MIT Sloan Management Review discusses "How the Analytics Gap Decides Who Wins"

It's not the data that's the problem.  It's the people.  So says a fascinating article in the winter 2011 issue of “MIT Sloan Management Review” (http://sloanreview.mit.edu/the-magazine/articles/2011/winter/52205/big-d...).  This extensive article written by several IBM researchers explores how organizations are utilizing analytics to differentiate themselves from their competitors and to become top performers in their industries.  The article profiles results of a massive survey of more than 3000 business executives in multiple countries and industries, evaluating their use of analytics in different areas (finance, operations, strategy, marketing, etc.), the barriers to their adoption, and recommendations to facilitate their success.
Types of organizations
The article categorizes organizations based on the following three stages of analytics adoption:

  • Aspirational, using analytics to justify actions, primarily in finance, operations and marketing.  Key obstacles include lack of understanding of how to obtain value from analytics, executive sponsorship, and the culture of hoarding information.  These organizations are limited in their ability to gather, aggregate and analyze information, and frequently make decisions based on intuition rather than robust data and rigorous analysis.
  • Experienced, using analytics increasingly for strategy and business development.  Their primary use of analytics is for revenue growth, and major limitations are lack of technical skills for the analytics, as well as lack of effective data governance and ownership policies.  They have developed processes for capturing in analyzing data, but have limited capabilities to share it on a wide scale.
  • Transformed, using analytics to prescribe actions rather than to justify them.  These organizations use their analytical capabilities to create a competitive advantage over others who can’t extract knowledge from their data, or act as quickly upon it.  The major limitations of these organizations are lack of management bandwidth to competing priorities, and accessibility of the data continues to be an issue.  However, they have developed strong abilities to capture and analyze the data, as well as to share it.

Recommendations for success
The article recommends by specific steps in implementing analytics in an organization.

  • First, don’t start with the data.  Instead, start with an organizational challenge that can be solved by improved analytics.  Make sure that the analytics achieve a specific business purpose, such that “quick win” can be demonstrated.
  • Target the specific data necessary to meet this challenge, using the most readily available data.  Limit the scope of data collection, if necessary, in order to produce a timely result.  More comprehensive approaches that assemble large masses of data and spend significant time cleaning it up often lose momentum before results can be achieved.
  • Don’t try to use yesterday’s technologies to present to tomorrow’s results.  Data visualizations, such as dashboards and scorecards, simulations, and other techniques that enhance visualization of analytical results can pay big dividends in creating value.
  • Also, don’t attempt to replace your existing analytics capabilities with new ones.  As new capabilities are developed, existing one should continue to be supported.
  • Finally, while great successes can come in selecting specific areas having the most potential for benefit from analytics, don’t lose track of the big picture.  Make sure you know how each piece of the analytic system fits together, by developing information governance policies data architectures, and analytical tool kits that will be commonly applied throughout the organization.

Enhancing analytics in healthcare organizations
So where do healthcare organizations start in building its analytical capabilities?  Some organizations go for the “whole enchilada”, bringing in an enterprise-level data warehouse and business intelligence infrastructure.  While this approach works well for the right organization, it’s not the only way to begin.  One nice characteristic of Singletrack Analytics’ approach to BI is that it can be relatively easy to pick a specific organizational area with the data-related problem and build a solution to address that particular issue.  In this way a “quick win” can be achieved, which will build confidence in the ability of analytics to provide solutions to the organization’s other issues.  The area selected can be an operational area, such as a hospital department, a related organizational unit such as an ACO, physician hospital organization or PACE program.  It can also be a function within the organization such as providing improved analytics from the hospital’s cost accounting system, or enhancing the capabilities of the disease management team to identify and manage patients with chronic diseases.  Starting with small projects minimizes the cost barrier, as well as the demands on already-overtaxed IT staff.  It allows building a small team of trained users that can gradually encompass other projects within the organization.  While an enterprise BI solution may encompass the hospitals may enterprise-level systems, there will always be niche areas that fall outside of its boundaries, and for which targeted solutions are the best approach.

Browse through the business section of your local bookstore (if it still exists) and you’ll probably see an entire shelf dedicated to books like “Competing on Analytics”, “The New Know”, “The Decision Tree” and others on analytics, business intelligence and data-driven decision-making.  You’ll see why these techniques are creating value for many businesses.  Maybe yours should be one of them.