A REPORT by MIT’s Technology Review Insights indicates that decision-makers tend to excel when they deploy analytics and machine learning.
The report, produced in association with Databricks, canvassed 351 senior global data officers and includes interviews with Total, Estée Lauder, McDonald’s, L’Oréal, CVS Health and North-western Mutual.
The findings:
- Just 13 percent of organisations excel at delivering on their data strategy – by paying attention to data management and architecture that enable them to “democratise” data and derive value from machine learning.
- Analytics and machine learning capabilities, coupled with advanced data technologies, help end-users to make better-informed decisions.
- Machine learning’s impact is limited by difficulties managing its end-to-end lifecycle, and scaling is complex. A significant challenge, according to 55 percent of respondents, is the lack of central storage.
Top data priorities over the next two years fall into three areas, supported by wider adoption of cloud platforms: improving data management, enhancing data analytics and machine learning, and expanding enterprise data, including streaming and unstructured data.
If respondents build a new data architecture for their business, the most beneficial aspect would be a greater embrace of open-source standards and open data formats, the findings show.
“Creating the right architecture is the first step in a huge business transformation,” says report editor Francesca Fanshawe. “There are many models, but ultimately the aim should be (for) simple, flexible, and well-governed.”
Full report: Building a high-performance data and AI organization
