When business leaders read about (and tackle) Big Data, there is a lot to take in.
The field is developing so dynamically that many of the industry buzzwords will not have existed until a few short years ago. Just a short list of some programming languages is enough to make most business leaders dizzy. R, C, Python, Java, Julia, Scala, Ruby ….. just a few of the languages that our grandchildren might be learning at high school. There will be many others; you can be sure of that.
There is one language in which every Data Scientist should be fluent: Business
As recruiters, we, of course, assess our candidates for the hard, technical skills. We look at the projects that they have completed. How they rate on Kaggle. We can do rigid technical competency checks to ascertain their professional level. That is all measureable. You either have the knowledge and the skills or you don’t.
However, the difference between a good Data Scientist and a GREAT Data Scientist is often not found in their technical ability or their amazing mathematical genius. Nope. Data Science exists to provide a service to business and business is run by people. If Data Scientists cannot comfortably communicate with their non-expert colleagues and bosses, then their effectiveness is greatly reduced. They need to be able to speak easily with people, to understand, to interpret, to translate.
They have to understand the issues of their business and give guidance in providing the data to reach the best solutions. They have to be adept at facilitating a continuous dialogue loop – from business to the Data Science / Big Data teams and then back to the business. Great data scientists will not just address business problems; they will pick the right problems that can have the most value to the organization.
They have to be able to present their findings in a clear and simple way – in the language of their business. Not all people understand the technical jargon. The candidates who can explain what they have achieved without blowing my mind with jargon are those who usually go far. Accurate numbers and graphs are one thing, but only the data scientist understands them well enough to be able to draw the crucial business conclusions. They have to interpret and translate.
Many mid-level candidates struggle with this initially. They have not had much senior management interaction and have mostly been fairly insular in terms of their work circle within a company. The solution going forward is to give them more exposure to the business, and to introduce the value of Big Data to their respective mid-management colleagues across all departments.
The organizations making the most of Big Data are now integrating their Data Science teams far closer with the rest of their business. They will grow up together as a team and learn to talk to each other more effectively.
They will learn to speak each other’s language.
Matt Reaney is the Founder and Director at Big Cloud. Big Cloud is a talent search firm focussing on all things Big Data and helps innovative organisations across Europe, APAC and the US find the talent they need to grow.
Hi Matt – while your intent in theory has merit ( Data Scientist should be fluent in Business ) in practice very few will become masters so that’s why you have smart and competent C level people, product / project managers, etc that understand enough of the Data Scientist world to provide fusion.
I agree that Data Scientists should communicate effectively with their non-expert colleagues and bosses. But more importantly the non-expert colleagues(managers, engineers, others involved in the project) should have a fundamental knowledge of data science concepts inorder to understand the findings projected by data scientists.
For example, If a data scientist proposes to improve a particular business application by extracting knowledge from data, manager should be able to assess the proposal systematically and decide whether it is sound or flawed. This doesn’t mean that the manager will be able to tell whether it will actually succeed — for data mining projects, that often requires trying — but the manager should be able to spot obvious flaws, unrealistic assumptions, and missing pieces
I agree with Mr Prakash,
However my view of the future may differ.
The internet of things, increasing automation and increasing complexity of the support functions will mean that those hired will fill positions from the grass-roots upwards. The data scientist will be the next decades MI / BI manager. The data scientist will learn infrastructure and a variety of tools from the data storage level to the data presentation level. The data scientist will inevitably learn how to tie all these tools together.
Working with the “Business” will invariably lead to sector or business knowledge. Here consultants who work as part of off-shore models who have knowledge of the various verticals of a variety of businesses will have the advantage. Since they will know business process as well as the data that supports such processes.
Old organisations may struggle those most to foster the learning and mastery / evolution of the data scientist.
However newer departments or new businesses or small businesses may be the best place since their environment will be one of constant change.