A new survey of C-suite data, IT, and senior tech executives finds that just 13% of organizations are delivering on their data strategy. The report, which was based on a survey of 351 respondents at organizations earning $1 billion or more in annual revenue, found that machine learning’s business impact is limited largely by challenges in managing its end-to-end lifecycle.
MIT Technology Review Insights and Databricks conducted the survey, which canvassed companies including Total, the Estée Lauder companies, McDonald’s, L’Oréal, CVS Health, and Northwestern Mutual. Among the findings was that only a select group of “high achievers” — the aforementioned 13% — delivered measurable business results across the enterprise. This group succeeded by paying attention to the foundations of sound data management and architecture, which enabled them to “democratize” data and derive value from AI and machine learning technologies, according to the report’s authors.
“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,” report editor Francesca Fanshawe said in a press release.
Democratization of data
Every chief data officer interviewed for the study ascribed importance to democratizing analytics and machine learning capabilities. This, they said, will help end users make more informed business decisions — the hallmarks of a strong data culture.
The respondents also advocated embracing open source standards and data formats. But what remains the most significant challenge is the lack of a central place to store and discover machine learning models, 55% of executives said. That’s perhaps why 50% are currently evaluating or actively implementing new, potentially cloud-based data platforms.
As Broadridge VP of innovation and growth Neha Singh noted in a recent piece, many firms try to develop AI solutions without having clean, centralized data pools or a strategy for actively managing them. Without this critical building block for training AI solutions, the reliability, validity, and business value of any AI solution is likely to be limited.
Organizations’ top data priorities over the next two years fall into three areas, all supported by wider adoption of cloud platforms, according to the report. These are: improving data management; enhancing data analytics and machine learning; and expanding the use of all types of enterprise data, including streaming and unstructured data. “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,” Fanshawe continued.
The MIT and Databricks findings come after Alation’s latest quarterly State of Data Culture Report, which similarly discovered that only a small percentage of professionals believe AI is being used effectively across their organizations. A lack of executive buy-in was a top reason, Alation reported, with 55% of respondents to the company’s survey citing this as more important than a lack of employees with data science skills.
The findings agree with other surveys showing that, despite enthusiasm around AI, enterprises struggle to deploy AI-powered services in production. Business use of AI grew a whopping 270% over the past several years, according to Gartner, while Deloitte says 62% of respondents to its corporate October 2018 report adopted some form of AI, up from 53% in 2019. But adoption doesn’t always meet with success, as the roughly 25% of companies that have seen half their AI projects fail will tell you.
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