Manjeet Rege, professor and chair of the Department of Software Engineering and Data Science at the University of St. Thomas School of Engineering and director of the Center for Applied Artificial Intelligence, spoke with CIO on why organizations must modernize their data strategies to successfully support enterprisewide artificial intelligence initiatives.
From the article:
IBM’s data strategy for the AI era included multiple changes to longstanding approaches, enabling it to build what (Vice President and Chief Data Officer Ed) Lovely calls an integrated enterprise data architecture. For example, the company retained the concept of data owners but “helped them understand that the data is an IBM asset, and if we’re able to democratize it in a controlled, secure way, we can run the business in a better, more productive way,” Lovely says.
As a result, IBM moved from multiple teams managing siloed data to a common team using common standards and common architectures. Enterprise leaders also consolidated 300 terabytes of data, selecting needed data based on the outcomes the company seeks and the workflows that drive those outcomes. ...
- Rethink Data Ownership
“Traditional models that treat data ownership as a purely IT issue no longer work when business units, product teams, and AI platforms are all generating and transforming data continuously,” (research manager for IDC’s Global DataSphere) Adam Wright explains. “Ideally, clear accountability should sit with a senior data leader such as a CDO, but organizations without a CDO must ensure that data governance responsibilities are explicitly distributed across IT, security, and the business.”
It’s critical to have “a single point of authority for defining policies and a federated model for execution, so that business units remain empowered but not unchecked,” he adds.
Manjeet Rege, professor and chair of the Department of Software Engineering and Data Science and director of the Center for Applied Artificial Intelligence at the University of St. Thomas, advises organizations to reframe data owners as data stewards, who don’t own the data but rather own the meaning and quality of the data based on standards, governance, security, and interoperability set by a central data function.
