Data Quality Assurance Methods: To QC, or not to QC… (or is it QA?)

July 5, 2018 — Dave Coates

You have probably heard by now, but as of March 30th this year, Wind Lake Solutions is now SSP Wind Lake! We’re very proud of our history at Wind Lake serving numerous customers, with some of our relationships approaching a generation in duration. Over the years we have provided a variety of different services, along with our Joint Use management application SPANS (much more on that in a future post). However, more than anything else at SSP Wind Lake, we do data!

In our corporate tenure, we have completed a large variety of projects and seen a lot of data:

  • Mapped 400,000 work orders for our customers
  • Added over 2 million gas services to a customer’s Esri / ArcFM™ geodatabase
  • Fully connected what had been a disconnected electric dataset
  • Verified and updated phasing information in the GIS across the customer’s entire service territory
  • Added thousands of CP test wire reads to a customer’s GIS
  • In addition to millions of mainstream gas and electric features, we’ve converted:
    • Telecom data
    • Electric transmission data
    • Downtown mesh network data
    • Real estate/easement data
  • As well as, a whole variety of other datasets from paper and other sources to GIS.

If it’s a type of data that resides in a utility’s GIS somewhere, chances are we’re familiar with it.

Quality is Paramount

During these and other projects, across all the datasets we’ve encountered and the variety of data services we’ve delivered, there is one single unifying characteristic – and that’s QUALITY. Quality is relevant to every dataset, to every service, to every customer interaction and that’s just as true for your internal data, services and customers as it is for ours when we’re working with a client. As someone involved with your organization’s GIS and the users of that GIS, you have the chance to significantly influence the overall quality of each users’ experience. Quality pervades all aspects of your GIS, good, bad or somewhere in the middle.

The quality of the data within a GIS has always been important, but never more so than now. Today’s sophisticated GIS applications, like AMI, Main Renewal Prioritization and ADMS, depend on extremely high-quality GIS data to deliver reliable, accurate results. Additionally, regulatory bodies are demanding accountability on the part of the utility operator, driving the need for complete and accurate facility data to support justifiable decision making. Furthermore, as GIS data becomes more widely available within a utility, we’ve become increasingly dependent upon that data and its overall reliability.

What does this mean to you and your GIS? Quality is paramount.

So how can you influence the quality of your utility’s GIS in a positive way? Based on our combined 450 years of GIS data experience with countless different systems and processes, that’s what we’ll answer here and in future posts on building and maintaining quality. We’ll cover fundamental concepts from quality thinking as it relates to internally maintained data, to new datasets imported to the GIS and data which flows into your GIS from outside sources. We’ll address “field feedback” and how you can encourage field personnel to provide data updates when the GIS is incorrect. We’ll also dig a little deeper into some specific practices that we have employed over the years that have proven very successful in furthering overall data quality.

Diving Deep on QA/QC

For today’s post, I’d like to discuss a common quality related acronym that you see all the time, namely QA/QC. Of course, this stands for Quality Assurance/Quality Control, but what does this really mean? Are QA and QC the same things or are they different? I’d like to suggest that they are quite different and each very important in their own right. I’d actually prefer if the acronym was QC/QA, because in my mind, that’s the proper order. Quality Control is about doing things in-process to produce quality results; it’s about doing whatever can be done to force quality into a process. Quality Assurance is a post-process activity which is undertaken to verify and measure the quality level of the output from a process. Quality Assurance gives you a score. QC = during, QA = after.

Practically speaking, when we’re talking about GIS data, QC would include things like:

  • Having the GIS Technician run a trace on the data they’ve just added to the GIS
  • Limiting attribute values to specified domains or ranges whenever possible
  • Using a tool to immediately compare the shape length and measured length of a newly added conductor or main, and report back when those lengths differ by more than a specified percentage.

A complete list for your situation might be very different and would likely be much longer.

Quality Control also includes higher-level process things like empowering the GIS editor to stop work on a particular task if they notice data anomalies, or sending a work order back to the field technician/designer if data is missing or in error. Ideally, QC encourages a way of thinking on the part of those involved in a process: how can we make this better or more efficient? Can the process be improved so that I get what I need sooner? Can documentation be produced to make our results more consistent? Quality Control needs to be an ongoing focus within any process for that process to produce optimal results. Obviously, focusing on the quality of something you are building, like GIS data, while you are building it, will certainly improve the results.

But Quality Control, this in-process focus, isn’t enough by itself. How do you know if your processes and results are consistently good enough? You can go by anecdotal information, like how often you get complaints from users of the GIS, but this type of feedback is not always constructive or insightful. That’s where Quality Assurance comes in. If you have a post-production process in place whereby you analyze, test and score the quality of a batch of work, you know where you stand with that work. If that process includes tracking error types and frequencies, you can use that information to tell you where to focus more energy within the process itself. Maybe its additional training (for certain individuals or everyone involved in the process). Maybe it’s a better tool to catch a certain type of error at its source. A well-designed Quality Assurance plan provides you with the information you need to enhance your Quality Control and, over time, informs you if the process changes you are implementing are actually resulting in quality improvement.

In summary, Quality Control and Quality Assurance go hand-in-hand as part of an overall quality management program. Focus on process improvements to enhance quality, then measure to see how well they worked.

As to the question in the title, the answer is a resounding “Yes! And it’s both!”

More to come on this next time, particularly more specific suggestions on establishing a QA process for your work. Thanks for reading!

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