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Decades (at least) of business analytics writings have focused on the power, perspicacity, value, and validity in deploying predictive and prescriptiveanalytics for business forecasting and optimization, respectively. How do predictive and prescriptiveanalytics fit into this statistical framework?
A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data. To do this at scale, you have to use AI/ML services for decision-making.
85% of AI (marketing) projects fail due to risk, confusion, and lack of upskilling among marketing teams.(Source: AI Adoption and DataStrategy. Lack of a solid datastrategy. In order to adopt AI solutions for your business, the best way forward is to first ensure that you have a strong datastrategy in place.
Data intelligence has thus evolved to answer these questions, and today supports a range of use cases. Examples of Data Intelligence use cases include: Data governance. Cloud Data Migration. Privacy, Risk and Compliance. Let’s take a closer look at the role of DI in the use case of data governance.
Data security The travel industry collects, transmits, processes, and stores a wide range of personally identifiable information (PII) from customers, which are of interest to bad actors, as cybercriminals target this valuable data. Otherwise, they risk a data privacy violation. Curious to see Alation in action?
In Prioritizing AI investments: Balancing short-term gains with long-term vision , I addressed the foundational role of data trust in crafting a viable AI investment strategy. Like most, your enterprise business decision-makers very likely make decisions informed by analytics.
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