The term “data sci­en­tist” typ­i­cal­ly con­jures up images of a lone nerd stashed away in a base­ment, end­less­ly scru­ti­niz­ing spread­sheets and writ­ing byzan­tine white papers. There was a time when this descrip­tion rang true. But now it’s obso­lete. Today, more com­pa­nies than ever before are using data sci­ence to spot trends and find cus­tomers. And data sci­en­tists are now expect­ed to bal­ance their analy­sis with imag­i­na­tion and busi­ness savvy.

Demand for data sci­en­tists far out­paces sup­ply. So firms across the coun­try are anx­ious­ly wait­ing for col­leges to start churn­ing out data gurus. This is sil­ly. Thanks to today’s data sci­ence resources, any­one can be a data sci­en­tist.

Why Businesses Need Data Scientists

With the arrival of big data – the wealth of infor­ma­tion made acces­si­ble by high-speed com­put­ers linked to the Inter­net – data sci­ence has become one of the most impor­tant occu­pa­tion in the world. Har­vard Busi­ness Review even went so far as to declare the data sci­en­tist the “sex­i­est job of the 21st Cen­tu­ry.” Data sci­en­tists now serve cru­cial busi­ness func­tions:

• They are the inter­preters, turn­ing vast quan­ti­ties of raw infor­ma­tion into an action­able sto­ry.
• They are unique­ly equipped to rec­og­nize key indus­try trends and oppor­tu­ni­ties for growth.
• Their work occa­sion­al­ly verges on the out­right artis­tic, cut­ting through the staid sta­tus quo to imag­ine ground-break­ing new prod­ucts and strate­gies.

That’s why it’s no sur­prise that busi­ness­es are now aggres­sive­ly recruit­ing ana­lyt­i­cal Sven­galis.

Brands Are Finding Valuable Consumer Insights

For instance:

• In the wake of the recall of their Chevy Cobalt due to faulty igni­tion switch­es, Gen­er­al Motors cre­at­ed a mas­sive new data ware­house specif­i­cal­ly to improve prod­uct qual­i­ty.
• Boot mak­er Tim­ber­land improved its prof­its by 15 per­cent by scru­ti­niz­ing con­sumer sta­tis­tics and tar­get­ing a promis­ing new niche: out­doors-lov­ing urban­ites.
• Airbnb, which con­nects trav­el­ers to low-cost lodg­ing, uses data sci­ence to fea­ture the hosts most like­ly to accom­mo­date each guest. The imple­men­ta­tion of this mod­el improved book­ing con­ver­sions by almost 4 per­cent.

But There’s A Problem

As big data grows, busi­ness demand for data sci­en­tists will soon exceed sup­ply. In fact, the Unit­ed States will be short 1.5 mil­lion big data ana­lysts and man­agers by 2018, accord­ing to research from McK­in­sey. Com­pa­nies can’t afford to sim­ply wait until col­leges pro­duce enough qual­i­fied data sci­en­tists. Luck­i­ly, there’s a bet­ter strat­e­gy.

Grow A Fleet Of Data Jedis Internally

Intel­li­gent and dili­gent employ­ees, regard­less of their train­ing, can be data sci­en­tists if giv­en the right tools. These tech­nolo­gies turn high­ly tech­ni­cal infor­ma­tion like “F‑scores” and “p‑values” into eas­i­ly intel­li­gi­ble data sto­ries. They make iden­ti­fy­ing prof­it pain points a sci­ence. When a CEO won­ders whether unfriend­ly staff or over­crowd­ed park­ing lots are hurt­ing sales, Joe Schmo from mar­ket­ing can now pro­vide the answer. Rather than brib­ing a PhD grad­u­ate to come rack his brain over data anom­alies for weeks or months, com­pa­nies can use such a tool to empow­er their exist­ing mar­ket­ing team to iden­ti­fy and solve data puz­zles in a mat­ter of sec­onds.

The Model Of The Future

Con­sid­er a tech­nol­o­gy pro­vid­ed by Editd, a Lon­don-based data com­pa­ny. By search­ing every­thing from social media to run­way reports, Editd gath­ers 53 bil­lion data points on what’s in style in the fash­ion indus­try and pro­vides cus­tomized dash­boards for retail­ers. Editd right­ful­ly main­tains that “peo­ple should­n’t have to be data sci­en­tists to under­stand the insights.” One of its sat­is­fied cus­tomers, the British-based online retail­er ASOS, used these dash­boards to boost sales by 33 per­cent over a sin­gle quar­ter. This is the mod­el of the future. In fact, research from the ana­lyst firm Gart­ner pre­dicts that over the next two years the num­ber of ama­teur data sci­en­tists – that is, work­ers with no for­mal edu­ca­tion in this area empow­ered with these new sophis­ti­cat­ed data tech­nolo­gies – will grow five times faster than the sup­ply of their tra­di­tion­al­ly trained coun­ter­parts.