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Give data scientists clean data. Give them clean data to analyze, instead of having them spend top wage dollars on scrubbing and prepping data. Recent reports reveal that data scientists spend, on average, 80 percent of their time cleaning data rather than analyzing it. Not only is this preventing the highest use of wage dollars, but it is irritating and frustrating the scientists, who are walking out.
“Doing data science and managing data science are not the same, just like being an engineer and a product manager are not the same,” reports Datasciencecentral.com. Managing data science requires collecting and aggregating data from multiple sources, cleaning and prepping it for analysis, and then preparing reports based on the analysis provided by the data scientists. To combat this problem, Gartner estimates that by 2019, 90 percent of large companies will have an executive, such as a chief data officer, on the management team responsible for managing data science. The race to drive competitive advantage through better use of information assets is leading to demand for efficiency – which means demand for clean data. According to Gartner, “With the explosion of datasets everywhere, an important task is determining which information can add business value, drive efficiency or improve risk management,” but this cannot be accomplished without first prepping the data.
Data governance must include data quality. Enterprise management strategy should be based on business strategy, such that business rules determine data quality rules. In order to monetize data and obtain ROI on data collection investment, efficiencies must be achieved to get scrubbed data to the data scientists for analysis, and to do so without delay.
This is why demand for Naveego’s Complete Data Accuracy platform is exploding. In the Hadoop world of big data, and given the “new data” explosion of business metrics now including data from sources not managed by traditional master data management installations (such as IoT data), there is a need for Naveego’s data quality solution to bring together and manage new data (and traditional data) from disparate sources, scrub it, and deliver it ready for data scientist consumption.
Katie is a former IP attorney at Microsoft, and leverages her Silicon Valley background and industrial engineering skills, for start-up scaling and growth. She has been recognized at U.S. Congress as a business leader for innovative business models.