Woody Zuill remembers his first job, where he learned not only how to water plants and repair hoses, but—more importantly—what makes a good work environment. Here are some lessons for management that reflect much of the Deming philosophy, without quoting Deming. Could this all be common sense, or golden rule, or something?
Last weekend’s edition (Jan. 26-27) of The Wall Street Journal featured an article by Bill Gates (“My Solution to the World’s Biggest Problems: Measure them!”), in which he pointed out the importance of measurement systems. “From the fight against polio to fixing education, what’s missing is often good measurement and a commitment to follow the data. We can do better. We have the tools at hand,” he says.
Mr. Gates’ emphasis on measurement and data analysis is a nod to the quality movement – where many of the tools for tracking improvement were developed. SPC charting, SPC software, and other tools for continuous improvement share many of the aims of the Gates Foundation albeit in differing venues. Charting software, that can take the drudgery out of looking at vast amounts of data, makes it more likely that trends and problems are immediately apparent.
PQ Systems has been thinking about ways to make data easy to understand. If you find yourself “drowning in data but starved for information,” SPC software solutions, such as CHARTrunner Lean and SQCpack, provide just the ticket to respond to the challenge.
Data flows into spreadsheets and database tables at an astonishing rate. It is stacking up in personal computers, laptops, tablet computers, database servers, in the cloud, and even in smart phones. In these growing mountains of data there is insight to be found–insight that can lead to gains in quality and ultimately in profitability.
How do we extract the value in this data?
Many applications allow you to import data. For example, when using Excel, you might query a database and put the results into a text file. Next, you might import the text file into an Excel document. The result of this action (importing data) is that you’ll have two copies of at least some of the data–the original copy in the database and the new copy in the Excel document. Once the data has been saved in Excel, it has lost its connection to the original source of the data.
An important question is: “What happens next month?”
The typical answer is “I will use the same process to import data for the new month into my Excel document.” Many people do this. However, the second, and subsequent monthly imports can involve a complex workflow. You probably want to keep the prior month’s data. You have to carefully append data each month in a way that preserves data you have previously imported in your Excel document.
An alternative approach, used by CHARTrunner Lean, is to leave your data where it lies. In this scenario, there is only a single copy of the data in the original data source. Chart definitions are set up to request the most recent data directly from the original data source. This data is not imported; rather it is requested (from the original data source) any time a user wants to see the chart.
This workflow prevents data duplication and ensures a relevant chart any time during the month. When a user wants to see the chart, the most current, fresh data, is fetched from the original data source–and the chart is rendered. No need to wait for the end of the month when the data analysts do their next monthly import and update cycle!
Data is flowing into spreadsheets and database tables at an astonishing rate. It is stacking up in personal computers, laptops, tablet computers, database servers, in the cloud, and even in smart phones. In these growing mountains of data there is insight to be found–insight that can lead to gains in quality and ultimately in profitability. It is increasingly important when we ask questions of this data, and that we do so carefully and with knowledge about how that answers get generated and how they come back to us from the data source.
Heard the discussion over the ways they may be related?
Do you suspect that statisticians sometimes throw around the jargon of the trade just to confuse you? Or are you one of those stat-heads who loves the questions that emerge from data analysis, as well as the rules that statisticians apply to that analysis?
If you’re the latter, this blog post may be of interest to you.
The visual expression of charts brings numbers to life and offers a way to quickly absorb and make information-based decisions. Find out how you can create a chart-driven culture at your organization that can lead to better understanding of data and the ability to transform it to knowledge.
Knowing what a variety of processes are doing in real time can more effectively facilitate process monitoring and reduce waste. Quality professionals are finding that a “dashboard” format helps them do this—especially when you can go behind the dashboard graphic to get the real picture from control charts. Put yourself in the pilot’s seat and chart your own course of quality improvement using StatBoard.
Knowing the difference between different kinds of charts depends on not only understanding the relative advantages that each offers, but also knowing what information one wants to derive from the data. As we saw in an earlier column, it is critical to know how the data can be analyzed with respect to specific information that is required, and perhaps to anticipate other uses that the data might serve at the same time. Steve Daum discusses the differences between run charts and control charts, and offers perspectives on the benefits of control charts.
Inadvertent clicks on default settings can wreak havoc with measurement systems, costing time and money. Up-front action will head off the mess.