flywheel effect analytics

Analytics and “The Flywheel Effect”

If you are a doubter on the power of leveraging data and analytics, you belong to an increasingly small group of non-believers. While we are all starting to agree on the power of analytics, actually implementing a data and analytics program is more challenging than it may seem. From the importance of having a clearly defined data strategy to taking the right steps to develop a data-driven culture, they are many nuances to getting started with analytics.

One of the best suggestions I have when starting an analytics program is to identify quick wins. Identifying these “low hanging fruit” is a highly recommended way to build some early momentum. For those of you familiar with Jim Collins’ book Good to Great, you may remember his discussion of “The Flywheel Effect”. In this post, I’ll quickly highlight how “The Flywheel Effect” applies to analytics and how some early wins can create long-term success.

What is “The Flywheel Effect”

Below is Jim Collins’ description of “The Flywheel Effect”:

Now picture a huge, heavy flywheel. It’s a massive, metal disk mounted horizontally on an axle. It’s about 100 feet in diameter, 10 feet thick, and it weighs about 25 tons. That flywheel is your company. Your job is to get that flywheel to move as fast as possible, because momentum—mass times velocity—is what will generate superior economic results over time.

Right now, the flywheel is at a standstill. To get it moving, you make a tremendous effort. You push with all your might, and finally you get the flywheel to inch forward. After two or three days of sustained effort, you get the flywheel to complete one entire turn. You keep pushing, and the flywheel begins to move a bit faster. It takes a lot of work, but at last the flywheel makes a second rotation. You keep pushing steadily. It makes three turns, four turns, five, six. With each turn, it moves faster, and then—at some point, you can’’t say exactly when—you break through. The momentum of the heavy wheel kicks in your favor. It spins faster and faster, with its own weight propelling it. You aren’t pushing any harder, but the flywheel is accelerating, its momentum building, its speed increasing. – Jim Collins from his blog

Like a flywheel, starting an analytics program from scratch can be hard work. Like trying to go uphill on a bike in sixth gear, it can seem like no matter how hard you pedal you aren’t going anywhere. But, once you start to get the wheels turning, it becomes easier to go faster and faster and faster. Eventually, it becomes so easy to keep momentum that you feel as if the bike is pedaling itself! This is Jim Collins’ “Flywheel Effect”.

Quick, Early Wins are Essential

Starting a data program certainly has the feel of Collins’ flywheel. In our world, getting some early, quick and (ideally) easy wins is a great way to start turning the wheel. For organizations with a relatively low business intelligence maturity, quick wins are plentiful. Identifying and successfully achieving those early wins is crucial to ensure buy-in and begin to foster a data-driven culture.

How Can I Identify the “Low Hanging Fruit”?

The next logical question, of course, is how do I identify these quick wins? In my experiences, the easiest way to identify a quick win is to simply ask analysts and operational staff what types of reports they produce on a recurring basis. When performed organizationally, I call this a “report inventory”. The average $1 billion financial institution I work with has at least 5,000 hours that could be saved by automating recurring reporting processes. This adds up to hundreds of thousands of dollars in FTE time cost that could be reduced. Imagine how much an employee would be raving about your BI team if you could give them half of their week back! (And imagine what that employee could do with the ability to be that much more productive and efficient?!).

These reports can be automated using tools like SSIS, SSRS, or other visualization and BI tools your organization may own (like InformationBuilders, Tableau, etc.). Not only will these quick wins start to build momentum for your analytics program (turning the flywheel), you will also make some colleagues of yours quite happy!


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2 Comments

  1. Pingback: What's Holding You Back from Being Data-Driven? - The Knowlton Group

  2. Pingback: Podcast #8 - Analytics and "The Flywheel Effect" - The Knowlton Group

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