Data Quality Analysts invoke a blend of technical, adaptive and interpretive skills to verify that incoming and outgoing data meet expected quality standards. In the event of quality issues, they work with internal and external team members to determine the origins of those issues and provide recommendations to solve them.
Data Quality Analysts verify incoming and outgoing data in order to meet expected quality standards. They interpret various data streams, analyze the results and provide root cause reports to communicate the scope and depth of any issues. Data Quality Analysts will help work with other team members to determine the origin of quality issues and provide recommendations to solve them.
Data Quality Analysts need to exhibit a blend of technical, adaptive and interpretative skills in order to be successful.
After graduation, most Data Quality Analysts pursue specialized training or additional certification to enhance their skills. They may focus on statistics, scripting or programming, where they can be more versatile in serving your business needs.
Frequently a career in Data Quality Analysis is the first step in a career that often leads to more advanced roles such as Data Analyst or even eventually Data Scientist.
Data Quality Analysts work closely with data culled from a variety of different sources, transforming complex data into more useful formats for use in other parts of the business, so they should have a bachelor’s degree in Mathematics, Computer Science, Economics or Statistics.
Skills in leveraging the power of data wrangling tools such as Alteryx are essential for a Data Quality Analyst.
Free open source tools are increasing being used to perform data quality analysis. Those include tools such as KNIME, Talend and Pentaho.
Data Quality Analysts should know how to execute complex queries in SQL (structured query language).
Data Quality Analysts are often required to know Python, as well as Java, Perl and C/C++. Python is relatively easy to learn, and it is supported by an active community. Python has been gaining on R in popularity in recent years, though both of these open-source languages are popular.
Data Quality Analysts may want to add NoSQL such as MongoDB or HBase to their skillset, as these systems work quickly with large volumes of data and are easily scalable for a more customized approach.
Unstructured data from reviews, social media comments and email can be a gold mine of information for Data Quality Analysts, but it doesn’t always fit neatly into traditional data tables. That’s why they may need to leverage the capabilities of ETL.
Being able to communicate their findings is a key responsibility for Data Quality Analysts, and in order to do so, they need to know which tools will work best for their applications.
Tableau is an essential software package that can present the data and showcase the derived insights. It allows Data Quality Analysts to show the results in a variety of visual formats.
ggplot2 allows Data Quality Analysts to plot trends on a graph with unique color-coding to help distinguish between key points. The findings can then be processed directly as a PDF or object that can be easily disseminated to shareholders.
How much does a Data Quality Analyst make per year? Depending on skills, experience and additional certifications, some of the best-known job-search companies have this to say about salaries:
Xperra Data Quality Analysts leverage the latest technology. They apply AI and machine learning to interpret big data for accurate data analysis, helping you make more reliable data quality decisions, faster and more dependably than ever before. They’re your team of experts who are responsible for:
Xperra’s Data Quality Analysts devise custom answers to your data issues and problems by helping you understand potential data loopholes and provide real solutions to maximize your accuracy.