Data quality metrics best practices

The amount of data we deal with has increased rapidly (close to 50TB, even for a small company), whereas 75% of leaders don’t trust their data for business decision making. Though both are two different stats, the common denominator playing a role could be data quality.

With new data flowing from almost every direction, there needs to be a yardstick or a reference point to measure data quality — enter data quality metrics.

Data quality metrics are quantitative values that reveals the consumability % of data in all dimensions. It’s like a building inspection report with all nitty-gritty details of cracks, crevices, and other structural/internal defects, only with more deeper insights.

Any data team that wants to ensure data reliability and build trust in data through data quality management techniques, should make quality metrics measurement a periodic activity.

Data quality metrics & why they are important

Data quality metrics

Any metric is a numerical value needs to fall under a particular range to meet predefined conditions. In our case, to consider as high-quality data.

Here are some data quality metrics & measures an organization should track and why its important.

Accuracy metric: % of data accuracy. How much of the data in each dataset carries original values. Example: Number of correct email addresses/contact numbers in a customer database. Accuracy is crucial for high reliability in processes like customer communications, targeted campaigns, etc.

Consistency metric: how much is the data consistent across systems and databases in % is consistency metric for data quality. With multiple tools in place, the chances of contradictions in values are plenty, especially with use cases like inventory management, financial reporting, etc. Example: product pricing being recorded as $100 in one and $120 in the other system.

Timeliness metric: the number of changes or updates in data that’s updated as soon as the change occurs. This is the measure of having up-to-date and reliable data. Timeliness metric is essential for decision-makers to have the latest or near-real-date available. This metric matters a lot for use cases like order fulfilment, stock market, and other similar dynamic environments.

Completeness metric: % of fields with available data. For example, a customer record with incomplete contact details. Completeness metric will help you evaluate and ensure that there aren’t any data gaps, and the available information is holistic.

While accuracy, consistency, and timeliness are key data quality management best practices, the acceptable thresholds for these metrics to achieve passable data quality can from one organization to another, depending on their specific needs and use cases.

There are a few other quality metrics too: integrity, relevance, validity, & usability. Depending on the data landscape and use cases, data teams select the most appropriate quality dimensions to measure.

Why are data quality metrics important?

Data quality metrics are important as otherwise you wouldn’t know if data could lead to accurate decisions. It could bring to surface potential compliance risks of not meeting regulatory standards.

To understand the importance of data quality metrics, we need look more into the negative outcomes of bad data and how worse it could turn out.

High operational costs: poor quality data leads to 41% of resources and cost wastage. This could be taken in two ways.

It takes resources, labor, and tools to fix bad data. Also, there will be processing & storage costs, which could have been avoided.

Bad data leads to bad decisions, leading to lost opportunities, dreadful mistakes, cost & resource wastage.

Operational inefficiencies: time goes by fixing data errors, duplication, and incomplete entries. Thus, business users don’t get reports on time and the effects snowball and spread beyond the data team.

Reputation takes a hit: the aversion and hesitation customers feel toward companies that make data security compromises is indescribable. Not only does this cause poor experience for them but also erode their trust and drive them away from the brand, which is difficult to recover from. It doesn’t even take a cyber fault; a simple misspelling or a duplicate email is enough to leave a negative impression.

Data quality metrics vs. Data quality dimensions

Data quality metrics and data quality dimensions are closely related; but aren’t the same. The purpose, usage, and scope of both concepts vary too. Data quality dimensions are attributes or characteristics that define data quality. On the other hand, data quality metrics are values, %, or quantitative measurements of how much well the data meets the above characteristics.

A good analogy to explain the differences between data quality metrics and dimensions would be the following. Consider data quality dimensions as talking about a product’s attribute – it's durable, long-lasting, or has a simple design. Then, data quality metrics would be how much it weighs, how long it lasts, and the like.

Best practices to maintain data quality

Data quality metrics alone can help you enhance its usefulness? No. Tracking only shows the progress and shortcomings; you need to take the following steps to improve data quality and sustain them.

Identify your challenges

Every solution starts with a problem. Identify the pressing concerns – missing records, data inconsistencies, format errors, or old records. What is that you are trying to solve? Turn this challenge into your goal statement and that into a use case. let's assume your bottleneck is inaccurate or inadequate inventory reports which leads to stocking issues. Then, the goal statement is ‘making reports reliable by addressing the accuracy errors’. Based on this, set up goals for data quality metrics to achieve clean, accurate, and consistent data.

Use data quality tools

There are many tools available to fix or improve data quality, be it open source or cloud based & fulfilling a variety of requirements from profiling to cleaning to validation. You could leverage them to automate data quality monitoring, profiling, and quality management. Some examples for data quality tools include Informatica, Talend, and OpenRefine; they are powerful tools for de-duplication, advanced cleansing, & validation against a set of defined rules.

Maintaining data integrity

Data integrity is a part of data quality management – keeping data accurate, complete, and consistent from creation to discard. This stage is critical to safeguard your data from security & compliance risks and requires more than tools. Go back to your data governance framework and enforce validation rules to prevent unauthorized access. Ask these questions to strengthen it further - Are there any encryption measures in place to protect sensitive data? Is the sensitive data anonymized enough to help business use cases without revealing private info? Is the metadata being updated and managed well? All these questions are essential to ensure that data is used to its fullest extent in a secure manner.

Make it accessible to everyone

Where there’s easy data accessibility, there’s collaboration & consistent usage. Breaking siloes and making data accessible beyond teams will bring more stakeholders into the picture, offering everyone a holistic view. When every user is made accountable for data they access, less errors and inefficiencies take place. And having such a centralized place reduces the chances of fragmentation, consistency errors, and duplication.

Monitor and refine it

Data quality management is more of a marathon than a sprint. Measure your data quality metrics using trackers and data quality dashboards. Set up automated alerting when there is an anomaly, notifying relevant teams. Occasionally, review the progress, make changes, and make sure it’s aligned with current business goals.

Final thoughts

IoT, cloud, ETL, digitization, automation, AI, data-driven culture, so and so many aspects. So many digital transformation goals. Yet, the base of it all is data. Good, relevant data to be precise, without which the above goals would fall down like a house of cards. So having good-quality data isn’t just a nice-to-have; it’s indispensable.

Data quality metrics is one way to look back and assess the data health & stay away from errors, cost wastage, delays, and bad decisions. Adding the best data quality practices like metadata management, data audits, and automated quality checks can strengthen it further, creating a stronger foundation for every data initiative.