Building Enterprise Data Analytics – 10 considerations

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I attended the 2014 Minneapolis CDO Executive Summit as a guest of the MN State CIO and Director of Innovation.   The event featured an extensive number of speakers sharing their experiences in establishing practices around data aggregation and analytics.   The real world perspectives from corporate professions was very insightful to the challenges and levels of investment required to make these initiatives successful.   As I’m currently working on extensive models around data & analytics regarding economic development and innovation metrics I found the networking opportunities at the event invaluable and have led to a number of working groups that continue on since the event.
The event resonated with the years of experience I spent on strategic initiatives with companies,  specifically in the areas of building the data analytic organizations and systems.  Here is a quick top 10 considerations I constantly ran into:
  1. These are new capabilities that have to be developed.  Not to be confused with existing IT data organizations that are maintaining operational systems.  It takes more time, money, and commitment than most organization understand and will require most areas of the company to participate vs. an isolated team approach.
  2. Funding the practice will span not only technology, but a wide range of skills sets from data architecture,  system integration, analytics, etc.   Many companies underestimate the amount of time/cost business subject matter experts will need to be involve and participate in developing the insights that come from the analytics.
  3. While existing system integration seems like a large task,   there is a significant amount of information and meta data that your organization has NOT been capturing over the past decades.   One mitigation approach is to find  strategic partnerships that can leverage their data to create combined data sets that are vastly more valuable than a single organization and can cover history data gaps.
  4. Integrating to public data sources is a vastly under utilized resource.   Many state governments are working to improve systems, apis and crowdsourcing efforts to create more value out of this data.  I have also seen companies create portions of internal data and created their own public share.  This has paid big dividends in terms of the crowdsourcing solutions that have come out of that data and the strategic partnerships that it has attracted.
  5. An organization should not under estimate the value of analytics of data outside of ones core customer demographics.  It might be the key to understand what you don’t know about the market and customer needs.
  6. Channel data is a frequent  point for data collection investments, but many orgs fail to capture the data for cross channel analytics.
  7. Look past just the data in the channel, but into the value chain of systems, orgs, and partners that the channels trigger.  Many organizations do not understand the operational considerations that channel activation puts on their own organizations.  Especially when adding new channels.
  8. Partner and supply chain data is a great source of understanding the capabilities of your partners during times of crisis, economic instability and market disruptions.   Look for those anomalies to understand how better to support the strengths and weaknesses in your own business ecosystem.   Pilot additional partners to compare performance and capability variences.
  9. While many companies are focused heavily on the business analytics,  perhaps of of the biggest areas of corporate improvement comes from the aggregation and analytics of internal collaboration of systems and departments.   I’ve seen great organization strategies come from the study of internal communication, spending, budgeting, governance, project planning, and project outcomes metrics just to name a few.   How good a dashboard to you have in watching organizational behavior and performance over time.  What data are you not capturing about your own organization today?
  10. One of the most frequent differences that I found working crossed thousands of organizations was the fact that companies didn’t really understand the maturity levels of their competition in this area.   Most companies would attend a conference and come away feeling that everyone was struggling with similar problems.   While this is likely true,  they where missing the point that other orgs had already committed significant investment to the aggregation of data even though they where still very immature in their capabilities to exploit it.   In many cases they didn’t realize their competition already had gained years of data collection in new and strategic data partnerships and they hadn’t even begun yet.   Most fail to consider the value of time in terms of data collection.   It is hard to go back in time and get the data you failed to capture and your falling behind every day that passes.

In future blogs I will dive more deeply into each of these considerations to explore how different organizations approached each area and what outcomes came for their efforts.

See more images of the event and speaker agenda in the gallery


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