The Data Heads

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.

Over at the LinkedIn Continuous Improvement, Six Sigma & Lean Group, I posted a discussion question that generated some great responses. Here was my original question:

Lean Six Sigma practitioner’s ad hoc data survey
Recently I was talking with a black belt who told me “I know exactly what you mean by the data shuffle. Three hours of my 13 hour day yesterday were spent doing the shuffle.”

The data shuffle is all the compiling, massaging, and manipulating data that you do to get something useful so that you can make meaningful business decisions.

My experience is that all that busy work undermines the cultural transformation that is needed in Lean Six Sigma deployments. In other words, if data were easier to get to, the corporate culture would be more apt to make data driven decisions.

I have a couple of blog posts on the topic.

For an example of the data shuffle, see http://www.hertzler.com/blog/dataheads/index.php/2008/12/doing-the-data-shuffle/

For more background on the relationship between data and cultural transformation, see http://www.hertzler.com/blog/dataheads/index.php/2009/03/hitting-my-forehead-with-the-palm-of-my-hand/

Is the data shuffle alive and well at your organization?
How much time do you spend doing the data shuffle for your projects?
Does the data shuffle undermine cultural transformation?

Here are excerpts from some of the responses:

From Shaun Wurzner:

Regrettably, [Six Sigma] arrived at a point where managers and business leaders had to demonstrate Six Sigma improvements and for fear of at best, never being promoted or at worst, being let go…. Incorporating the fear of employment with continuous improvement was in my opinion, a disastrous train wreck. As a result, managers (and I AM GENERALIZING) felt compelled to generate data to show improvement. Regrettably, the more paperwork and reports, the better chance of being viewed as a valuable results oriented contributor…

To tie back into Evan’s original question, another fundamental requirement of Six Sigma was the ability to access data to characterize process, identify sources of variation, and drive process improvement through various means… The ability to capture timely data continues to be one of the fundamental issues that challenges companies and practitioners alike…. I can tell you that so many companies drive their business without fundamental data collection and analyze tools. As an example, how many times have you seen someone apply an algorithm to a data set without checking it for normality and these are statisticians or quality “experts”. Not to fault them, but everyone is so preoccupied with the end results that they by-pass some of the fundamentals.

This is a powerful indictment: “Managers feel compelled to generate data to show improvement.” Wow.

But why should we be surprised? It is entirely consistent with Deming’s 14 Points, especially #8 (Drive out Fear.), #10 (Eliminate slogans, exhortations, and targets for the work force.), and #11 (Eliminate numerical quotas for the work force and numerical goals for management.)

Maybe it is time to dust off your 1982 edition of Deming’s “Out of the Crisis”. We did not learn those lessons well.

And from Terri Jostes:

The 7 types of waste are now known as the “7 +1″ or 8 types of waste with the addition of “Creativity” or “Human Potential” as the 8th type of waste. All the wasted effort of overprocessing, rework and motion (in the form of moving data from one database to another) associated with the “data shuffle” causes our intelligent, highly trained, well-paid green belts and black belts to spend countless hours in unproductive, frustrating activity.

Why do companies allow this to happen? In some companies, it is seen as a rite of passage for a belt or a necessary, if unsavory, part of the job. Some belts even like doing this type of work – they’re good at it! Of course they are…they spend a lot of time doing it!

Evan’s right…It’s time to be strategic about our data gathering, compilation and analysis. In order to create a “data driven” culture, clean, reliable data has to be readily available to all levels of the organization. Letting the “data shuffle” continue in our organizations guarantees lost opportunity in our improvement activities and operational performance.

I love that idea: “Rite of passage.” Did you have to go through this rite? What is your story?

And from Forrest Breyfogle:

Evan, you indicated that the data shuffle is all the compiling, massaging, and manipulating data that you do to get something useful so that you can make “meaningful business decisions.” If the result was to make meaning business decisions that would be one thing and would be waste in that the analysis was not performed efficiently. However, it appears to me that the problem is worse in that often there is playing games with the numbers to make a situation appear better than it is… We need to blend analytics usage within an overall business governance system.

Excellent point: This isn’t just an efficiency issue; we need better governance. Can better data help that?

And from Terry Burton:

Today’s turbulent economy requires much more targeted Lean and Six Sigma efforts that produce an accelerated level of tangible results. That means a rapid and perfected “value-added” execution. We need to stop the data shuffle, and the training of the masses and put the money on the table – Or it’s all just another bandwagon! Too many organizations are stuck on this”mad belt” and “data shuffle” mode with their Lean and Six Sigma deployments.

One of the largest inefficiences we observe that compounds the data shuffle is that people are using multiple versions of the truth (facts). This is prevalent in organizations where the formal enterprise system has broken down, or where people struggle with their own personalized kluge spreadsheets to get (data shuffle) the facts. Some people tend to grab and shuffle the data that is available, rather than think through the specific data elements needed to solve the problem.

Think about the obvious. Not only are these folks inefficient due to data shuffling, but they all have different versions of the facts when they’re done! Now this leads to the wrong actions and firefighting, and the data shuffle becomes a viscious cycle. Their organizations would be better off if they made their “data shufflers” sit there and do nothing! Unfortunately, these organizations are not improving at the rate of the economy so despite all the belts, they are falling behind.

The largest challenge with Lean and Six Sigma is quickly thinking through and acquiring the right data to make the right decisions and get the right results. Some of our clients have reached this “utopia:” Real-time, visual event-driven metrics, a single version of the facts, decide-act-measure in real time. Based on true potential, we see that well over 80% of Lean and Six Sigma deployments are failing. People are hanging up the window dressing and getting their belts, there’s a lot of data shuffling and charts, but cultural transformation is not happening.

This economic meltdown is a great opportunity to rethink the “what’s” and “how’s” of your Lean and Six Sigma deployments and shift into a higher gear of new results.

Important concept: “A single version of the truth.” This reminds me of a conversation I had with a test engineer at an audio amplifier company who told me “In the past, if I wanted that data, I had to go mine it myself… People would be reasonably questioning my political motives for saying ‘We had 94% first pass yield last week. Now we have this standard called Hertzler, and you can go get the same data I just got…”

And from David Back:

I can only echo the points made and pass on some of my experience with management teams. Management by facts is a major change for many leaders and it is far more than setting up data cemeteries and then having specialist analysts or Belts providing insight in parallel to routine business processes. Managers need to buy in to the strategic nature of the change and be equipped with the clear concepts to act in this way. The size of this step is generally underestimated, old habits die hard ! It falls to all of us that understand this issue to promote the change at every possible opportunity.

“Data cemeteries…” What a great term.

And from Ken Place:

Although I agree that “the data shuffle” is a problem, I see more instances where data is just not available, what is available cannot be used, and new or additional data collection activity is required before an effective Lean or Six Sigma effort can/should even begin. As a Master Black Belt, many of my Black Belt students come to the first week of class ready to hit the ground running with a project. When we discuss the details of “Good” data they realize that they must return to square one and generate some history of the current state before progressing. Rather than massage the data to make it useful I find they must disregard current data and re-collect. Clearly non-value added but seldom fixed at the root of the problem, The Business Management System (BMS). In my opinion the BMS, formerly known as the QMS, should be designed with the correct, robust metrics in place for each process. Those metrics should reflect the health of each process as it contributes to the system, so that a company is able to asses where their next opportunity for continuous improvement exists. Without ongoing correct measures of at least efficiency and effectiveness, projects are not properly prioritized and work cannot begin right away. Yes metrics should be strategically established and available at any time for accurate monitoring, measuring and continuous improvement efforts.

At the risk of over-simplyfying, build the measurement system and the the rest will be easier.

What do you think? Is the data shuffle alive and well at your organization? How much time do you spend doing the data shuffle for your projects? Does the data shuffle undermine cultural transformation? You can leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

Forrest Breyfogle posted a great question over at Linked-In:

Does our corporate leadership’s relationship-building strengths make it a challenge for them to truly understand and resolve the fundamental system improvement and re-engineering needs of the financial crisis?

From examination of the myers-briggs personality types we note that not everyone thinks the same. One observation is that some people tend to be systems thinkers and others are not. Systems thinkers, according to the Myers-Briggs Type Indicator [MBTI] manual, are those persons who have NT temperament and make up only 15% of the U.S. male population (female is less). My hypothesis is that high level government and business decision makers do not tend to be system thinkers because it takes a lot of relationship building to move upward. If this is true, many of our overall decision makers can have a very difficult time approaching the economic crisis as a system issue that needs process improvement/re-engineering. What are your thoughts?

I think Breyfogle may be on to something. I’ve taken the MBTI, and I’m an NT. I realize that I think differently from a lot of people.

What I’ve learned, however, is that ready access to real-time data helps people who are not systems thinkers see connections they would otherwise miss. That’s why having a good theory of data and making data more visible and accessible is so important. That is how you turn data into knowledge.

The follow-up comments are interesting and reveal a wide range of opinions about the underlying causes (and subsequent improvements needed) of the recession.  My own opinion?

What the MBTI doesn’t address is the question of values raised by some other commentators. Is the term ‘free market’ used in the way classical economists advocated: a market free from monopoly power, business fraud, political insider dealing and special privileges for vested interests? Or is it used in the more modern sense: free for predators to exploit victims without public regulation or economic policemen?

I own a small business and I don’t favor regulation, but I’m outraged at the way the marketplace has become free to enable Ponzi schemes and other scams to proliferate.

As a data guy and a systems thinker I would like to see us make better business decisions based on systems theory and data. But it has to be exercised in a free market that is fair and equitable, and that doesn’t reward theft.

What’s your personality type? Do you agree that it affects the way you view problems?  Which type of ‘Free Market’ do you advocate? You can leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

A recent article on SearchDataManagement.com about a Gartner Group conference on Business Intelligence (BI) discusses the fact that business intelligence is probably the number one priority for CIOs, but most companies have not translated that prioritization into high value.

That conclusion doesn’t surprise me. I’ve been arguing for sometime that most businesses under-utilize their data assets. Here are a couple of blog posts, looking at topics like BI as an oxymoron, technology and culture, and the key drivers of Best-in-Class manufacturing.

In this latest story, I especially like this prescription for addressing the problem:

IT workers must reconsider how they deliver information to end users. Traditionally, on one end of the spectrum, users either access information through static reports or through ad hoc queries, Schlegel said. Instead, IT should focus on developing interactive reports to meet both demands.

On the other end of the spectrum, more sophisticated users often create their own spreadmarts, which by definition fall outside the view of IT, to make up for the limitations of ad hoc queries. IT departments should develop data discovery environments that empower users to do the analysis they need, but which also let them connect that analysis back to the organization.

That sounds to me like a dashboard that users can drill into to get to the underlying data. It sounds to me like an analysis wizard that guides users to the underlying sources of variation in a process.

IT people do sometimes lose sight of their real goal. As one conference attendee, Chad Erman, head of BI for Southwestern Energy put it, “We noticed a lot of people think in terms of reports, instead of BI or key metrics. What I constantly had to remind them … is: What is the question you’re trying to answer? Then work to achieve that goal.”

Getting business leaders the right tools can go a long way to enabling that shift.

What about you? Are you getting high value from BI? If not, what are your road blocks? You can leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

A couple years ago we sponsored a research report by the Aberdeen Group on best practices in Six Sigma deployments. You can download a copy of the report, and a companion white paper that I wrote for the report called “Leveraging Technology to Transform Culture.” For me the most astonishing thing about that 2006 report was the disconnect between the challenges people say they face in their Six Sigma deployments and their responses to those challenges.

Most of the challenges people face are cultural:

Most people respond to those challenges directly by doing a checklist of initiatives: train employees, introduce change gradually, assign senior management as champions, engage outside consultants, steal talent from the competition, and so forth.

This frontal assault has been going on for years and it isn’t working. That’s the forehead smacking part of this.

Years ago I realized that making data more visible and accessible changed the way people look at themselves, the people around them, and the problems they face. Somehow just making the data visible takes away the personality and political dimensions – the cultural barriers – and helps people focus on solving problems.

Making the data very accessible – visually on the screen in a control chart or a dashboard – and making it available in real-time is a huge benefit. It breaks down all kinds of barriers.

My customer, Royce Binion, then Director of Operations at BAE Systems Controls in Fort Wayne, put it most succinctly when he said to me years ago “Real-time access to accurate, actionable data is the number one tool that has enabled us to move to a data driven culture.” This was way back in 2000 when his plant won the Industry Week’s 10 Best Plants award, and a few years before they would win the Shingo Prize.

This came up again for me this week when I attended a webinar hosted by the Aberdeen Group. They’re doing follow up research to see what has changed in the last 2 1/2 years, and they wanted to share their preliminary findings on best practices in Lean Six Sigma deployments. (If you’d like to participate in the study, you have until April 30. You get a free copy of the report if you do.)

What struck me as I heard this briefing is how little has changed. Cultural challenges are still at the top of the list, followed closely by IT and technology challenges. People still don’t seem to be connecting the dots.

Today I wrote to Cindy Jutras, the author of this research, to get her take on it. She wrote back:

in spite of all the data and IT related challenges from the previous slide (about the challenges people face in Lean Six Sigma deployments), there was not an appropriate response to those challenges. I agree that visibility is key. And our results support that. In general we found those with True Six Sigma have 110% better visibility than Industry Average and 580% better visibility than Laggards. Not surprisingly, they deploy far more IT tools than those not performing as well.

How about you? Are you using real-time access to accurate, actionable data to transform your culture? You can leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

Dr. Neil Polhemus, CTO at StatPoint Technologies (and publisher of StatGraphics) contributed a great article in the current issue of Quality Magazine about selecting statistical software for Six Sigma. In it he lists four criteria for selecting the right statistical package:

1. How strong a background in statistics does the typical operator have?
2. What types of data are operators most likely to encounter?
3. If data are mined for information, how easily can multiple approaches with multiple options be tried?
4. How easy is it to create a report or presentation that can be shared with other colleagues?

I’ll answer each of these questions for GainSeeker Suite before the end of this post, but first I want to surface an unspoken assumption in the article and add a couple of criteria that I think Dr. Polhemus missed in his list.

The unspoken assumption is that one statistical package will serve all the needs of a Six Sigma deployment. My experience is that there are at least two broad categories of statistical software, and each has their place in Six Sigma.

Two Categories of Statistical Software

One category of statistical software is Advanced Statistical Analysis tools. Dr. Polhemus’ article outlines criteria for this group. Products in this category include StatGraphics, Minitab, JMP and others. These systems were developed (originally) for statisticians. Often Black Belts (BBs) and Master Black Belts (MBBs) depend on these packages for in-depth work in the Analysis phase. These systems are less useful for selecting projects. For the most part they operate poorly in the Control Phase. Put another way, these tools are of less use to the Champions and Business leaders who charter projects, and also of less use to Green Belts (GBs) and Process Owners who inherit and live with a project when it is completed.

The other category of statistical software is what I call Real-time Enterprise SPC Solutions. It will come as no surprise that GainSeeker Suite falls into this second group. This category comes out of the Real-Time Statistical Process Control (SPC) world. These packages are designed for ongoing data collection and analysis in a continuous improvement (kaizen) environment. These tools are, first and foremost, a tool for process owners and Green Belts. They are also tools for Champions and Sponsors (business leaders) who are chartering projects and driving business performance.

While there is some overlap between the two categories, they are more complimentary than competitive. In fact, they should readily share data. Data should be especially portable from a Real-time Enterprise SPC solution to the Advanced Statistics Solution. That way BBs and MBBs can readily tap into the enterprise data sources to support their efforts.

So here are the additional criteria that you should look for when you’re selecting a Real-time Enterprise SPC System.

Criteria 5: What does it take to get new data into the system?

Advanced Statistical Analysis Packages begin with an assumption that data are in a file, in rows and columns. In this view, data are static: generated once, analyzed in some way and then saved in a folder somewhere. Real-Time Enterprise SPC Packages assume that we are tapping into a live stream of data. Each new data point contributes to our understanding of the process. (Some of the Advanced Stats Packages are beginning to recognize this, but their core competency is in analyzing a static data set.)

The ability to readily incorporate new data is what makes Real-time Enterprise software so effective in the Control Phase. Users set up automatic data collection once and then monitor the results for exceptions.

Keep in mind too that the system should collect data at all levels of the organization. Good systems will make it easy to collect and manage data from the shop floor to the executive suite. This makes it easy to capture high levels of business metrics which can be used to help prioritize projects.

When selecting a statistical package, be sure to ask:

• Can the system tap into any data source, including front-line process owners, gages, a wide variety of text files and databases, PLCs, PDAs, cell phones, and so forth?
• Can the data entry process be controlled so that only valid data can be entered into the system, in a reliable and repeatable way?
• Can data be collected automatically and without human intervention?
• Is it easy to set up and manage these data collection processes to meet all the various needs across my business?

Criteria 6: Does the system automatically test new data for real-time process shifts?

Real-time doesn’t just refer to the process of connecting to data sources and readily incorporating new data. It also refers to statistically evaluating all new data for expected variation. This is an essential tool for understanding processes. If the system does detect a change or shift, it needs to automatically communicate that to people and systems that can do something about it.

When selecting a statistical package, be sure to ask:

• Does the system automatically detect process changes using appropriate statistical tools?
• Does the system automatically let me know there is a shift through email, pagers, on-screen displays, or other appropriate means?

Criteria 7: Are Data Stored in a Robust Relational Database?

The word “Enterprise” in our category name (Real-time Enterprise SPC Solutions) tells us that we’re not looking for a point solution. There are some fine packages out there that do SPC with Excel spreadsheets. But these programs can create a data management nightmare when you are managing all the data in your business (not to mention the risk of defects being introduced in a spreadsheet environment).

An enterprise system builds a data warehouse in a relational database. This makes it possible to tap into a rich data set for selecting and prioritizing new projects. It also makes it easier to share data (and best practices) across the organization.

When selecting a statistical package, be sure to ask:

• Are data stored in a robust relational database structure with a flexible hierarchy?
• How fast are retrievals on large data sets?
• How easy is it to group or segment data?

Criteria 8: How Easy is it to Slice and Dice the Data?

A good Real-time Enterprise SPC System will collect data at multiple levels of the organization. It shouldn’t be confined to the down and dirty shopfloor data.

By capturing this data – along with information about the data – think of it as demographic information – you can slice and dice the data to find opportunities to improve the system.

At high levels it might mean viewing Overall Equipment Effectiveness (OEE) by Line, and then drilling down into various machines or sliced across all shifts. In a transactional environment it might mean tracking cycle times across all offices, or within offices by customer service rep. Being able to easily slice and dice the data makes it easier to understand the relationship of all the parts.

When selecting a statistical package, be sure to ask:

• How easy is it to drill into various subsets of the data?
• Are automatic analysis wizards available to help prioritize and focus your attention on the critical variables?
• Can you data be rolled up into dashboards and other high level summary views for easy monitoring?
• Can data be easily tagged with demographic information?

These additional four criteria are a good starting point for rounding out your tool box of statistical software for Six Sigma.

Additional information

For more information, check out these white papers and case studies:

• Freeing Six Sigma from the ‘Data Shuffle’
• Data grouping and drill down
• Walking the talk – How Micropump built a comprehensive data system to empower their people with data
• Aberdeen Lean Six Sigma Benchmark Report
• The Role of Real-Time Data in Improving Profitability and Customer Satisfaction
• Building a Six Sigma Measurement System in Financial Services

How GainSeeker performs against Dr. Polhemus’ criteria

I promised at the start of this post that I’d address how GainSeeker Suite performs against Dr. Polhemus’ criteria.

1. How strong a background in statistics does the typical operator have?

GainSeeker Suite serves a wide population of users, and assumes that the typical user has little or no background in statistics. Furthermore the system assumes that the user has many other tasks to perform besides statistical analysis.

2. What types of data are operators most likely to encounter?

GainSeeker Suite is targeted for engineering and manufacturing applications. The product is in use in some purely transactional environments too. The system isn’t particularly robust for managing survey data, but does very well with cycle times and defect/error tracking. The system is not designed for the R&D community.

3. If data are mined for information, how easily can multiple approaches with multiple options be tried?

GainSeeker is an interactive system. Users do not need to write programs that are submitted for execution. Having said that, it is not intended to function as an advanced statistical tool. Instead it readily ports data to other software systems including advanced statistics packages. Of course Gainseeker is an excellent tool for automatically updating databases and analysis.

4. How easy is it to create a report or presentation that can be shared with other colleagues?

GainSeeker pays particular attention to sharing data, analysis and reports with various user communities. Users can access data over the web, including mobile devices, and output can readily be delivered by email. In addition, GainSeeker includes a new Enterprise Dashboard module that provides easy-to-understand role-based summary knowledge.

What do you think? Are there other Critical to Quality Characteristics that we haven’t mentioned?

As I talk to people in many different businesses, I’m often amazed at how much time they spend extracting, massaging, and scrubbing data for analysis and reporting. I call this “The Data Shuffle“. A friend of mine at Minitab calls it “The stuff we do that we call our job.”

My informal surveys of Six Sigma Black Belts and Green Belts tell me that there is often a 20:1 or 25:1 ratio of massaging and scrubbing data to analyzing that data. And Six Sigma people aren’t the only ones doing the data shuffle. I’ve seen it in all kinds of environments and for all kinds of reasons.

Any way you look at it, The Data Shuffle is not value added activity. It is a business cost that enables better communications with customers and better business decisions.

So why might you not want to show this post to your boss?

Because if you’re doing the data shuffle this post points out that the data shuffle can be eliminated through automation, and that part of your job can be eliminated.

And in this economy I’m guessing you don’t need any more reasons to have your boss eliminate your job.

So what can you do instead?

OK – here is the gutsy move:

Start by studying this matrix:

(You can read more about the Cost Value Matrix.) Chances are good that if you’re doing the Data Shuffle, you’re down in quadrant D where the cost of data is High and value of the data is Low.

There is also a good chance that right now your company is very interested in reducing costs. If we automate the Data Shuffle we can shift towards Quadrant C.

So take this matrix to your boss and say something like:

“Look – I know the company is interested in saving money. The economy is tightening up all around us and we need to lean out operations so we’re more competitive… blah blah blah.”

Then say: “I know how to cut some significant Non Value Added costs out of our system. I can help you do that, but if I’m successful, it will eliminate part of my job. Instead of working myself out of a job I’d like to invest this freed time in helping the business make better use of that data. By making better use of the data we can reduce scrap and material costs, improve customer satisfaction… blah blah blah. In other words, I want to help us move up to Quadrant A.”

This gives your boss three options: business as usual, or eliminate the data shuffle and your job, or eliminate the data shuffle and free up time to do more important and value added work. If he chooses business as usual you might want to keep your resume up to date because of the way the economy is going. If he chooses to eliminate the data shuffle and your job, you will at least have learned how to eliminate the data shuffle and you may be able to take that skill into a new business that will appreciate the value of moving to Quadrant A. And if he chooses to reinvest in you and in process improvement, everybody wins.

So give me some feedback: How much time do you spend on the Data Shuffle today? How would this strategy work in your organization?

I’ve been following Forrest Breyfogle for some time. You may know that he came out with a couple of the definitive text books for the Six Sigma DMAIC process several years ago. I have a couple of them on my shelf.

In the last few months, I’ve bumped into Breyfogle at a couple of conferences and he is onto something really important. At the risk of oversimplifying it, he has come to realize that Six Sigma and Lean Six Sigma are, in and of themselves, too narrow in scope. All too often DMAIC projects fix one thing and break something else, and seldom do you find links from individual projects to ultimate business performance.

This is made worse because these process improvement efforts are mostly divorced from the implementation of business dashboards and scorecards. Furthermore, strategic goals are too often created in a vacuum at an executive retreat with little connection to customers and their real needs. (Six Sigma guru’s may be harrumphing in the background, but please, go read his website. There’s a lot of truth in his words.)

Breyfogle proposes an Integrated Enterprise Excellence (IEE) system that helps organizations execute the Three ‘Rs’ of Business: Doing the Right Things, doing them Right, at the Right Time. You can read more about IEE at his website and blog.

You find IT (information technology) and the CIO (Chief Information Officer) at the heart of IEE. Breyfogle “gets” the role of the CIO and IT in continuous improvement. He has a great white paper on this topic, and his latest blog post touches on it.

I haven’t had time to see how far Breyfogle takes his prescription for what IT needs to do to enable business excellence. What I’ve read so far seems very consistent with our customer’s experiences and my vision for how data and IT can – in Breyfogle’s words – “be the catalyst for new improvement initiatives.” It is also consistent with the research we’ve seen out of the Aberdeen Group on the role of real-time data in manufacturing excellence.

Breyfogle thinks he may be on to the “Next Big Thing”, and he may be right. I intend to keep my eye on it.

Andrew Downard had a great post last week on the iSixSigma blog about the maturation of Six Sigma. He argues that Six Sigma has an “Act II” problem. Act II is all about institutionalizing Six Sigma so that it actually delivers on it’s promise. Downard argues that the skill set required for Act II is very different than what is required for Act I.

My language for this is the difference between Explorers and Settlers. In US history the explorers came from outside, mapped the terrain, and then moved on to the next place. Settlers came in and put up a cabin, plowed the earth, and settled in to make a difference. The actors in Act I are Explorers. The Settlers don’t show up until Act II.

But there is another way to look at this. If you do a Google Image Search for Six Sigma DMAIC, and then tally the images by type you get a very interesting perspective on Six Sigma. Of the first 40 images, about one half of them depict DMAIC as a linear process. Here is a typical graphic:

DMAIC as a Linear Process

The other half of the images, however, are cyclical. This one is typical:

DMAIC as a Cyclical Process

I wonder if how we picture DMAIC indicates whether we’re an Explorer or a Settler, or in Downard’s terms, whether we’re in Act I or Act II.

I think it may also lead us to different conclusions about how we view data and data systems. If you’re an Act I Linear Explorer, I’m guessing you’ll see data and data systems as something that you need to conquer. Data is a wild river that holds some great fish that you can eat, but essentially it is blocking you from your next destination.

If you’re an Act II Settler, that same data river is something that can help drive subsequent cycles of the DMAIC process. In Settler terms you can use it to water your crops, drive a new sawmill, and (once you’ve invented them) power electric lights and a toaster.

If you’re an Act I Explorer, tools like GainSeeker Suite won’t make much sense to you. Who wants to build a hydro-electric dam when you’re just trying to get across the river?

But if you’re an Act II Settler, being able to connect to disparate data sources, collect and alarm based on statistical process control rules, and analyze for root cause of process variation becomes essential.