Bridging the Actionable Analytics Gap


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Deriving insight from historical customer and prospect interactions (online and offline) has several implications, many of which can positively affect bottom line revenue or profit if understood and implemented correctly. Presenting the insight in a form so that the business constituencies may easily digest the information, and start to visualize how it may be implemented to effect bottom line revenue (or profit), is one of the biggest barriers in insight and analytics adoption today. What are the primary issues in bridging that analytics gap? Skills, tools or resources? Yes, you need the right tools and resources, with the right skills, but those are table-stakes and many institutions are still not bridging the analytic divide well with all three in place.

Assuming skills, tools and resource gaps are fulfilled (I know, a big assumption … a separate topic) what are the typical issues why institutions are not getting the biggest analytic bang for their buck?

In many cases the primary issues are organizational alignment and business processes. Many organizations have great IT, analytics and marketing departments, staffed with top talent, but not all of them are driving the incremental revenue and profit they are capable of to the organization.

What is the easiest way to start to bridge that gap? Get everyone in a room and talk. I know that may sound silly but bringing departments and individuals together (and not just the heads of the departments – you need representatives from all sections of each department) will allow you to identify the gaps, determine solutions, and most importantly build alignment as an organization and not just within business units.

This is not one meeting. This should be an ongoing discussion, with varying members of each department present. It is natural for individuals to focus on their tasks and departments only which, let’s face it, does turn into an ‘us-versus-them’ mentality at times which, in turn, creates huge gaps in the organization. And these gaps are not built over time by ill-will. They are built by competing priorities throughout departments for the most part.

Getting everyone in a room to visualize what they could do together, if they work together better, is truly enlightening. It is usually rough at first, and it may be silly and simple, but it works as everyone naturally wants to provide the biggest impact they can to the organization.

A few tips to get started:

  • The IT (database group), Analytics and marketing departments (and any other business functions that should be included – Risk, planning,..) should all be well represented
  • Each department should come with a list of initial grievances
  • Using the grievances, along with a list of overarching goals developed by the full group, determine 1) the revenue streams that need to be optimized / opened and then 2 )the issues that are minimizing those revenue streams
  • Use an iterative discovery and assessment process (pain point identification > process improvement > solution development) to create a list of projects and categorize those projects into 2 dimensions – difficulty to implement and business impact (each low to high).
  • Map the projects into that 2 dimension grid and verify the assignments then prioritize the projects based on their assignment. In most cases you will initially focus on the bottom right part of the grid (easy to implement and high business impact).
  • When determining the initial projects try to select a project that requires all departments to participate as that will better facilitate further improvements as cross functional teams typically bond well as they see other department issues and concerns.
  • Continually reevaluate the projects and prioritization grid

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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