It can’t be stressed enough that if your company wants to do the cool stuff with data like AI, machine learning, analytics, you have to have control over your data in a form of a solid data governance framework. It’s similar to wanting to build a fancy facade on your house, but you haven’t built the foundation – practically impossible.
It’s been several years since the term CDO first appearing on the CXO map. We’ve also previously covered presentations from other speakers that have had the position of a CDO and tried to depict what a Chief Data Officer does, their mandate in the organisation as well as challenges related to the position.
When it comes to data governance policy, companies are aware they need one, but few of them know where to start. A lot of them are at a crossroads when it comes to creating a policy that works for their organisation. But the first instinct of reaching to a standard policy wouldn’t work because it won’t meet your corporate strategy.
Data quality is considered as the highest commandment in data management. And it’s with a strong purpose. Only quality data is useful data, and to be quality it must be consistent and unambiguous. All data that is gathered, stored and consumed during business processes directly impacts the success of the business.
Everyone agrees that there is value in data science and advanced analytics. But still, companies are struggling to see that value in their business. Francisca Zanoguera from Expedia at her presentation at the Data Innovation Summit 2019, draws attention to a McKinsey study showing that only 8% of companies have managed to implement machine learning into their processes and only 12% have managed to go beyond the experimentation phase.
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