There are two opposing points of view…
Virtually any article today about big data inevitably turns to the notion that the country is suffering from a crucial shortage of data scientists.
What seems to be missing from all of these discussions, though, is a dialogue about how to steer around this bottleneck and make big data directly accessible to business leaders.
While difficult to generalize, there are three main roles served by the data scientist: data architecture, machine learning, and analytics.
The solution then lies in creating fit-to-purpose products and solutions that abstract away as much of the technical complexity as possible, so that the power of big data can be put into the hands of business users.
Data scientists are changing the way decisions happen by making better use of big data. Rather than finding ways around them, we need to make data science more accessible as a profession and need to provide easier tools for data scientists.
We build new systems that are flexible and dynamic and create more new jobs — such as data scientists — to analyze and build models for these new systems. It is obvious that in such a world, where static models cannot keep up, data scientists will be indispensable.
…data scientists are the designers and the content creators of today, not the software engineers or the IT bottleneck.
We need data scientists, and we need hundreds of thousands of them. They will do their magic, create new ways of experiencing life, products and services…
New, simpler tools will no doubt come along over time and it is something to look forward to.
I’m choosing to concentrate on the analytics angle of a Data Science role – know the right questions to ask, know how to state the questions so that you are delivered the answers you want to get and then be able to interpret the answers correctly, so that relevant decisions can be made. That is the ultimate goal.