First came the data analyst, then came the Data Quality specialist. Data analysts require accurate data to develop business intelligence and, unless someone else is cleaning the data, they must also perform this task. It is time-consuming and interferes with analyzing the data for useful business intelligence.
Enter the Data Quality specialist, who takes responsibility for providing high-quality data by using specialized tools and best practices. These professionals measure the data’s quality and work with data stewards to develop plans that ensure its reliability, consistency, and completeness.
The Data Quality specialist should also work directly with the data steward to promote the storage of high-quality data.
In combination with their other responsibilities, a Data Quality specialist may act as an assistant for the data analyst, or the data analysis team. Still, their primary responsibility is to ensure the quality and accuracy of the data. The Data Quality specialist is often responsible for examining the source of the data to verify its accuracy, and for correctly transferring information on paper to an electronic format. This allows it to be stored in a database or the cloud.
The Data Quality specialist is not a management position.
Data Quality specialists will work closely with data creators, data consumers, and data stewards to assure data is usable, trustworthy, timely, and formatted correctly. They are responsible for learning, understanding, and following the Data Governance program’s rules and policies to develop, store, and maintain high-quality data.
They may also be responsible for using Data Governance tools to track and manage the data’s quality. Additionally, a Data Quality specialist may be responsible for profiling the data and analyzing the user requirements.
Regarding Data Quality management, Garry Moroney, the founder and former CEO of Clavis Insight, stated in an interview:
“In today’s highly interconnected world, bad data entered into a web-form or into a frontline business application, such as a call center system or a new product application, immediately spreads through a company’s information systems. The negative impact of the defective data and the cost of fixing it later – or not fixing it and leaving it to hamper important business processes – can be huge. The only solution is to implement controls to ensure that data is right the first time.”
What Is Data Quality and Why Is It Important?
Data Quality refers to a measurement of the data’s accuracy and completeness. The emphasis on Data Quality in data-driven businesses has increased as the volume of data has steadily increased and become more intricately linked with a business’s operations. High-quality data allows businesses to avoid errors in their data that will promote faulty assumptions and poor decision-making.
The impact of poor-quality data can have significant consequences for businesses. It is often the source of operational glitches, inaccurate analytics, and short-sighted business strategies. Examples of the damage poor-quality data can cause include additional (and unnecessary) expenses when products are sent to the wrong addresses and/or lost sales opportunities due to incomplete customer records.
Another issue that develops as a result of poor-quality data is a lack of trust in the data. Staff, business managers, and corporate executives working for organizations with data of low quality cannot develop business intelligence, or long-term planning, because the data cannot be trusted.
The Skills Needed to Become a Data Quality Specialist
On average, a Data Quality specialist earns an annual salary of $60,907 in the United States. An entry-level position starts at $37,011 a year. Experienced workers can earn up to $101,350 a year.
Some organizations have advertised for a Data Quality specialist but have included data analyst responsibilities in the job description. This is a reflection of the confusion that exists regarding the two positions’ responsibilities, which originally belonged only to the data analyst.
The basic skills needed to be a Data Quality specialist include:
- Experience in dealing with the Data Quality principles needed to deliver high-quality data assets
- Experience with Data Quality tools
- Experience in finding and resolving issues with Data Quality
- Experience with data profiling and data integration tools
- Experience with automation processes
- Experience with query languages (SQL is a common query language)
Note that the key word in each requirement is “experience.”
Gaining Experience and Certification
A certification provides credentials that verifies a person’s knowledge and skills. Fortunately, there are courses and combinations of courses that provide both experience and a certificate. Some educational opportunities for aspiring Data Quality specialists are:
- Several online training centers (including DATAVERSITY) offer comprehensive, multi-course Data Quality programs and certifications. Topics include Data Quality practices, principles, challenges and solutions, tools, and more.
- The Great Learning Academy offers free data language courses and certifications. These are typically only two-hour courses and should be considered supplemental certifications, which are added to another certificate.
- DataCamp has a free four-hour course titled Introduction to SQL, which teaches the basics of SQL (the most popular query language) and how to manipulate data using relational databases.
The Future of Data Quality Specialists
With the evolution of computer, software, and internet technology, the importance of transforming data to support accuracy and quality has increased significantly.
Data Quality specialists can expect a certain amount of job security as the amount of data that businesses collect continues to grow. With the increasing number of connected devices and sensors, high data quality is more important for intelligent decision-making and business intelligence. Those organizations that use a Data Quality specialist to manage and organize their data will have a better understanding of its value.
Businesses have learned the importance of accurate, high-quality data in their decision-making process and are giving it a high priority.
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