Many brands and businesses are putting all their eggs in the AI-mastery basket, thinking once their skillsets are firmed up and data quality and productivity improve, then all the promises of AI will be revealed and everything else will fall into place. Well, it sure won’t hurt, but new research from MIT Technology Review Insights urges leaders to keep their eye on the real ball—the ability to make better decisions that propel the whole business.
The new report, Modernizing Data with Strategic Purpose, in partnership with tech consultancy Thoughtworks, explores how data strategies and modernization initiatives misaligned with the overall business strategy—or too narrowly focused on AI—leave substantial business value on the table. The research draws on a global survey of 350 senior data and technology leaders along with in-depth interviews conducted with experts on data strategy and modernization.
The key findings include:
AI isn’t the only reason companies are modernizing the data estate
Better decision-making is the primary aim of data modernization, with nearly half (46 percent) of executives citing this among their three top drivers. Support for AI models (40 percent) and for decarbonization (38 percent) are also major drivers of modernization.
Data strategy is too often siloed from business strategy
While only a small share (22 percent) of surveyed organizations lack a fully developed data strategy, no more than 39 percent say their data strategy is in complete alignment with the key objectives of the business. Data teams need to do more to bring other business units and functions into strategy discussions.
Data strategy paves the road to modernization
It is probably no coincidence that most organizations (71 percent) that have undertaken a data modernization in the past two years have had a data strategy in place for longer than that. Modernization goals require buy-in from the business, and implementation decisions need strategic guidance, lest they lead to added complexity or duplication.
Top data pain points are data quality and timeliness
Executives point to substandard data (cited by 41 percent) and untimely delivery (33 percent) as the facets of their data operations most in need of improvement. Incomplete or inaccurate data leads enterprise users to question data trustworthiness. This helps explain why the most common modernization measure taken by our respondents’ organizations in the past two years has been to review and upgrade data governance (cited by 45 percent).
“Leaders highlight data modernization’s focus on improving decision-making, driven by factors such as AI and decarbonization. However, a gap exists as organizations struggle to align data strategy with broader business goals,” said Laurel Ruma, global director of custom content for MIT Technology Review, in a news release. “By reviewing and upgrading data governance, organizations can improve operations, enhance data quality, and build trust.”
“Only a third of leaders reported complete alignment of their data strategy with business strategy goals, which is key to organizations maximizing the business impact from their data,” said Danilo Sato, global head of technology for data and AI for Thoughtworks, in the release. “By applying effective and scalable data engineering practices alongside a modern data platform, organizations will start realizing how data plus AI as a force multiplier can be a powerful driver of growth and competitive advantage.”
