What Can You Do with Data? Posted on : Mar 30 - 2015

I gave a speech about marketing and data science last week. Afterwards, one of the CMOs came up to me with panic in his eyes. He said, and I quote, "Help!"

I get it. Big Data and Data Science are overused catch phrases that can mean anything anyone wants them to mean. But the hype doesn't change the facts. We are being overwhelmed with data, and I can assure you that if you don't know what to do with it, your competition will.

Data Science Can Help

The fundamental goal of data science, a mash-up of three foundational skills - domain expertise, mathematics and computer science - is to turn information into action. You can learn about how they work together in my article, Are You Ready for Data Science?

The truism "If you ask the wrong question, you're guaranteed to get the wrong answer" is often tossed about in data science meetings as profound insight. It is not. It is an axiom.

Importantly, the obverse is almost never true. Asking the right question might be a path to enlightenment, but it certainly does not guarantee finding the right answer. To attempt that, you need the appropriate analytic tools and techniques. This is where mathematics and computer science add value.

An Overview of Analytic Techniques

What are the technical members of your data science team going to do with your data? Transform, learn and predict. So, in the interest of searching for the right answers, let's review a few common classes of analytic techniques and see how you might put them to good use.

Transformational Analytics

Aggregation - a class of techniques used to summarize data including basic statistics such as mean and weighted averages, median, Gaussian distribution and standard deviation. Other aggregation techniques include probability distribution fitting (the repeated measurement of variable phenomena - remember "method of moments" and "maximum likelihood" from Stats class?) and good, old-fashioned plotting points on a graph. View More