Analysis that goes nowhere. It’s the dirty little secret inside the Big Data craze. And it’s a problem technology can’t solve. These are projects with a strong business case, a good story, and solid analysis that still goes nowhere.
It can be a frustrating paradox. Leaders sign up for the project, but when it comes time to take action, they don't. It’s tempting to think that leaders aren’t being honest when they talk about wanting to be more data driven. But more often than not its not an honesty issue, but rather a leadership issue.
It takes action to generate results from analytics. Decisions alone don’t cut it. The space between decision and results is truly the analytics red zone.
We all know it takes action to generate results from analytics. Decisions alone don’t cut it. Active leadership is required to ensure that the right actions are taken and results are achieved. This space between decision and results is essentially the red zone, the analytics red zone.
I’m cursed with being a Chicago Bears fans. Depressingly, the Bears are terrible at scoring in the red zone. (inside the 20-yard line) It's the same for analytics projects. Far too many die in the analytics red zone. These projects get stuck in the quagmire of inaction and are ultimately declared a failure.
If you want to improve your organization’s chance of pushing through the analytics red zone, you have to play good offense. That includes overcoming the four most common leadership challenges that prevent organizations from getting results from their data projects.
1. A Lack of ‘Edge’ - When Jack Welch was Chairman and CEO of GE he created a 4E leadership model to help him evaluate and drive his leadership team. One of those E’s was called Edge. Leaders with Edge have the courage to make tough yes-or-no decisions, know when to stop assessing, and will make a tough call, even with less than perfect information. A leadership culture that lacks Edge makes getting through the analytic red zone very difficult. These cultures will often use decision stalling tactics like ‘analysis paralysis’ or play ‘hot potato’ to avoid stepping up to take ownership of problems and solutions. In these situations, its best to keep your projects small, pick your projects wisely early on, and coach leaders step by step through implementing the solution. After a few successes you can use these wins to model success for other leaders.
2. Resistance to Change - The very essence of being a data driven leader is change. Trying something different to solve the problem. And where there’s change, there’s resistance. It may take the shape of leaders not believing there’s a real problem, or it's even solvable. Or it may be a passive-aggressive leader that takes small or insignificant actions in an effort to demonstrate they ‘tried’ but it didn’t work. The best tactics for overcoming this type of resistance is sponsorship and transparency. You need a leader who ‘gets it’ and has enough political clout to help move things forward. It also requires transparency related to follow through. Measuring, evaluating, and adjusting the actions taken creates a learning cycle. This will bubble up the real change barriers so they can be discussed and addressed.
3. Zero Risk Leadership - Zero risk leaders love the comfort of the way things are. These type of leaders want your analysis to prove beyond a doubt both the problem and solution before they'll entertain any path forward. This usually happens in cultures that are intolerant of mistakes. Leaders don’t want to be in a position where they could be perceived as being wrong or weak. The zero risk mentality is diametrically opposed to effective analytics. Analytics should be a learning process for everyone. Whether an action works or not, you always learn something. You can break this cycle by utilizing prototypes and pilots in analytics roadmap. These mini-projects can help develop more realistic leadership expectations about risk and reinforce the idea that even though success isn’t certain, you can’t win if you don’t play.
4. No Execution Mindset - Results come from execution, and analytics projects are no exception. Unfortunately, too many leaders believe their job is done once ‘the decision’ is made. This is very common in command and control cultures. Analytic projects without active business leadership will wither and die. This challenge often leaves senior leaders wondering why their decision didn’t net anything. It also reinforces the zero risk mentality. Everyone has a role to play in an execution mindset. There should be a systematic process that facilitates rigorous discussion about the hows and whats, with tenacious follow through, and accountability. An analytic team has an important role to play in measuring results, facilitating decision making, and enabling follow through. For analytics leaders, there should be as much work to do in supporting execution as there is in analyzing the problem. Some times even more.
If you really want to improve your organization results from it's data projects, then do what they do in sports, measure it. See what percentage of your analytic projects make it through the red zone and net meaningful results for your business. You might be surprised by what you learn.
Latest posts by Phil Kelly (see all)
- Five Ideas to Improve your Analytics Returns - October 24, 2018
- 5 Reasons to Take a Portfolio Approach to Analytics - September 27, 2018
- How Small Provider Networks Can Be Data-Driven without Breaking the Bank - May 11, 2018