Gain Support for Predictive Analytics
The decisions you make in business may never be perfectly right, but, you can strive to become less wrong. Predictive analytics provides decision makers with a system to continually improve decision-making, while eliminating some of the inefficiencies of non-analytical trial and error.
However, the ability of predictive analytics to systemize an organization’s cumulative knowledge can be threatening to experts who value their accumulated knowledge over continual learning. When beginning a predictive analytics initiative, the most vital key for gaining political support is to maintain focus on the business problem, and never the technology. By focusing on the specific problem, you will deploy predictive analytics only if it is the right tool for the job. This ensures that the initiative has a greater likelihood of success, has the support of key internal stakeholders, and because predictive capabilities are leveled at a specific target the initiative gains executive buy-in. “A business problem exists, and this is how we are going to solve it.”
While predictive analytics may be ‘the best man for the job’, expect there to be resistance to implementation. Often, this comes from individuals who rely on their accumulated knowledge as a competitive advantage against their team-mates. In light of this situation, quite possibly the most effective method of gaining support, is to focus predictive analytics on a specific, defined business problem. Your initiative must be dialed in on solving vital, business critical issues. This way, dissenters to implementation will be seen in the light of hindering the future success of the organization.
Consider the following case study. One of our clients had a division that is responsible for sourcing materials for production. They had a group of commodity traders that were responsible for sourcing materials at the best price possible. While their expert traders had a great track record of forecasting the market, their best traders were nearing retirement. Also, while the company had made significant investments into business intelligence, the amount of data required to make an informed trade had been growing exponentially for the last ten years. The future of the organization required developing a system that made learning from the cumulative knowledge of the organization easier. However, trying to come in with predictive analytics was politically challenging, as it could be threat to both business intelligence and the company’s best commodity traders. To overcome that perceived threat, we had to focus on the business problem and make a case that eliminating the problem was essential to the continued success of the organization.
Our client had to systematize the knowledge of those traders nearing retirement, and also develop a solution to find valuable patterns in the increasing flow of data. The solution that we developed was simple. We built a model that forecasted the direction of commodity prices. Before our model, traders and business intelligence had been spending a significant amount of time determining the direction of the market. Now, by leveraging predictive analytics, traders and business intelligence have moved up the value chain. Instead of spending the majority of their time trying to determine the direction of the markets, they spend the majority of their time quantifying the direction of the change. This resulted in the improved performance and value of both business intelligence and the commodity traders.
In conclusion, the key to gaining political support is to define the business problem in the context of its importance to the continued success of the organization. If solving the business problem is not essential to the success of the organization, it may not be worth addressing. If it truly is important, and predictive analytics is the best candidate for the job; internal opposition will be seen as coming from selfish protectionists who threaten the continued success of the organization.