Data Leaders Must Face the Challenge of Fragmentation in Analytic Data Infrastructures

Dresner Advisory Services defines Analytic Data Infrastructure (ADI) as the underlying infrastructure that provides the data management and operations needed for analytic content for business intelligence (BI) and analytic use cases. ADI includes on-premises and cloud-deployed platforms such as data warehouses. Understanding ADI capabilities, uses, and adoption helps prioritization planning, developing, and execution of a BI and analytics strategy.

Data-engineering pipelines and data integration compliment ADIs and provide the means to simplify and speed up access by BI users, for many use cases, to relevant, qualified, and governed analytical content. Data-engineering pipelines provide the data connections that enable BI users to access their analytic content, regardless of the ADI from which the content is sourced.

Our data show greater fragmentation of ADI capabilities, priorities, and deployments, driven by the divergence of organizational and functional use case requirements and a lack of data governance. As a result of this fragmentation, BI users find it increasingly difficult to locate and access the analytical content they need. Our data show that more than 50 percent of organizations have BI users that have a difficult-to-impossible task of accessing the analytical content they need.

However, our data also show that 96 percent of organizations are extremely or somewhat satisfied with their current approaches to data integration and data-engineering pipelines.

How can both of these seemingly contradictory data points be true? The reality is that many data leaders now face and must manage a previously unrecognized challenge: the ADI development-fragmentation-redevelopment cycle.
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