Show me the numbers

MIT research measures data-driven decision making, analytical culture and humans vs. machines

I always enjoy discovering research that shows where business models are headed – or even what direction the economy as a whole will take. That’s why I found Erik Brynjolfsson’s presentation at The Premier Business Leadership Series in Amsterdam so intriguing. I also had a chance to catch up with the director of the MIT Center for Digital Business in an interview. As he further explained his research and views, I immediately transformed into a university student soaking up a fascinating talk, asking myself things like, “what might this mean for the future of business?” or “I’ve never thought about the grand-scale effect of technology on innovation and the economy in this way, tell me more!” Big, cool stuff. Check out these nuggets we discussed.

Brynjolfsson spoke about his research that quantifies the performance impact of making data-driven decisions. He found organizations that rely on data-driven decision making performed at rates 4-6 percent higher than competitors in the same industries.

Another point Brynjolfsson drove home was the cultural management shift from making gut-driven decisions to applying fact-based decision models. I asked him if his study on the measurable impact of data-driven decision making is driving this cultural shift, and here’s what he said.
In addition to the cultural considerations of how to make decisions, Brynjolfsson spoke about big data, and emphasized the importance of “nanodata.” He explains how granular data is just as significant as the amassed data organizations face every day.

In his recent book, Race Against the Machine, Brynjolfsson and co-author Andrew McAfee talk about how the digital revolution is accelerating innovation. More specifically in a book excerpt found in the article “Thriving in the Automated Economy,” the two conclude that “weak human + machine + better process was superior to a strong computer alone” and, more remarkably, “superior to a strong human + machine + inferior process.”

In my view, since productivity relies so heavily on innovative and fine-tuned processes, organizations should proactively develop processes to support their data-driven decision making journey, rather than establishing them as an afterthought.

The authors also show why “labor’s race against automation will only be won if we partner with our machines.” Now that seemingly contradicts a similarly named band, Rage Against the Machine, to his book, doesn’t it?

Just how does analytics play into all this? Brynjolfsson explains.

Are you using analytics to keep up with technological innovation? And how important is process in your organization, no matter what analytical stage you’re in? Now you’re in the student seat. Raise your hand by commenting below.

Tags: ,
  • Facebook
  • del.icio.us
  • Twitter
  • Digg
  • LinkedIn
  • email

3 Comments

  1. Peter Dorrington, Director of Marketing Strategy (EMEA), SAS
    Posted June 7, 2012 at 6:30 pm | Permalink

    Hello Anna,
    Great post.
    I was fortunate enough to hear Erik Brynjolfsson give this presentation and I was particularly impressed by the rigour of his research; Erik doesn’t just claim that Data-Driven Decision-Making (Business Analytics) makes a difference to business, he has the data to prove and quantify it.
    Secondly his point about people and machines working in concert to deliver superior results is one I think is incredibly important as the West looks for ways to adapt to labour cost and commodity price pressures from the developing nations: it is people that bring the art and the insight to the science of analysis and this is a potential source of value-add (not cost reduction) that can drive revenues in these global markets.

Post a Comment

Your email is never published nor shared. Required fields are marked *

*
*

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <strike> <strong>