Background: The Data Cost / Value Matrix
How Data Driven is your Organization?
The Data Cost / Value Matrix is a useful framework for evaluating your company's ability to be data driven. While a full-featured Gap Analysis provides great value for understanding the best opportunities to improve your quality data systems, the Hertzler System's On-line Gap Analysis is a powerful tool for "Do It Yourselfers." You can plot your score on the Data Cost / Value Matrix and consider suggestions for prioritizing ways to help your organizations build more effective data systems.
The Data Cost / Value Matrix Explained
The Data Cost / Value Matrix describes how data driven your organization is. On the horizontal axis the Cost of Data is positioned from High to Low. On the vertical axis the Value of Data is rated from Low to High. The Data Driven Organization lives in the upper-right quadrant, where data are inexpensive and of high value. Unfortunately, most businesses fall in one of the other three quadrants: data costs too much, or data fails to deliver real value, or data is too expensive AND it fails to deliver value.
Companies in Quadrant B gain high value from their data, but pay way too much for the knowledge. Many Six Sigma companies fall into this bucket. For the most part these people understand how to maximize the value of the data. They know how to use the full array of statistical tools. They apply proven, disciplined techniques of project management and statistical problem solving to get to the bottom of chronic, entrenched problems. Payback from these programs can be huge.
However, many Six Sigma Belts spend an inordinate amount of time scrubbing and massaging data in order to get something useful. This is Six Sigma Data Shuffle.
Oddly this pattern is seen as normal in the Six Sigma world. If somebody in your organization has to copy files from one folder to another, reorganize the data in some new format (convert from .csv to .xls) and then scrub the data so that all descriptive fields match (make McConnell, F. and Frank McConnell into F. McConnell), all before you copy it into the final software program, where you have to group it into appropriate samples and manually enter specifications before you can begin to analyze the data, then you are paying too high a price for clean data.
Folks in Quadrant C seem to pursue data for data’s sake. They build elaborate data collection systems that effectively protect themselves from their customer. This data provides little or no additional value to the business. An example of this is the quality manager who once told me, "If the customer calls with a complaint, I’ll print out a blast of several thousand data points and email it to him. He gets real quiet when he discovers I have the data." The fact that these data are rarely used to make improvements to the process (and thereby eliminating the customer’s complaint) doesn’t seem to concern this manager. Data are cheap, but the only value comes from being able to "snow" the customer. Often this data is cheap because it is what CAN be collected, not what SHOULD be collected.
The third camp (Quadrant D) has the worst of both worlds: they pay a high price for data, but have almost nothing to show for their efforts. This is typical of mature organizations with a long tradition of inspecting quality into a product. These businesses have enormous file cabinets full of hand written data sheets. Data are written on an inspection sheet and then filed away. Getting to the data is a laborious process requiring the patience of Job and the dogged determination of Wile E. Coyote.
While the matrix is useful for understanding the big picture, we need more detail to get guidance on what to do about your current reality. The Eight Aspects of Data Cost and Data Value provide that detail.
Eight Aspects of Data Cost and Data Value
In a manufacturing environment, there are four aspects of the Cost of Data and four aspects of the Value of Data. These are illustrated on these graphics.
Four Aspects of Data Costs
Complete
We collect all the data we need, and no more than is necessary.
Automated
We write down very little data. In fact, we type very little data into computer systems. We use bar codes, RFID or other identification technologies. We capture data from digital equipment whenever possible. Wherever possible we have eliminated human interaction with data collection, and we are confident through data driven statistically valid measurement system analysis that the data are reliable.
Integrated
We have specialized data systems to run various aspects of our business, but we don't have silos of data that are used for only one purpose when the data can be useful to other applications. Put another way, data is never entered more than one time anywhere in our business.
Accessible
Anyone can get to the data they need at any time. We don't have to rely on specialists to write special queries or export data. We've learned that our people do not need to be programmers to make good use of data.
Four Aspects of Data Costs
Product Release & Control
We use data to validate that our products are acceptable for shipment. This data is primarily accept/reject type data, and may be based on either measurements or some other kind of pass/fail criteria. The pass/fail criteria is based on the voice of the customer.
Process Control
We apply statistical process control tests to key products and processes. These activities use the Voice of the Process to determine the stability of our process. We react immediately to instability and unexpected variation.
Continuous Process Improvement
We use data to close the loop on our processes and drive continuous improvement. All of our people are trained to use this data to look for hidden sources of variation and correlation between key input and key output variables.
Data Visibility & Transparency
Our data is readily visible at all levels of the organization. Every stakeholder, from process owners to the leadership team, can put their fingers on the performance data that matters to them. Information is summarized in easy-to-understand dashboards that help them separate signal from noise so they don't react to the wrong things. Furthermore, they can readily get to the underlying data to better understand the drivers of their key metrics.
For a deep dive into these details, please read our white paper "Exploring the Eight Aspects of the Data Driven Business."
Take the test
Return to the Free On-line Gap Analysis
GainSeeker
Suite Version 8.1
