The Data Heads

I ran into a statement the other day that really got my attention:

“The value of data and statistics is not in the numbers or the charts, but in the conversations we hold around them.”

I found this statement in an article by David Shaked called “Creating a Bridge Between Deficit-based and Strength-based Problem Solving: the Journey of a Six Sigma Master Black Belt.”

What got my attention is that this is one of those simple statements that emerge on the other side of complexity.

I’ve sometimes erred on the side of complexity. In this blog and elsewhere on our website I’ve written a lot about the value and cost of data. You can take a short, eight-question evaluation of how well your organization uses data. You can find lots of case studies of how people use data to make better business decisions and make huge improvements to business processes and products.

All of these are great tools and topics, but sometimes they may make it harder to see the forest for the trees. Shaked’s statement encompasses all the complexity and opens the door for a new set of questions. It takes you through complexity to simplicity.

Our Data Cost – Data Value Matrix (take test here) describes four aspects of data value: Product Control, Process Control, Continuous Process Improvement, and Data Visibility & Transparency. These first three describe specific types of conversations we can have around the data in our business. Each represents a level of value that we can get from data. In other words, if the only thing we use data for is to control product, then we’re having a fairly limited conversation about our business.

On the other hand, if we’re using data for process control or continuous process improvement, we’re having a much richer conversation about our business – and we’re getting a lot more value for our data.

It is that third arena that Shaked really begins to push the boundaries of traditional Lean Six Sigma (and TPS / TQA / CPI) thinking. In this article, and in others published on his site, he begins to introduce a strength-based approach to problem solving. Built on the subset of Organizational Development called Appreciative Inquiry, Shaked turns the burner up on Continuous Process Improvement by asking “What is it we really want more of?”

Appreciative Inquiry is a rich theoretical and experience-based approach to organizational change. It is based on the fundamental principle that organizations move in the direction of their questions. In other words, if you ask positive questions you move in a very different direction than if you ask negative questions.

So if we ask “Why do we have such poor employee morale around here?”, you’ll move in that direction and you’re likely to increase poor morale. On the other hand if you ask, “What examples do we have to excited, engaged employees who are giving their very best of their unique strengths and capabilities?” you’ll move in a very different direction.

Shaked argues that the same holds true when we’re talking about defects and problems.  In other words, if we really want Continuous Process Improvement, wouldn’t it be better to talk about the times when we are getting exceptional performance, rather than the times that things are broken? Peter Drucker famously said: “The purpose of management is to create an alignment of strengths so the organization’s weaknesses are irrelevant.”

For me this opens some interesting questions:

• What examples exist now where continuous improvement is focused on increasing yield or throughput, rather than reducing defects?
• Does a shift in focus impact the way people think about and attend to process improvement?
• Are we collecting the right data for moving to what we want, rather than moving away from what we don’t want?
• Do we need to think about data analysis differently? Do we need new tools, or just think about the tools from a different direction?

I’m sure there are other questions. Perhaps in future posts we’ll dive into this more.

What about you? What questions does this trigger for you? Use the ShareThis button below to mark this page, leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

I mentioned in yesterday’s post announcing the release of GainSeeker Suite Version 8 that I have been working on a case study about the new report writer.

You can read the case study here, and download a PDF version to share with others.

A little bit of the back story:

This project came about during a training class we held for Valeo (the subject of the case study) early in the new year. During the class, Stacey (who is quoted extensively in the case study) made the comment “You’ll never find a kid who wants to grow up to be a data jockey.”  What a great comment.

Mel (the guy from our staff doing the training) was intrigued and scratched away at it. What did he mean by data jockey? Why had he become a data jockey? Who cared about the results of his data jockey work? What difference would it make if we could eliminate the data jockey work? What would it take to eliminate the work?

At the time, Version 8 hadn’t been released, and Mel had had only minimal exposure to the power of the new Dynamic Reports module. He came back and started asking his colleagues “So this is what they really want. Could Dynamic Reports handle it?” Dale, one of our senior developers whom I sometimes call Obi-Wan Kenobi, put together a prototype and we were off to the races.

In manufacturing circles it not at all uncommon to talk about “The hidden factory.” The hidden factory is rework. Another case study about our customer Titleist includes this analogy from the rubber and plastics industry:

Another benefit of reduced scrap is that equipment is freed to do productive work. A shop with a 5 percent scrap rate and 20 molding machines has one machine dedicated to making scrap. Using real-time production data to eliminate scrap is the equivalent of buying a new machine.

Working as a Data Jockey is a hidden factory in our offices. Like a machine producing scrap, it is not value added. Eliminating the data shuffle – freeing the data jockey – pays huge benefits to your organization. The case study outlines some of them.

What about you? What is your experience as a Data Jockey? What have you done to eliminate this hidden factory in your office? 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.

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.

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.

One of the really important aspects of the Data Cost / Value Matrix is how reducing the cost of data can help you get more value from data.

This came up again the other day as we were debriefing following a recent deployment for a new customer. This customer is about to undergo a major cultural transformation because we helped them automate real-time data collection and analysis. In the process, they will shift from a Product Control to a Process Control way of looking at the world. Ultimately this will reduce costs, increase customer satisfaction, and (we hope) drive new business.

This customer makes electronic devices. Their products are complex pieces of equipment, and at a couple places on the production line, the various component parts and the final assembly pass through an automated test stand. This test stand runs the unit though dozens – even hundreds of electrical tests. A unit has to pass all of those tests to be released to their customer.

Sounds like a winner, right? One hundred percent inspection. Nothing bad ever escapes to the end user. And we have the data to prove it. Boy, do we have data. Entire databases of rows and columns of numbers. Every one of them in spec.

Except it doesn’t work that way. Some units pass all the tests with flying colors – and then fail out of the box or early in the life of the product. Here is what the data for one test parameter looks like plotted on a histogram (this isn’t actually the customer’s data – it’s just representative):

In this example, everything is well within spec – it should all be good.

When you look at this data in time series on a control chart it looks very different:

Viewed this way, this data tells a completely different story. If you look closely you can see a couple of signals that the process is unstable, even though everything meets spec. In a complex environment with dozens of critical – and interconnected – variables, these unstable values often directly correlate to the early life and out of the box failures.

Learning to rely on the control chart is the shift from Product Control to Process Control. It is often a tough shift to make – especially when everything is in spec.

What makes it even harder, however, is the sheer volume of data they need to sift through to find those signals. Hundreds of test parameters, hundreds of units tested every day. Automation is the only way you can keep up with the data. By automating the data collection and analysis process you can empower people to make that cultural change.

What about you? How can reducing data costs make it easier to get more value from your data? Use the ShareThis button below to mark this page, or leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

If you haven’t been following it, the discussion about the data shuffle has been continuing over at LInkedIn.

Laura Wright posted a comment yesterday:

Is there SOME value to the ‘data shuffle’? E.g., deep knowledge that can help the green or black belt discern nuances to their process analysis that otherwise wouldn’t be had…and so a better solution comes to light? Don’t get me wrong – I do believe data shuffle is wasteful…but some fruit can be gleaned from the exercise.

I think this is a great question, and I agree with her comment. But I also wanted to push the topic out a little further.

I was still trying to formulate a response when Terri Jostes weighed back in with a comment that said what I wanted to say far better than I could have:

I agree with Laura in that there is no substitute for an intimate knowledge of your data. Understanding where it came from, what it means and the process used to acquire the data is absolutely critical. But after that’s been figured out, a mechanism for streaming process data to managers and process improvement experts has to be put in place to free your belts from the ongoing task of “cleaning up” the data or linking files from multiple databases so it can be used.

In the interest of full disclosure I need to point out that Terri is a former user of the GainSeeker Suite. She comes to this after having lived with the data shuffle and found a different way of life. Actually some years ago I wrote up a case study about the experience of an unnamed Master Black Belt (who I just ‘outed’) at a financial services firm. Here is a link to read the case study, Building a Six Sigma Measurement System in Financial Services. At the end of the case study is a section on Lessons Learned, and the first lesson addressed this very point. Here is an excerpt:

Upstream manual data collection – According to the MBB who led the cycle-time-reduction initiative, the initial effort of capturing data manually first paid huge dividends as the deployment progressed. By engaging in manual data collection, the MBB 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.

While an automated system has proved invaluable for collecting and analyzing massive amounts of transactional data, it is essential to develop an intimate, hands-on relationship with data in order to understand the system that produced it. This principle applies to any initiative or project that is focused on deriving long-term, leveraged benefit from an automated measurement system.

This lesson was reinforced later when the MBB implemented a similar measurement system in another part of the business. In this second application, she believed she knew enough about the system to go straight to automated data collection, but she discovered that there was no shortcut to forming a thorough understanding of the data by collecting it manually first. The second application took far longer to deploy, with many more false starts before realizing success.

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