The Data Heads

In the weeks leading up to American Thanksgiving and the rest of the holiday season, I found myself buried in a couple of projects related to the release of GainSeeker Suite, Version 8; and a course in Appreciative Inquiry. In the process, I got out of the blogging groove. (Version 8 is pretty cool. It includes some brand new modules that will help you get more value from your data, and make it even easier to use. More on the new version in subsequent posts. Likewise, I’ll write more about my interest in Appreciative Inquiry in the weeks and months to come.)

Now my editor at Quality Magazine is prodding me, so it is about time to get back in the rhythm, even though I still have some tasks on Version 8.

My last post at Quality Magazine on ‘Defining Quality’ (which was a revision of ‘What are they thinking…?’ published here) generated a number of thoughtful comments from readers. I really appreciate hearing other perspectives on the questions I posed in the post, especially this comment:

Only in the world of utopia are there processes with zero variation and where only Green or on target values are produced. In the real world you have to look at each process and determine the ability and cost required to reduce process variability. In some cases it may be more cost effective to use an inspection system (like a vision system) to inspect out defects, then it would be to reduce the variation that creates these defects. The rule I use is if prevention is not practical and if detection methods are effective and reliable, then the inspection method is the right choice. When detection is difficult or not reliable, then prevention efforts must be taken.

This got me thinking about a model for inspection that I’ve found helpful in recent years. Here it is:

Bear with me because this graphic still needs some explanation.

If you look at this carefully, you’ll see that it is a typical ‘Input - Process - Output’ diagram. If you look at it even more carefully you’ll see that it is there are two loops on the graphic. The question isn’t so much which loop is right and which is wrong. The question is: Which loop is primary? Which loop is emphasized?

Both loops start in the middle with an observation or a measurement. The right-hand loop is the Voice of the Customer. I’ve highlighted it here in yellow:

The right-hand loop compares the observation to the customer’s requirements and asks “Does this meet the customer’s requirements?” If it passes, you can ship the product. If it doesn’t, you have a couple of options.

If your product fails to meet specs in a manufacturing environment, your options are to Scrap, Rework or Downgrade the product. In a transactional world, your options are to Remediate, Replace, or Compensate. In either situation your options for response are always reactive and wasteful.

I see people tolerate this waste for all kinds of reasons. Perhaps there are other, more expensive issues that need to be addressed before this problem can be tackled. Perhaps the cost of getting rid of a problem seems too high. Perhaps they’re just used to it and can’t imagine any other way of doing business. Some of these reasons are probably better than others, and I’m really not here to pass judgment in this blog. The point that I want to be clear about is that the right hand loop - the Voice of the Customer Loop - captures waste and protects the customer. There is nothing wrong with that (actually there are some good things about it). But it doesn’t prevent the problems from recurring.

The left hand loop starts at the same place, but has a very different impact. This is the Voice of the Process Loop, highlighted here in green:

The Voice of the Process Loop also requires an observation or a measurement, but here is the crucial difference. Where the Voice of Customer Loop compared the observation against specifications, the Voice of the Process Loop compares the observation against what is expected.

On what do we base our expectations? Well you can guess that it isn’t a specification - or anything that is derived from a specification or a requirement.

We base our expectations on our past experience with the process. This is why we call it the Voice of the Process. The best way to tap into our past experience with the process is with the humble control chart.

The control chart tells us what we need to know about the process. If the data we observe shows no patterns, no shifts in mean, and no more variation than we’ve experienced before, then we have reason to conclude that the process is stable. Once we know the process is stable, then we can still ask ourselves “is there a way to improve (reduce) chronic variation? This can lead to improvements in the process or the inputs to the process.

If the process tells us that it isn’t stable, then we can (and should) address that. We can focus our efforts on improving the process or the inputs to the process.

Using the left-hand loop - the Voice of the Process Loop - is how you improve processes and ultimately reduce or even eliminate the need for the Voice of Customer Loop. In an ideal world our processes are well understood and stable, and we don’t need to check against specs because we know that we’ll always meet customer requirements.

In the meantime, we live in the real world. In the real world, inspections against specifications are a reality and will probably be around for a long time. They’re useful and I would be the last to advocate their complete elimination.

But they don’t lead to process or quality improvement, or to an elimination of the waste associated with failure to meet requirements. To get there you need to pay attention to the Voice of the Process. You need to stabilize your processes and then systematically reduce chronic variation.

Where is your emphasis? Which loop do you follow? What are the biggest challenges you face or have faced in shifting your emphasis to the Voice of the Process Loop? Use the ShareThis button below to mark this page, leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

Last Friday evening I drove up two hours to Valpo University. The Business Department at Valpo has an annual lecture series, and this year’s topic was Sustainable Business. I have no real connections with Valpo, but when I got a notice about the lecture from an associate, I followed the link to a video clip about the keynote speaker, Ray C. Anderson. If you’ve followed my posts with the tag “Environmental Sustainability” you won’t be surprised that I made the trip.)

Anderson gave a compelling talk. He is an industrialist - actually an industrial engineer by training. His company, Interface Global, is a $1B carpet and floor coverings company. Fifteen years ago Interface began climbing what he calls “Mount Sustainability”, with the goal of meeting the United Nations definition of sustainability by 2020:

”Sustainable Development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs”.

I really appreciated the fact that Anderson is rooted in the industrial world and is actually doing something. At the risk of oversimplifying it sometimes seems like there are two camps in the environmental debate: the environmentalists who have all the theories and spend their time throwing stones and distrusting the industrialists, and the industrialists who focus on “the message” and are mostly not doing much besides “greenwashing.”

Anderson had the passion of an environmentalist and the discipline of Six Sigma Black Belt.

His talk oscillated between the macro and the micro. On the macro level he addressed issues of species extinction, depletion of resources, and the burden of industrial waste. On the micro level he dove into details of specific projects (very Six Sigma-like) where his company has reduced energy use and waste, switched to renewable power sources, and taken responsibility for recycling their products at the end of the product life cycle.

Another thing that impressed me was the fact that there were probably about 100 undergrad business students who gave up their Friday night to come to an evening of lectures. How cool is that?

I bought Anderson’s latest book, Confessions of a Radical Industrialist. When he signed it we talked briefly about all the gap between his company and the manufacturers who didn’t seem to see beyond the end of the quarter, much less a five year plan, much less to the next generation. His parting comment? “One mind at a time. One mind at a time.”

I don’t think I’ve ever plugged a book before in this blog. I urge you to buy Anderson’s book, and watch some of the videos floating out there on the internet. Pass it around your office and to your CEO.

And he got me thinking about what’s next on Hertzler Systems’ sustainable business journey.

One mind at a time.

Use the ShareThis button below to mark this page, leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

Back in August I gave a sneak preview of a new case study that I was working on. Yesterday I finally completed it and published it on our web site. You can read the entire study and download a copy to share with colleagues.

My favorite quote?

“We can’t credit GainSeeker with all of these benefits. We still had to do the work. But we would never have been able to capture the changes we needed to make if we didn’t have GainSeeker. We’d never have been able to do any of this if we didn’t have the system. So truly it deserves the credit. GainSeeker is the tool that enabled our people to make the changes.”

Use the ShareThis button below to mark this page, leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

At today’s web seminar How Best-in-Class Food Processing Companies Drive Profits, Increase Efficiency and Reduce Risk, my colleague Tom Albrecht offered a number of free resources for individuals who would like more information. (If you missed the live presentation, you can still view the recorded version.) We decided to put links to all of these resources on one page so that you can use this as a starting point.

Here are the resources:

• The Aberdeen Group’s Event Driven Manufacturing Intelligence Benchmark Report.
• A supporting white paper that examines the role of real-time data in improving profitability and customer satisfaction.
• The Aberdeen Group’s Cost of Quality Benchmark Report.
• Free gap analysis software that helps you rate your ‘Gap to Entitlement‘ to a data driven organization
• Case Studies and video presentations from McCormick Flavor Division, Hormel Foods, Consolidated Biscuit, and Del Monte Foods. Additional case studies and white papers are available from the Portfolio pages on our website.

Of course, if you’d like to link to this, share it with a friend or make a comments, please do so. Use the ShareThis button below to mark this page, leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

This week at the Quality Expo I ran into an old friend who described a business transaction that left me shaking my head.

He told me about a company that went to their customer and negotiated a 10% price increase for the product they supplied. The customer agreed to it because the supplier was using the increase to fund new high-speed vision inspection equipment. The new equipment would enable the supplier to 100% inspect the product they’re supplying and guarantee that the customer would receive only good parts.

On the surface it’s hard to argue against that. Evidently the customer had been very frustrated with this supplier because they had had to put up with a lot of defects. They seemed eager to shift the effort to inspect and sort good from bad to the supplier, and were even willing to share in the cost. They must have felt 10% was a pretty good deal. (Given all the estimates out there that total cost of poor quality is 30 - 40% of sales, I can see how they could reach that conclusion.)

So why am I shaking my head?

If your definition of good quality is “no bad parts” then this is a perfect solution.

But “no bad parts” is only one definition of quality. And it is the wrong one.

Don’t get hung up on the “parts” language. At the risk of over-simplifying, it doesn’t matter if you’re talking about the diameter of a metal part, the time it takes to close a call in a support center; the amount of yellow ink printed on a magazine cover, the weight of peanut butter in a jar, or the sales of a particular product by a sales rep. All of these are processes, and all have targets and acceptable limits (specifications).

Regardless of the product or the service, “no bad parts” is a poor definition.

Maybe a picture would help:

What the graphic shows is that the green dot is our target and when our output is on target, it is the best that it can be. It also says that the further you get from target, the worse the quality.

“No Bad Parts” says that “Best” and “Fair” are the same. They’re not.

Think about this: A product that is at the yellow dot is closer to the red dot than the green one. It is closer to Unacceptable than it is to Target. When you’re out there in the boondocks of your specifications, you’re a long ways from target, and it doesn’t take much to push you over the edge.

A far better definition of good quality is “on target with no variation.”

Twenty-seven years ago W. Edwards Deming published his 14 Points to guide businesses “Out of the Crisis”. Point 3 of Deming’s 14 points was “Cease dependence on inspection to achieve quality.” My friend’s story tells me that we’re still depending on inspection to achieve quality. Is there any doubt that we’re still in crisis?

How about you? What examples do you have of inspecting quality into a product? How is it working for you? Use the ShareThis button below to mark this page, leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

If you’re connected to the foods industry, mark your calendar for October 6 at 1pm EDT.

That’s the date we’ve set for our brand new web seminar “How Best-in-Class Food Processing Companies Drive Profits, Increase Efficiency and Reduce Risk”

Over the years, my colleague Tom Albrecht (our VP Bus Dev) has worked with a lot of people in the foods industry, and he has had an amazing range of experiences. I don’t think there’s much he hasn’t seen in one form or another.

I’ve asked him to try to boil it all down to the essence and present it to you on Oct 6 in 45 minutes or less.

This isn’t going to be a sales pitch. Tom is going to share his experience with best-in-class customers and back it up with research provided by the Aberdeen Group. It will be solid content that your team can sink their teeth into.

The seminar is free, and will be delivered to your desk top. But you need to register by following this link. You can also read our press release here.

Hope to see you there. Use the ShareThis button below to mark this page, leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

I just got off the phone with Kris Deckard, the Director of Ready Indiana. Ready Indiana is my state’s effort to help Indiana businesses “Engage, Elevate and Educate our Workforce.”  In the last few months we’ve seen a modest uptick in customers calling us from across the country with the news that they’ve received a training grant from some regional or state agency for training and workforce development. I wanted more of that, so I had tracked Kris down using Google.

These training grants seem like such a win-win.

I don’t think we have any customers who are over-trained; people will always be able to get more value out of a tool that they really know and understand.  Knowing more about the tool can translate directly into higher individual worker productivity and effectiveness. If you’ve been following my last few posts you know that that can turn into huge increases in throughput and quality, and huge decreases in downtime, rework, repair, and other wastes.

But in a recessionary economy, a lot of manufacturers are squeezing every gram of copper out of every penny they have. Training budgets are one of the first things to get slashed. So investing in training is one use of stimulus money that makes an awful lot of sense. Of course I’m biased, because the money eventually trickles down to me and enables me to create and retain jobs. Like I said, I wanted more of this.

Kris confirmed that American Recovery and Reinvestment Act (ARRA) does have substantial funds available for workforce development. Actually the buzz word these days is “Incumbent Worker Training”.

In Indiana there is a special grant called the Skill Enhancement Fund (SEF - these people seem to be huge believers in the wonder of acronyms). SEF will pay out up to $200,000 every two years to employers so they can train or retrain their workers.  The grants are especially supportive of manufacturers. Eligible training activities specifically mention “Quality-Assurance Skills: Skills that are intended to increase the quality of the company’s product (Statistical Process Control [SPC], Total Quality Management [TQM], ISO and QS).”

So if you’re one of our Indiana customers, contact me and I’ll introduce you to Kris. She’ll guide you through the application process maze.

Which brings me to the bad news, if there is bad news. There is no centralized clearing house for this information. It seems very regionally based. Actually Indiana seems progressive compared to some other states where grants are administered through regions that may be as small as a couple of counties. So navigating the maze may be difficult.

Have you checked what’s available in your area? What is your company doing to develop you and your staff? Use the ShareThis button below to mark this page, leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

I’m working on a case study with one of my customers that I think you’ll be interested in. I’m just beginning to put it together now, but I thought you’d appreciate a sneak preview. I’ll let you know when the final article is ready.

Last fall this customer came to us with a sizeable integration and customization project. It came at a time when the financial and manufacturing world seemed to be falling down around us. I was, frankly, surprised that they wanted to spend that kind of money at the same time that banks and investment firms were collapsing, the stock market was imploding, and businesses were shedding employees like autumn leaves.

But we worked with him through our standard process of defining the project and formalizing a Statement of Work. We launched the project right around the new year. During that process, my customer agreed to meet with me in six months to do a post-mortem on the project. He said he’d be willing to open his books so we could evaluate - objectively - whether the project was paying for itself.

We finally got together last month - seven months after we finished our deployment. True to his word, he did open his books to me and demonstrated - with CFO-approved numbers - that he had paid for the initial investment in less than three months.

Many organizations look for a two-year payback. He had achieved his in an eighth of that time.

Now, seven months into the project, he had documented an ROI of 171%.

That got my attention.

We started by reviewing the work we had done with his team.  This was a truly collaborative effort. His engineers had done an exceptionally fine job of building the foundation for the project, and then worked with my staff to implement the solution. Together they did a fantastic job of automating and integrating a variety of work flows and data systems. The result was a streamlined process for tracking repair and rework processes across multiple departments.

It was the classic tactic of “reduce the cost of data”. I knew that going into the debriefing meeting. And I expected that the ROI would be based on the efficiencies gained by eliminating islands of data, removing duplicate data entry, and integrating disparate data systems.  I expected that he paid for the project by eliminating staff (I knew the company was going through a downsizing concurrent with our project) through automation.  Clearly we were helping this customer move laterally on the Data Cost / Value Matrix from expensive data to low cost data.

As we dove into the data, I found a number of surprises.

First, he didn’t eliminate any jobs because of this project. As he reduced rework he reassigned the rework staff to more productive activities. They shifted from non-value-added status (overhead) to value-added production staff.

Second, reducing the cost of the data contributed only about 2% to the ROI. It was such a puny number. I had expected reducing the cost of the data would account for maybe 50% or 60% of the cost savings.

The lion’s share of the ROI came from improved throughput. Cheaper, more reliable, and more accessible data enabled his staff to drive defects out of the process. Reducing defects increased first pass yield. This resulted in lower WIP (work in process), faster product delivery cycle times, and improved order to cash cycle times.

How are you looking at ROI? Do you ever understate (as I was tempted to do) the benefit you get from the value of the data? Use the ShareThis button below to mark this page, leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

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.