Work = Data x Quality-squared

E=MC2 is perhaps the most famous equation of the 20th century if not of all time, and so I couldn't resist appropriating it to convey the relationship between process and data.

 I in no way equate the significance or rigor of my formula with Einstein's; merely taking advantage of the similarity in form is assist in remembering the relationship it represents.

In my version "W" stands for the work embodied a given process, "D" for the data that is used or generated by that process, and "Q" the quality of that data in terms of how completely and correctly it represents the process.

Implicit in this equation is the idea that data and process are related, and that processes can be converted to data, and data back into processes. Another way of looking at this is to think of data as simply "dehydrated" process value. It takes effort to convert a process to its essential data, but once converted that data is easily transportable and reusable - it represents significant value, which when released can perform a tremendous amount of future work!

Let's look at a specific example common to many organizations; the question of who has performed work for a particular client. In this example:

• W0 represents the activities involved in establishing and maintaining a relationship with a client (e.g. calls, lunches, engagements, meetings, etc.) as well as any work performed for that client,
• D is the data generated through all processes represented by W (e.g. email address, phone, meeting notes, work performed, $$ billed, etc.)
• Q is the quality (measured by correctness and completeness) of the data collected

 The higher the value of Q, the more closely the data D can be converted back into a full-fidelity representation of all process work W0. That in itself is valuable. But the multiplier doesn't stop there. D can be used to support many other processes (e.g. Marketing assessments of client-base by market, industry, etc.) - assuming a sufficiently high value for Q. In fact, the impact of Q on the future Wf that can be performed by when D is rehydrated is not linear but exponential - a larger investment in the quality of data collected returns much more than its incremental cost in terms of future work value.

It stands to reason then, that the wise course is to capture the data D at the highest quality Q practical so that it can be reused in as many ways possible to realize the highest multiplier in future work Wf.

Just as with Einstein's equation, where the amount of power released when matter is converted back to energy at the atomic level can be huge - so too when data is "rehydrated" it can generate tremendous work value.

For this potential to be realized fully, the data must be "pure" - i.e. it must be of a sufficiently high quality. For example, in the case of who has done work for client "X" - all instances of work (completeness), along with the precise nature of the work must be captured (correctness), for the data to have much value. Absent sufficient quality, "rehydrated" data cannot be trusted to perform much work. The trap too many firms fall into is to collect large quantities of poor quality data that cannot be used to generate future process value.

"It is always better to collect a smaller amount of higher quality data than vice versa because high quality data can be used in almost limitless ways, while poor quality data has zero, or even negative value!"

As noted above, poor quality data can actually have negative value, since its use may lead to costly or damaging actions. It may appear that incomplete data is better than none, and that could be the case as long as the uses to which that data is put are limited do not assume completeness. It is never the case that poor quality data should be collected!

The bottom line is that data and process are simply two sides of the same coin - but the ability to convert between the two is a function of the quality of the data captured. When care is taken and Q is high, a stream of subsequent process work value can be generated from data captured. This ongoing stream of value may be exponentially greater than that of the original process work performed.