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In the world of metrology, there has been a longstanding confusion regarding data management vs. data collection. According to the Data Management Association (DAMA), “data management is the development, execution, and supervision of plans, policies, programs, and practices that control, protect, deliver, and enhance the value of data and information assets.” In contrast, data collection is simply a task.
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Collecting data quickly and accurately is a great capability, but there are no benefits unless the data is presented in an understandable way to the intended audience. Of course, the presentation of data means different things to different people depending on their job. Typically, the person creating a widget does not care what the scrap rate is for the current month. However, a quality assurance person does care about the scrap rate as well as the nature of the problem. For instance, if the bore size of the widget is out of tolerance. Both of these scenarios require different actions from the same data set. This is an exercise of data management.
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Data Collection Without Analysis is Waste
Shaun calls it data management; I call it data analysis. Either way, without analysis, data collection is just a form of unnecessary processing. From a Lean perspective, it's just waste. Almost every company I work with has too much data and too little analysis. Too much data collection is a form of overproduction; again, from a Lean perspective it's waste.
Also much of the data is collected in a form that's hard to analyze. It's in badly formatted Excel files or buried in large data collection systems. Most data has to be exported, cleaned up and prepared for analysis.
I have also found that most people don't know how to use Excel's PivotTable function to summarize the data or how to use a tool like the QI Macros to turn the PivotTable into actionable improvement projects using control charts and Pareto charts or histograms. Consultants often speak about "low-hanging fruit", but most people don't know how to use Excel to find it.
I have created videos explaining how to use Excel PivotTables at <a href="http://www.qimacros.com/Moneybelt/pivottable-examples.html">www.lssyb.com</a>. Stop simply collecting data and start analyzing the data to find the invisible low-hanging fruit in the data you've already collected.
Nice bit of clarification
That's a good point KNOWWAREMAN, to say that Data Management is analysis of aggregate data and then hopefully taking an action from it or at least deciding what are your triggers to take an action.
For example-- you can do data collection part by part and look at pass/fail data in isolation, but take no actions to aggregate the data. To aggregate the data from multiple parts (as suggested) and look at trends can be useful-- but only if you plan to take an action based on what you see happening.
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