The Data Heads

Last week a colleague sent me a link to a new white paper that you should take a look at. It is published by SAP and titled “Reaping the benefits of next generation dashboards.” You can download your own copy from The Dashboard Spy.

The white paper describes the problems it sees with current dashboard and business intelligence solutions (they are inflexible and too cumbersome to use). And it offers a punch list of features for what it describes as the “Next Generation Dashboard”. Here is the list:

Next Generation Dashboards must:

• Be easy to build and customize
• Provide a consolidated view from any data source
• Leverage visualization to make information easy to consume
• Offer engaging interactivity for further analysis
• Provide the information in a personalized and easy to understand format
• Allow developers to extend new features or integrate to new technology

The white paper concludes with a list of the benefits users can expect to see from these next generation dashboards.

As I read the report I wondered how this vision of the Next Generation Dashboard matched our vision of the data driven organization as defined by the Data Cost / Value Matrix. (If you haven’t already taken the Free On-line Gap Analysis you might want to do that before you read more. It only takes a few minutes.)

The Data Cost / Value Matrix  identifies four aspects of Data Cost and four aspects of Data Value. You can read more about this at the background page.

Let’s take the four aspects of Data Costs and see how the white paper approaches them:

Complete: We collect all the data we need, and no more than is necessary.

The white paper seems to begin with the assumption that we have all the data that we need, and that all data are good, reliable, and necessary.

My experience is that most organizations are smothered in data. Typically it is the wrong data. All too often organizations focus their attention on the data they CAN get, and do not spend enough energy on the data they SHOULD get.

When we make the wrong data more actionable we have gained nothing.

I think the Six Sigma Master Black Belt described in this case study from a financial services firm was right on track when she engaged in manual data collection first because she “was able to gain valuable insight into the nuances of the various operational definitions used by the process owners, and in the way the information system supported or did not support those definitions.”

This white paper overlooks this issue.

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.

The white paper is very strong on the first part of this because of its emphasis on the integration and interconnectivity of data systems. At the same time, it seems unaware of Measurement Systems Analysis and the contribution it should make to this process. It may be the MSA is too technical and therefore beyond the scope of this kind of white paper. However, the world envisioned by the white paper - where everything is fully automated - overlooks the premise that we need to be thoughtful about our data.

One of my favorite business quotes is by Peter Drucker: “Nothing is worse than making more efficient what should not be done at all.”

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.

This is one area where the vision of the next generation dashboard is in close alignment with the Cost / Value Matrix.

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.

Clearly the white paper is aligned with this aspect of reducing the cost of data. This comes up several times in the article, as in this quote: “the next generation of dashboards empower non-IT professionals to design and connect business data to a dashboard interface.”

So the white paper endorses integration, accessibility, and automation. It seems to fall short on the issues of completeness and data reliability.

Lets turn out attention to the four aspects of Data Value.

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.

The white paper jumps on this with both feet. Under the heading “Leverage visualization to make information easy to consume” it suggests a product release and control strategy as one of the fundamental ways users should consume information: “In addition to robust data visualization, next generation dashboards provide methods to visually alert a user when performance indicators are out of tolerance, then enable the exploration of details with point and click simplicity.”

This is a great example of a Product Release & Control approach to the world: Test a result against the tolerance ( tolerance = specifications = Voice of Customer) and alert the user when something fails. Clearly this is a huge advantage to companies to get this kind of information - especially if it is provided in real time so that prompt corrective action can be implemented.

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.

On the issue of Process Control, the white paper falls completely silent. There is no indication that the authors understand this critical point of delivering value with data.

It may be that this is an oversight. More likely it is a point of value that is not appreciated by the authors. Making this a point of value assumes that the user understands the difference between Voice of Process and Voice of Customer. In my experience this distinction is not commonly understood. Even people who have been through Six Sigma training or who are certified quality engineers sometimes confuse the issue. We readily understand “outside the specs”. We’re far less likely to embrace or distinguish the more abstract “out of control”.

This is an important short-coming in this vision of the Next Generation Dashboard.

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.

According to the white paper, the fundamental benefit of implementing Next Generation Dashboards is to improve processes:

Notably, C-level executives use business intelligence to improve processes, ensure compliance, optimize marketing efforts, and increase sales. And department managers can use the information to improve their operations and monitor the performance of their groups.

In another section of the white paper, the authors note that Next Generation Dashboards should “enable the exploration of details with point and click simplicity.”

Clearly these are signs of commitment to continuous process improvement. I wonder, however, if the proliferation of dashboard tools will be matched with a similar effort to make sure people use the data in meaningful ways.

My mother spent her career teaching fourth grade. I’m old enough to remember the alarm bells she rang when pocket calculators were first introduced: “But will these kids actually understand the answers they’re coming up with? Or will they just get wrong answers faster and assume that they’re right because a computer spat it out at them?”  The older I get, the more I see what she warned against. This situation is a grown up version of the same problem.

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.

This is actually a pretty good summary to the Next Generation Dashboard White Paper. Clearly the authors “get” this vision.

In summary, the white paper is in alignment on many aspects of the Data Cost / Value Matrix. There are a few points where it falls short. Most notably if falls short in its vision of the importance (or the real cost) of complete and accurate data, and the value it places on the Voice of Process.

The fundamental assumption of this white paper is that business processes can be characterized by data. If we set aside the (very important) questions about the reliability and repeatability of data for just a minute, the question that comes to the forefront is “what theory shall we apply to the data that characterizes this business process?” Or, in the words of Dr. Deming, “By what method?” shall we reach our goals?

If we fail to attend to the Voice of the Process, our efforts will certainly be suboptimized. GainSeeker Suite and GainSeeker’s Enterprise Dashboard implement all of the requirements outlined by the white paper for the Next Generation Dashboard and they make it very easy to pay attention to the voice of the process.

What do you think? How important is the Voice of the Process in your dashboard? What are you doing today to build dashboards for your business?  Use the ShareThis button below to mark this page, leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

In my last post I shared the Data Cost / Value Matrix and described companies that I’ve known that live in each of the four quadrants. While most people aspire to Quadrant A (Low Cost and High Value data), most don’t live there in reality. Many actually live in Quadrant D (High Cost and Low Value).

While these anecdotal descriptions of the four quadrants are useful, they don’t offer much guidance on what to do about your current reality.

For that reason, I developed a quick and dirty Gap Analysis that helps you quantify where you are today. Used properly, the Gap Analysis can point you to some actions that can help you become more data driven.

(I use the term Gap Analysis because it helps you evaluate the Gap in your current performance and your performance potential. Sometimes I like to say that your performance potential is how things would work if God ran the process.)

Anyhow - here is the Gap Analysis Tool. Just answer the four questions below, then click Continue to answer four more questions. Then Click Results to see where you score.

Once you have your score, use the Back button to review your answers to each question and plot your strategy for improving your business.

Here are some questions that might help you develop a new strategy:

• Is your score better on the horizontal axis (Data Cost) or on the vertical axis (Data Value)? If you’re firmly in Quadrant C (Low Cost and Low Value) it is obvious that you need to work on increasing the value of your data. If both scores are low, look for the low hanging fruit. Often this is found in data completeness and automation. Automation will free up time from the data shuffle so that you can work at making better use of the data that you’re collecting.
• Where are your lowest scores? Often bringing one score up from Never to Seldom or Seldom to Sometimes will do a lot to improve your performance.
• Are your scores balanced across all eight categories, or are some significantly better than others? As a rule, I’d encourage you to seek a balance across all aspects, rather than strive for excellence in one aspect at the expense of the others.

These are just some of the ways you can use this data to become more data driven. Here is some information about a more complete Gap Analysis that we can help you with too.

In the meantime, does your score on this Gap Analysis reflect the reality of your business? Tell me what you think. Use the ShareThis button below to mark this page, leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

I came up with the Data Cost / Value Matrix to help me explain what organizations can do to become more Data Driven.

On the horizontal axis we have cost of data, going from High to Low. On the vertical axis we have the value of data, going from Low to High. The Data Driven Organization lives in the upper-right quadrant, where data is inexpensive and of high value.

Unfortunately, the Data Driven Organization is in sharp contrast to most businesses. Many business leaders don’t realize that they can have it both ways: high value data at low cost. While there may be an infinite number of ways companies fall short of being data driven, our experience is that they fall into three broad camps. I’ve seen these companies, and maybe you have too.

Some gain high value from their data, but pay way too much for the knowledge. They’re in Quadrant B on the Cost / Value Matrix. This is typical of many Six Sigma companies. Six Sigma companies, for the most part, understand listening to the Voice of the Process better than many other organizations. 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 is huge.

However, many Six Sigma Black Belts spend an inordinate amount of time scrubbing and massaging data in order to get something useful. We call this effort the 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 MINITAB where you have to group it in 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.  See our white paper “Freeing Six Sigma from the Data Shuffle” for more on this topic.

Others (Quadrant C) pursue data for data’s sake. They build  elaborate data collection systems that effectively protect the user from their customer, but provide little or no additional value to the business. An example of this is an automotive supplier who told me “If the customer calls with a complaint, I’ll print out a blast of several thousand data points and email or fax it to him. He gets real quiet when he discovers I have the data.” The fact that this data is rarely used to make improvements to the process (and eliminating the customer’s complaint) doesn’t seem to concern this manager.

A 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 may 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 Wiley Coyote.

In a future post I’ll share a quick test that you can take to determine just how Data Driven your company is. In the meantime, what’s your best quess? Which quadrant does your business live in?  Use the ShareThis button below to mark this page, leave a comment, tweet me, schedule a conversation, or call 800-958-2709.