Big Idea: Competing With Data & Analytics

Better Decisions with Smarter Data

Business and academia pair up to teach managers how to add intelligence to their gigantic datasets.

Renee Boucher Ferguson Reading Time: 3 min 

Topics

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.
More in this series
Smart Data
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.
  • Actionable – data must drive an immediate scalable action in a way that maximizes a business objective like media reach across platforms.

Topics

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.
More in this series

References

1. In May, MIT Sloan will offer its first online professional education course, Tackling the Challenges of Big Data, aimed at technical professionals and executives.

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Comments (4)
Corey Weiner
The above makes a valid point - data is present, yet many orgs lack either the wherewithal or discipline to check on the data as the survey concludes.

Two quick points, however:

1) I collected data for five years for Nielsen. Nielsen is famous for media research, but they engage in various market studies and info gathering for various industries.

Statistics, trends and pie charts DO NOT translate to risk management and/or better important decisions per se.

How would you like to be a VP or higher analyzing data for a year, implement a program and it fails? 

Doesn't happen? It does. Ask B of A how they made out acquiring Countrywide Mortgage or Merrill Lynch despite calculations and a ton of projections.

2) General Colin Powell came to Santa Barbara and spoke about his time as Joint Chiefs of Staff chair and Secretary of State in about 2007.

He said in general, a decision-maker usually has 70 percent of what she / he needs and then has to make a decision; but has to defer to instinct.

Must I remind any readers of the vast resources the US government taps for data?

Data / Information resources beyond those the commercial sector can likely fathom exist. Visit Gen Dynamics, SAIC or Booz Allen Hamilton. They will confirm it I'm sure.

Data to analyze is good. But, truth be told, data is trendy the last few years because techies have exploited ways to gather and synthesize it. And then sell it to other companies.

Advantages, however, not written in stone. Except, again, for those selling "big data" or peripheral services.

Corey Weiner, B2B Editor, Published by AllBusiness, Sales & Marketing, Business2Community, AOL and others.
Leslie Brokaw
Thanks Roland -- 

We have corrected the opening sentence to the article with the original publication date of 1970 for "Future Shock." 

Leslie Brokaw
Contributing Editor, Digital Media
MIT Sloan Management Review
Roland Roxford
Future Shock was first published in 1970, not 1984.
Peter Burgess
I was a corporate CFO a long time ago. Most of my work involved data for decisions, and the power of modern information technology should mean that really great decisions are being made for society and the economy. I think, however, that most would agree that decisions are not getting made in this way, but are getting made in ways that are resulting in historically high socio-economic inequality. 

In order to find the needle in the haystack there has to be a haystack. The only haystack that exists is the one that enables corporate business performance (and global security surveillance) but no haystacks have been built full of data about important elements of people's quality of life, resource depletion, environmental degradation, performance of the built environment, performance of the enabling environment and the knowledge dimension of everything. 

Data scientists only start their work after there is a haystack to study. In my view, we have to put together some other very important haystacks ... and do it soon. 

Peter Burgess - TrueValueMetrics
Multi Dimension Impact Accounting