High-Performance Data Management Is Critical for Delivering Business Results With Cloud Data and AI Platforms, Says MIT Technology Review Insights

A new report by MIT Technology Review Insights explores how decision-makers from leading organizations excel by deploying advanced cloud-based technologies, including analytics and machine learning capabilities.

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The report, “Building a high-performance data and AI organization,” is produced in association with Databricks and is based on a survey of 351 global senior data officers as well as in-depth interviews with data and analytics leaders at organizations including Total, The Estée Lauder Companies, McDonald’s, L’Oréal, CVS Health, and Northwestern Mutual. The findings are as follows:

• Just 13% of organizations excel at delivering on their data strategy. This select group of “high-achievers” deliver measurable business results across the enterprise. They are succeeding thanks to their attention to the foundations of sound data management and architecture, which enable them to “democratize” data and derive value from machine learning.
• Technology-enabled collaboration is creating a working data culture. The chief data officers interviewed for the study ascribe great importance to democratizing analytics and machine learning capabilities. Pushing these to the edge with advanced data technologies will help end-users to make more informed business decisions—the hallmarks of a strong data culture.
• Machine learning’s business impact is limited by difficulties managing its end-to-end lifecycle. Scaling machine learning use cases is exceedingly complex for many organizations. The most significant challenge, according to 55% of respondents, is the lack of a central place to store and discover machine learning models.
• Enterprises seek cloud-native platforms that support data management, analytics, and machine learning. Organizations’ top data priorities over the next two years fall into three areas, all supported by wider adoption of cloud platforms: improving data management, enhancing data analytics and machine learning, and expanding the use of all types of enterprise data, including streaming and unstructured data.
• Open standards are the top requirements of future data architecture strategies. If respondents could build a new data architecture for their business, the most critical advantage over the existing architecture would be a greater embrace of open-source standards and open data formats.

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“Managing data is highly complex and can be a real challenge for organizations. But creating the right architecture is the first step in a huge business transformation,” says Francesca Fanshawe, editor of the report. “There are many models an enterprise can adopt, but ultimately the aim should be to create a data architecture that’s simple, flexible, and well-governed.”

“The past year has been an accelerant of change as data-driven organizations look to adapt, innovate, and future proof their technology and architecture investments,” says Chris D’Agostino, global principal technologist at Databricks. “Now more than ever, enterprises need a modern data analytics strategy that is open, flexible, and empowers everyone across the organization to make faster, more informed decisions with a unified view of all their data—whether that’s using machine learning and AI algorithms or straightforward SQL and business intelligence reporting.”

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