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How Data Quality Impacts Manufacturing Productivity


By: Gavin O’Heir

When it comes to data quality, there’s an old database programmer’s saying that is just as true today as it was 30 years ago – “Garbage in, garbage out.”  In fact, data quality is even more important today as manufacturing systems and operations have become increasingly sophisticated and software-driven. The hard truth is companies run on data and if your data is faulty and unreliable, manufacturing productivity slows down, quality suffers, and profitability can be impacted. High-quality data is your company’s greatest asset.

But what exactly is “data quality” and how do you measure it?

Data quality is a perception or assessment of your data’s ability serve a specific purpose. This is generally determined by how data meets the following criteria:

  • Completeness
  • Accuracy
  • Relevance
  • Reliability
  • Accessibility
  • Consistency
  • Proper format

The quality of the data that is used by a business is a measure of how well its organizational data practices satisfy business, technical, and regulatory standards. Organizations with high data quality use it to increase efficiency, enhance customer service, and drive profitability. However, organizations with poor data quality find their efficiency and productivity hampered by inaccurate reports and flawed business plans, resulting in bad decisions that are made with outdated, inconsistent, and invalid data.

How much does all this cost companies in terms of manufacturing productivity and revenue? Gartner estimates that the average company can lose as much as $8 million annually through poor data quality. Inaccurate, incomplete, and missing data forces workers to compensate for poor quality data or create workarounds when using operational and analytic applications that can hinder and even delay production, costing companies big in terms of current and future revenue. 

Poor data quality has a ripple effect that can run throughout the enterprise and impact the following areas:

Production, including increased workloads and processing time, as well as decreased throughput and end-product quality. 

Internal and external confidence can take a hit when workers, vendors, and customers can’t trust the data they’re receiving. For example, when you run reports, do you find errors and inconsistencies in the data? This can have a direct effect on organizational trust, business forecasting, reporting, and overall brand value. 

Risk and compliance can be affected by poor data quality, resulting in diminished credit and capital investment, increased competitive risk, and potential industry and regulatory compliance issues.

Financial security can be impacted by increasing operating costs, decreasing revenues, missed business opportunities, cash flow problems, and performance- or quality-based penalties and fines.

Improving your data quality

It should come as no surprise that data quality is directly affected by how data is entered, stored, and managed. Verifying the reliability and effectiveness of your data is known as data quality assurance (DQA) and is central to manufacturing productivity and ensuring the accuracy of your business intelligence and business analytics. 

Some companies adopt a “set it and forget it” approach to data input and management. They’ll put a data entry process in place and assume it will consistently yield the type and quality of data they require. This is a mistake. The way data is processed, managed, and analyzed are living, breathing activities that need to be constantly examined and evaluated to ensure data relevance, thoroughness, and accuracy. It’s vital to ask the following questions on a regular basis:

  • Are we gathering all the data we need?
  • Is it all relevant and appropriate for the tasks we need it to accomplish?
  • Is it consistent and accurate?

In order to maximize and maintain data quality and efficacy, it’s important to periodically “scrub” it – a process that ensures all the data being utilized is current, standardized and unduplicated – as a cornerstone of your DQA strategy. Don’t have a data quality assurance strategy? You definitely need one. We’ll discuss the importance of DQA and how to develop an effective strategy in our next blog post.