Every big company now manages a proliferation of sites, apps, and technology systems for interacting with buyers and managing everything in the business, from customers and clients to inventory and products. These systems are spitting out data continuously. But even after multiple generations of investments and billions of dollars of digital transformations, organizations struggle to use that data to improve customer service, reduce costs, and speed the core processes that provide competitive advantage.
Is Your Data Infrastructure Ready for AI?
Four steps for streamlining and scaling your systems.
April 28, 2020
Summary.
Creating an ontology is an essential investment to prepare your enterprise to realize the benefits of AI and machine learning. Gone are the days when businesses should simply allow a number of small AI projects to blossom independently: for these projects to be competitive they need to draw on data from across the company, data stored in many different forms in many different systems. An ontology defines these connections in a way that AI can take advantage of. Businesses will be best positioned to build ontologies if they identify and research pain points first–areas where the data connections are most needed–before beginning to set the organizing principles for the ontology itself.
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Accelerate your career with Harvard ManageMentor®. HBR Learning’s online leadership training helps you hone your skills with courses like Digital Intelligence . Earn badges to share on LinkedIn and your resume. Access more than 40 courses trusted by Fortune 500 companies.
Excel in a world that's being continually transformed by technology.