Capping IT Off

Capping IT Off

Opinions expressed on this blog reflect the writer’s views and not the position of the Capgemini Group

Building an Analytic Culture Part II: Why Big Data can seem like the mysterious land of Oz. A subjective history of information management

Information Management can be the mysterious land like Oz to IT personnel and organizations patterned to deliver point solutions such as ERP with recognizable “owners”, defined requirements and predefined outcomes that are often solved by implementing or integrating modules or suites from ERP vendors. Information Management on the other hand is seen as this obnoxious area where everybody is a stake holder, has an opinion, an interest and often has hard to define outcomes. Part of Information Management’s mystery comes from the fact that it was born out of the “Intelligence” departments within the military and then adopted by companies who patterned themselves on the military model and structure. Demanding Line of Business executives required more and more of their staff support groups in terms of charts, graphs, reports and data. Staff support organizations expanded to support this need by creating specific Intelligence units within their divisions. Shared Service IT often had a very limited role in real intelligence gathering and dissemination except as a source of raw data for the staff support Intelligence organizations. In the late 90s and 2000s, companies who had achieved a lot of efficiencies by implementing COTS solutions for many problems began looking for COTS solutions to solve the “Intelligence problem”. They came back and implemented a bunch of solutions with strange names like “Data Warehouse”, “ETL”, “Reporting”, “Master Data Management” etc but there could find no panacea COTS package that solved this “Intelligence” problem.

When staff support organizations came for Decision support, IT departments simply added them to their normal lifecycle and process.  For a time, this process worked well as Staff Support organizations simply adopted the Data Warehouse as yet another albeit more comprehensive source of information and continued with their intelligence pursuits. Over time, Data Warehouses stored so much information that they became integral many operational processes within business units. This often required more IT ownership of these solutions from a support and SLA standpoint. As IT took ownership of information management solutions, IT began “hardening” the solutions against failure. Again initially, this seemed to work well since on paper, avoidance of failure is very important to the success of business functions. However and over time, avoidance of failure turned out to be a major hurdle to internal innovation. Many companies are stuck in a conundrum unable to support two seemingly divergent requirements of supporting hardened production applications from their data warehouses while facilitating a culture of experimentation and analytics. 

The market and evolution punishes those that protect the status quo while failing to adapt and evolve. In order to succeed organizations need to become nimble and support a culture of experimentation where change and uncertainty is normal while also supporting hardened predictable applications that support operations. It requires CIOs and IT departments who have often had a NASA like perspective for all of IT to understand that their big data ideation center requires a different mindset from their mission critical ERP and Operational support applications. This change in culture can be very hard for organizations who have had a culture of ruling by fiat and optimizing their processes and structure along functional lines for years.

The nimbleness and degree of experimentation that an organization facilitates through Big Data and other information management technologies can have a significant impact on the direction, morale and momentum of the company. Success through and adoption of Big Data may depend more on the context, structure and processes with which Big Data and Analytics is deployed and less on the platforms and technologies that are selected.  
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Goutham Belliappa

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