Building high-performance data and organizing artificial intelligence


In this context, effective data management is one of the foundations of a data-driven organization. But managing data in an organization is very complex. With the advent of new data technologies, the burden of legacy systems and data silos increases, unless they can be merged or fenced off.

Architecture fragmentation is a problem for many CDO’s, not only because of silos but also because of the variety of on-premises and cloud-based tools that many organizations use. Along with poor data quality, these problems combine to deny enterprise data platforms – and the machine learning models and analytics that support them – of the speed and scale needed to deliver the required business outcomes.

To understand how data management and the technologies that depend on it have evolved amid such challenges, MIT Technology Review Insights surveyed 351 CDOs, chief analytics, chief information officers (CIOs), chief technology officers (CTOs), and other senior technology leaders. We also conducted in-depth interviews with many other senior technology leaders. Here are the key findings:

  • Only 13% of organizations excel at implementing their data strategy. This select group of “top performers” delivers business results that are measurable across the organization. They succeed thanks to their interest in the foundations of sound data management and architecture, which enables them to “democratize” data and derive value from machine learning.
  • Technology-based collaboration is creating a working data culture. The CDOs interviewed for the study attach great importance to democratizing the analytics and ML capabilities pushing it to the edge using advanced data technologies will help end-users to make more informed business decisions – the hallmarks of a robust data culture.
  • ML’s impact on the business is limited by the difficulties in managing its life cycle from start to finish. Scaling up ML use cases is very complex for many organizations. The biggest challenge, according to 55% of respondents, is the lack of a central place to store and discover ML models.
  • Businesses are looking for native cloud platforms that support data management, analytics, and machine learning. Enterprise’s key data priorities over the next two years fall into three areas, all of which are underpinned by a broader 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 most important requirement of future data engineering strategies. If respondents can build a new data architecture for their business, the most significant advantage over the existing architecture will be greater adoption of open source standards and open data formats.

Download the full report.

This content was produced by Insights, the dedicated content arm of MIT Technology Review. It was not written by the editorial team at MIT Technology Review.



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