Before companies can profit from big data, they often must deal with bad data. There may indeed be gold in the mountains of information that firms collect today, but there also are stores of contaminated or “noisy” data. In large organizations, especially financial institutions, data often suffer from mislabeling, omissions, and other inaccuracies. In firms that have undergone mergers or acquisitions, the problem is usually worse.
Contaminated data is a fact of life in statistics and econometrics. It is tempting to ignore or throw out bad data, or to assume that it can be “fixed” (or even identified) somehow. In general, this is not the case.
When I came home from Afghanistan with the military, I was ready to focus on the things I cared about most. I saw how short life could be, and it seemed as if I didn’t have much time to waste. I had been a serious songwriter and musician since my early teens, but recommitted myself to this passion after this deployment. During my transition out of the military, right before entering MIT Sloan, I ran into a guy named Chris Dorsey at a neighborhood blockparty. Within a few minutes, Chris found out I was a songwriter, and I found out Chris was a drummer. After the party, I sent Chris a bunch of my home-recordings and, within a month or so, we headed into the studio to record our music. We experienced a number of inefficiencies while in the studio; but, after recording, we were caught in what felt like a never-ending feedback loop that surrounded the music we had recorded. It was difficult to review the audio data outside the studio (the way we needed to review it), to iterate and polish our music. This, of course, translated into our spending more time in the studio, and our spending more money than we had ever planned to spend. Every independent musician knows this struggle; and, while in the studio, Chris and I laid the initial seeds for AudioCommon—the company we would later co-found to enable Cloud collaboration during the earliest stages of the music creation process.
Compared to five years ago, the amount of data we now generate is huge. Some companies collect that data, but more often than not they don’t do anything with it. Business analytics is an important tool to help organizations harness the power of that data. By unlocking its value, you can do things like improve profits, predict consumer behavior, better understand markets, and make more informed decisions. Most importantly, it can give you a competitive edge.
For those of us in the field of operations research, data analytics is a huge and exciting area. It’s a critical tool for businesses moving forward. As a result, we’re offering MIT Sloan’s popular Analytics Edge course on the MITx online, interactive learning platform this spring. We want to share the cutting-edge knowledge generated at MIT on this important topic with people around the world. Read More »
As we continue to recover from a global recession and look to the future, it’s imperative that we build more entrepreneurial-driven academic institutions. Not only will this provide the foundation for much-needed innovation, it also will strengthen economies by providing jobs and fostering sustainable growth in enterprises.
Lessons can be learned from universities around the world about accelerating entrepreneurship. They can provide the model for how to create clusters of commercially successful startups around research-driven institutions. However, the success of that model largely depends on the role of the business school within that university setting.