Many mid-market executives worry about being behind the trend when it comes to data analytics. In fact, it has great potential to improve decision making and therefore performance, profitability and value. We also see a lot of excitement around data analytics, making it easy to jump without a deliberate and flexible approach.

Data analysis is the transformation of information into practical knowledge that can lead to competitive advantage. There are three types.

Descriptive analysis answers questions like, “What happened and why?”

Predictive analytics takes a step into the future, answering

“What if …?” Prescriptive analytics goes one step further and targets specific actions to take based on the outcome you are looking for. Google Maps is a good example here. Enter your destination and, based on your current location, traffic, available road network, and previous preferences.

All three types of analysis reduce the personal biases and preferences that we so often use to make decisions. As good as our instincts are, analytics reduces uncertainty. They can also lead to increased efficiency and revenue. Businesses use analytics to experiment with real-time pricing based on demand, inventory, and data on how much different customers will pay.

Manufacturers use data analytics for preventative maintenance to reduce downtime and avoid shortages. Others use it to understand absenteeism patterns and predict plant changes that may be insufficient.

Align questions with business strategy

Any project or initiative must align with your profitable growth strategy. This ensures that the questions you ask and the answers you discover directly contribute to gaining a competitive advantage or solving a particular problem. What are your key performance indicators (KPIs)? When investing in a data analytics program, you need a systematic way to measure progress against your business goals.

Organizational infrastructure (people and processes) is the biggest challenge for many companies. The results of your data analysis projects will drive a change in the way people work. Old spreadsheets and processes disappear and this can make people uncomfortable. Leadership commitment and participation are critical to smooth transitions.

The cost of undertaking data analytics initiatives is often a concern, but the investment does not have to be prohibitive. The amount to invest should be based on the type of business.

Reduce focus to start

We recommend that companies start with a pilot test. Focus on one aspect of the business and identify specific questions to answer. To find that suitable first project, look at your internal and external stakeholders: shareholders, customers, staff, suppliers, and others.

Develop a plan about what information you will retrieve to answer the question and how you will apply the results. The pilot you carry out must be important to a key group and will allow you to move the needle on the KPIs, in alignment with your strategy.

Formalize your program by involving the right people, including leadership. Your IT staff and many other functional areas should be involved, but not lead the charge. Data analytics is not “an IT thing.” Create a litter box, make mistakes, learn, and then expand the scope to tackle more.