The Truth About Big Data
An oft-ignored piece of Data Science: SCIENCE
Though “data science” has been a buzzword from nearly the first time it was spoken, there are tangible, material benefits to leveraging data inside and outside your organization.
Data science transcends hype when implemented and leveraged properly, but the hype is strong.
History of the Hype
The concept of “Big Data” popped up in late 2010 and has reached new highs for references this year, per Google Trends. The terms Data Science and Data Scientist lagged behind, starting their ascent almost exactly two years later.
The sizzle proceeded the steak, driving many big companies to try to leverage the increasing amount of data being collected.
Drew Conway took an early crack at defining what Data Science really means in a presentation on Sept. 27, 2010. The Venn Diagram has been used regularly since then. Though the hype hadn’t really ramped at that point, Conway was already trying to put a damper on the buzzword to focus on the deeper meaning.
Conway’s chart has recently been replaced with a new version that has popped up in LinkedIn feeds over the past several months. (We haven’t been able to track down the originator. Let us know in the comments if you know the original source.)
This new diagram tries to layer in “Communication” as a new layer, as well as give some more specificity beyond “hacking skills.”
Diagram still not right
We asked ASMGi Data Scientist Peter Malave to weigh in, and his first reaction was one of exasperation: “My first observation would be one that would seem too obvious: There is no ‘Science’ set.”
A Ph.D. in Physics, Malave says science truly impacts nearly every aspect of the job: “When approaching a new set of data, the data scientist may not know which model might best suit the problem. Investigation of the data in a scientific way starts to shed light on the answer. And that’s just the data exploration part of the pipeline. There’s also the feature discovery, model building, testing, and validation that incorporate scientific approaches.”
Practical IT Innovation
We’re not going to try to redraw the diagram, in part, because we have a much more straight-forward way of looking at data science. Frankly, if we needed a Venn Diagram to explain a service, we would never suggest one of our clients pay for it.
Data Science takes data, both proprietary and widely available data, and creates findings that can be leveraged to make better business decisions. This concept long pre-dates 2010, but recent innovations have made the data even more powerful.
Beyond creating reports or business recommendations with this data, ASMGi has been working to automate real-time crunching of this kind of data.
That allows, for example, an entry-level sales person make more targeted offers to the prospect they’re talking to with just a few pieces of information run through an algorithm.
It can also be leveraged to guide spending decisions with inputs of a customer care agent when speaking to a client.
When you consider the volume of data that can be leveraged to make more profitable sales pitches and decisions on resources to spend, this can become a highly lucrative business. It also tucks in perfectly with innovations in Customer Experience and User Experience.
The path to purchase can be individualized. That means each prospect can be automatically segmented and treated differently based on future profitability calculations.
And loyalty can be valued, not in a generic way, but in a very specific and business-minded way.
This is the kind of innovation that can impact revenues, expenses and profitability directly.
If you’re using data science in your business today, ask yourself if it is based on hype or on pragmatic execution. If it is too much of the former, let’s have a conversation about how we can help you turn that Big Data into Big Profits.