Working with spreadsheets and analyzing data is no longer reserved only for those who crunch numbers. Today, all fields are relying more heavily on making data-driven decisions and utilizing spreadsheet modeling as a tool for growth. Donna Haeger, a Cornell professor of economics and management, sat down with eCornell’s Chris Wofford to discuss the growing impact spreadsheet modeling is having on business.
What follows is an abridged version of that conversation.
Wofford: How does spreadsheet modeling relate to business analytics? How do we distinguish the two?
Haeger: The spreadsheet modeling piece is really taking the unstructured data. We’re structuring it into an organized fashion. The business analytics piece is really the data-driven decision-making that we’re doing, so making the decisions on the model are what we’re doing when we’re performing business analytics. If we’re using optimization, we want the result of the model to tell us what we should do – how many of a particular product we should produce based on our criteria and our goals. We could also do predictive which is a forecast, like a simulation.
Wofford: What are some typical obstacles? For some people, this is very fresh and if you’re really starting to take your analytics and your modeling seriously, what are the typical obstacles that people run up against when they’re first starting to think about this as a strategy for their company?
Haeger: I think the biggest obstacle today is how much data we have. We’re drowning in data. I always tell my students data is not the problem. We have so much data that we don’t know what to do with it. Most of the startup companies that are working in data analytics are basically becoming specialists in spreadsheet modeling and other types of data modeling so that they can answer questions. And I like to say that every company that has data, which is every company at this point, they’re swimming in answers to questions they haven’t even begun to ask – and that’s a pretty amazing place to be.
Wofford: What is your experience as far as when you work with students? Can you just speak to that broadly about this as a career path or if somebody’s actually already established in a position, how might it benefit them to learn about this?
Haeger: That’s an interesting question and I get this all the time – things like what job titles am I looking for. I have not found a position, an internship or a permanent job, that does not involve data in the business realm as of late. And so that’s interesting because I like to say that this whole thing about data is ubiquitous, like it’s everywhere. There are very few jobs right now that do not relate to having some data literacy, so understanding how to take data and turn it from data to information, which is structuring the data and then analyzing it and turning it to some knowledge is it’s really hard to find a position where you don’t need to know how to do that.
In fact, we’re starting to hear that people are being encouraged to learn a programming language on top of being able to be working with the data.
Wofford: What is that?
Haeger: When you’re working with spreadsheet modeling, you have a lot of control over the data that you receive, however, it depends. Everyone has a choice. I call it a Venn diagram. We’ve gone from where we used to send an email to IT and say, “I need some data, please send it to me” and the business people would get the data from IT.
Wofford: I mean if we talk about data literacy across an organization, for example, there’s certainly a case to be made that everybody should be to literate in some way so we know what we’re talking about. Are visuals where it’s at?
Haeger: We all love pictures, right? I think most of us are visual and even if we’re not visual having the spreadsheet model – and when we say spreadsheet model, it could be a pivot table, table, columns, rows, a chart – when we turn it into a visualization, we’re answering a lot more questions in one image. When we illustrate it this way and if we do a good job with it, it’s much easier for people to answer their own questions and interact with the visual. We’re starting to actually create dashboards now where we’ll create several different pivot tables, pull out some visualizations, put them all on one tab and create slicers where the individual isn’t just looking at three or four images but also being able to hit the slicers and interact with the data. So now you’re answering thousands of questions by interacting with what you see on the dashboard.
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