Change Management, A Key To Big Data Success
While businesses are getting excited about big data and advanced analytics, in most companies, data-analytics success has been limited to a few tests or to narrow slices of the business. Very few have achieved what we would call “big impact through big data,” or impact at scale. While new tools and improved approaches such as visualization, machine learning and advanced analytics software are offering ways to deal with the challenge of achieving scale, they are not sufficient, believes David Court, Director McKinsey in his latest article, where he states that for big data success, businsses should be beyond new tools, and focus on adapting the organization, which includes more focus, more job redefinition, and more cultural change.
Focus on change management
Democratization and the power of new tools can help overcome frontline doubts and unfamiliarity with analytics. However, in addition to gaining confidence, managers need to change their way of making decisions to take advantage of analytics. This is the heart of the change-management challenge—it is not easy, and it takes time. The implication is that to achieve scale, paradoxically, you need to focus.
“Trying to orchestrate change in all of a company’s daily decision-making and operating approaches is too overwhelming to be practical. In our experience, though, it’s possible to drive adoption and behavioral change across the full enterprise in focused areas such as pricing, inventory allocation, or credit management. Better to pursue scale that’s achievable than to overreach and be disappointed or to scatter pilots all over the organization,” states Court.
He believes leaders should ask themselves which functions or departments would benefit most from analytics and deploy a combination of new targeted solutions, visualization tools, and change management and training in those few areas. One telecommunications company, for example, focused on applying analytics to improve customer-churn management, which held the potential for a big bottom-line impact. That required the company to partner with a leading data-storage and analytics player to identify (in near real time) customers who would churn. Once the models were developed, a frontline transformation effort was launched to drive adoption of the tools. Moreover, customer-service workflows were redesigned, user-friendly frontline apps were deployed, and customer-service agents received training for all of the new tools.
Automating part of the jobs of employees means making a permanent change in their roles and responsibilities. If you automate pricing, for instance, it is hard to hold the affected manager solely responsible for the profit and loss of the business going forward, since a key part of the profit formula is now made by a machine. As managerial responsibilities evolve or are eliminated altogether, organizations will have to adapt by redefining roles to best leverage and support the ongoing development of these technologies, says Court.
At insurance companies, claims managers no longer process all claims; instead, they focus on the exceptional ones, with the highest level of complexity or the most severe property damage. Again, focus is required, since job redesign is time consuming. And it can be taken on only if the automated tools and new roles have been developed and tested to meet whatever surprises our volatile world throws at them.
Build a foundation of analytics in your culture
People have been talking about data-driven cultures for a long time, but what it takes to create one is changing as a result of the new tools available. Companies have a wider set of options to spur analytics engagement among critical employees. A leading financial-services firm, for example, began by developing competitions that rewarded and recognized those teams that could generate powerful insights through analytics. Second, it established training boot camps where end users would learn how to use self-service tools. Third, it created a community of power users to support end-users in their analyses and to validate findings. Finally, the company established a communications program to share the excitement through analytics meet-ups, leadership communications, and newsletters (which were critical to maintaining long-term support for the program). Creative adaptations like these will help companies to move beyond the hope that “we are going to be a big data company” and to root cultural change in realistic action, says Court.
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