Data science is one of the most recent buzzwords that is gaining popularity in tech circles. With the number of job advertisements on the rise, one may think that data science and their professionals, data scientists, would become one of the most sought-after professionals in the technology job market over the next decade. Indeed, there are strong reasons to believe so, and surprisingly the cause won’t be the big companies, but the small and medium-sized ones, which are eager to find ways to interpret the data they now collect at an exponential rate.
Every company, big or small, is surrounded by data
Some people only relate data science with social networks like Facebook or Twitter; in general, networks with huge amounts of user information that is primarily used to deliver better advertisements. But that is only one side of the coin —albeit an important one— The democratization of the Internet has had two important consequences for almost any company, even if it is not related to technology:
- Companies can now reach a greater number of potential customers from all over the world, with many different backgrounds and interests.
- Mobile devices are always connected and are also extremely portable, which means that the number of transactions and customer interactions per product and service has increased substantially in recent years. One example of this is the mobile impact in e-commerce, and 2015 may be the year of Apple Pay.
How data influences decisions
Arguably one of the most important values that a company can have is its ability to make good decisions. This includes how to respond to the competition, how to plan future products and services, and so on. Data science specialists guide decisions by ensuring that the right questions are asked on data. Like Alice in “Alice in Wonderland”, if you ask the wrong questions on data, it does not matter which decision you take.
A data scientist uses math concepts to extract insights from data, but this is not limited to standard data mining that target users for marketing purposes or fraud detection, like banks have been doing for a long time. It is more like treating each internal or external business problem from a data perspective. For example, a company may discard a long project even before starting it if rigorous data analysis shows that it may not generate enough value. For this purpose, I think that a multidisciplinary team is needed with not only data scientists, but also economists, psychologists, and engineers.
To sum up, data science is here to help businesses in many ways. The first step an organization should take is to come up with a methodological way to extract insightful information from data. Once the organization has the specialists for this task, the next logical step is to close the gap between data and decision-making, by viewing each business case as a data problem.
What do you think? Do you think investing in data science gives companies a competitive advantage? Do you think data science can be applied to decision-making inside an organization? Join the comments below.
Daniel is a software engineer at Fon and PhD student at the Artificial Intelligence and Software Engineering department of Complutense University of Madrid. His research interests are computer vision and machine learning. He is specially interested in the applications of the emerging field of deep learning.
You can reach him at LinkedIn, or Twitter.
(Image credit: Dataviz by Andy Lamb, via Flickr)
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