With many companies obsessing about how to turn their data into value, we find they spend too much time on the “data” and not enough time on the “people” side of the equation. Getting the people side of the equation right, however, requires doing two things right: 1) Hiring the right people for the right roles and, 2) building a "customer-service" mentality towards working with the people who will be using the Big Data insights, i.e., those in decision-making roles or on the front-line.
Big Data talent is a critical issue. By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills, according to the McKinsey Global Institute. While different companies will have different talent needs, they need to focus on several key roles for an advanced analytics bureau.
Nothing is as demoralizing as working on something that no one uses, and that includes your Big Data team. So your team needs to discover and provide useful insights to internal business owners. That requires thinking of your business owners as your customers. As any good retailer will tell you, you need to understand your customers to be successful. That means having regular meetings with them to discuss progress, understand their needs, and get feedback on the results of the generated models. Always ask yourself, “Who in the business will be helped by my analytics?” and “Do they agree you helped them succeed?”
We also see Big Data initiatives fail because the internal customers don’t have confidence in the team and don’t trust the models. Trust starts with being transparent. Be completely open about who is working on what. Provide estimates of realistic finish times. Be clear about trade-offs when determining which models to build so your internal customers make an informed decision that will get to the best end product.
To ensure adoption of a service bureau culture, measure personal performance by business success not just volume or speed as too often happens. Track how many new models were used by internal customers to drive new results. Some companies have developed bonus criteria for members of their Big Data teams based on how quickly and broadly a model was adopted by the internal customers rather than how innovative the model was. This approach prevents the classic war of words: “I built a brilliant model. It is not my fault no one is using it!” It also nips in the bud the problem of building analytics for its own sake rather than for business impact.
Creating a successful analytics team requires both the right people and the right culture. When it comes to Big Data, your teams should spend less time worrying about crunching it and more time focused on serving it.