Continution to previous post - How to Address Big Data Challenges (PART 5 of 6)
Identifying and correcting data quality concerns
When data quality issues sneak into big data systems, analytics algorithms and artificial intelligence applications built on big data might produce poor results. As data management and analytics teams strive to pull in more and various types of data, these issues may become more serious and difficult to audit.
As it grew to 500,000 clients, Bunddler, an online marketplace for hiring web shopping assistants who help people buy things and arrange shipping, came into these issues.
Big data was used to deliver a more personalized experience, expose upselling opportunities, and track new trends, which was a key growth driver for the company. A major concern was managing data quality effectively.
You must regularly check and correct any data quality issues. Duplicate entries and mistakes are prevalent, particularly when data is collected from multiple sources. An intelligent data identifier was designed to assure the quality of the data they collect.
It identifies duplicates with minor data deviations and reports any possible typos. As a result, the accuracy of the business insights derived from data analysis has improved.
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