Here’s Why Analytics Strategies Fail, and Ideas on How to Succeed
04/09/2019 by Tricia Aanderud Modernization - Analytics
When introducing an analytics strategy to your organization, it may fail for reasons that the leaders do not understand. Change is difficult for organizations. It is scary to move away from an approach that got you to where you are or has always been successful in the past. The reason for failure may merely be the culture resisting the strategy. Other times it may just be fear of change or admitting you don’t know.
The leadership team does set the organizational direction, but they also may inadvertently be rewarding a culture with opposing values. If everyone agreed on an analytics strategy in the most recent meeting, you might think, what else is there …?
Imagine being tasked with transforming a vast conglomerate into a data-driven powerhouse. A colleague of mine had just that responsibility, and as he described the situation he endured to me, he shook his head noting the entire experience was full of political landmines. Sometimes meetings were nothing short of trench warfare. The wounds were emotional, and you could see they left marks on him. He said it was the most stressful point in his career because he knew this program would ensure the organization’s success for years to come. Three members of the leadership team did not fully support the program and worked to undermine him throughout the implementation. They told the CEO it was a great idea while passively resisting the change — this went on for over a year.
The eventual resolution? The CEO was a firm believer in statistics and wouldn’t let the program fail. After months of no results, the secret naysayers either left the company or were asked to leave. The analytics program could move forward without resistance, and it is part of this Fortune 500 company’s success strategy today.
The senior leaders must set the strategy and keep the organization inside the proverbial rails. Otherwise, it is too easy for the culture to resist change and drag the company in the wrong direction.
It’s not always clear why resistance occurs. In the above scenario, it could have been a burdened culture pushing back. Perhaps the naysaying leaders had spent years building an empire and felt the change threatened their livelihood. Maybe the leaders trusted their actual life experience more than the data. Perhaps they thought that the money being spent on the analytics programs would be better spent on other equipment or processes.
When starting an analytics strategy, it is important to remember that it can be expensive and time-consuming at the outset. While organizations are encouraged to seek low-hanging fruit projects that can generate quick wins, the truth is that it may be months before any actionable results are available from a longer-term analytics strategy. During this period, the management team is willfully diverting organization resources from other high-profile projects. If funds are tight, then this situation alone may cause friction. It may not be clear to everyone how the change is expected to help.
When nearly 45% of workers generally prefer the status quo over innovation, how do you encourage an organization to move forward? If the workers are not engaged or see the program as merely just the latest management trend, it may be tricky to convince them. Larger organizations may have a culture that is slow to change due to its size or outside forces.
One customer of ours, who handled the change well, spent a year talking about an approved analytics tool before moving forward. The employees had time to consider the change and to understand the new skill sets needed. Once the entire team embraced the change, the organization moved forward swiftly to convert existing data and reports into the new tool. In the end, the corporation is more successful, and the employees are still in alignment with the corporate strategy.
Resistance to analytics may come from a poor understanding of statistics or how data-driven processes can help organizations. Sometimes leaders don’t like to admit a lack of knowledge. Statistics enjoy a reputation for being difficult to understand and often are accused of being damned lies.
Successful organizations use education and coaching to help the leadership team. Their leaders may have last thought about statistics during freshman year at college, so it is helpful to conduct Statistics 101 training to level set within the organization and across the leadership team. It strengthens the team by allowing them to discuss what analytics means to the organization. It also reduces their fears about the change.
After a collective understanding is achieved, an analytic advocate or analytic coach spends time with the management team. Typically this is person is a third-party who does not have any loyalties other than ensuring the team can effectively use data for decision making.
The person attends meetings to suggest and review the data analysis needed. The advocate coaches the team on what questions to ask about the data, what other data would be helpful to consider and helping to identify any flaws in the analysis. This method allows leaders to feel confident in their new skill set.
If using data to support decisions is a foreign concept to the organization, it’s a smart idea to ensure the managers and workers have similar training. This training may involve everything from basic data literacy to selecting the right data for management presentations. However, it cannot stop at the training; the leaders must then ask for the right data to support conclusions that will be used for key decisions across the business.
These methods make it easier to sell the idea and keep the organization’s analytic strategy moving forward.
When introducing a data and analytics strategy, the organization must plan carefully. Management must fully explain the change including the expected benefits. Every organization is different. The culture at a smaller company may be nimbler and more adaptable to change while larger organizations need years to change. Regardless of size, culture, and the obstacles ahead, management must be prepared to deal with resistance to help the organization move toward data-driven decision making.