This is the first article in a multi-part series discussing the strategic considerations and crucial technical details that senior managers and CxOs need to consider in an enterprise-wide analytics infrastructure modernization strategy . We share observations and insights we ’ ve gleaned in our role as a partner in these journeys with multiple clients .
This article focuses on large data warehouses , covering their current state in the enterprise , barriers to modernization , the case for modernization and a set of strategic considerations that highlight any data warehouse modernization approach .
The Old Octopus and Its Tentacles
If we walked into a Fortune 500 company today and investigated the analytics infrastructure that supports its financial reporting , OLAP slicing and dicing for business intelligence , and advanced analytics and dashboards for the CXOs , we would most likely find a massive , clunky old data warehouse churning noisily at the heart of it all . The legacy data warehouse , like an old octopus , extends its tentacles into the deeper corners of the organization , either feeding on or spewing out data in various shapes and forms . Most enterprises pay millions in annual licensing fees and employ hundreds of ETL developers , DBAs and report writers to support , maintain and modify all the data feeds going in and coming out , and the thousands of static and dynamic reports hungrily consumed by business teams all over the company .
If we sat down with the business users who are the supposed beneficiaries of such a large recurring investment , we would most likely hear a long list of usability , performance and time-to-market issues , as users openly discuss their deep dissatisfaction with their dependence on the IT organization .
So , why do businesses still keep this extremely expensive , lumbering , coughing and wheezing data warehouse that has clearly outlived its purpose ?
Barriers to Modernization
Here are the three most powerful factors that play a dominant role in maintaining the data warehouse status quo .
Lack of a Defining Event
Discrete pockets of dissatisfaction rarely coalesce into a voice powerful enough to force the organization to start thinking about disrupting a legacy system that has become an unruly octopus . In our experience , we rarely see a push toward data warehouse modernization without a defining event--either an
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