In today’s digital world, business users need easier ways to explore data, uncover new insights and make informed decisions instantly from any device. However, achieving this can be difficult for many organizations even with the best of IT infrastructures. What if the data is just bad? DATAVERSITY brought this interesting information to our attention in their article, “How is Bad Data Crippling Your Data Analytics?”
With big data, the Internet of Things, and real-time analytics, the chances of acquiring huge volumes data at high speed is assured, but quantity does not always determine quality. The current data governance processes of many organizations are still unable to identify and remove the inaccuracies in high-speed and high-volume data, thus leaving serious data quality issues.
One way to correct data quality issues like these is to research each inconsistency or ambiguity and fix it manually. Obviously, that is not practical on a large scale. A more time and cost efficient approach is to use automated data quality tools that can identify, interpret and correct data problems without human guidance.
Melody K. Smith
Sponsored by Access Innovations, the world leader in taxonomies, metadata, and semantic enrichment to make your content findable.