OxOs and boards recognize that their organization’s ability to generate actionable insights from data, often in real-time, is of the highest strategic importance. If there were any doubts on this score, consumers’ accelerated flight to digital in this past crisis year have dispelled them. To help them become data driven, companies are deploying increasingly advanced cloud[1]based technologies, including analytics tools with machine learning (ML) capabilities. What these tools deliver, however, will be of limited value without abundant, high-quality, and easily accessible data.
In this context, effective data management is one of the foundations of a data-driven organization. But managing data in an enterprise is highly complex. As new data technologies come on stream, the burden of legacy systems and data silos grows, unless they can be integrated or ring-fenced. Fragmentation of architecture is a headache for many a chief data officer (CDO), due not just to silos but also to the variety of on-premise and cloud-based tools many organizations use. Along with poor data quality, these issues combine to deprive organizations’ data platforms—and the machine learning and analytics models they support—of the speed and scale needed to deliver the desired business results.
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