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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.

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