Big Data is, I think, a phenomenon that’s impacting just about every business these days. And as we look at all of the data that we have – just think about it for a moment. We have data about the marketplace that might come from industry analysts like Milt Gartner or Forrester. We have economic reports that talk about where people are expecting to spend money and make capital expenditures on things like IT.
We have geographic information. We have a lot of external data that we can draw on. But we also have a lot of internal data. We have information about purchase history from customers. We have information about what they’re still using. We have information about how often do they refresh hardware. We have information that comes from our own discussions with them about what’s important to some of our big clients. What are their strategies? Where are they trying to go?
We have all of these vast amounts of information and what we’re really trying to do is to figure out how can we make that information available in a way that’s consumable to our business. So just think for example if you’re a sales operations manager as an example, and you have a challenge. You have a number of salespeople on your team but how are you going to optimally deploy those across the opportunity? You know, do I assign three people to a big client? Do I assign them to a number of different small to medium business accounts? How am I going to optimize my revenue using these salespeople that I have?
And that is a classic kind of sort of big data challenge and problem. Because what you want to do is you want to marry all of these disparate sources of data and to be able to come up with an algorithm that says I can optimally place them in different places.
So one of the things that my team has done, and within IT, is to build an environment – think of it as being an internal Cloud where we deploy technologies such as Cognos for business reporting and analytics. SPSS which is the statistical analysis packages that we can deploy into this Cloud environment. We can provide all of this wealth of information that we have, connect it to that environment and then we can work with the business to say, “Okay, what business problems do you have? How can you now start to analyze all of this data to be able to find answers to that.
So I gave you one example in the sales deployment question which is a very numerical analysis. You know, I’ve got all these facts and figures and I’m trying to come up with an algorithm. Now if you’re more into the marketing team a lot of the information and data you have actually is not numbers, it’s text. It’s information. What are people saying about us on social media? Y
How can you mine that information so that again you can identify trends, you know, as sentiment – all of a sudden going from very positive to very negative. Is there something that we’re seeing more discussion out there about maybe we did a new product announcement. Is there something that’s happening there?
Now the marketing team can look at that kind of information and determine the effectiveness of different marketing programs. Are people getting the message? Are they out there talking about things. These are examples of how we’re trying to bring both that internal data, the external data, the structured data in terms of rows and columns and numbers – marrying that with text data as well providing much, much richer analytic experience.
In Their Own Words is recorded in Big Think's studio.
Image courtesy of Shutterstock