Very few companies are able to deliver on their data strategy

Only a small minority (13 percent) of organizations are able to properly deliver on their data strategies, despite understanding the importance of data in generating actionable insights.

This is according to a new report from business continuity solutions provider Databarracks and MIT Technology Review, based on a poll of 351 C-level executives, which states that scaling machine learning remains a challenge, despite the “explosion of data” that most industries have experienced.

For almost two in five (39 percent), the collaboration between data science and production is inadequate, while more than half (55 percent) claim they lack a central place to store and discover ML models.

To make better use of their data, enterprises are seeking cloud-native platforms, the report further claims. Almost half (43 percent) of respondents see increasing the adoption of cloud platforms, to support data management, as one of the top strategic initiatives for the next 24 months. 

But there are many challenges ahead. Organizations are struggling to achieve a solid return on investment for their business intelligence initiatives, with just 12 percent claiming to have achieved an optimal price/performance for their analytics workloads.

Approximately half of the executives polled also said they are in the process of evaluating or implementing new data platforms to address the challenges they are facing today. 

If they could build a new data architecture for their business, half would opt for open-source standards and open-data formats, the report concluded.

”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,” the report stated.

“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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