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Big Data Analytics: For Many, It’s a Departmental Affair

I’ve recently been interviewing users and business sponsors, asking them about their new practices with advanced analytics, plus the special role of big data. When I ask people to talk about critical factors that make or break or success, they usually come around to a common issue that needs sorting out. It’s the fact that most analytic applications are departmentally focused (often departmentally owned and funded) and they satisfy department requirements, not enterprise ones.

Give me a minute to explain what I’m hearing from users, as well as why big data analytics is progressively a departmental affair:

Analytic applications are departmental, by nature. Just about any analytic application you think of is focused on tasks, data domains, and business opportunities that are associated with specific departments. For example, customer base segmentation should be owned and executed by marketing and sales departments. The actuarial department does risk analysis. The procurement department does supply and supplier analysis.

Most data warehouse (DW) and business intelligence (BI) infrastructure is not designed for advanced analytics. In most organizations, it is, instead, designed and optimized for reporting, performance management, and online analytic processing (OLAP). This enterprise asset is invaluable for “big picture” reports and analyses that span enterprise-wide processes (especially financial ones). And it’s capable of satisfying most departmental requirements for reporting and OLAP. But, in many organizations, the BI/DW infrastructure cannot (and, due to its owners, will not) satisfy departmental requirements for advanced analytics and big data.

Many departments are deploying their own platforms for big data and analytics. They do this when the department has a strong business need for analytics with big data, plus the budget and management sponsorship to back it up. Just think of the many new vendor tools and platforms that have arisen in recent years. Data warehouse appliances, columnar databases, MapReduce, visual discovery tools, and analytic tools for business users all supply analytic functionality that user organizations are demanding at the department level. And all are built from the bottom up to management and operate on big data. Obviously, big data analytics can be implemented on older, more traditional databases and tools, as well.

Put it all together, and this user and vendor activity reveals that big data analytics is progressively a departmental affair, implemented on departmentally owned platforms.

So, what do you think? Does the trend toward departmental big data analytics make sense to you? Let me know. Thanks!

Posted by Philip Russom, Ph.D. on May 10, 2011


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