Technology driven initiatives to create the one, golden, data store across the enterprise more often than not fail to deliver to the expectations of business users and the promised ROI. Data quality, lack of domain context, functional data silos that overlap but speak different languages and dialects combine to disappoint the business users and AI/ML engines. In consequence, business users continue to work their spreadsheets to clean and combine data to run the business day-to-day and to mine for revenue and margin insights. In addition, value analysis across functional areas such as Sales, Market Access, Contract Ops, and Finance stays out of reach. And to top it off, just as with humans, pervasive issues with an enterprise’s data throttles the effectiveness of AI. This paper explores the root causes and proposes approaches and governance models to improve the odds of adoption, efficacy and ROI.
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