Successful Strategic Planning for Big Data

Prior to Perficient, I had previously worked at HP/Knightsbridge Consulting and had worked on creating the Knightsbridge/HP BI/MDM methodology. One of my many colleagues on this really large endeavor then was Mike Mansur, who is now the Worldwide Competency Lead for HP’s Global Methods for EIS. Mike recently offered his views on the importance of a comprehensive BI strategy in an interview recorded in the TDWI.org website.

Companies today have often fallen short of the ROI that they expected from BI, due in large measure to their inability to put together an effective information strategy. This lack of a BI strategy has resulted in the siloing of department-level BI initiatives, instead of being driven as it should be, by effective business participation.

This failure is not all due to the recent developments. In fact, organizations have had challenges with information and BI for years, including incomplete data, master data inconsistency, poor data governance, etc. Organizations had traditionally simply chosen not to put in the requisite time, money and effort needed to address these problems.

Big data has brought these issues to the fore, where they can now no longer be ignored. The BI approaches of prior times have proven insufficient to provide “the speed and agility required to integrate the various data types we are dealing with today, analyze data in real time, and generate the intelligence required by today’s face-paced, rapidly changing business environment.”

There is level of complexity to the business and IT challenges that big data presents that requires mastery of multiple competencies and technologies. But, the first challenge is to define priorities, based on a sound BI strategy. The business value must be identified, along with an understanding of the cost of implementation, to build a successful BI roadmap.

All key stakeholders need to be engaged, and ownership needs to be shared by both business and IT. The big data social media component, for example, requires marketing’s input on how to leverage the information. Both short and long term value can only be delivered through a scalable and adaptable architecture.

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This focus generates pressure on IT to keep up with the technology alternatives to meet the demands of increasing data volumes, variety, and complexity. The pressure on the business side is to define the value justification for the needed investments.

There is increased competitive pressure, as well, because companies that are not able to extract customer information and insights from the many social media sources available today will lose out to competitors who do.

The other challenges are integrating this data with traditional data types, providing context to the data, and feeding insight back into business processes, where it really starts to make a difference.

Mansur offers the following guidelines for organizations to achieve a comprehensive and business value-driven business intelligence program:

A) Connect and exploit previously untapped or inaccessible information.

B) Realize greater return on IT investments by realigning and leveraging siloed, uncoordinated BI activities.

C) Increase business agility with a cohesive, aligned BI strategy, to be better positioned to adapt to changing business needs, customer demands, and capitalize on emerging market opportunities.

D) Institute a business-driven vision for BI; incorporate a thorough business vision for BI rather than only IT’s perspective of what the business wants.

E) Ensure that the previous disconnects between the business and IT that hampered success are not repeated.

For more detailed information on all of the above, also visit the TDWI.org website.