Many Factors Drive Analytical Data Infrastructure (ADI) Plans and Decisions
As organizations plan, develop, and execute a business intelligence (BI) and analytics strategy, they also need to include an assessment of their analytical data infrastructure (ADI) options.
We define ADI as: a set of technology components for integrating, modeling, managing, storing, and accessing the data sets that serve as sources for analytic/BI consumers, e.g., analytic/business applications, tools, and users.
Developing a business and technical strategy for cross-functional, multiple-use-case BI and analytics projects is more difficult than ever due to the:
- Range of innovation
- Variety of ADI platform capabilities
- Diversity of use cases
- Absence of corporate standards and governance
When deciding on ADI priorities and selection criteria, important factors to consider include:
- Scale and performance
- Security and privacy capabilities
- Data modeling and management capabilities for relevant data types
- Data-integration support
- Analytical features embedded in the ADI
- Development features and services
- Deployment options
- The two primary factors driving most ADI platform decisions are (a) security of the data and analytic workloads and (b) scale and performance that can meet digital business transformation needs.
- The current ADI “standard” for most organizations can’t adequately address new and existing BI and analytic use cases, associated data workflows, and multiple business preferences and priorities.
- Different business functions have different priorities, deployment preferences, and BI and analytics use cases—all of which will complicate ADI platform decisions.
- Many organizations are still evaluating whether to—or when to—migrate business processes and related BI and analytic tools and data infrastructure workflows to cloud services.
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