Building Your Own Data Quality Process



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As organizations continue to rely heavily on newer technologies, automation, performance analysis, and other critical business components, the streamlined collection and use of data has been an essential element.

With data in all its forms becoming a driving force in modern-day business operations, companies must ensure that the quality of their data is up to par and that they can continue to rely on it.

But why is data quality important, and how can businesses actively improve it? Below we'll define exactly how data quality affects your business.

What is Data Quality?

The term data is quite vague, and it can be represented in different ways for every organization. For example, data collection can be used in various areas, including:

  • Software Development

  • Marketing Campaigns

  • Sales Engagement

  • Healthcare Research

  • Transportation Cost Analysis

While each of these business use cases utilizes data in varying ways, all of them collect data for a specific intended purpose of use. Data quality is a measurement of the overall utility that a dataset has, qualified by dimensions like consistency, completeness, and accuracy.

Why is Data Quality Important?

Data Quality Process

As businesses begin executing their digital transformation strategies, the quality of the data a company collects is closely associated with its overall business health. Being able to make the right business decisions all begins with high-quality data.

However, as businesses begin to grow, poorly formated or inconsistent data pools can impact specific initiatives that the company is driving negatively. For example, when developing a website and improving its UI (User Interface), inconsistent data flow during A/B testing phases can lead the organization to make costly and potentially damaging changes that are unpreferred or unwanted by their customers.

This makes it imperative that organizations take active steps to improve their data quality process. However, before you start building your own data quality process, you need to know how to measure data quality successfully.

How to Measure Data Quality

When trying to understand the overall quality of the data you’re compiling, there are a variety of characteristics to measure. These data features include completeness, reliability, accuracy, timeliness, and relevancy.


Completeness refers to the verification of a dataset to ensure enough information is gathered to serve the intended purpose of the compilation.


Reliability is a characteristic defined by comparing one dataset to a similar one in order to ensure there are no contradictions between sources.


Accuracy is a measurement that should be applied in all data quality evaluations and is used to determine whether or not the presented information has apparent errors.


Timeliness refers to the accuracy of data associated with specific periods of time, and measures how "available" your data is when being compiled in specific frequencies throughout the year.


As organizations collect and store their information in more than one place, measuring consistency becomes essential to ensure any duplicate information doesn't conflict with itself over time.

Best Practices for Building Your Data Quality Process

Data Quality Process

Regardless of the size of your data management project, establishing best practices for your data quality process is essential to get the most value out of the information you collect. Below are five practical steps you can take to maximize the quality of your data and minimize deficiencies:

1. Define Your Needs

Before venturing into any type of data collection project, it is essential to first establish the goals you're trying to achieve. While the information you're looking to extract may have a broad spectrum, you're still able to build rules or thresholds associated with the data to ensure it meets your long-term needs.

2. Set Up KPIs

Metrics are an essential part of developing and maintaining high-quality data pools. Most importantly, however, when paired with your data management goals, key performance indicators will enable you to establish and track milestones to ensure your data is performing as it should.

3. Assess Your Data

Once you've established your data goals and have the metrics in place to measure them over time, it's important to now assess where your collection efforts are today. Depending on whether your data meets all of the quality measurements or if your goals are being met, you'll be able to identify where your organization needs to improve, if at all. This will give you insight into where you need to adjust your systems, processes, or overall data management strategy.

4. Address and Adjust Inefficiencies

Once you've assessed your data management needs, putting a plan in place to address any inefficiencies that exist is the next logical step. However, depending on the scale of your project and how many adjustments are needed, this process can often be the most time-consuming. One of the first things organizations can do to lighten the load around improving their data quality process is by working with outside development teams who can quickly assess and make necessary adjustments to outdated or poorly configured data management projects.

5. Take Preventative Action

As your data quality gets back on track, it's essential to deploy best practices associated with preventing deficiencies down the road. Regular data audits and assessments are one way you can ensure your data is maintaining its accuracy and consistency over time. Deploying root cause analysis when any data deficiencies surface is another way to discover what caused original data integrity issues, giving you insight into specific areas that need to be addressed. This will minimize the chance that these issues resurface or cause problems.

Final Thoughts

While collecting or managing business data pools can be an important key to long-term success, the quality of that data will play a vital role in helping to make the right business decisions.

Building your own data-quality process requires due diligence and a focus on completeness, reliability, accuracy, timeliness, and relevancy in all your data sources. However, by following these basic best practices, you'll be able to get real value out of your data management projects and build a sustainable path for business success.

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