Information technology – we’ve done a lot with technology, and until recently almost nothing with information. In fact until just four or five years ago IT was synonymous with networks, Java, data management and arcane terms such as Service Oriented Architectures. Not a single mention of information. Well, unless you have just come back from an extended vacation on Jupiter the change of emphasis that is currently taking place cannot have gone unnoticed.
Most businesses are creaking at the seams with applications that address business administration – you know, the Enterprise fill-in-the-blank Management type of application. But today the focus has shifted to applications such as data mining, business intelligence, big data, text mining and statistics. While this may just seem like a new bunch of technology it betrays a shift of emphasis, because these technologies are truly concerned with information, and not just data collection and management.
A new role has also emerged – that of the data scientist. And if you think there is a disconnect between IT and the business, just wait until you have a team of data scientists talking about entropy, support vector machines, and regression splines. But this is the language of information and as various commentators have said (Thomas Davenport included) data science will be the sexiest profession of the 21st century.
This is all highly disruptive from a career point of view. Businesses are already clamoring for people who can analyze their data and tease out the patterns that would spell improved profitability, and we are barely off the starting blocks. Online MSc’s are being offered to managers who see the challenge. If you are going to manage this stuff, you had better know something about it.
This move to data science, although being hyped to death right now, is here to stay. It represents a convergence between need, technology, method and science. Data analytics tools are maturing, the methods are finding implementation through more capable hardware, and the frontier of what they can do is advancing through a massive global research effort.
Anyone over the age of thirty is in danger of being left behind (unless you are already a data scientist). It’s time to jump on board this particular train, which means some education, some technology and a different understanding of what the data in your organization means. Demand for such people is already high and in the US various projections show a shortfall of up to 60% of need in just five years time. Guess who is going to be earning the big bucks five years from now!