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!
Do you have data that’s an anomaly or special cause that you want to exclude from your analysis? Do you want the ability to temporarily exclude certain data from your data set analysis? Special causes and outlying data can occur in any data collection process, learn how to easily handle these situations.
A control chart with a regular pattern demands some analysis. Regardless of the pattern that emerges when data is charted using SPC software, it should trigger an alarm and generate efforts to gather more information about the process. Learn what steps you should take to discover why a non-random pattern is emerging.
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.
CHARTrunner Lean is the next generation of charting software from PQ Systems. The software generates process performance charts from a variety of data sources to help you optimize your processes and demonstrate proof of quality performance.
Charting your data has never been easier! We’ve taken an innovative approach to charting by creating a brand new user interface. This provides a simplified user-experience that further streamlines the process of creating charts that can be interpreted and shared with others.
Data can be complex, and CHARTrunner Lean allows you to more quickly interpret what your data means and make data-driven business decisions. To learn more, watch an overview video or attend a free demo.
You are looking at a chart. You are going through an analysis and interpretation process. What data is being represented? How important is the data? Does the chart signal any changes? Does the chart show anything that is “bad” or “good?” Does the chart offer proof of quality?
Ultimately, you want to answer the question: is any action required based on what I see?
Now, think about the workflow leading up to this. How did the chart get created? How was the data gathered? What part of the process was difficult or error prone? Would it have been possible for you to miss this chart among your other tasks?
At PQ we’ve been pondering questions like these for more than twenty years. We are working hard on our products and services to reduce friction in your quality improvement processes. If you have a charting story to tell, please share it with us; who knows, it may lead to the next great quality improvement solution.
If you’ve forgotten what control charts are and why they’re important, this three-minute video will remind you how this critical tool can help you demonstrate proof of quality performance, whether you produce a service or a product.
In a heartbeat, you’ll understand the difference between special cause variation and common cause variation—and you’ll learn what to do about it and how data speaks to you about managing your processes.
You may want to show this short, just-released, snappy video to your boss: