Our survey research and case studies repeatedly show successful analytics implementations, and subsequent analytics use, rely on factors outside the reach of technology. Culture, the prevalence of a performance management methodology, and non-executive involvement in driving analytics use all play a role in driving successful analytics use. One important factor to success includes a higher degree of training on the meaning of data. This means driving consensus, not just during a data governance project, but continually as people make decisions from the data.
One of the most successful implementations of a performance management methodology I have ever personally heard about was when I interviewed the CFO at a $20 billion global services organization that had implemented a formal performance management methodology to ensure strong long-term financial growth. The company relies on the office of the CFO to coordinate and manage KPIs and performance guidance for its regional, line-of-business, and departmental managers. Business managers are continuously informed by finance about the KPIs for which they are responsible, and at the same time, managerial input is fed back to finance to inform the operational plans.
As part of managing enterprise performance, the finance department works to forecast the financial performance required to meet goals and then delivers and monitors the performance indicators that guide managers. During shorter time cycles, managers have the flexibility to adjust operational policies to ensure they meet shorter-term goals. The finance department then analyzes performance from all business groups and provides a presentation back to managers on a monthly basis to help keep them informed on the meaning of the data and to suggest actions they can take to improve performance where needed.
However, this closed-loop methodology goes beyond simply monitoring performance for managers and providing incentives to meet goals. It provides a learning environment where processes, policies, and decisions are continuously improved. "Although most of our KPIs don't change from year to year, we constantly evaluate the validity and quality of each KPI," said the CFO. "Furthermore, we work with a team of decision makers from lines of business and finance to determine what performance levels should be increased or decreased for the coming year to channel management focus."
Yes, technology is pivotal to making better decisions, but with some decisions there's a lot to be said for a little hand-holding by the CFO. This can be especially true for strategic decision as shown in the figure below depicting the IDC decision management model. At IDC, we use this model to describe the characteristics of decisions that need to be made within a corporation. It helps to illustrate the different types of technologies that are needed to support them. For example, strategic decisions are highly collaborative, made infrequently and have a higher degree of risk associated with them when compared to a single transactional decision. If organizations support decision makers with relevant facts and hope they come to the best decision they run the risk of letting biases creep into the decision-making process. The CFO and the finance organization have a pivotal, collaborative role to play in guiding organizational decisions.