Data science is everywhere, so why no data scientists to be seen?

Data science is everywhere at the moment. Nearly as everywhere as big data, but not quite. Books out there are making the concepts behind statistics and predictive analytics more and more accessible not only to those in business making decisions everyday but also to the average man or woman on the street.  Try Super Crunchers by Ian Ayres, Moneyball (the book  or the film which has the advantage of featuring Brad Pitt and therefore making the business of statistics much sexier than it has been),Freakonomics or the newer Superfreakonomics or pretty much anything by Malcolm Gladwell. All of these books have their roots in story telling related to analytics of some sort. So people are talking about the data science thing, reading about it (even if the books don’t call it that) and they probably see it everyday when a personal recommendation is served up to them via Amazon or similar.  But what’s driving this sudden surge in the coolness of analytics?

I think it’s probably because we have no choice.  There’s more data out there now than we can possibly review and it grows quickly and consistently.  This big data thing where we track and store EVERYTHING means there’s a critical need for the people, skills and tools to make sense of it.  To mine it if you will.  This flood of data is both an opportunity (in business terms) and a huge challenge to business and publicly funded organisations alike. Easily available data now exists to answer pretty much every business question one might have. Compare this to the situation not so many years ago when you would have to conduct a specific survey or study in order to get the information you wanted.

Now the data exists because businesses save the details of every click, transaction, page view, loyalty card scan and so on. It’s now also relatively cheap to store data in large volumes for long periods of time.  And, crucially, the analytical tools you need to make sense of it are easily accessible in terms of cost, processing power and ease of use. This means there’s so much more opportunity do proper marketing tests and act on their results through the application of rigorous analytics.

To do this however business do need to buy some analytics software – it’s just not possible to get any meaningful insights by simply eyeballing your data or analysing it by hand anymore and a spreadsheet won’t cut it either.  Businesses also need to buy or develop the skills.  People need to be able to understand not only how to pull together the data and perform a credible piece of analysis but also how to translate the results into meaningful business terminology and communicate them to non-technical stakeholders.

This is why there are so many job ads out there for data scientists.  People need them.  And it’s not JUST statisticians or analyst they are looking for now. They need people who have enough technical ability to get to grips with data, databases and data querying, combined with enough business acumen to understand how to apply their insights.  AND, as if that wasn’t enough, they also need to be agile and skilled analysts. A data scientist is an individual who can build a bridge from raw information to actionable insight.

Harvard is now teaching a Data Science course, which lists on its syllabus the following tangible lessons:

  • Wrangle the data (gather, clean, and sample data to get a suitable data set).
  • Manage the data in a way that gives you access to big data quickly and reliably.
  • Explore the data so you can generate a hypothesis.
  • Make predictions using statistical methods such as regression and classification.
  • Communicate the results using visualization, presentations, and interpretable summaries.

So interestingly not dissimilar to the Steps in a CRISP DM project (Cross Industry Standard Process for Data Mining).

I believe you can teach these skills in house. Take an intelligent, relatively data literate member of the team and train them up.  Teach them how to use some software but importantly package up projects and allow them to investigate the data.  No-one learns more about data nuances than by getting on with it and trying things out.

Contact us at Smart Vision if you want to talk about skills training or software or just for some no strings attached advice.  We’ve been doing this a long time and like to talk about it!

 

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