The leading car manufacturers are currently investing huge amounts of money integrating Big Data knowhow into their manufacturing processes. In the future, it will also undoubtedly have a direct impact on the driving experience – and before long your car will drive itself.

If your in-car computer tells you that your engine is 70% likely to fail, most people would want to do something about it. If it tells you that your braking distance is 85% likely to cause a rear-end collision, you might drive with a little more caution…. Data has the power to change behaviours – more than education or prohibition ever could.

[bctt tweet=”Data has the power to change behaviours – more than education or prohibition ever could.”]

This emerging trend in Big Data will be driven by the businesses who will stand to profit from it (they will sell more cars, parts, etc), but it will soon move across to other areas of our lives, and it may not be universally welcome.

If, after a detailed examination of your marriage, you were told that you had a 95% chance of getting a divorce over the next five years, would you feel any less determination to work at it? When emotions are turned into cold and hard facts, they are much harder to ignore. “We’ll get through it, we’ll be fine” might be so much harder to believe in. There are lots of things in life that aren’t just about the “data”, but they will be analysed anyway.

On the other hand, within the healthcare sphere, preventative predictions will cause a revolution in the way we understand out bodies and the affects of what we are doing to them. If you understand that not going on that morning run has a 75% chance of shortening your life by three years, most of us would rush to put on our trainers…. If you understand that a stroke was imminent, you could rush to a hospital and get yourself treated before it took hold.

From a recruitment perspective, what if your employer could tap into your online habits to tell when you might start to look for a new job. The moment you start to browse certain websites is the beginning of the end. What may have been a flirtation with temptation previously might now be viewed as a heinous betrayal. “How dare you check out their career page. You’re obviously not fully engaged anymore. Goodbye.” Sounds silly, but a variation on this may not be so far away.

With the advent of the “Internet of Things,” this predictive analysis could turn us into little robots, governed by algorithms and not by our hearts. There could be a Google Glass type headset to tells us how we should be interacting with others based on their reactions. It could analyse all the tiny “micro-expressions” that our intuition takes for granted and give us hints as to how we should be behaving…. This for me would be the beginning of the end!

Preventative predictions will form a big part of the Big Data revolution, but we should be careful not to let their conclusions get out of hand.

(Image credit: Teradata)

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.

Follow @DataconomyMedia

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.

My first article on “How To Become A Data Scientist” explored the basic four different types of Data Scientist – Data Business People, Data Creatives, Data Developers and Data Researchers (as per the O’Reilly study “Analysing the Analysers”). It highlighted the need for a data science team with diverse and complementary skill sets. It is clear that no one “superstar” can fulfil all the required roles, and it is up to us as recruiters to understand the requirements of any organisation to ensure that there aren’t any gaps in their capability.

Therefore, in this piece, I wish to assess in more detail the primary skills of each type of Data Scientist, investigating in which areas they might collaborate – thus starting to compile a basic profile of each role. I’ll be following up next time with the different routes to becoming a Data Scientist.

To recap: Data Business People (DB) are leaders and entrepreneurs. Data Creatives (DC) are multi-talented artists and hackers. Data Developers (DD) areprogrammers and engineers. Data Researchers (DR) are scientists andstatisticians.

As you can see by the following graphic, there is a usually a stronger skill set for each Data Scientist group. As recruiters, it is important that we identify not only the general skill set of our candidates, but also where they have particular strengths.

There are certain areas in which each type will collaborate. For example, Data Creatives might work with Data Researchers on Statistics, Data Developers might work with Data Creatives on ML/Big Data work, while Data Business People may work fairly independently on the Business side.

The skills of a Data Scientist can be broken down into 22 sub-sections, and I offer my interpretation of the key skills for each of the four Data Scientist types. This is my subjective view and of course is open to debate.

Algorithms (ex: computational complexity, CS theory) DD,DR

Back-End Programming (ex: JAVA/Rails/Objective C) DC, DD

Bayesian/Monte-Carlo Statistics (ex: MCMC, BUGS) DD, DR

Big and Distributed Data (ex: Hadoop, Map/Reduce) DB, DC, DD

Business (ex: management, business development, budgeting) DB

Classical Statistics (ex: general linear model, ANOVA) DB, DC, DR

Data Manipulation (ex: regexes, R, SAS, web scraping) DC, DR

Front-End Programming (ex: JavaScript, HTML, CSS) DC, DD

Graphical Models (ex: social networks, Bayes networks) DD, DR

Machine Learning (ex: decision trees, neural nets, SVM, clustering) DC, DD

Math (ex: linear algebra, real analysis, calculus) DD,DR

Optimization (ex: linear, integer, convex, global) DD, DR

Product Development (ex: design, project management) DB

Science (ex: experimental design, technical writing/publishing) DC, DR

Simulation (ex: discrete, agent-based, continuous) DD,DR

Spatial Statistics (ex: geographic covariates, GIS) DC, DR

Structured Data (ex: SQL, JSON, XML) DC, DD

Surveys and Marketing (ex: multinomial modeling) DC, DR

Systems Administration (ex: *nix, DBA, cloud tech.) DC, DD

Temporal Statistics (ex: forecasting, time-series analysis) DC, DR

Unstructured Data (ex: noSQL, text mining) DC, DD

Visualisation (ex: statistical graphics, mapping, web-based data‐viz) DC, DR

The success of your Big Data organisation will depend on how your team functions within these 22 distinct areas. Collaboration between distinct work streams is the key to success and it is vital that you recruit and retain “T-shaped” individuals – i.e. with a solid general skillset plus one or two “stand-out” skills.

The next post will explore the different routes to take in becoming a Data Scientist.

We can help compile an audit of your Big Data organisation. Are you sure that you don’t have any gaps? If you do, Big Cloud can help you find the right people to fill them!

Sources: http://www.oreilly.com/data/free/files/analyzing-the-analyzers.pdf

Follow @DataconomyMedia

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.

(Image Credit: Jorge Franganillo)