Think First, and Second
Many analytics organizations (and by default, their leaders) have developed a set of behaviors which treat analytics solutions as a project or a model. From our research, our engagements with clients, as well as hundreds of Analytics Maturity Assessments (not to mention our discussions with thought leaders like Brian T. O’Neill) I am convinced that data analytics solutions that are developed with a product approach are more successful. It’s one of the 5 Areas for Analytics Leaders to Obsess About and Brian and I covered it on webinar recently. And while a fully planned and ruthlessly implemented product approach across the enterprise delivers the max value, you can get a lot of the value from a product approach by just starting to adopt product thinking. To change any behavior, you first need to change how you think. In fact, changing how you think is so essential, so fundamental, that psychologically speaking, there even two types of thinking (precontemplation and contemplation) that precede any new behavioral action.
User, Utility & Unrelenting
As a leader you can start your organization’s behavioral change by changing your and your team’s own thinking first, and without loud proclamations that shout, “now we will develop analytics products!” That’s just sowing confusion. Focus on revising your thinking across three broad areas.
Sharpen your focus on the user, which in most cases is a functional business leader looking to improve their part of the business. While nearly 100% of analytics leaders say they do this, fewer than 60% of business leaders agree. If you have ever had your team bemoan how the “business doesn’t get it” some part of your org is in that 40% and you want no part of your org in that 40%. Less seasoned analytics folks often feel pressured to completely understand the problem that’s being explained to them, quickly. They want to appear smart, to show they understand the business and they want the functional leader to trust them. Rushing to conclusions, however, is a big mistake. Help model a better way of thinking and put a focus on curious over smart. Why is the problem a problem? How do you know it’s a problem? Why is the problem more of a problem than other problems? How do they solve the problem now? Why does that solution not work well? Have they seen a solution to a similar problem that’s attractive and why? Even seemingly fluffy questions like, how would life be better if you solved this problem? Asking too many questions is unlikely, asking too few is common. Even with as many questions as an inquisitive toddler, stay open to the possibility that you still don’t perfectly understand the problem.
Validate the utility first. Don’t think about building a great product, think about building a good product, with the potential to be great. Good products solve an important problem better than any known alternative. Great products do so in a way that’s enjoyable (easy, fast, etc.). So, first and foremost, ‘good’ means they provide actionable insights with a degree of certainty that compels the user to act. Of course, they should be easy to use and fast, more specifically, they should be easier to use and faster than the current alternative. Too often teams spend too much of their limited time and resources aiming for a perfect looking, ‘game changing’ tool, compared to the time they spend making sure they are solving the right problem correctly. Remember, you think you understand the problem, but you remain open to the possibility you don’t.
Be unrelenting on two fronts. Have you really understood the problem and is the product that solved that problem really the best solution? Our experience shows that mostly the solutions are well done, but where there are failures or lack of any real business benefit, it’s because there was not enough understanding of the problem. Be open to this possibility. Be humble that you and your team might have not understood the problem well enough and be empathetic that the it’s tough for the business leader to clearly explain the problem. One of the best parts of product thinking is that it removes the temptation to put the business leader on the hook to perfectly express their problem and your team to perfectly understand it and deliver a perfect solution. There is no such thing as a perfect product (Go ahead, try to think of one. You can’t). Developing both a clearer problem statement and a more effective solution is always on the table.
Even though IIA research and experience points to the value of product thinking, it’s a developing concept in analytics. The three points above are intended to get you started not to be comprehensive. There’s a lot of discussion out there about the differences in product thinking, product management and design thinking, among others. Some of the discussion is nonsense, including semantic debates and positing that one role (Product Manager or UX Designer or Data Scientist, etc.), is the only role that ensures success. If you want to expand your understanding, be careful out there. Look to experts like Brian, who is mentioned above, and to sharp insights from more in-depth examinations like this one (A Design Thinking Mindset for Data Science). Mostly though, I restate a simple concept from Apple, that the folks who do something more impactful think different.
Drew has close to 20 years of experience, having worked on both the business side of analytics, leveraging insights for business performance, and on the delivery side of analytics driving the use of enterprise analytics. As the lead of Analytics Leadership Consortium, Drew drives engagement with analytics executives and top analytics practitioners in the IIA Community to help them lead their firm’s journey to analytics excellence.
Before joining the IIA, he led the Enterprise Data Analytics and Governance function at IKEA’s global headquarters in Europe. He leveraged analytics in various leadership roles across the IKEA value chain in both the United States and Europe. He received his MBA from Penn State and his undergraduate degree from Boston University.
About The Analytics Leadership Consortium (ALC)
The Analytics Leadership Consortium (ALC) is a closed network of analytics executives from diverse industries who meet to share and discuss real world best practices, as well as discover and develop analytics innovation, all for the purpose of improving the analytics maturity of their firms and securing the business impact they deliver.
You can view more posts by Drew here.