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Models of enterprise intelligence
By Ken Collier, Mark Brand and Pramod N
Today’s leading companies are increasingly data-driven. These organizations aspire to have data power all their decisions and actions and are used to validate (or invalidate) business hypotheses and ideas.
Data has, as the saying goes, become the enterprise’s lifeblood. But data alone isn’t sufficient to guarantee success — being able to use data to produce tangible outcomes for business is the real value driver and for that, we are seeing the world moving more towards intelligence. The use of machine intelligence to drive business outcomes is a central theme. Aligning the use of intelligent insights with business goals to drive better decision making challenges most organizations — even for relatively basic functions. At the same time, advances in artificial intelligence, data engineering, and cloud computing have made it feasible to execute decisions in more sophisticated ways. This includes making decisions in greater number, faster, more nuanced, informed by more varied inputs and richer logic, and using more autonomous learning and iteration. Business leaders that understand how to use new technology to align their organizational intelligence to their desired outcomes will secure a substantial advantage.
In this series of articles, we’ll be exploring the idea of an Intelligent Enterprise: what it is; the role of your IT systems and your approaches to data; and how your teams and ways of working enables you to create maximum value from data. We’ll be publishing new articles in this multi-part series every few weeks, covering a range of practices, disciplines, techniques that combined enable companies to use data to drive better intelligence and create an Intelligent Enterprise.
In this first article, we’ll be covering models of enterprise intelligence: to understand how businesses use data to derive insights today, and how they can improve.