The quality of its data can make or break an organization
High quality data can lead to benefits such as better decision making, enhanced customer service, improved business processes, and greater competitiveness. Poor quality data, on the other hand, can potentially lead to financial ruin for organizations.
In a May 2019 report, research firm Forrester Research Inc. noted that a lack of data quality is one of three challenges holding companies back from achieving their aspirations with artificial intelligence (AI). The other two are talent scarcity and a trust gap.
Most enterprise AI models don't make it into production, and many stall at the pilot or proof-of-concept phase, Forrester said, even when they show value.
The path to enterprise AI is full of twists and turns, false starts, and lessons to learn, the firm said. Surely without data quality, AI and other advanced technologies can not live up to their expectations.
Research firm Gartner Inc. in June 2019 published a report on “Peer Lessons Learned: Implementing Data Quality Tools,” which said data quality tools enable enterprises to optimize their data quality through activities such as generalized cleansing, parsing and standardization, profiling, and monitoring of data.
Among the peer lessons learned are identify data quality use cases and engage business users; evaluate tools based on compatibility, cost and proof of concept results; build strong relationships with the vendor and implementation partner; build data quality teams strategically and prioritize professional development; and prioritize data governance and explore all tool capabilities for maximum benefit.