Bridging the Actionable Analytics Gap > Part2: Back to Basics


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I provided thoughts on how to bridge the actionable analytics gap assuming the analytic skills, tools and resource gaps are fulfilled and yet a large analytic divide still may exist in your organization. In this post I would like to go back and address analytic skills, resources and tools. I’m not going to provide suggested solutions, as all environments differ, but, I would like to provide key considerations as there are many analytic buzz words and hype in the marketplace, some of which are valid and some should be hampered.

Analytic resources require several skills including database, technical and analytic skills as well as a strong business acumen. A few key considerations to keep in mind-

  • A strong subject matter expertise (business acumen) is critical to solving specific business problems. In fact, an organization that manages analytic contests has stated that their contest winners tend to have a strong knowledge of the business problem they are trying to solve.
  • Data Expertise: Analytics requires a very strong understanding of data, and the ability to transform the data in varying environments. If a situation exists where the analytic resource(s) require the data to be ‘pushed’ and / or ‘transformed’ before they touch the data, an assessment should be done. In many cases this may not be an analytic skill gap – it could be an organizational or technical gap. Regardless, if this is happening, take some time to determine the root cause(s) as analytics needs to access the data in raw form in order to maximize insights.
  • Analytic resources with stagnating skills are being left behind. Good analytic skill requires the ability to use new technologies to glean information from the data, derive insights and make the insights actionable. These technologies and analytic skills are evolving at a rapid pace and one of the primary reasons is the need for the business to take in more and more data.

The analytic tools should be considered next in my opinion. Start with analytic resources and skills, then based on the best resources / skills you can acquire, as well as your specific environment and analytic needs, determine the best analytic solution / tool(s). Several nice analytic solutions are available from high cost solutions to no cost open source solutions. Key considerations when assessing potential analytic solutions-

  • Categorize your business opportunities (cost savings & profit optimization), map to available analytic methods with your analytic team and ensure that your analytic solution of choice can handle all those analytic methods. Also anticipate future business needs.
  • The analytic solution must integrate seamlessly with your database system(s). Data push and pull should be native to the database of interest (target database or an analytic snapshot) in order to streamline the full process.
  • Analytic solutions are now being deployed directly within customer interaction systems. Your analytic system of choice should be able to integrate its analytic solutions into your customer interaction / execution systems.
  • Your current analytic resource skills should be considered when determining an analytic solution of choice but that should be a secondary criteria. Current skill sets may be considered but should NEVER hinder progress.
  • Beware of bells-and-whistles: An easy point-and-click interface is always nice but that can come at a cost and point-and-click is rarely as powerful and dynamic as code. At times a sexy interface is needed but always keep the decision criteria simple > focused on price and value provided.

Republished with author's permission from original post.

Roman Lenzen
Roman Lenzen, Partner and Chief Data Scientist at Optumine, has delivered value added analytical processes to several industries for 20+ years. His significant analytical, technical, and business process experience provides a unique perspective on improving process efficiency and customer profitability. Roman was previously VP of Analytics at Quaero and Director of Analytics at Merkle. Roman's education includes a Bachelor of Science degree in Mathematics from Marquette University and Masters of Science in Statistics from DePaul University.


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