What a Mentor Taught Me About Being an Analyst - Part One

As I transitioned to becoming an industry analyst 25 years ago, I was fortunate to have a mentor that took me under his wing and taught me some important lessons that still guide me to this day.

“Mike” was a pretty polarizing figure. He was outspoken and could be sharply critical. His advice was pointed and could be harsh. You either liked him or you didn’t. But, if you asked him for help or feedback, he was supportive and generous with his time.  I decided early on to ask him for help, which ultimately made me a better analyst.

So, 25 years later, here are several of those lessons:

1)     Teach a process: Provide the tools to see a problem in a new light and successfully tackle it.

2)     Challenge “sacred cows”: Go against the grain of conventional wisdom, providing a broader and more informed perspective on a topic.

3)     Develop an expert: Arm people with insights to make them respected subject-matter authorities within their organization.

4)     Help save money: Supported with critical information and skills, help people make better investment decisions for their organizations.

These are the set of guiding principles behind our research and our upcoming Real Business Intelligence conference (July 11th and 12th on the campus of MIT in Cambridge, MA):

Our inaugural conference is designed to be small and intimate. It’s a single track event intended to ensure that each attendee receives 100% of the benefit of being there - as well as meaningful networking with peers and faculty.  

All sessions have been curated and staffed to support these guiding principles and each will help to make attendees subject-matter authorities and will help to save their organizations money. Many will also teach process and will challenge conventional wisdom.

Here are some examples of sessions that will teach process:

-         In the Performance-Directed Culture Workshop, Bill Hostmann and I offer a process for diagnosing an organization’s readiness, with some recommendations, towards becoming more fact-driven, transparent and collaborative. This session brings alive the diagnostic which I developed for my second book, Profiles in Performance, Business Intelligence Journeys and the Roadmap for Change.

-         In Secrets of Building Actionable KPIS, Mico Yuk presents a process for developing meaningful and impactful performance indicators using her famous BIDF methodology, which is documented in her book, Data Visualization for Dummies.

-         In David Dadoun’s session, Creating and Sustaining a Governance Program, he shares what a governance program actually entails, the associated benefits, and key lessons learned in developing a successful initiative. David is Aldo Group's Director of Business Intelligence and Data Governance.

-         In Jim Ericson’s Moving Business Intelligence to the Cloud, he’ll share our core research on the topic, as well as case studies and a decision structure to decide if you should move and to what degree. Jim is a VP and Research Director with Dresner Advisory Services, and coauthors much of our industry research – including Cloud Computing and Business Intelligence, in its sixth year of publication.

-         In Chuck Hooper’s Great Storytelling with Data, he teaches us the processes and components involved in telling any story, whether you are selling a product or service, asking for a raise, or, are an executive presenting to the media. Chuck documents these techniques in his book, 59 Minutes to Great Storytelling.

-         In Nathan Kollett’s Self-Service Business Intelligence: Separating Hype from Reality, he’ll discuss the do’s and don’ts of self-service Business Intelligence, focusing on the idea that, although technology is necessarily at the center of any BI analytics platform, it is the people and processes that ultimately make it work and enable you to transform your business. Nathan is a Senior Product Manager within the Analytics group at Wayfair.

In a future article, I’ll share with you some of those sessions which challenge many of the “sacred cows” in the industry!

In conclusion, Real Business Intelligence will deliver much more than a typical conference. We will deliver an experience, with a taste of the mentorship that I experienced early in my career.

I hope you can join us!

Best,

Howard

Read three great articles and attend a webinar before attending Real BI Conference

Before attending our upcoming Real Business Intelligence Conference (July 11 & 12), please read these three relevant articles that were published this week:

- Despite functionality gains, use of cloud BI tools elevates slowly - Craig Stedman, SeachBusinessAnalytics

- Mathematician warns against weapons of 'math' destruction - Nicole Laskowski, SearchCIO

- Data skeptic Cathy O'Neil explains why we need to regulate algorithms - Nicole Laskowski, SearchCIO

Also, consider attending our conference preview webinar, next Tuesday (6/27) at 1:30 PM EDT where we'll have several of our external faculty on hand to present, including Mico Yuk and Chuck Hooper! 

Mico Yuk (author of Data Visualization for Dummies) and Chuck Hooper (author of 59 Minutes to Great Storytelling) will each share a preview of their materials planned for the conference.

I'll also be there and will share additional information about the event, scheduled for July 11th and 12th on the campus of MIT in Cambridge, MA.

At the live event there will be opportunities to win free conference passes and more!

Hope to see you there!

Best,

Howard Dresner

Real Business Intelligence Conference Co-Chair

Social Media in Business Intelligence: Bold Steps or Sidesteps?

What a difference a year makes! Among the many prioritized business intelligence technologies and initiatives our company tracks in annual market studies of what is significant and strategic to BI, social media analysis has remained a back-burner issue for years. But now there are other factors at play. Battles in the U.S. presidential campaign played out over a year on Twitter, attracting people like a magnet; and now the new White House administration is using Twitter to dramatically alter perceptions and even present alternative facts. 

So, social media BI is now a hot topic, thanks in large part to politics over recent months. Are we at an inflexion point of significant change in the progress curve for social media adoption in BI and analytics? Is it postured to experience a boost in 2017 as an area of BI investment? I posed these questions to launch a recent discussion among the participants in one of my Friday #BIWisdom tweetchats on Twitter. The group was lively, and their tweeted comments show they had given thought to this even before I raised the questions. 

Compelling current data comes from social BI, someone tweeted. A participant responded, I could easily see it leading to reactive management if not used wisely. Another pointed out, So many companies ban social media, so how could it effectively work in BI? Someone else advised that the use cases for social analysis need to be understood and agreed to before trying it in BI.

As I told the #BIWisdom folks, most companies I have spoken with that are using social BI are doing so with a discrete cloud app in marketing. The group agreed that makes a lot of sense since sensitivity analysis, managing the message and competitive analysis are perfect social use cases. That makes it a business need now, not a luxury or add-on, stated an attendee. Another tweeted, There is much potential. But there are challenges in synthesizing and classifying the information and combining it with other data for analysis.

Honing in on the use of social BI in marketing, someone tweeted, Marketing is best used as a tool for tactical and strategic business alignment. But since social BI is operational, there is a high risk of misuse and of false positives. Someone added, But if marketing is the product, social BI takes on a whole new dimension. Another tweeted, Tied with that is whether social sentiment is your product (politics, for example) or whether its simply a marketing channel.

I commented that part of the issue is incompatibility between sentiment and facts, and its hard to make key decisions based on how a group might feel. One of the #BIWisdom group asked, How can companies bridge that gap to get an understanding of the indications both are pointing to?  

We then veered into opinions on whether social media's focus on sentiment will change and become more substantive. Opinions varied:

    If an organization relies on listening through social media and then adjusts, it can be beneficial.     People often take more to social media to complain or seek customer service rather than praise.      This is important. It could lead to a lot of effort in fighting perception fires rather than growing the business.     What if others take the lead of President Trump and start sharing statements of policy, etc.?     Well probably see more of it in the future. People appreciate transparency.     Social BI is a specialization of data, using data gathered entirely from outside the organization.     It could lead to trying to shoehorn someone into analyzing something that isnt their specialty. This is likely part of the reason many organizations dont put a lot of effort behind it. But it can be worthwhile.

The #BIWisdom groups conclusion is that there a lot of low-hanging fruit for social BI. And vendors are doing demos around the possibilities in social media analysis. But determining how to actually show value is difficult at this time. 

Bottom Line: Social media in BI isnt going away. But are we at an inflection point in 2017 where companies will choose to take bold steps? Or will companies sidestep social media BI initiatives  like a boxer avoiding a harmful blow? The key issue is how to tie it to critical business outcomes.

I believe organizations need to pay more attention to social media analysis as it becomes a centerpiece for government policy. The larger question is whether this will become the norm for all kinds of institutions. 

My advice for companies considering in investing in social media analysis is to become aware of the capabilities but dont try to reinvent the wheel as this movement evolves. Politicians often outsource the analysis of its social media data. Similarly, companies stepping into the social BI waters cautiously may benefit by leveraging outsourced resources, lessons learned and expertise already available. 

Learn more about this and many other topics at our upcoming Real Business Intelligence® conference, July 11-12, 2017 on the campus of MIT. 

Howard Dresner is president, founder and chief research officer at Dresner Advisory Services, LLC, an independent advisory firm. He is one of the foremost thought leaders in Business Intelligence and Performance Management, having coined the term “Business Intelligence” in 1989. He has published two books on the subject, The Performance Management Revolution — Business Results through Insight and Action, and Profiles in Performance — Business Intelligence Journeys and the Roadmap for Change. He hosts a weekly tweet chat (#BIWisdom) on Twitter each Friday. Prior to Dresner Advisory Services, Howard served as chief strategy officer at Hyperion Solutions and was a research fellow at Gartner, where he led its Business Intelligence research practice for 13 years.

Front-runners for Business Intelligence Investments in 2017

Every year at this time, I look forward to insights from Twitter participants in our Friday #BIWisdom tweetchats to glean their “boots on the ground” perspectives of what’s new and what has abiding interest for the upcoming year in enterprise BI investments and intentions. At a couple of recent tweetchat sessions, I asked the #BIWisdom group some questions about technologies and initiatives for 2017 related to BI and analytics.

Self-Service BI

I asked the group whether they think information catalogs to support self-service will be an important theme in 2017. I suspect a challenge in an organization of size is simply "finding stuff" and a lot of time is wasted searching for data, models, reports, etc. or duplicating efforts.

They noted that most companies’ focus on self-service depends less on investment than on increasing collaboration between IT and the business. One participant tweeted that she hopes 2017 will be the year that the business users and IT groups coordinate regarding self-service. And someone added it’s still common for IT and business users to have divergent goals and even definitions of self-service BI. Another participant tweeted that companies need easier self-service analytics, and that is tied to “a relentless focus on usability and better exploitation of collaboration in the context of BI.”

Natural Language Analytics (NLA) and Natural Language Processing (NLP)

In December 2016, we released the report on our first annual market study of NLA in BI. This is an area that has been around for quite some time in BI but has not yet fully emerged. But with the advent of Siri, Alexa and GoogleHome, this is a hot area. Will 2017 see a big movement in NLA?

The group’s consensus opinion aligned with our market study report – NLA and NLP adoption are greater than a few years ago, but they are not yet mature. One participant tweeted that he had seen some novel demos but few practical use cases have been implemented.

Still at a nascent level in BI, some of the #BIWisdom tweetchat group were not sure who are target users for NLA / NLP in a business context. Another participant responded that users would be “anyone having to write a report – business analysts, accountants, etc.” Our market study revealed that the main interest for this capability is among less technologically savvy users.

The group agreed that, although probably not in 2017, NLA / NLP could be the next “big thing” for certain BI applications and users.

Big Data

I reminded the tweetchat group that big data use cases now exist and there were big increases in adoption in the business intelligence space in 2016. I asked whether they think big data will continue to be an important investment for organizations in 2017. And will the term “big data” finally become just “data?”

It’s still distinct but is becoming mainstream, someone tweeted. Technologically, big data is different, but it’s becoming more functional. Another #BIWisdom member tweeted that the demands for better data governance should continue because of big data. However, much of governance depends on people and process.

Overall Investments

Will budgets for business intelligence and analytics increase for #2017? Some of the group agreed that they expect investments in BI education, software, consulting and new hires to be the same in 2017 as it has been for the past few years. Others indicated overall investment in BI is increasing at their company, primarily because of the need to refresh hardware and software. No one indicated a decrease in spending for 2017, and one member commented that “BI is seen as table stakes” in his company’s industry.

Bottom Line: My final question to the #BIWisdom group was “What still limits BI deployment or use in 2017? Is it cost? Lack of investment?” They responded that the biggest limitations they encounter are (1) the difficulty in governing data, tools and applications and (2) issues around perceived vs. real value.

With a greater emphasis on self-service BI functionalities this year, governance becomes even more crucial. This is not a new issue; in fact, I blogged in 2014 about whether it’s possible to have self-service BI and governance too. Although data discovery tools enable line-of-business user insight, companies need to maintain centralized control to ensure proper usage of data. Establishing a Business Intelligence Competency Center (BICC) is an effective way to ensure self-service BI does not become a company vulnerability.

When it comes to BI value, survey respondents in our annual Wisdom of Crowds® Business Intelligence Market Study, consistently state that making better decisions is their primary goal for business intelligence. Achieving this goal is the value or return on investment, and it manifests itself in two financial aspects: cost savings or revenue generation. However, some companies are not successful in achieving these goals. I’ve blogged in the past about two related challenges – dangling BI initiatives and ineffective Key Performance Indicators (KPIs) used to communicate a BI strategy.  

I think a great resolution for 2017 is to end BI’s deployment limitations, and I’ll check back with the #BIWisdom tweetchat tribe in December for opinions and examples of companies making progress in this goal. Meanwhile, please feel free to contact me with your own success stories through this year.

Learn more about this and many other topics at our upcoming Real Business Intelligence® conference, July 11-12, 2017 on the campus of MIT. 

Howard Dresner is president, founder and chief research officer at Dresner Advisory Services, LLC, an independent advisory firm. He is one of the foremost thought leaders in Business Intelligence and Performance Management, having coined the term “Business Intelligence” in 1989. He has published two books on the subject, The Performance Management Revolution — Business Results through Insight and Action, and Profiles in Performance — Business Intelligence Journeys and the Roadmap for Change. He hosts a weekly tweet chat (#BIWisdom) on Twitter each Friday. Prior to Dresner Advisory Services, Howard served as chief strategy officer at Hyperion Solutions and was a research fellow at Gartner, where he led its Business Intelligence research practice for 13 years.

 

 

The Magical BI Adoption Formula: Embedded BI

One of the aspects I appreciate about the #BIWisdom tweetchats I conduct each Friday on Twitter is that the participants dont sidestep any issues. Of course, we cant be too long-winded in a 140-character tweet anyway, but often their comments are pithy  like the woman who dubbed embedded BI as the magical BI adoption formula in the recent tweetchat about the embedded BI market study report we recently published. Thats a much faster, bottom-line way to convey what Ive advocated for several years  that it could help get more eyes on the data and is a great way to engage users in BI in the context of a familiar application.

But, of course, there is also great value in having BI instrumentation above the applications. What about top-down BI vs. embedded BI? Is it a non-issue? Are both necessary? Those are the thought-provoking questions I posed to #BIWisdom participants recently. 

Someone tweeted, I submit that companies often think embedded BI has the same capabilities as the rest of the BI spectrum. But thats not true. And I agreed with him. Users mostly want to view content and interact or navigate using embedded BI. 

Another tweetchat attendee added he believes the power of embedded BI is that it gives direct insight and provides more context.

Embedded BI is the technological capability to include BI features and functions as an inherent part of another application. The pervasive perception among the tweetchat attendees, as evident in their tweets below, is that the benefit is users dont need to know theyre using BI. 

    They have easy access to tools as part of their daily workflow.     It enables users without having to go through a big rollout.     The big thing is people dont like switching applications to an analytics tool, so embedded BI is attractive.     The ease of use makes users happy. Quick results at their fingertip.

But the perception of embedded BI among tweetchat attendees differed around the actual use. The following tweeted discussion in particular highlights the differences:

    Embedded BI is needed for analyzing, acting and optimizing on business objectives.     It is needed more for operational daily use as part of the business transactions. Finance, operations  take your pick. Its complimentary to ERP bundled reports.      So, adoption of embedded BI is like bundled packaged reports with ERP that users dont like or use?     Embedded BI apps need to interact with other BI data stores or within transactional apps.     Data monetization is a driver of increased embedded BI.

I added to that discussion with data points from the report on our recent embedded BI market study:

    Companies are embedding BI in portals to extend access to employees and within specific apps (ERP, for example).      Interaction with live objects is key, presumably in synch with other app content.     It depends on the use case, but embedded BI interacts mostly against traditional data stores. 


Some companies advocate data monetization as a driver, but most applications of embedded BI are for internal, employee-facing applications. Very few companies seek to expose internal data to external constituents via embedded BI. And monetizing data is tough for companies; there are many issues to contend with that have nothing to do with technology.

Bottom Line: A tweetchat member commented a year ago that the consequences of the push towards embedded BI are interesting and will keep growing in 2016 and beyond. Among BI topics, embedded BI is pretty hot right now in 2017. Im particularly interested in it because it helps realize the vision of Information Democracy, or equal access to actionable insight for all. As I explained, in my book, The Performance Management Revolution: Business Results Through Insights and Action (John Wiley & Sons, Inc., 2008), the goal of achieving Information Democracy cannot be achieved just through technology; it also requires empowering individuals. 

Our recent #BIWisdom tweetchat discussion aligned with this goal. A participant tweeted, With end-user empowerment, you may finally have a data-driven culture. And another in the group added that BI insights have business value only if theyre acted upon and impact decisions. Sharing data increases that likelihood. 
 

Learn more about this and many other topics at our upcoming Real Business Intelligence® conference, July 11-12, 2017 on the campus of MIT. 

Howard Dresner is president, founder and chief research officer at Dresner Advisory Services, LLC, an independent advisory firm. He is one of the foremost thought leaders in Business Intelligence and Performance Management, having coined the term “Business Intelligence” in 1989. He has published two books on the subject, The Performance Management Revolution — Business Results through Insight and Action, and Profiles in Performance — Business Intelligence Journeys and the Roadmap for Change. He hosts a weekly tweet chat (#BIWisdom) on Twitter each Friday. Prior to Dresner Advisory Services, Howard served as chief strategy officer at Hyperion Solutions and was a research fellow at Gartner, where he led its Business Intelligence research practice for 13 years.

Is Artificial Intelligence the Future of Business Intelligence?

There is a lot of buzz these days on artificial intelligence (AI) in business intelligence products. At a recent tweetchat of my #BIWisdom group of users, vendors and consultants, a participant asked: Is AI becoming a part of everything we do, the way analytics have been added to apps of all shapes and sizes across the business and personal space?

Although companies tend to overreact to emerging technologies, AI is not a new technology.  One of the tweetchat participants commented that AI is crossing a chasm, either in awareness or approach and that perhaps its evolution to this point is due to the prevalence of data scientists. Another member of the group agreed, tweeting that the data scientists craze has helped more organizations embrace machine learning and AI to get deeper insights. He added that analytics doesnt give enough deep insights.

Learn more about this topic at our upcoming Real Business IntelligenceTM conference, July 11-12, 2017 on the campus of MIT. Keynote speakers include Dr. Cathy O'Neil author of the recent book "Weapons of Math Destruction".

As our discussion progressed, I tweeted that I question whether AI is mature enough for generalized approaches in the business intelligence space.

Someone in the group asked, If all organizations see views of the same information, where will a competitive advantage come from? My AI is better than yours? His observation reminds me of what happened with ERP  if everyone adopts it, then everyone has the same benefits and limitations.

As with any technology, there are pros and cons. One the people attending the session that day tweeted a concern: I would hope organizations using AI for a long time, such as NASA, have developed some standards and guidelines.

Others discussed concerns about whether vendors should embed AI into their BI products. It could result in users thinking of AI as BI and taking it for granted, someone tweeted. Another in the #BIWisdom group countered that view with an opinion that vendors making AI invisible to users would help increase adoption. An observation from someone else was that some vendors probably will differentiate their products by not labeling AI. And another concluded that maybe it doesnt matter if users know. That was followed with another tweet: AI is a way to give users something they need but didnt know they wanted; its super useful for novice BI users.

Learn more about this topic at our upcoming Real Business IntelligenceTM conference, July 11-12, 2017 on the campus of MIT. Keynote speakers include Dr. Cathy O'Neil author of the recent book "Weapons of Math Destruction".

I brought up another concern: AI has the capability to transform industries. So how can organizations audit AI to make sure its working properly? Does this make data scientists even more important with responsibility for monitoring and auditing algorithms? And there are legal complications; so who gets sued when AI fails, especially if vendors embed AI in their products?

The group responded with some interesting tweets:

    Future accountants will study auditing and need to get AI auditing attestation.     The regulations around AI would be fascinating. We're still struggling with net neutrality, so I cant imagine having government regulations around AI.     Would AI algorithms be able to think beyond a regulation?     Would the regulations be black and white, so to speak? Would they be in the form of guardrails or more like safety nets?     This is why you need data scientists rather than accountants. Accountants think in black and white.     Which business unit would develop and maintain the AI?

Bottom line: In my mind, all the concerns about AI come down to determining the goal of artificial intelligence in BI products. Will it be used to guide users, augmenting human capabilities? Or will it be used to replace them? 

The answer depends on whether artificial intelligence is better suited than humans for doing data analysis. Its important to keep in mind that artificial intelligence and cognitive machine learning change the game. These technologies can go beyond just enabling business intelligence; they can advise people. Arguably, AI wouldnt have bias or private agendas like humans, so it could be totally objective. But in reality AI could be trained to be biased. It could inherit the biases of a programmer or the entity funding the BI initiative. This could be an issue where AI augments human analysis by recommending new data sources and helping with metadata. The old maxim, he who has the gold makes the rules, is a key consideration. Already we have examples of differences is recommending data sources: Amazon uses Alexa, Apple uses Siri, Google uses Google Now and Microsoft uses Cortana. 

As organizations embrace artificial intelligence more and more to gain deeper insights, will those insights be objective or have built-in bias? As AI is crossing a chasm in awareness, organizations must keep in mind that it is not a mature technology and they should consider the impact of bias, just as they would be careful with bias in advice from humans.

Learn more about this topic at our upcoming Real Business IntelligenceTM conference, July 11-12, 2017 on the campus of MIT. Keynote speakers include Dr. Cathy O'Neil author of the recent book "Weapons of Math Destruction".

Howard Dresner is president, founder and chief research officer at Dresner Advisory Services, LLC, an independent advisory firm. He is one of the foremost thought leaders in Business Intelligence and Performance Management, having coined the term “Business Intelligence” in 1989. He has published two books on the subject, The Performance Management Revolution — Business Results through Insight and Action, and Profiles in Performance — Business Intelligence Journeys and the Roadmap for Change. He hosts a weekly tweet chat (#BIWisdom) on Twitter each Friday. Prior to Dresner Advisory Services, Howard served as chief strategy officer at Hyperion Solutions and was a research fellow at Gartner, where he led its Business Intelligence research practice for 13 years.

Introducing The Real Business Intelligence Conference

Many organizations focus upon technology as a means of enhancing user perspective and fact-based decision-making. And, while technology is an important enabler, it’s a relatively small part of the equation. We know that the essential ingredients for success lie with people, process and organization - enabled by technology. We’ve long believed that there’s a need for a fresh in-person forum for business and IT leaders to help achieve success with information, business intelligence and analytics.

Accordingly, the Real Business IntelligenceTM conference is different from others you may have attended because it's NOT about technology. Rather it's focused on strategies for success with business intelligence, analytics, performance management and information management (e.g., people, process and organization supported by technology).

Designed as an interactive executive forum, we’ll be focusing upon topics which enable attendees to help their respective organizations become more performance-directed and information-driven. Our aim is to teach new processes, share new ideas and help "move the needle" within your organization with pointed and actionable advice. The full agenda for the event can be found here.

We’ve assembled an impressive (and perhaps unconventional) faculty that you won’t see or hear anywhere else. They are among the best in the industry, picked by Bill Hostmann and me (conference co-chairs) to ensure a rich learning experience. All are prominent thought leaders in their disciplines and each will deliver important insights and actionable, real-world advice. A full list of our faculty can be found here.

As a very small and intimate forum (300 attendees) we are limiting attendance to very senior business and IT leaders. This means that there will be many more opportunities to have meaningful interactions and build relationships with peers and faculty members.

The Real Business Intelligence conference will take place on the campus of MIT at the Tang Center July 11 – 12, 2017. Please visit www.realbusinessintelligence.com for more details and to register.

Looking Past the Hype of Big Data’s Impact on Business Intelligence

Over the years of our weekly #BIWisdom tweetchats, one of the topics that continues to pop up in our tweeted discussions is big data. The perception of its value in business intelligence has changed over time. In 2015, most participants in our tweetchats observed that a few real use cases existed but adoption of big data was mostly modest and still characterized by hype as much as substance. One of the participants tweeted that “Gartner had predicted we’d be done with the term big data by February 2015, but they were wrong.”

In January 2016 when I asked the #BIWisdom tribe to share their BI resolutions for the year, it was clear that many of their companies were looking to use big data to solve business problems. High on their agendas were:

“Which of our customers are not profitable for us and why?” “What percentage of our customers are experiencing the level of service we intend?”

Five months later, the big data talk among our group revolved around monetization – how the lines of business could make money from the results of analyzing the data. By the end of summer 2016, participants were tweeting about the role of big data and BI enabling digital transformation. And this month the group agreed that the real impact of BI’s use of big data will be in the Internet of Things.

I told the group that I believe the Internet of Things presents one of the best use cases for big data. In fact, the findings in our Wisdom of Crowds® Big Data Analytics Market Study reveals that big data resonates strongly with organizations that are IoT advocates.

One of the #BIWisdom group pointed out that big data also will have a big impact as it feeds artificial intelligence and machine learning.

But it’s not all a rosy picture, as evident in some tweeted opinions from the group:

“It gets harder over time to see the boundaries and scope of business intelligence technologies and concepts.”  “Hype still seems to surround a lot in the BI and big data world these days. Users need to be able to see past it.” “The biggest challenge today is the business question. A lot of bright statisticians have data but need help understanding what are the relevant business questions.” “I’m starting to wonder if the power and accessibility of big data tools is hiding poor business understanding.” “It doesn’t help that the volume and velocity of data are going up exponentially from IoT sensors, streaming big data, that are not all useful.”

The group agreed with a tweet that every organization using data should have a training plan in place. But not everyone agreed with this tweet: “Incumbent upon the rollout of a new business intelligence tool or solution is that users get an explanation of how it delivers on the requirements they set forth.” Rebuttals were that education on how business intelligence and use of data can help must come before education on BI tools.

Delving further into this important area, I asked, “What are the top BI education areas? What do end users need to know vs what is nice to know?” Responses included:

“They need to know how to develop publishable reports, create beautiful visualizations, how to find and fix data and how to solve a quantitative problem.” “They need to know the theory and practice of BI. If they don’t know at least the 10k-foot view, how can they effectively do the practice?” “We crafted a curriculum at our organization. It was hands-on with real data. Having content baked in (through image overlays and short videos) helped.”

Bottom line: As one of the #BIWisdom group tweeted, “Big data has left an indelible mark on the BI industry. It brings awareness back to the power of BI.” I believe this points to the need for education around data fluency. Of course, as the group shared, there are many forms of education.

The greatest enabler – or greatest impediment – to success in business intelligence is people. So education needs to be a core component of any BI strategy and organizations must train users on BI principles. Users need to understand when and why to ask what business problem to solve and then how to proceed using data to find the answer.

Finally, as I’ve blogged many times, if an organization has established a Business Intelligence Competency Center (BICC), user training should be part of the BICC charter.

Advanced and Predictive Analytics 2016

This past August, we released our third annual Advanced and Predictive Analytics Report - building upon our previous years' work and highlighting the ongoing changes to this space.

We define Advanced and Predictive Analytics (APA) as including statistics, modeling, machine learning and data mining to analyze facts to make predictions about future or otherwise unknown events. Our new APA report also introduces the role of citizen data scientist, which describes a role that might be business analyst or BI user but a person who is nonetheless able to generate models for advanced and predictive analytics.

Here are three high level takeaways to keep in mind as you evaluate existing or potential APA investments in your organization:


1. Selective Use: While certain organizations are well-invested in advanced and predictive analytics, overall penetration and current use remains low at just 24 percent (and declined slightly year over year). Coupled with future plans, we can conclude that APA will not be a widespread practice in the near future. Going forward, large organizations are most likely adopters. Statisticians/data scientists, business intelligence experts and business analysts are the likeliest users of advanced and predictive analytics.


2. Main Memory over Big Data Analytics:  Despite the marketing noise around big data analytics, in-memory and in in-database analytics are the most important scalability requirements to respondents, followed distantly by in Hadoop and MPP architecture. This sentiment is well sustained over time and the vendor industry is well aligned to serve demand.


3. Most Important Features:  Mainstay features including regression models, clustering, textbook statistic and geospatial analysis are the most important analytic user features/functions. A range of data preparation features, led by de-duplication, set operations and complex filtering are important to users, but interest has cooled slightly year over year. Feature interest is again strongest in large organizations.
 

You’ll find much more in our report, all free to the users who filled out our survey, and this is just one of over a dozen reports we’re producing this year. For those that are not a part of our research community, the report is available for purchase at www.predictiveanalytics.report.

Best,


Jim Ericson
Vice President and Research Director
Dresner Advisory Services

Best Approaches for Developing KPIs for Business Intelligence

I recently asked a provocative question in one of my weekly #BIWisdom tweetchat sessions: In the age of discovery and cognitive BI, are Key Performance Indicators (KPIs) going to remain relevant?

Someone tweeted a quick answer: If you dont have KPIs, how do you know if youre succeeding? Another participant commented that most places where she has worked had KPIs and it was sometimes part of the appraisal process. 

Some #BIWisdom attendees who are BI users as well as vendors and consultants shared their opinions about whats wrong with KPIs. They mentioned that the average BI user doesnt understand what makes up a good KPI. And most companies have too many KPIs. Or that KPIs are misused and then drive the wrong results. Someone else tweeted about the problem of KPIs using absolutes and not normalizing and that normalizing KPIs helps prevent irrational goals and the resulting bad behavior that can follow.

I asked for the groups opinions on how to create effective KPIs. Their answers below may serve as a brief overview checklist or framework for developing your organizations KPIs. 

Where do you start?
    You start with the right business users and identify their main problem. 
    Start with one KPI that drives everything else and then expand from there.
    As more line-of-business managers start to measure, add micro Key Value Indicators (KVIs) that roll up under top-level KPIs.

Do you start with goals or data?
    Start with the users main goal. Data limits value.
    Existing data may not support the goal.
    Start with goals and use benchmarks to help set targets.
    KPIs need to align to objectives or goals. As an example, the KPIs for the objective of good design might be task, visual appeal and completeness.

What is the overriding guideline?
    KPIs need to be individually actionable.
    KPIs must be dynamic because how you measure your business changes over time. Whats relevant now might not be relevant in 18 months.
    KPIs should be updated regularly to align with shifting objectives. A KPI can remain the same, but the target might change. 

Bottom line: KPIs are more than a way of measuring success with business intelligence. They also serve as a very effective tool for defining and communicating the BI strategy.

Over the years, I've noticed two issues that can lead to ineffective KPIs. First, KPIs need to be unobtrusive, natural and intuitive; otherwise, they can end up as another job for users to do. Second, organizations that have been previously unsuccessful with BI tend to use prepackaged apps, meaning a vendor has defined the KPIs for them.

In my opinion, creating good KPIs require guidance. Thats why organizations often engage consultants to help with the process. 

Its no secret that Im a big proponent of BI Competency Centers (BICCs), and Dresner Advisory Services conducts an annual market study to assess the management and results of BICCs. A BICC is a group within an organization charged with defining, delivering, documenting and promoting best-practices in business intelligence. Another way of thinking about it is that a BICC organizes BI initiatives for success. Ideally, the BICC is the champion of KPIs that really matter.
 

Howard Dresner is president, founder and chief research officer at Dresner Advisory Services, LLC, an independent advisory firm. He is one of the foremost thought leaders in Business Intelligence and Performance Management, having coined the term “Business Intelligence” in 1989. He has published two books on the subject, The Performance Management Revolution — Business Results through Insight and Action, and Profiles in Performance — Business Intelligence Journeys and the Roadmap for Change. He hosts a weekly tweet chat (#BIWisdom) on Twitter each Friday. Prior to Dresner Advisory Services, Howard served as chief strategy officer at Hyperion Solutions and was a research fellow at Gartner, where he led its Business Intelligence research practice for 13 years.

Click here to view our catalog of premium research products

Collective Insights 2016: Three Takeaways

In April, we published our inaugural Collective Insights research report, which builds upon our view of Collaborative Business Intelligence by adding an important, emergent dynamic to the mix: user governance. Where collaborative business intelligence is a process that develops a common, shared understanding that improves decision-making, user governance - the policies and controls for directing content creation and sharing - improve information consistency and accelerates that group-based decision-making.


Here are three high level takeaways to keep in mind as you evaluate the collaboration and user governance initiatives in your organization. 
 
1. Collaboration is Important: A solid majority (65 percent) considers collaborative BI either "critical" (21 percent) or "very important" and say collaboration does translate to BI success. Collaboration (14th) and governance (11th) rank above the middle of 30 topics under study at Dresner Advisory Services with support well ahead of cloud, big data etc. and other "hot" topics. Sharing , annotating and co-authoring are top collaborative BI requirements. Industry support for content co-creation and collaboration features is robust and maturing.

2. User Governance is Especially Important: Governance of BI content creation and sharing is extraordinarily (90 percent-plus) important to respondents in 2016  and correlates strongly to success with BI. Only 1 percent says governance is "not important." Vendor sentiment for managing and governing BI content is also extremely strong. Top governance feature requirements relate to access control and sign-on, but other controls are also important.

3. Enterprise Frameworks Adrift: Respondent interest in enterprise collaborative frameworks (e.g., SharePoint, Google Docs, Salesforce Chatter) is not as strong as might be expected and trails sentiment toward collaboration and user governance generally. After Sharepoint (49 percent), fewer than one in three respondents use any OTS enterprise collaboration framework and, in our sample base, only a handful of products have more than 10 percent adoption.  
 
You’ll find much more in our report, all free to the users who filled out our study, and this is just one of a dozen reports we’re producing this year. For those that are not a part of our research community, the report is available for purchase at www.collectiveinsights.report.

Best,


Jim Ericson
Vice President and Research Director
Dresner Advisory Services

Cloud Computing and Cloud BI 2016: Three Takeaways

In March, we publishd our fifth assessment of the cloud BI arena, which finds us at a period of expectation leveling. Attitudes toward cloud BI and cloud generally remain at mid-tier importance among BI priorities. Sentiment has cooled slightly from the blue-sky days, consistent with maturing technology markets.

Here are three high level takeaways to keep in mind as you evaluate the cloud and cloud BI initiatives in your organization.  

1. Slow March to Public: Across five years of data, actual use and favorable attitudes toward future use of public (multitenant) cloud BI have steadily increased and future. plans for public cloud BI use now slightly eclipse private cloud use. Industry support has likewise shifted toward public cloud year over year. Future cloud BI investment will also be slightly higher for public than for private cloud models; few plan decreased cloud BI investments in any model.

2. Cloud BI Still Looks Like BI: Regardless of delivery model, users have consistent expectations for BI tools and functionality. Traditional BI functionality (advanced visualization, ad-hoc query, dashboards and self-service) lead the list of the most-required cloud BI features.  "Data blending" has become a more popular term, "self-service" less so. The most important architectural feature for cloud BI is relational database support, followed by open client connectors, automatic upgrades and connectors to on-premise apps.  

3. Industry Has Your Back: Though vendor enthusiasm toward cloud and cloud BI has also eased a bit, it remains higher than that of end users and will continue to drive adoption. Support for BI features and architecture generally meets user demands. The industry also appears responsive to user preferences for subscription models versus perpetual license and maintenance. A notable problem remains support for security standards, which is weak but improving. 

You’ll find this and much more in our report, all free to the qualified users who filled out our survey and this is just one of over a dozen reports we’re producing this year. For those that are not a part of our research community, the report is available for purchase at http://cloudbi.report

Best,

Jim Ericson
Research Director
Dresner Advisory Services

Jim Ericson is a research director with Dresner Advisory Services. Jim has served as a consultant and journalist who studies end-user management practices and industry trending in the data and information management fields. From 2004 to 2013 he was the editorial director at Information Management magazine (formerly DM Review), where he created architectures for user and industry coverage for hundreds of contributors across the breadth of the data and information management industry. As lead writer he interviewed and profiled more than 100 CIOs, CTOs, and program directors in a 2010-2012 program called “25 Top Information Managers.” His related feature articles earned ASBPE national bronze and multiple Mid-Atlantic region gold and silver awards for Technical Article and for Case History feature writing.

Examine the DNA in Your Business Intelligence Implementation

The media and news outlets over the past few years have highlighted powerful results from DNA research and studying the genetic features, components and characteristics of human cells, viruses, etc. Have you ever thought about the DNA of a business intelligence implementation? It’s not a far-fetched idea. Medical practitioners’ ability to diagnose diseases early on or diagnose a person’s genetic susceptibility to certain diseases has greatly improved thanks to DNA research. Why not apply the thinking to BI “DNAcomponents” that result in a success or failure?

I define a failed BI initiative as one that doesn’t reach its full potential. The implementation phase is fertile ground for a potential BI failure. In fact, a poor approach to BI implementation is one of the most-often cited factors by respondents in our annual Wisdom of Crowds business intelligence market studies.

So it was no surprise to me when a participant in one of my recent Friday #BIWisdom tweetchats asked this question: “Has anyone here had a BI implementation success from a bottom-up approach rather than a top-down approach?” He added that he had only see top-down approaches as successful.

Another participant tweeted that he had seen successful implementations from both bottom-up and top-down approaches, but he also tweeted that “obviously the top-down approach is a smoother slope.”

Someone else tweeted that a bottom-up approach works well in line-of-business areas like finance. But another participant questioned that assessment and asked, “That makes sense, but does it hold when investment is required?” Then came the comment that “in those cases, investment is already made. They’re just taking advantage of what they already have.”

There was no shortage of opinions that day on the initial question about the best implementation approach, with opinions coming from BI users, vendors, and consultants. Here are a few more of their tweeted comments; which ones do you agree with?

- “In my experience, a bottom-up approach comes about when the top executives want to do something but can make a decision.”

- “I would expect there would be more success with data discovery and exploration in a bottom-up approach.”

- “A bottom-up approach usually requires a strong partnership with the vendor – investment in prototypes, etc. to prove value.”

- “Is a bottom-up approach often due to groundswell for a single product or a general culture change?”

- “Some bottom-up approaches are due to a data-driven product innovation that was not necessarily looked at as a BI evaluation. And the success of that data product opened the door to become a BI standard.”

- “A bottom-up approach usually results in fragmented solutions across the enterprise.”

- “Organization size makes a difference. In smaller orgs, staff may cover multiple roles which simplifies things for a bottom-up approach.”

- “Does it matter whose need the BI initiative is satisfying? What if it needs a more robust solution but the CXO can’t provide that? Lower levels will find a ‘good enough’ solution.”

The tweetchat session closed with two people summarizing their real-world views:

- “It always boils down to the best available option to meet a need. If people need it, they’ll find a way.”

- “The window for bottom-up approaches may be closing as established/incumbent platforms beef up their discovery offering.”

Bottom line: Despite the many successful BI programs and initiatives that companies are experiencing – and those successes just keep building even greater value over time for those companies – there are still some BI failures. Here are three tips for an effective top-down approach for a more effective BI implementation.

1. Senior management must first decide if the BI initiative is strategic or tactical and whether it involves a course correction or is it a new course of action. 
2. Change management is essential because BI initiatives involve a significant shift in power over information. Conduct change management at both the strategic and tactical levels. And don’t forget to eliminate political noise, an often-present challenge because some executives fear the shift in information power.
3. Think of the BI initiative as though it’s a complete business. For business success, marketing is key. It’s important to include “internal marketing” to communicate the results of the initiative. Thus, always make sure to structure your initiative so you can track the connection between the initiative approach and/or goals and the end results.

Howard Dresner is president, founder and chief research officer at Dresner Advisory Services, LLC, an independent advisory firm. He is one of the foremost thought leaders in Business Intelligence and Performance Management, having coined the term “Business Intelligence” in 1989. He has published two books on the subject, The Performance Management Revolution — Business Results through Insight and Action, and Profiles in Performance — Business Intelligence Journeys and the Roadmap for Change. He hosts a weekly tweet chat (#BIWisdom) on Twitter each Friday. Prior to Dresner Advisory Services, Howard served as chief strategy officer at Hyperion Solutions and was a research fellow at Gartner, where he led its Business Intelligence research practice for 13 years.

Click here to view our catalog of premium research products

End User Data Preparation: Three Takeaways

Our latest report on end user data preparation is our second assessment of the landscape and is already one of our most popular reports. We define end user data preparation as “a self service capability for end users to model, prepare, and combine data prior to analysis.”

The “end user” in this case usually sits at the decision-making end of the business and demands creative autonomy to manipulate internal and external data resources free of IT dependency. These roles draw a bright spotlight and more than a little executive scrutiny.

As you review the current state and future needs for end user data preparation in your organization, here are three high level takeaways to keep in mind as you dig into our latest findings.

1. Tip of the spear: Though it sits somewhere in the middle of business intelligence technology and initiative priorities, end user data prep consumers are key players in optimizing organizational initiatives through their analysis, but they need to move data and perform various manipulations and ETL tasks in order to get there. They look for insight in multiple files and databases – and more than half at least occasionally enrich data from third-party sources. Think analysts driving sales, marketing and other high demand revenue growth, campaign or cost initiatives along with a variety of free-roaming power users. More than seven in 10 respondents in sales and marketing (and IT) say data prep is either critical or very important and 65 percent of all respondents “constantly” or “frequently” make use of end user data preparation. R&D says it’s important as well, which hints at its trajectory.

2. Results fall short: Most organizations say their current end user data prep efforts and processes could be much better and they would welcome improvements. Only about 12 percent say their ability to model, prepare and combine data is “highly effective” and more than one-third say their abilities are somewhat or totally “ineffective.” Depending or organization size, role, geography and location, end users want better interfaces, access to common files and databases, the ability to combine data along with scheduling, monitoring and testing.

3. Needs and Gaps: The product vendor community gets the message. All the vendors we sampled in our report say end user data prep is “critical” or “very important” and they are investing to make data prep more fruitful and less burdensome process. The results range from good to not so good however: better in areas of integration and output formats; mixed in usability features; and a bit worse in areas of data manipulation. We identify the hot spots and provide a list of rankings by capability in our summary.

You’ll find this and much more in our report, all free to the qualified users who filled out our survey and this is just one of over a dozen reports we’re producing this year. For those that are not a part of our research community, the report is available for purchase at http://enduserdataprep.report

Best,

Jim Ericson
Research Director
Dresner Advisory Services

Jim Ericson is a research director with Dresner Advisory Services. Jim has served as a consultant and journalist who studies end-user management practices and industry trending in the data and information management fields. From 2004 to 2013 he was the editorial director at Information Management magazine (formerly DM Review), where he created architectures for user and industry coverage for hundreds of contributors across the breadth of the data and information management industry. As lead writer he interviewed and profiled more than 100 CIOs, CTOs, and program directors in a 2010-2012 program called “25 Top Information Managers.” His related feature articles earned ASBPE national bronze and multiple Mid-Atlantic region gold and silver awards for Technical Article and for Case History feature writing.

Click here to view our catalog of premium research products

Collaborative Business Intelligence is a Diamond in the Rough

Here’s a serious number: $31.5 billion a year. Unfortunately, it’s not a revenue growth indicator. It’s how much Fortune 500 companies lose per year because of not sharing knowledge, according to an IDC study. Consider that $31.5 billion in light of the necessity for idea/insight sharing in the Digital Age, “Sharing Economy,” and innovation happening through the Internet of Things, and it’s easy to see that the number could quickly grow even larger.

The goal of collaboration is to improve an outcome; collaboration results in decision-making information (and even solutions to business problems) that are greater than one person can create. Certainly this is essential in this new business world where human-performed work is quickly becoming more knowledge based and sharing business intelligence and experience is a key to success in innovation and agility,.

So why the $31.5 billion annual loss? Why isn’t collaboration happening more? I posed this question to my #BIWisdom tweet chat group on Twitter one Friday. And I shared with them two findings in our 2015 Wisdom of Crowds Market Study on collaborative BI:

" In most organizations, sharing business intelligence insights is still ad hoc and inconsistent in nature. 
" Only 14 percent of the study respondents reported their organizations use collaborative features in BI tools – even though these features are often included free.

As you’ll see in the following recap of their tweeted conversation, it’s clear that the BI users, vendors and consultants among the #BIWisdom attendees have differing opinions on where the blame lies for these findings.

It’s the BI vendors’ fault:

" “I wonder if BI vendors are giving people capabilities that line up with the way people collaborate, when they do.”
" “Most users just want to do simple things – annotate, share, discuss. Vendors need to keep it easy. Tools have failed to adapt to the people’s collaboration habits.”
" “Why embed proprietary collaboration features in each BI tool? There should be a standard collaboration tool.”
" “There’s an entire industry of collaborative vendors. I’d rather see integration than having BI vendors reinvent the wheel.” 
" “Some of the vendors are somewhat immature. Collaboration is not something you can just lay over the top of a BI tool.”
" “Generic tools always win. The collaborative BI features need to work with standard office apps in daily use and existing tools such as MS Office to increase early adoption.”

Management is to blame:

" “Collaboration improves when management makes sure there are fewer barriers/friction for sharing.”
" "In most organizations today, the CIO / CTO holds the ultimate BI power. Do we need another C-level person to make people share and imbue collaboration in the culture? Perhaps a “BIO” (BI Officer)?"
" “A Business Intelligence Officer could spend more time on obtaining insights and less on the technology than a CIO / CTO.”
" “I imagine collaborative BI rides some sort of BI maturity curve. Collaborative BI is a higher level than just trying to get insight into what’s going on in your biz.” 
" “Confidentiality is a huge issue. Could initiate some collaborative workflows via social media but need privacy at some point.”
" “Collaboration needs to be differentiated between compliance-oriented discussions vs. brainstorming or other collaboration.”

The problem lies with the users:

" “Practices and policies may help some, but the adoption solution lies with the user. Collaboration requires a different mindset and corporate culture where people are comfortable with transparency.”
" “Some people try to use data and knowledge as weapons and not tools.”

My opinion? Collaborative BI is like a diamond in the rough that hasn’t yet been cut and polished, so it doesn’t shine and hasn’t reached its potential value. But the value is definitely there.

Bottom line: Businesses and governments need shared perspectives on insights derived from business intelligence. Insight built collaboratively adds value faster and achieves faster consensus and better buy-in. Most business challenges are multi-faceted and often cross-functional. Effective collaboration not only can build a deeper understanding of the problem but also can be the basis for a solution to be embraced by employees at all levels of the organization and make the solution more doable. And it also leaves behind an audit trail of sorts.

But collaboration technology doesn’t make an organization collaborative. Collaboration doesn’t work in organizations where information is power that leads to hidden agendas, internal politics and hoarding information.

In cutting and polishing the collaboration BI diamond, remember that trust and mutual respect are critical to collaboration, as is a sense of shared responsibility.

The corporate culture should emphasize or encourage collaboration enough that employees believe in the process, are not wary of being transparent and see collaborative behavior as being in their own best interests. Publicizing successes and what can be achieved through collaboration can help drive this kind of culture. But there’s nothing more important than the leaders removing collaboration barriers and also modeling collaboration in their decision making.

Howard Dresner is president, founder and chief research officer at Dresner Advisory Services, LLC, an independent advisory firm. He is one of the foremost thought leaders in Business Intelligence and Performance Management, having coined the term “Business Intelligence” in 1989. He has published two books on the subject, The Performance Management Revolution — Business Results through Insight and Action, and Profiles in Performance — Business Intelligence Journeys and the Roadmap for Change. He hosts a weekly tweet chat (#BIWisdom) on Twitter each Friday. Prior to Dresner Advisory Services, Howard served as chief strategy officer at Hyperion Solutions and was a research fellow at Gartner, where he led its Business Intelligence research practice for 13 years.

Click here to view our catalog of premium research products

What a difference a year makes! Among the many prioritized business intelligence technologies and initiatives our company tracks in annual market studies of what is significant and strategic to BI, social media analysis has remained a back-burner issue for years. But now there are other factors at play. Battles in the U.S. presidential campaign played out over a year on Twitter, attracting people like a magnet; and now the new White House administration is using Twitter to dramatically alter perceptions and even present alternative facts. 

So, social media BI is now a hot topic, thanks in large part to politics over recent months. Are we at an inflexion point of significant change in the progress curve for social media adoption in BI and analytics? Is it postured to experience a boost in 2017 as an area of BI investment? I posed these questions to launch a recent discussion among the participants in one of my Friday #BIWisdom tweetchats on Twitter. The group was lively, and their tweeted comments show they had given thought to this even before I raised the questions. 

Compelling current data comes from social BI, someone tweeted. A participant responded, I could easily see it leading to reactive management if not used wisely. Another pointed out, So many companies ban social media, so how could it effectively work in BI? Someone else advised that the use cases for social analysis need to be understood and agreed to before trying it in BI.

As I told the #BIWisdom folks, most companies I have spoken with that are using social BI are doing so with a discrete cloud app in marketing. The group agreed that makes a lot of sense since sensitivity analysis, managing the message and competitive analysis are perfect social use cases. That makes it a business need now, not a luxury or add-on, stated an attendee. Another tweeted, There is much potential. But there are challenges in synthesizing and classifying the information and combining it with other data for analysis.

Honing in on the use of social BI in marketing, someone tweeted, Marketing is best used as a tool for tactical and strategic business alignment. But since social BI is operational, there is a high risk of misuse and of false positives. Someone added, But if marketing is the product, social BI takes on a whole new dimension. Another tweeted, Tied with that is whether social sentiment is your product (politics, for example) or whether its simply a marketing channel.

I commented that part of the issue is incompatibility between sentiment and facts, and its hard to make key decisions based on how a group might feel. One of the #BIWisdom group asked, How can companies bridge that gap to get an understanding of the indications both are pointing to?  

We then veered into opinions on whether social media's focus on sentiment will change and become more substantive. Opinions varied:

    If an organization relies on listening through social media and then adjusts, it can be beneficial.     People often take more to social media to complain or seek customer service rather than praise.      This is important. It could lead to a lot of effort in fighting perception fires rather than growing the business.     What if others take the lead of President Trump and start sharing statements of policy, etc.?     Well probably see more of it in the future. People appreciate transparency.     Social BI is a specialization of data, using data gathered entirely from outside the organization.     It could lead to trying to shoehorn someone into analyzing something that isnt their specialty. This is likely part of the reason many organizations dont put a lot of effort behind it. But it can be worthwhile.

The #BIWisdom groups conclusion is that there a lot of low-hanging fruit for social BI. And vendors are doing demos around the possibilities in social media analysis. But determining how to actually show value is difficult at this time. 

Bottom Line: Social media in BI isnt going away. But are we at an inflection point in 2017 where companies will choose to take bold steps? Or will companies sidestep social media BI initiatives  like a boxer avoiding a harmful blow? The key issue is how to tie it to critical business outcomes.

I believe organizations need to pay more attention to social media analysis as it becomes a centerpiece for government policy. The larger question is whether this will become the norm for all kinds of institutions. 

My advice for companies considering in investing in social media analysis is to become aware of the capabilities but dont try to reinvent the wheel as this movement evolves. Politicians often outsource the analysis of its social media data. Similarly, companies stepping into the social BI waters cautiously may benefit by leveraging outsourced resources, lessons learned and expertise already available. 

Learn more about this and many other topics at our upcoming Real Business Intelligence® conference, July 11-12, 2017 on the campus of MIT. 

Howard Dresner is president, founder and chief research officer at Dresner Advisory Services, LLC, an independent advisory firm. He is one of the foremost thought leaders in Business Intelligence and Performance Management, having coined the term “Business Intelligence” in 1989. He has published two books on the subject, The Performance Management Revolution — Business Results through Insight and Action, and Profiles in Performance — Business Intelligence Journeys and the Roadmap for Change. He hosts a weekly tweet chat (#BIWisdom) on Twitter each Friday. Prior to Dresner Advisory Services, Howard served as chief strategy officer at Hyperion Solutions and was a research fellow at Gartner, where he led its Business Intelligence research practice for 13 years.

Are BI Vendors Missing the Boat for Customer Success?

There are moments when you realize that you discovered something important. That happened to me recently after I spoke with business intelligence vendors that have customer success programs – yet their customers remain unhappy. Of course you could make a case that the customers didn’t train their users well enough on the BI tools or that the implementation wasn’t smooth or they had bad data.

I brought up this issue in one my recent Friday #BIWisdom tweet chats, where participants include vendors, customers, analysts and consultants. Their immediate reaction was similar to the aftermath of a storm. Quiet. The usually boisterous group with quickly tweeted opinions was quiet and didn’t tweet for a few minutes. I could almost hear their minds churning through their thoughts. Their initial tweets finally came, and it was easy to see that neither customers nor vendors had much experience with customer success programs.

“What is the definition of what a customer success team should do?” asked a participant. “Customer success is an all-in thing, more culture than a program,” someone else tweeted. “It’s a way to upsell and get more business from the same customer,” commented a vendor. “It’s a relationship, not a program,” tweeted an analyst. “Partnership and a stake in the outcome for both parties are what’s needed for mutual success,” tweeted another participant.

My own comment was that it means customers get value and expected outcomes from the software, have a positive experience with the vendor and choose to continue being a customer.

Further evidence of the need to rethink customer success programs was on display when someone asked who the customer success team should report to. Opinions varied widely:

- “Since the team should be multi-channeled they would report to the chief marketing officer in my opinion.”
- “I find it problematic when the CSM team reports into Sales; I see it as a neutral team that needs to have the voice of the client.”
- “They need to report to the Support, Engineering or Quality function. Wisdom says don’t let the fox guard the henhouse.”
- “The customer success team should be independent.”
- “Customer support spans Sales, Marketing and Support and needs all three to be truly successful. But they should report to a central function.”

I commented that I like the idea of reporting to a quality function; however, they need to have authority to drive change.

I also asked the group why customer success efforts often fail after vendors believe their customers are safely tucked away after implementation and will remain loyal. Their opinions:

“It’s difficult to quantify success in BI. It’s different for each customer a vendor interacts with; one glove does not fit all.” “It has to be an elastic program not held to an organized set of parameters or rules.” “Some vendors create customer success programs after problems arise and as an afterthought.” “One challenge is structural: salespeople and sales engineers sometimes have more incentive for new biz than for nurturing existing customers.” “There’s no handholding of the client after the sale.”

And I agreed. In our Wisdom of Crowds market surveys, we find that one of the areas where most users complain about vendors is the follow-up after the sale.

Bottom line: Overall, the tweetchat discussion suggests that a good deal must change in the area of business intelligence customer success programs.

What BI vendor doesn’t want opportunities for upsells and cross-sales with a happy customer? And what customer’s business doesn’t want to be successful with its business intelligence investments and initiatives? But each business is unique and communications must include the vendor gaining an understanding of what “success” with the BI product means to each customer. If the customer is not happy, future roll-out and expansion will be dramatically hampered. Software vendors must interact with their customers in a personalized manner and build a relationship. One of the participants in the #BIWisdom tweet chat commented that his company refers to it as “customer nurturing.”

And by the way, the team needs to maintain a long-term view of the customer’s business in order to identify opportunities for upsells and renewals, let alone reduce churn. So, as a tweetchat participant pointed out, salespeople need incentives for customer nurturing, not just incentives for bringing in new business. Don’t overlook the fact that cultural change is involved in establishing and maintaining a customer success program.

That brings me to an important conclusion: Cultural change is hard. It’s better to start off with customer success as a core tenet rather than establishing a program after customer problems arise.

 

Howard Dresner is president, founder and chief research officer at Dresner Advisory Services, LLC, an independent advisory firm. He is one of the foremost thought leaders in Business Intelligence and Performance Management, having coined the term “Business Intelligence” in 1989. He has published two books on the subject, The Performance Management Revolution — Business Results through Insight and Action, and Profiles in Performance — Business Intelligence Journeys and the Roadmap for Change. He hosts a weekly tweet chat (#BIWisdom) on Twitter each Friday. Prior to Dresner Advisory Services, Howard served as chief strategy officer at Hyperion Solutions and was a research fellow at Gartner, where he led its Business Intelligence research practice for 13 years.

 

Click here to view our catalog of premium research products

6 Things to Know When Increasing User Adoption of BI

Wouldn’t it be great if every time we start a new endeavor experts pave the way to our success with a list of “never do this” and “always do this” advice? Especially when an endeavor appears deceptively simply but has hidden pitfalls. That’s the case with efforts to gain deeper penetration – increase user adoption – of business intelligence solutions within an organization. And there’s a lot of that going around these days.

Some companies have found their footing with BI after initial projects to see if it delivers on its promises and now are off and running. Others started out with just executives and management as users and now want to spread BI across the organization to gain even greater beneficial insights. And a third segment are completely new to BI and want to make sure their implementation and user adoption are successful.

In a couple of sessions of my Friday #BIWisdom tweetchats, I asked participants to share their real-world kernels of wisdom on success in increasing user adoption. Here are their tweets about difference-makers that will help your organization’s user adoption efforts not to go into a tailspin.

Never do this

1. Management should never forget to explain how BI will not only help the company but also help employees do their jobs. Often, people are a barrier to success. Some people are averse to change and some are straight-up afraid of BI and how it might impact their job. Fear is a potent force! At the onset, management needs to assure users that BI is here to help, not get them fired.

2. Never implement the BI solution in a manner that makes users completely change their ways in how they work. Instead, embed the BI solution in the flow of their day-to-day life. Make sure they don’t have to go looking for it.

Always do this

3. People don’t think of themselves as ‘BI users.’ Managers always need to encourage employees to use the BI solution. One way to do this is to encourage them to look for the story that the data tells. Business users have good insights into how to make an impact on the business. Always help them understand how data can create value and impact.

4. Always provide user training. You need to ensure a level of user competency. A pitfall in increasing user adoption is to believe the vendor’s sales rep who says your employees won’t need much training because the tools are easy to use. Some self-service tools are overstated as to their ease of use, and tools are not a one-size-fits-all solution. Also, always remember that educating employees for BI solutions should not focus just on the technology; make sure their training is job or analysis focused.

5. Always provide recognition for employees whose insights from BI contribute to the organization’s success. They need to be nurtured – and even promoted if possible – within the organization. When employees perceive that using the BI solution is a path to recognition and advancement, user adoption increases.

Bottom line: Adding my own difference maker to the suggestions of the #BIWisdom tribe, #6 goes in the “always do this” list. Always remember how critical a BI “evangelist” or “champion” is to success in user adoption. Fear and lack of trust present the greatest – unspoken – barriers to user adoption.

The evangelist might be a trailblazer, one of the early adopters in the company who can share stories of proven success. Or it may be an executive in the C-suite who demonstrates – not just tells, but shows – how a data-driven culture is a winning strategy for the company.

And, finally, increasing user adoption often requires a “marketing” effort. For the greatest success, the marketing needs to explain not just “why” but also “how.” When employees understand how they can contribute to the success, there’s a better chance they’ll jump on board.

 

Howard Dresner is president, founder and chief research officer at Dresner Advisory Services, LLC, an independent advisory firm. He is one of the foremost thought leaders in Business Intelligence and Performance Management, having coined the term “Business Intelligence” in 1989. He has published two books on the subject, The Performance Management Revolution — Business Results through Insight and Action, and Profiles in Performance — Business Intelligence Journeys and the Roadmap for Change. He hosts a weekly tweet chat (#BIWisdom) on Twitter each Friday. Prior to Dresner Advisory Services, Howard served as chief strategy officer at Hyperion Solutions and was a research fellow at Gartner, where he led its Business Intelligence research practice for 13 years.

 

Click here to view our catalog of premium research products

Internet of Things Makes Some Aspects of BI More Crucial

My favorite latest sensor, trakdot.com, lets me know where my luggage is when I land from an air flight. It’s just one in a myriad of devices in the Internet of Things, which delivers vast amounts of diverse information concerning surroundings, people and various assets. The IoT’s opportunities for businesses are exciting, and it’s already clear that sensor data is and will continue to be a game changer in many areas.

The Internet of Things makes business intelligence solutions and analytics essential, so we decided to explore the IoT implications in one of my recent #BIWisdom tweetchat sessions. We started with this question: What does the IoT do to BI corporate infrastructure?

Someone tweeted that it will challenge many legacy setups that were “built in knee-jerk fashion.” Another commented that the data preparation / modeling stage will be more crucial than ever before.

Then came another question: “Is the IoT a way that Hadoop will finally be accepted by many as part of the hybrid data?” Good question. We agreed that Hadoop will likely be a part of many IoT solutions in a growing mix of overlaying technologies, tools, concepts and methods.

And the age-old question of whether “ownership” of BI should reside in IT or the business units came up as someone queried: “Will the IoT drive BI back into the IT realm, or can it still be managed by the line of business?” Another participant in the group responded that the “extra layers of intricacy will probably drive it up to a C-level imperative for a while, which might help integrate with IT.”

As I shared with the group, from the research we conduct in our annual market studies, IoT BI so far is usually a line-of-business initiative driven by sales and marketing.

The nice thing about the IoT, tweeted a participant, is that it gets us even closer to where the data is generated and gives us access to data in real time. “Operational BI is the Holy Grail, and IoT helps get us there.” Someone else put it another way: “The beauty of the IoT and analytics is the volume of data from more sources. It will be easier to see repeat patterns and project what to expect, which will lead to better decision making.”

And that’s the challenge. How will we analyze the data that sensors bring us?"

The group quickly voiced their opinions and, as you can see, they weren’t all in agreement:
" “Raw data can definitely mislead the unqualified. It will be up to BI to add knowledge to produce intelligence.”
" “To analyze all that data in real time at massive scale, with value add, requires massive computing power – the cloud, for example. Or it will require streaming capability.”
" “A business may not want to land all the data, just the outliers.” 
" “But there has to be intelligence in the system to winnow out what the outliers are.” 
" “Maybe you land the abnormal patterns for a closer look and better predictive information.”
" “Reducing it to a subset will help with real-time data.” 
" “Agreed, but you still need processing power to determine what is abnormal. What if all sensors alarm at once, for example?”

Our #BIWisdom session concluded with two forward-looking comments. 
" “The data volume will be huge. I think businesses underestimate the impact on processes.”
" “There will be a lot of privacy concerns around the IoT data. As with any new technology, it can be used for noble or nefarious purposes. It will take a while for the laws to catch up – if they ever do.”

Bottom line: The possibilities of the good things that businesses and industries will be able to achieve through IoT data boggle the mind. But it comes with complexities and challenges.

Leveraging that data to advantage will require the entire BI analytics tool chest – descriptive, predictive and prescriptive tools. Most of all, the cloud is the key to the IoT. Many interesting data sources will be external to organizations and massive data will be stored in the cloud.

The areas of BI that will be most crucial in an IoT world are end user data preparation, predictive analytics, mobile BI, and location intelligence. In fact, the IoT will put location intelligence functionalities in the spotlight; this has been a BI sleeper until now.

There certainly will be a sea change in how we treat data, consumers, analytics and BI. Inevitably, we’ll be faced with good and bad effects from the IoT; regardless, businesses and BI teams must prepare for it.

Howard Dresner is president, founder and chief research officer at Dresner Advisory Services, LLC, an independent advisory firm. He is one of the foremost thought leaders in Business Intelligence and Performance Management, having coined the term “Business Intelligence” in 1989. He has published two books on the subject, The Performance Management Revolution — Business Results through Insight and Action, and Profiles in Performance — Business Intelligence Journeys and the Roadmap for Change. He hosts a weekly tweet chat (#BIWisdom) on Twitter each Friday. Prior to Dresner Advisory Services, Howard served as chief strategy officer at Hyperion Solutions and was a research fellow at Gartner, where he led its Business Intelligence research practice for 13 years.

Click here to view our catalog of premium research products

Collaborative Computing and BI,2015: Three Takeaways

Recently we published our fourth annual Wisdom of Crowds Collaborative Computing and Business Intelligence Market Study.

At the highest level, collaborative support for group-based analysis remains a mid-tier BI priority in 2015, behind dashboards and data warehousing, yet ahead of topics including cloud BI, big data and social media analysis. A majority consider both collaborative BI and enterprise frameworks, at minimum, important.

Collaboration is integral to most business activities. Today, organizations have more tools and channels by which to collaborate than at any time in history. Our report documents – from traditional to contemporary – the many ways individuals share business intelligence in group collaboration. We asked users and the industry to explain what they prioritize and what makes them successful. We pay special attention to collaboration features baked into BI software and the extensions and frameworks that support group projects and decision making.

Here are three takeaways to keep in mind as you evaluate collaboration and collaborative BI in your own organization

1. State the obvious. Few would dispute the observation that very successful organizations always seem to work well together, and a central finding supports this. Though adoption is low, organizations that consider themselves successful at business intelligence and able to take Action on Insight are also most likely to use collaborative features built into their BI tool. Organizations that are least successful at BI are least likely to use BI collaboration (and very unlikely to try).

The Dresner Advisory view is that collaborative BI creates organizational memory. A documented and closed-loop process that can be audited and referenced maintains decision context and improves training. If not relevant to all working processes, collaborative BI can be a foundation for establishing and sustaining best practices in core business activities

2. Utility creates users. Like the items on a desk, information workers instinctively reach for the fastest, most effective ways to do their job well with others. They use tools that seamlessly enhance annotation, co-authoring, highlighting, file sharing, storytelling and other uncomplicated upgrades. If a new tool is not an improvement on email or the phone, it will either not be adopted or old ways will die a slow hard death. Closely consider the report’s findings in industry criteria, user requirements, vendor rankings and how your existing or potential supplier compares.

3. There is no cost barrier. The number of vendors charging their customers for collaborative
capabilities within BI products fell to an all-time low of 9 percent in 2015. Many powerful collaboration frameworks and file-sharing services are also free to use. That said, establishing BI collaboration best practices is a cultural and leadership challenge that should never be underestimated. Even if old habits die hard, adopting change (because it is better) is always preferable to enforcing it.

You’ll find this and much more in our report, all free to the qualified users who filled out our survey and this is just one of over a dozen reports we’re producing this year. For those that are not a part of our research community, the report is available for purchase atwww.collaborativereport.com

Jim Ericson
Research Director
Dresner Advisory Services

Jim Ericson is a research director with Dresner Advisory Services. Jim has served as a consultant and journalist who studies end-user management practices and industry trending in the data and information management fields. From 2004 to 2013 he was the editorial director at Information Management magazine (formerly DM Review), where he created architectures for user and industry coverage for hundreds of contributors across the breadth of the data and information management industry. As lead writer he interviewed and profiled more than 100 CIOs, CTOs, and program directors in a 2010-2012 program called “25 Top Information Managers.” His related feature articles earned ASBPE national bronze and multiple Mid-Atlantic region gold and silver awards for Technical Article and for Case History feature writing.

Click here to view our catalog of premium research products

Cloud Computing and BI 2015: Three Takeaways

Cloud-based business intelligence is now an ingrained practice at many organizations – maybe even your own. More than half of the respondents to our 2015 Cloud/Cloud BI study agree that cloud BI is important, very important or critical to their operations and planning. More than half use or plan to use cloud BI in the near future.

But other organizations are plainly and sometimes painfully stalled. The “great wave” of cloud BI is almost surely going to arrive piece by piece and not universally. Right now, private cloud models are preferred but some departments crave public cloud solutions. Organizations are wrestling third-party data that sometimes arrives with its own tools and analytics. Standards and security are known and unknown hurdles to adoption.

Here are three takeaways to keep in mind as you evaluate cloud BI in your own organization:

1. Cloud BI is still BI. Interest in cloud BI features is increasing, but, public or private, users want the same kinds of functionality in their on-premise enterprise BI platforms. Self-service, dashboards, ad-hoc query, integration/ETL and production reporting capabilities are at the front of the line. Needs vary by function but actual feature priorities are almost identical for organizations of different size. Certainly, time to value and performance goals push users toward cloud BI, but the message is, “give me what I need to do my job and don’t reinvent the wheel.” Sales and marketing are going to lead the cloud BI push so look and learn from their experience.

2. Cloud BI terminology matters. Our report goes into some detail concerning public, private and hybrid cloud adoption and there’s valuable trending data within. We also note that different users and the vendor community parse these words differently. For example, if you want to blend data that is on and off-premise, you’d necessarily employ a hybrid cloud. Though organizations plainly want to pursue this strategy, they’re less inclined to pursue something called a hybrid model. The more thoroughly you discuss terms and distinctions and what they imply for your own BI program, the more confidently you’ll move down that path.

3. Security is the only elephant in the room. Old news, yes, and it’s truer than ever: security is overwhelmingly the single biggest concern and reason why organizations are not moving toward cloud BI. Corporate leaders and decision-makers with jobs at risk in the event of a breach are most aware, yet events have shown closely held data is just as vulnerable as data in a cloud. A majority of respondents to our cloud BI survey are simply unaware of relevant industry standards which often center around security as well. To get cloud BI off and running, we’ll need to take the discussion into the open and address the boardroom as well as the rank and file.

You’ll find this and much more in our report, all free to the qualified users who filled out our survey and this is just one of over a dozen reports we’re producing this year. For those that are not a part of our research community, the report is available for purchase atwww.cloudbireport.com

Best,

Jim Ericson
Research Director
Dresner Advisory Services

Jim Ericson is a research director with Dresner Advisory Services. Jim has served as a consultant and journalist who studies end-user management practices and industry trending in the data and information management fields. From 2004 to 2013 he was the editorial director at Information Management magazine (formerly DM Review), where he created architectures for user and industry coverage for hundreds of contributors across the breadth of the data and information management industry. As lead writer he interviewed and profiled more than 100 CIOs, CTOs, and program directors in a 2010-2012 program called “25 Top Information Managers.” His related feature articles earned ASBPE national bronze and multiple Mid-Atlantic region gold and silver awards for Technical Article and for Case History feature writing.

Click here to view our catalog of premium research products

Will 2015 be the Year for Operational BI & Hybrid EDW?

Tweets flew back and forth quickly at one my recent Friday #BIWisdom tweetchats. The tribe really got into sharing their opinions of where BI is today – thanks to hopes for advancements in 2014 that were achieved or remain unfulfilled – and opinions about what BI technologies organizations will invest in during 2015.

Here are their top observations regarding what’s gaining buzz for growth and investments for 2015:

- Natural language processing (NLP)
- Infographics
- Streaming BI due to the Internet of Things 
- In-memory analytical sandboxes sitting above a hybrid EDW with traditional and non-traditional sources
- Operational BI

The #BIWisdom group took off on a discussion of operational BI. Someone questioned whether it will get bogged down in big
data hype and whether it can progress on its own. Another tweeted that “operational BI teams with data providers and
data integration (DI) tools are the next bastion of BI to increase its footprint.” That spurred this question: “If we combine these three sources, does it lead us to the hybrid EDW in a faster fashion?”

Another tweeted, “I like the idea of a hybrid EDW to combine everything, including big data.” Someone countered with "EDW
actually prevents good operational BI data from being used.

They concluded that the hybrid EDW allows for streaming but also historical analysis. And the hybrid EDW fits in because 
operational BI doesn’t have all the old historical data. The aim, a participant tweeted, “should be to support both in the hybrid EDW in order to allow best of breed.”

Bottom line: Looking back at adoption and advancements in business intelligence technologies in 2014, it’s clear that 
mobile BI became more mature; but it hasn’t yet replaced desktops. It remains important to BI, as it’s the way to engage more users and younger users; and I believe we’ll see this phenomenon playing out in 2015. Inroads were made into adopting collaborative BI in 2014. But it wasn’t the year for social BI as some analysts had predicted (and some vendors hoped). There was significant success in 2014 where application vendors embedded BI.

What’s ahead for BI technologies in 2015? I agree with the #BIWisdom group that operational BI will be big in 2015. But 
infographics? Probably less so. During 2015 we’ll continue to invest in what we think are the most important areas of research such as cloud BI, advanced and predictive analytics, collaborative BI, embedded BI, location intelligence and mobile BI. New hot topics for 2015 will include enterprise planning, big data analytics and end-user data prep – which will publish this month!

Personally, my favorite predictions are those that say a new technology will completely displace an old one. Never happens. 
If that were true, Microsoft’s Excel wouldn’t still be at the top of BI tools users like.

Howard Dresner is president, founder and chief research officer at Dresner Advisory Services, LLC, an independent advisory firm. He is one of the foremost thought leaders in Business Intelligence and Performance Management, having coined the term “Business Intelligence” in 1989. He has published two books on the subject, The Performance Management Revolution — Business Results through Insight and Action, and Profiles in Performance — Business Intelligence Journeys and the Roadmap for Change. He hosts a weekly tweet chat (#BIWisdom) on Twitter each Friday. Prior to Dresner Advisory Services, Howard served as chief strategy officer at Hyperion Solutions and was a research fellow at Gartner, where he led its Business Intelligence research practice for 13 years.

Click here to view our catalog of premium research products

Watch Out for Business Intelligence "Gotchas"

When it comes to commentary with valuable real-world insights, I can always count on the participants at my weekly #BIWisdom tweetchats on Fridays. I kicked off a recent discussion with this question to the group: “What are the top five worst practices in business intelligence?”

It took only a few minutes for them to toss out a lot more than five. As I commented then, there are a bunch of successful overachievers who participate in #BIWisdom tweetchats!

I certainly don’t want to minimize the great successes organizations are having with business intelligence. But it’s a fact that some BI initiatives sputter. So let’s look at why a BI initiative sometimes doesn’t fully deliver on its promise. Failures, after all, are very instructive.

So here’s the list we compiled — 
Some of the worst mistakes organizations make in BI initiatives

Technology/tools: 
" Thinking the BI toolset will make up for not understanding the business
" Thinking BI tools will solve the business problems instead of using BI to solve the problems
" Generalizing solutions or tools for all types of users – BI is not a one-size-fits-all type of solution and many “tend to implement bright shiny objects with no real understanding of whether or not it’s a good fit with their organization”

Data:
" Thinking that data quality is a technical problem
" Thinking that data quality is not everyone’s concern
" Assuming some nice-looking charts from bad underlying data is actually good BI
" Believing the same visualization will work across different datasets
" Assuming that all the data is not relevant and some should be excluded

Insights:
" Having a mindset to shoot the messenger who delivers unanticipated insights
" Being afraid to share BI insights with customers and suppliers; this comment was followed by a tweet that “sharing the insights is a good way to cement ties in the value chain and it’s good business”
" Internal or external billing for every small change to a report, analysis, etc. – “it kills what analytics is about”
" Undertaking projects that depend on looking at the existing reports and recreating in BI with no change
Training:
" Knowing how important training is but still running out of funding for it 
" Believing a sales rep who says you don’t need much training – “remember, they make more from license sales”

Implementation/outset:
" Implementing BI technology without use cases
" Being unwilling to disrupt existing processes to gain the BI success
" Not resolving misalignment between IT and business users – “this results in fighting over scheduling priorities and diminished resources”
" Asking questions primarily in retrospect – “it’s much easier if questions come first”
" Not owning the biz problem – “an example: it’s in the data warehouse, so it’s not my job”
" Focusing solutions exclusively upon executives; but a tribe member tweeted that we can attribute this to a sales tactic in earlier days when it was the only way vendors could sell outside of IT since the executive team had the money to buy

Those are the frontrunners among the culprits that erode the achievable value in BI initiatives.

One of the #BIWisdom participants pointed out that many of these issues have the same root cause: lack of trust – either trusting the business users, IT or the BI “experts.” A lack of understanding about technology can breed distrust. And good communications between all involved can reduce misunderstanding up front.

The area of training I agree – recurrent training is essential for success. One of the participants tweeted that schools have finally caught on and are teaching for data enthusiasts. As she observed, these days, “everybody is a data generator and consumer. Computing and analysis are no longer synonymous with IT; they are a common way of life with everyone.” Millennials are changing the way we consume and report data, so a generational change is starting to make a difference regarding the importance of training.

Bottom line: It’s that time of year when the Internet is flooded with articles and blog posts of predictions for the upcoming year. As I often tell journalists and inquirers, I don’t have a crystal ball and don’t make predictions. But I’ll make an exception now – I predict that we’ll see even more success in BI initiatives in 2015 if organizations eliminate these “gotchas” from their practice.

Howard Dresner is president, founder and chief research officer at Dresner Advisory Services, LLC, an independent advisory firm. He is one of the foremost thought leaders in Business Intelligence and Performance Management, having coined the term “Business Intelligence” in 1989. He has published two books on the subject, The Performance Management Revolution — Business Results through Insight and Action, and Profiles in Performance — Business Intelligence Journeys and the Roadmap for Change. He hosts a weekly tweet chat (#BIWisdom) on Twitter each Friday. Prior to Dresner Advisory Services, Howard served as chief strategy officer at Hyperion Solutions and was a research fellow at Gartner, where he led its Business Intelligence research practice for 13 years.

Click here to view our catalog of premium research products

Have you Opened Your Business Intelligence Treasure Chest?

It happened so fast …. With one foot in the trap, it looked like he had utterly failed in his mission. … It all started nineteen years earlier when ….

Everyone likes a good story. Especially marketing teams in today’s leading businesses. They know that effective storytelling enhances brand and knocks down barriers to sales.

Similarly, it’s becoming a powerful way to distribute data and information in business intelligence initiatives. Several business intelligence vendors even promote storytelling as a needed component of data discovery.

So, with the participants in one of my recent Friday #BIWisdom tweetchats, we explored what’s happening today with BI storytelling. I started the discussion by stating that I think it’s about applying context to BI-derived content and that I see storytelling as an integral part of a broader collaborative capability.

Several agreed that storytelling is “sharing” and thus part of collaboration to bring people “through a data-driven journey” or bring the “results of statistical analysis into others’ workflows.”

Therefore, others added, collaborative features should be an integral (and easy to use) part of BI tools. But someone pointed out most BI tools today focus on the quantitative and technical areas, not experiential areas.

The discussion turned direction when a participant tweeted that storytelling is independent of any BI technology. “It’s a craft or an art, which is poorly understood and needs formal constructs,” he said. “That’s what bugs me,” someone else tweeted. “Vendors may add features to aid in storytelling, but it still needs the craft, the art of storytelling.”

One suggestion was that it might help if companies create a data template based on a narrative structure and enhancement of interactivity to enforce the story understanding. But someone countered that with an opinion that storytelling is both graphic and narrative but not necessarily interactive.

So what does the BI storytelling craft encompass? The #BIWisdom tribe’s opinions were that it must include all or most of these elements:

Be a highly condensed story with a beginning, middle and end that is relevant to the listeners Have a hero — someone who accomplished something notable or noteworthy Incorporate a surprising element, something that shocks the listeners out of complacency and shakes up their model of reality Stimulate an “of course” reaction and the listener should see the obvious path to the future; get the listener “from there to here” while believing they found their own way Embody the desired change process Inform and also motivate the listener to take action or want to know more Create a personal connection between the listener and the message in order to change the listeners’ opinion or inspire them to undertake difficult goals to improve things

That’s a tall order.

“Should storytelling be one of the main skills of a data scientist?” asked a tribe member.
Another stated it requires good analytical skills with a good balance with visual and narrative storytelling capabilities.

Is this combination of skills available broadly? Is storytelling an innate talent, or can people be trained to become great storytellers? Can technology make a BI business user a skilled storyteller?

What do you think?

Bottom line: Just as collaborative tools don’t make organizations collaborative, data storytelling tools don’t make users good storytellers. Does that mean that data storytelling in BI tools is a red herring? I don’t think so. I believe it’s a necessary — albeit today immature — feature set that will evolve to become more effective. And people can improve their storytelling skills with training.

Storytelling is like the surprise in a treasure chest — the key to buried riches in business intelligence outcomes. If your organization hasn’t opened this treasure chest yet, don’t continue to overlook it.

The bottom line, though, is the aftermath — what happens after the data is initially presented. The carefully crafted story will not only be insightful but will also cause a reaction that leads the listeners to take action. And therein lies your buried treasure or ROI.

 

Howard Dresner is president, founder and chief research officer at Dresner Advisory Services, LLC, an independent advisory firm. He is one of the foremost thought leaders in Business Intelligence and Performance Management, having coined the term “Business Intelligence” in 1989. He has published two books on the subject, The Performance Management Revolution — Business Results through Insight and Action, and Profiles in Performance — Business Intelligence Journeys and the Roadmap for Change. He hosts a weekly tweet chat (#BIWisdom) on Twitter each Friday. Prior to Dresner Advisory Services, Howard served as chief strategy officer at Hyperion Solutions and was a research fellow at Gartner, where he led its Business Intelligence research practice for 13 years.

Click here to view our catalog of premium research products

Dresner's Point: Organizations Need to Eliminate Data Sheep in BI

Perhaps a tag with “some assembly required” should be attached to business intelligence analytics tools.

We just released in July our Advanced and Predictive Analytics Market Study report in our Wisdom of Crowds series, and I wanted to explore the topic in more depth in one of my recent Friday #BIWisdom tweetchats. Our market survey found that awareness of the importance of BI analytics is high (90 percent), but adoption of analytics tools is in the early stages of deployment even though many of the tools have been available for decades.

I asked the tweetchat tribe about the current challenges that BI analytics face (from the users’ point of view) and, as usual, they tweeted a variety of opinions.

Several agreed that the biggest challenge is there are too many solutions and thus a lot of hype, which leads to confusion. Someone else commented that it’s not there are too many tools but rather that organizations haven’t found the right ones for their industry or segment specificity.

A dominant viewpoint among the group held that a lot of the analytics tools don’t scale or perform the way they were “told and sold,” especially when it comes to accessing multiple data sources. That comment generated a resounding thumbs-up response from several in the group. One person asked how it’s possible to “see through the PR fluff to the truth.”

Cost factors into the challenges too. Several agreed that user-based, per-seat license costs are too high. Another tweeted that license is never the biggest cost but is the first one looked at and often a driver. For that reason, vendors often discount license fees. But they rarely discount services such as implementation, maintenance and support, which are also significant.

The challenge that rose to prominence in our tweetchat is the lack of training and support for analytics tools. As the #BIWisdom tribe observed:
" A big challenge is data literacy. Users can see their stats but might not know what they mean.
" Companies are scrambling for analytics talent, and software companies are touting “everyone an analyst.” But not everyone is a data analyst. However, most users need to know how to adjust two or three key variables for better output. Data fluency among users is needed. Not everyone needs to be fluent in “talking” directly to the data, but every user needs a basic understanding. So a stratified approach is needed.
" Breeding a lifetime of data analysis starts with good training and support.

Most of the group agreed that education is playing a huge part in converting traditional data users to BI, but they dismissed the notion that it’s happening quick enough for the shift to analytics and predictive analytics.

And everyone agreed that all business people need education on critical thinking to become analytically driven. One of the tribe summed up the discussion: users lacking the ability to think critically are a big BI challenge for organizations today.

Bottom line: Organizations need to avoid what I call “data sheep” – creatures with a total reliance on software tools to present analysis and data. People still need to think. Knowledge of how to create a BI plot, for instance, and which type to use, is appropriate even if a tool automates it.

Sheep need the guidance of shepherds. Training in the principles of data analysis is necessary for BI analytics success, regardless of the tool. Also, even if a tool is ideal for an organization, the company culture will likely need to adapt, which requires education.

My opinion – and not stated sheepishly – is that all obstacles that stand in the way of business insights and users need to be minimized. The best way to achieve that is through training and support.

 

Howard Dresner is president, founder and chief research officer at Dresner Advisory Services, LLC, an independent advisory firm. He is one of the foremost thought leaders in Business Intelligence and Performance Management, having coined the term “Business Intelligence” in 1989. He has published two books on the subject, The Performance Management Revolution — Business Results through Insight and Action, and Profiles in Performance — Business Intelligence Journeys and the Roadmap for Change. He hosts a weekly tweet chat (#BIWisdom) on Twitter each Friday. Prior to Dresner Advisory Services, Howard served as chief strategy officer at Hyperion Solutions and was a research fellow at Gartner, where he led its Business Intelligence research practice for 13 years.

Click here to view our catalog of premium research products