Bi-Modal BI: Balancing Self-Service and Governance

                                   

The age old conflict.  IT needs centralization, governance, standards and control; on the other side of the coin?  Business units need the ability to move fast and try new things.  How can we get lines of business access to the data they need to for projects so they can spend their time focused on discovering new insights?  Typically they get stuck in a bottleneck of IT requests or spending 80% of their time doing data integration and preparation.  Neither group seems particularly excited to do it, and I don’t blame them.  For the analyst it increases the complexity of their tasks and seriously raises the technical knowledge requirements.  For IT, it’s a major distraction from their main purpose in life, an extra thing to do.  Self serve BI is trying to destroy the backlogged “report factories,” only to replace them with “data stores,”  which are sadly even less equipped for the job at hand.  Either way, the result is a painfully inefficient process, straining both ends of the value chain in any company that embarks on the data driven journey.

The Bi-Modal BI Answer?

An organization's ability to effectively extract value from data and analytics while maintaining a well governed source of truth is the difference between competitive advantage or sunken costs and missed opportunities.  How can we create an environment that provides the agile data access needed by the business users while still maintaining sound data governance?    Gartner has referred to a  Bi-modal IT strategy.  A big challenge with Bi-modal IT is that it pushes IT management to divide their efforts between ITs traditional focus and a more business focused agile methodology.

The DBA and Analyst Divide

Another major challenge in data access comes from the separation between DBAs and business users.  Although the technical side may have the necessary expertise to implement ETL projects, they often lack the business domain expertise needed to make the correct assumptions around context and how the data is regarded.  With so many projects competing for resources, we shouldn’t have to task a DBA on all of them.  Back to the flip side of the coin, data analysts and scientists want the right data for their tools of choice and they want it fast.  Even though there is growing set of data integration tools that allows individual business units to create and maintain their own data projects, this typically requires a lot of manual data modeling and can lead to siloed data or inconsistent metrics.  

Instead of controlling all of BI, IT can enable the business to develop their analytics without sacrificing control and governance standards.  So how can we get the right data in the hands of people who understand and need it in a timely manner?