Thursday, October 6, 2011

Micro-goals and Rapid Analytics

Let's face it, the future of our economy is uncertain.  With the wild swings in the stock market over the last few months, and the on-going turmoil in Europe, long range, rigid, business planning is not only nearly impossible, it is unadvisable.  Instead, many business are in "wait and see" mode, putting them in a position to be reactive to what the market brings. 

There is a middle ground, however: micro-goals.  Micro-goals represent short term targets within the context of a longer term, but fluid organizational vision.  For example, if the long range organization vision is to double annual revenue over the next five years - the micro goal would be to grow revenue by 3.7% in the next quarter (3.7% is the quarterly growth necessary to double revenue in five years).  The feasibility of this short term target is confirmed by baseline analysis of past results, executed with the help of analytic decision models and tracked real-time by business dashboards.  If, at the end of the quarter, the micro-goal is not met - or better if it is exceeded, then the fluid organization vision may shift to reflect the latest reality, with analytic decision models revisited to discover what they missed.

The methodology for applying micro-goals is to:
  1. Define a flexible and fluid vision and roadmap for the future
  2. Develop a process map and conduct baseline analytics of current practices
  3. Re-engineer the process for short-term improvement, and develop analytic decision models to evaluate the likely impact of changes
  4. Execute the improved process and monitor progress real-time, against decision model benchmarks
  5. Revisit and improve process and decision models, adjust the long-term vision and elevate performance
The key tool that supports this business practice is "rapid analytics".  While the long term fluid vision draw on professional judgment of the business owner or manager, it is business analytics that support the actual execution of micro-goals driving towards this vision.  Traditional "big data", enterprise business intelligence (BI) lead by IT will not cut it, however.  While IT plays a crucial role in providing the infrastructure to manage and store data necessary to feed business analytics, they are not in the best position to deliver "rapid analytics".  Instead this should be conceived of and generated by the business user and analytics experts - in direct response to their micro-goals.  Like the analytics that they produce, the technology to accomplish this must also be agile, easily deployed and programable and usable by business users.  These technologies exist today.

Micro-goals do not serve a specific business function, but instead require support from a number of often independent business functions.  As such data and analytics that support micro-goals must also cut across disparate data systems that tend to exist in stovepipes like their respective business functions.  In order to keep pace with the rapid cycles of micro-goals, data feeding the "rapid analytics" must too be agile, lightweight and flexible.  The best way to accomplish this is not through large data warehouses or data marts, but instead through virtual data stores that dynamically integrate data sources and apply to very specific micro-goals.

This methodology, that leverages business strategy, analytics and technology, keeps the benefit of a long term, big picture vision, without being blindly commited to unrealistic and unpredictable long term plans.  Additionally, focusing efforts on execution of short term micro-goals makes for a more flexible, agile yet intentional organization that is constant learning and evolving.