CU Employee CULytics Founder

What are your questions for CDO panel at the Summit?

Thanks @Brewster for putting together this list. Let me open it up to see what are the questions that audience would like to tackle.


  1. Did your CU make any previous attempts at analytics? Why did they fail? What could have allowed them to succeed?
  2. What was it that pushed your CU to invest more in analytics?
  3. Did your organization develop a strategy before diving into analytics?  If so, what was included in the strategy?
  4. What functional area of the organization (i.e. marketing, IT, lending, etc.) drove the inception of analytics at your organization?  Why?
  5. Where are your areas of opportunity to improve the use of analytics in the future?
  6. Was their strong executive support for analytics from the beginning?  If not, how were you able to gather that support?




  1. How important is cultural transformation to the success you have had or will have with data analytics?
  2. Did you hire external candidates to fulfill the technical skills required or develop internal skills.  Why did you choose that approach?
  3. With so much data available, how did you help your staff and peers overcome “paralysis by analysis”?
  4. What is more important to analytics success: the best technology or a strong culture/thirst for data?
  5. Did you get any pushback from some staff who were resistant to loosening their control on some datasets when starting the analytics journey?  If so, how did you overcome those challenges?



Process and Implementation

  1. What subject areas did your CU initially focus on leveraging analytics for?  Why those areas?
  2. How important was self-service analytics to your organization’s analytics efforts?
  3. What are your thoughts on the following statement: the fundamental building block of analytics is simply a well-defined question?
  4. How was your analytics program structured: centralized, decentralized or a hybrid setup?
  5. Did you monitor ROI when first building out your analytics program?  If so, what metrics did you use to gauge this ROI or utilization?
  6. Consistent definitions are critical – how were you able to begin to get buy-in for consistent use of key terms?
  7. How did you address data quality concerns that arose/continue to arise?
  8. What were some of the “quick wins” that each of you were able to produce for your organization through your analytics initiatives?  How were you able to identify those opportunities?
  9. How important was data transparency throughout different areas of the organization?
  10. How involved is your analytics teams with process improvement?
  11. How long did it take your organization to get to a point where you would define it as “data-driven”?



  1. What technologies/solutions did you leverage to get started with analytics?
  2. How were you able to justify the cost of implementing and training for the technologies used with your analytics teams?
  3. With so many BI/Analytics tools and providers out there, how did you come to the decision to use the tools/providers you did?


Industry Trends

  1. Getting started with analytics is all about taking that first step.  To begin, begin.  Why do you believe organizations aren’t taking that first step into analytics?
  2. Do you believe there are misconceptions about getting started with analytics throughout the industry?  What do you believe those misconceptions are and how would you help someone overcome them?
  3. Do you feel that an organization can survive without investing in analytics in the near future?


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  • CU Employee
    I love the last set of questions on getting started. My book, Cooking With Business Intelligence addresses these very questions. What I would be interested to know is what level of importance is placed on foundational data efforts in the journey to robust analytics. Who has embarked upon true data governance practices and has a full fledged data quality process in place in their organization and has it contributed to the success of their analytics practice if so?
    • CU Employee CULytics Founder

      These are interesting questions. Another way to put it is, what foundational elements need to be in place and what should be their maturity to ensure success with robust analytics.

      I see that there is a need to have a balance between data governance/quality and extracting value from data. If one goes too far into data-governance then business may not see enough value of the investment at the same time if there is no governance then trust in data hampers the analytics.

  • CU Employee

    How do you ensure getting the data model correct especially when combining multiple SOR's?

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