Learnings from the Field

I recently moderated a CIO Summit in Philadelphia on the topic of “Big Data: Moving from Monitoring to Optimization.”  Participating in the discussion were about 30 CIOs from very large to more moderate-sized organizations.  The discussion was lively and the participants were very open in sharing their concerns and issues, as well as their hopes and desires with respect to how big data could impact their businesses.

Big Data Business Maturity Index

I started the session by discussing the Big Data Business Maturity Index as a way to frame the conversation (see below chart).  I’m always curious as to where organizations sit on this index, and identifying the barriers that are preventing them from realizing the business benefits of big data.

Figure 1:  Big Data Business Model Maturity Index

I was not surprised that the vast majority of participants (95%+) felt that they were still at the Business Monitoring phase[1].  The group mentioned the below challenges as hindering their ability to advance up the big data maturity curve:

• Many participants talked about the rigidity of their current data warehouse and business intelligence environments.  Organizations are expending significant financial and human resources just to keep their existing data warehouse
• environments up and running, but the existing data warehouse environments lack the agility to easily add new users and new data sources while maintaining SLAs.
• There were lots of comments on the unfulfilled promise of self-service business intelligence.  The dream of self-service BI, where a business user can easily access the necessary data and create their own reports and analysis without involving IT, is still is a dream for many of the participants.  To quote one participant:  “We have to call the DBA every time we want a new report.”
• Many lack a strategy for how they would leverage unstructured data.  The CIOs were eager to get at the “insights” buried in their myriad sources of unstructured data, but feared that they were going to have to introduce an entirely different environment to manage and analyze unstructured data – that there is little hope that their existing data warehouse and BI investments can leverage unstructured data sources.
• One participant shared a story about how they were able to glean customer satisfaction issues from social media data.  They were able to uncover and act more quickly on addressing specific customer concerns. They felt that they had been successful in these interventions in converting frustrated customers into satisfied customers, even advocates in some cases.

Business Intelligence versus Data Science

The second challenge with which many of the CIOs were wrestling had to do with creating a data science capability within their organization.  While business intelligence would continue to be a critical role and capability going forward, expecting to evolve the current BI analysts into data scientists didn’t seem to be reasonable or timely (see chart below).

Below are some concerns raised by this group of CIOs about the challenges in creating or growing data science capabilities:

• In spite of the confusion on the clear definition of the role of a “Data Scientist,” everyone agreed that they needed more people with analytic skills and a scientific mindset to help them get more insights out of their data.  As one participant said, “we need more people who are not bound by conventional thinking.”  Many participants are hopeful that they can both hire to this Data Scientist role as well as groom or train existing Business Analytics into becoming Data Scientists.
• The idea of finding that “superstar” with the combined skills of a DBA (data czar who understands the data), a Business Analyst (who understands the business objectives and organizational execution challenges) and a Data Scientist (who understands the analytic capabilities) is unrealistic.  Consequently, the CIOs felt that they need to introduce new processes – with clearly defined responsibilities and expectations – across these three key roles.
• Participants were concerned about the resulting organization changes that they felt would be needed in order to get senior management to accept the insights about the business that the data was telling them.  Management needs to be receptive to “what the data can teach us about our industry and our business.”  To quote one participant, “we have always driven forward using the rear-view mirror.”

Summary

In summary, here were the key take-aways from the CIO discussion:

• Self-service BI is inhibiting organizations ‘ability to move more quickly into the big data space.  The overarching feeling was that this problem needed to be addressed before trying to introduce yet more data and analytics into an already frustrated user base.
• There is a need to build a big data business case to invest in the data science, analytics, and unstructured data capabilities.  The business case needs to clarify where and how to start the big data journeys and lay out a roadmap, especially with respect to how big data could impact the business and the potential ROI of big data people, data, and technology investments.
• “Do we have the right people in the right seats, and do we have the right seats at the table.”  Participants agreed that they need more Data Scientists and folks with strong analytic and statistical skills.  Where they are going to source those people is still unknown, but these data scientist roles are becoming more critical to leveraging the wealth of data that these organizations are accumulating.