Measurement & Analysis

Measurement and Analysis

Why?

A vital and strategic component of organizational performance improvement and management is the shrewd use of data and information, data to evaluate the actual effects of improvement trials or pilots, data to assure that implemented effective improvements sustain their effect, and data and information to assure the kind of operational transparency and feedback now known to be essential for sustained high performance. Performance management cannot occur without good performance measurement.

However, busy management, supervisors, and workers can only handle a limited amount of data without being overwhelmed. The important data get lost in the less important when too much data are generated. Furthermore, an imbalance in data components leads to imbalanced management as Robert Kaplan points out in his book, The Balanced Scorecard. Data generation takes organizational resources and attention, not to mention its analysis and presentation.

How?

Therefore, data for performance must, (besides being accurate and timely), be efficient, balanced and relevant to assure effective performance management. This is not an easy task.

The data must generally be analyzed, synthesized, and preferably visualized before being understood widely and easily.

We, in The BroadBaker Group think of data (numbers) simply as the beginning of an information chain of added value, ending in insight or understanding.

Adding value to each phase of the chain takes intellectual effort, skill and domain knowledge. If data can be converted to a meaningful graphic (pattern) it becomes information much more easily grasped. But that conversion to pattern information is insufficient. The pattern information must be given meaning, usually through recognizing what the pattern or graph is “saying.” That, in turn, becomes knowledge; it usually takes historical and/or professional/technical knowledge to do that. Recognition is involved. Knowing what the graph, image or description is saying, or giving it a name, is still insufficient. We must give meaning to the knowledge, understand the implications of the information we have learned in order to act properly. Gaining this insight for action is the highest purpose of data.

There are, of course, many tools available to assist in carrying out these steps. Our firm can assist or train in the use of these tools and we have in our alliance others who bring additional, even more specialized, data management and analytical capability, especially able to evaluate large databases. We can help our clients perceive the value (or lack thereof) in the data, and draw inferences and know the level of certainty (or uncertainty) of those inferences.

However, managing only one information chain (e.g., financial data) is grossly insufficient. Since the data must be broadly balanced to govern or manage well, many such chains must be managed, reflecting value streams, customer impact, and key operational functions. Errors can be introduced at many points. Wrong data, wrong conclusions; wrong analysis or transcription, wrong conclusions; wrong knowledge (or none), wrong conclusion; wrong inference, wrong insight, wrong action. Deadly. And the whole thing must be done efficiently.

Furthermore, good data enables more accurate and informed dynamic modeling if the client desires, greatly leveraging both the investment in the performance data system and in dynamic modeling.

Our competency

We can assist in thinking through the structure of the data system and work with the client to optimize the data system for effective and high performance management.