Effective operational analytics for use in digital transformation initiatives requires the use of clean data. In an Automation World article, Much Ado About Data Quality, Emerson’s Anil Datoo joins other suppliers in sharing his thoughts on ways to achieve the necessary data quality.
The article’s author opens noting:
…the age-old maxim “garbage in, garbage out” still reigns supreme. Even the most advanced machine learning algorithms are useless when fed poor quality data.
Anil, a vice president of data management shares that:
…around 70% of all data integration activities are spent validating, structuring, organizing, and cleaning data, a statistic that was echoed in an article on Big Data in The New York Times in 2014.
Given this time commitment:
…working to ensure that more data is in tip-top shape from its inception isn’t a bad strategy.
…beginning the transition with a small, targeted project, rather than diving in all at once.
“Our main recommendation is just to start and develop a small use-case. It doesn’t have to be cost prohibitive because there’s a lot of opportunity within operating environments; so if you can just target something that resonates operationally, would have a good return on investment, and can get the attention of operational stakeholders, you’re set… Continue to measure success along the way, be flexible, and expect to make iterative changes. There’s no simple answer to these problems, so it’s important to allow for that.”
Read the article for additional perspectives from the other suppliers of data quality and data analytics solutions. Visit the Operational Certainty and Data Management Consulting Services sections on Emerson.com for more on ways to drive operational improvements and developing low-risk project implementation strategies for effective data-management that measures progress on your digital transformation initiatives.
You can also connect and interact with other digital transformation experts in the IIoT & Digital Transformation group in the Emerson Exchange 365 community.