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Big data success in 2014: It´s the human talent part, stupid!

December 9, 2013

Leaders are 2.2x more likely to have formal career paths for analytics

Companies whose investments focus on growing talent in data sciences help not only their processes, but also the company as a whole. In our most recent global study on big data analytics, we concluded that the gap between the demand for analytics talent globally and the supply of analytics talent locally is one of the key obstacles to analytics implementations across all organizations. What was more striking was that one-third of respondents cited the lack of skills to analyze and interpret data into meaningful business actions as the top business challenge impeding better use of analytics within their organizations. People matter! Talent matters most! Especially in big data analytics. Yet many times we seem to forget the human differentiating factor.

At the beginning of my career, I was taught that the budget conversation is always the hardest conversation for an advisor to have with a client, but it's a necessary one. Well, as my daughter would twitter: #lessonlearned. Because it´s not. In my experience, it´s the “people-discussion” that is the hardest.

Remember the old joke in which two executives discuss the employee investment dilemma? “What if we train them and they leave?” asks the first executive. The second executive's reply: “What if we don´t and they stay?"

When working with clients, the more successful discussions around big data mostly aren´t about technology, nor are they about the data itself, nor are they about analytics. They are about the key questions that matter to a company, the business capabilities that are needed to enable processes in answering those key questions and the use cases that these capabilities apply to. Subsequently, the discussion moves to which big data will fuel these use cases. Seldom have I experienced the mentioning of ownership, accountability, governance, skills or talent. And, mind you, these are the successful conversations, with people who’s questioning minds and unconventional ideas are driving the transforming of those companies. Yet even then, employee development, talent scouting and governance are mostly on the backburner of this discussion.

Ultimately it comes down to the fact that business analysts are often unable to provide what the decision making process needs most: insight and understanding, not just numbers. Few executives that I have encountered make a decision from a pure number report or a spreadsheet; they want to know the background, the assumptions and some explanation in clear, simple language. Companies leading in analytics, however, have grown a new type of talent: the Data Scientist. Data Scientists need to be curious, greedy, driven to have deep, close looks at data and recognize trends. Like the Renaissance Man, Data Scientists must have a natural interest in almost every aspect of the business (and beyond), combined with a drive to grow and transform the organization.

We observed that analytics talent (Data Scientist, Big Data Analyst, etc.) in leading analytics companies served from within the business unit as a link between business and data. They would focus on questions like “what does big data tell us about our processes?” and “How can we perform better and faster?” A good Data Scientist does more than just analyze data and the creation of models. Unleashing the value of big data is only a small part of the job description. They understand both the business data as well as the statistical toolkit needed to analyze and visualize the data. They make it digestible and have the analyses fit a purpose, so one can see in one glance which data relationships matter and share the background (what’s excluded is as important as what’s included). Most of all, they explain the causality and impact in executive language, compelling reasons to act, thus transforming reactive analytics into predictive analytics.

Yet, most organizations simply store huge amounts of data, resulting in the nightmare for any Data Scientist: a dump of big data and a task to "go find stuff' with no context. Already this is quite difficult for the talented, yet becomes an impossible mission for the traditional data skilled. Former IBM chairman Thomas Watson Jr. knew how important it was to work with people who could separate their gut from data, who would question the way things are and challenge the status quo. Who would actually think. In fact, he even had a name for them: "Wild Ducks."  It again stresses how important it is to have a culture that fosters talent. If it doesn´t have a label, how can it be important?

The real value of big data lies precisely in combining analyses over different business units in a holistic way, without losing the big picture, and enabling the translation of the outcomes of big data analytics into action. Some say “Processes translate big data analytics into action." Personally, I prefer to think of a great collaborating team that translates big data analytics into action.

Let´s revisit our opening statement on the success of analytics leading organizations: they have more than twice the defined analytic career path! One might wonder why….