Data Is Useless Without the Skills to Analyze It
September 15, 2012 Editor 0
Do your employees have the skills to benefit from big data? As Tom Davenport and DJ Patil note in their October Harvard Business Review article on the rise of the data scientist, the advent of the big data era means that analyzing large, messy, unstructured data is going to increasingly form part of everyone’s work. Managers and business analysts will often be called upon to conduct data-driven experiments, to interpret data, and to create innovative data-based products and services. To thrive in this world, many will require additional skills.
Companies grappling with big data recognize this need. In a new Avanade survey, more than 60 percent of respondents said their employees need to develop new skills to translate big data into insights and business value. Anders Reinhardt, head of Global Business Intelligence for the VELUX Group — an international manufacturer of skylights, solar panels and other roof products based in Denmark — is convinced that “the standard way of training, where we simply explain to business users how to access data and reports, is not enough anymore. Big data is much more demanding on the user.” Executives in many industries are putting plans into place to beef up their workforce’s skills. They tell me that employees need to become:
Ready and willing to experiment: Managers and business analysts must be able to apply the principles of scientific experimentation to their business. They must know how to construct intelligent hypotheses. They also need to understand the principles of experimental testing and design, including population selection and sampling, in order to evaluate the validity of data analyses. As randomized testing and experimentation become more commonplace in the financial services, retail and pharmaceutical industries, a background in scientific experimental design will be particularly valued.
Google’s recruiters know that experimentation and testing are integral parts of their culture and business processes. So job applicants are asked questions such as “how many golf balls would fit in a school bus?” or “how many sewer covers are there in Manhattan?” The point isn’t to find the right answer but to test the applicant’s skills in experimental design, logic and quantitative analysis.
Adept at mathematical reasoning: How many of your managers today are really “numerate” — competent in the interpretation and use of numeric data? It’s a skill that’s going to become increasingly critical. VELUX’s Reinhardt explains that “Business users don’t need to be statisticians, but they need to understand the proper usage of statistical methods. We want our business users to understand how to interpret data, metrics, and the results of statistical models.”
Some companies, out of necessity, make sure that their employees are already highly adept at mathematical reasoning when they are hired. Capital One’s hiring practices are geared toward hiring highly analytical and numerate employees into every aspect of the business. Prospective employees, including senior executives, go through a rigorous interview process, including tests of their mathematical reasoning, logic and problem solving abilities.
Able to see the big (data) picture: You might call this “data literacy”: competence in finding, manipulating, managing, and interpreting data, including not just numbers but also text and images. Data literacy skills must spread far beyond their usual home, the IT function, and become an integral aspect of every business function and activity.
Procter & Gamble’s CEO, Bob McDonald, is convinced that “data modeling, simulation, and other digital tools are reshaping how we innovate.” And that has changed the skills needed by his employees. To meet this challenge, P&G created “a baseline digital-skills inventory that’s tailored to every level of advancement in the organization.” At VELUX, data literacy training for business users is a priority. Managers need to understand what data is available, and to use data visualization techniques to process and interpret it. “Perhaps most importantly, we need to help them to imagine how new types of data can lead to new insights,” notes Reinhardt.
Tomorrow’s leaders need to ensure that their people have these skills, along with the culture, support and accountability to go with it. In addition, they must be comfortable leading organizations in which many employees, not just a handful of IT professionals and PhDs in statistics, are up to their necks in the complexities of analyzing large, unstructured and messy data.
Here’s another challenge: The prospect of employees downloading and mashing up data brings up concerns about data security, reliability and accuracy. But in my research, I’ve found that employees are already assuming more responsibility for the technology, data and applications they use in their work. Employees must understand how to protect sensitive corporate data. And leaders will need to learn to “trust, but verify” the analyses of their workforce.
Ensuring that big data creates big value calls for a reskilling effort that is at least as much about fostering a data-driven mindset and analytical culture as it is about adopting new technology. Companies leading the revolution already have an experiment-focused, numerate, data-literate workforce. Are you ready to join them?
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