Data quality refers to the planning and implementation of quality management measures for the data that companies generate. The idea is that the data should fit the end goal of the data consumer’s needs – and must follow specific quality dimensions to be deemed fit for use. The role of a data quality analyst (DQA) is to ensure that the data being stored and passed on is in line with what the organization needs. A prerequisite to being a data quality analyst is being adept at using quality management techniques.

Data quality analysts have been common in the information technology (IT) sector for a long time, but they’re becoming more prevalent in the data space now. The need for such a role arose when businesses started to notice that despite having all the necessary measures in place, they still had poor-quality data seep into their systems, resulting in a tremendous waste of time and resources. The role of a data quality analyst was introduced to avoid this.

The most important part of a data quality analyst’s job is ensuring that the incoming data aligns with the business’s objectives. Since most companies have vast amounts of incoming data from different sources, it can be difficult to understand which ones can be used for the larger organizational goal and which can’t. 

Let’s look at the responsibilities of data quality analysts and the skills they need to thrive in the role.

What Do Data Quality Analysts Do? 

Data quality analysts are often confused with data analysts. Even though they sound similar, the difference between the two is vast. The former focuses on assessing data quality, whereas the latter focuses on analyzing already qualified data. Data quality analysts ensure that whatever data analysts use is of high quality.

Data quality analysts should look for six data quality dimensions to determine if their data is fit for use. They are:

• Completeness: a measure of the sufficiency of data
• Accuracy: assesses whether the data is factually correct and backed by a credible source
• Uniqueness: considers whether a particular dataset is devoid of duplication and overlap
• Consistency: the metric that helps assess whether the data in question aligns with formerly established and recognizable patterns
• Timeliness: a critical dimension of data that tells us a lot about how up to date the data is
• Validity: measures how relevant the data at hand is to its corresponding domain

The primary responsibility of a data quality analyst is to focus on improving current systems or introducing newer methodologies to analyze incoming data. It ensures that the accuracy and value of the data are maintained.

Here are a few more responsibilities:

• Review data loaded into the data warehouse: The IT landscape of most large organizations tends to be incredibly complicated. It is thus often riddled by data quality problems ranging from the data being misplaced, duplicated, or having other inaccuracies. Data quality analysts must review this data before loading it into data warehouses. The review helps accelerate the delivery of quality data through the early detection of errors.
• Implement processes to vet incoming data: Vetting incoming data is crucial to building data confidence. The hallmark of quality data is reliability. By standardizing incoming and existing data, data quality analysts ensure that the information is relevant to the users and can be retrieved in an accessible format.
• Maintain the referential integrity of the data: Data quality analysts maintain the referential integrity of the data at hand by deleting, renaming, and updating data as and when needed. If this responsibility is not fulfilled, it can take a toll on the accuracy and consistency of data – making it useless as time passes.
• Review the historical integrity of old datasets: It is incredibly easy for referenced data to become obsolete. It could be rendered incomplete by updated standards, making it outdated. Therefore, reviewing and maintaining the historical integrity of older datasets is imperative to maintaining data quality.
• Make recommendations to enrich existing data: Data quality analysts can enhance existing data by constantly updating them, adding contextual information into logs of referenced data, and more.

What Skills Are Necessary? 

Hands-on experience goes a long way in the job market; the more you have, the better. However, as data quality analysts are part of a data team, they don’t necessarily have to have many years of experience. Entry-level positions are available, and if you have a relevant degree, you have a better chance of stepping into the industry. As for formal education, the minimum requirement would be a bachelor’s degree in mathematics, statistics, or computer science.

However, seemingly unrelated STEM degrees can also stand out. Examples of these include physics, chemistry, and even biology. Hiring managers know that graduates have basic knowledge of mathematics and statistics, unless the experience is in an entirely unrelated degree or role.

Here are a few other skills data quality analysts need:

• Data profiling: This is effectively analyzing, examining, and reviewing source data. It is quintessential to any data quality analyst because it ensures quality (in terms of accuracy, correctness, consistency, etc.).
• Data discovery: Data discovery involves collecting relevant data from many sources and extracting value from it. It can be done by data quality analysts using multiple tools and systems. When done well, it can provide incredible insights that equip businesses with the information they need to stand out.
• Root cause analysis: Data quality analysts work with many people inside and outside the organization. In doing so, they typically become entrusted with promptly resolving any data issues and problems. They need to identify the root cause quickly; for this, they need to know the basics of Data Management (DM) and how different DM tools work.
• Data Quality measurement: Organizations need reliable information to make effective decisions. Poor-quality data can be detrimental, which is precisely why data quality measurement is a skill that every data quality analyst needs – and they need to understand the six dimensions mentioned previously. 
• Information chain analysis and management: Data quality analysts must understand how the data pipeline works and be able to manage it. In doing so, they can pinpoint relevant issues, analyze the right resources, and fix or monitor them as needed. It helps with maintaining data quality and managing existing pools of data.
• Cost-benefit analysis and ROI discovery: Conducting cost-benefit analyses is critical to boosting the data quality of existing data. Data quality analysts also need to know how to map the ROI of specific datasets to business outcomes so that leadership can make decisions accordingly.
• Use of data quality tools: Poor-quality data – be it in terms of inaccuracy, inconsistency, duplication, and more – can result in missed opportunities, loss of data confidence, and subpar decision-making. Many data quality tools on the market enrich business data, such as Ataccama ONE, Informatica, Infogix, and many more. Every data quality analyst should be acquainted with a few of them.

How Can Data Quality Analysts Impact Businesses?

As we’ve already established, data quality analysts can be quite resourceful for a business. Here’s a list of the kind of impact they can drive for businesses:

• Better decision-making: Data quality analysts are professionals with excellent problem-solving and analytical skills. Besides, they understand quality inspection in and out. They are experts at what they do, be it data profiling, data discovery, root-cause, or ROI analysis – which means that the resulting quality of the data is high. Using this, stakeholders can make more accurate and timely decisions.
• Improved relationships with customers: Having updated and accurate data about customer profiles regarding interests, preferences, etc., helps establish better-quality relationships with customers and build brand image.
• Reduced overhead costs: Data quality analysts can analyze information at a granular level and maintain its integrity. This rids businesses of the baggage and financial distress that inaccurate data imposes on them, significantly reducing overhead costs. 
• Increased revenue: By improving the quality of in-house data, companies can boost their operations – and revenue. They can use customer data to create personalized marketing campaigns, prepare for sales calls, and even offer customized recommendations once they’re in their funnel. It helps them increase customer retention and conversion while increasing revenue over time.
• More time saved: Companies can devote more time by automating current governance processes and assigning roles to the relevant data quality analysts without micromanaging each operation. Also, by using such tools, they can raise and resolve issues in real time. They would otherwise spend that time fixing data to increase its versatility and utility.
• Higher competitive advantage: Gartner predicts that by 2022, 70% of organizations will track their data quality metrics more aggressively. Those who don’t will fall behind. More and more businesses are employing data quality analysts because they are the best at obtaining valuable insights, metrics, and information.

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