Topics Data, AI, & Machine Learning

Analytics & Business Intelligence Competing With Data & Analytics How does data inform business processes, offerings, and engagement with customers? This research looks at trends in the use of analytics, the evolution of analytics strategy, optimal team composition, and new opportunities for data-driven innovation. See All Articles in This Section

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To make better data-based decisions managers need smart data — and big data training.

While the concept of information overload isn’t a new one — Alvin Toffler introduced it back in 1970, in his book Future Shock — it seems more relevant now than ever. Particularly for the growing number of organizations with a mandate to make more strategic and operational decisions based on data — or facts — in environments saturated with data.

There is so much data in market and non-market environments that it has become a cliché to note data generation and consumption in funny terms like exabytes (sounds like an orthodontist’s call to action) or zettabytes (a word that, for some people, may call to mind toothsome college fraternity zombies). The irony, however, is that there can be both too much data yet too little good data available when the time comes to make decisions.

In our recent data and analytics survey of about 2,500 professionals — part of our second annual research collaboration between MIT Sloan Management Review and SAS Institute — 60% of respondents agreed that senior managers are pressuring the organization to become more data-driven and analytical. At the same time, only 42% of respondents said they “frequently” or “always” have all the data they need to make key business decisions.

To cut through the noise to get the data that is most useful and timely requires smarter data, says Ali Fouladkar, a researcher at the Center for Studies and Research in Management (CERAG) and Ph.D candidate at the Doctoral School in Administrative Sciences within Université de Grenoble Alpes. Fouladkar defines smart data as data from which signals and patterns have been extracted by intelligent algorithms. Imagine the difference between a long list of numbers referring to weekly sales, versus a graph that tracks sales peaks and troughs during the same time frame, and you have the basic idea of what separates ordinary data from smart data.

In his research on data-based decision making, Fouladkar defines three key attributes that distinguish smart data from other forms of data. To be smart, data must be:

Accurate – data must be what it says it is with enough precision to drive value. Data quality matters.

– data must be what it says it is with enough precision to drive value. Data quality matters. Actionable – data must drive an immediate scalable action in a way that maximizes a business objective like media reach across platforms.



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