THE THIRD IN A BLOG SERIES: “CLOSING THE GROWING GAP IN ANALYTICS CAPABILITY AND EFFECTIVE USE”
Business Leaders Bear Some Responsibility for Gap
In the previous blog in this series, I encouraged analytics leaders to accept their obligation to bring down the hype around analytics in order to increase the volume and impact of their delivery. In this article, I am looking across the virtual table to the business leaders who use analytics to make better decisions. And since I think it’s important to be direct here, I will say business leaders need to do better and learn more about analytics techniques. I say this not out of spite, but out of care, inspired by a Dutch phrase that a fellow data professional and my former Dutch culture translator, Martine Huizing-van Etten, shared with me – “zachte heelmeesters maken stinkende wonden” or a “soft doctor makes a stinky wound.” Since I in work analytics, I can understand it if you find my faulting business leaders for part of the analytics and business divide to be wrong or ill-informed or plain garbage, but I hope whether fueled by rage or curiosity you keep reading.
It might be helpful to know that I have run large commercial and product development parts of a global company with direct and accountable impacts on the top and bottom-line growth of the company. And I’m proud of the work me and my colleagues did there. Still if I had known just a small fraction of what I know now about analytics, I would have done a better job. I would have used even more data (especially unstructured); I would have run many more experiments (using many more analytics techniques); and I would have pushed my data and analytics teams further, demanding they tried some of things that were then frontiers and are now more common (ensemble methods, for example).
Check Yourself Before You Wreck Yourself
There are many business leaders who have similar learnings to me, but since change starts from within, I would encourage you to think about yourself. It’s not uncommon to hear business leaders say to analytics professionals, “I don’t care about your algorithm.” It’s said so often and so quickly that it’s taken to be a universal truth, but it’s not so universal. While there is no doubt that the business outcome you’re driving towards should be your focus and a primary concern of your analytics partner, blanket statements that you don’t care about the algorithm is detrimental to your business, your partnership with the analytics team, and yourself. Sometimes the statement is said with so little thought it might well stem from ego or fear. When I was a young analyst working with basic statistical methods, some light predictive models and outputting into Excel, I had several senior leaders refuse to even learn how to use Excel filters. I had to output the results pre-carved based on what they might need. They were effectively too important to learn this new skill. When I hear some leaders discussing that they don’t need to know about analytics I hear echoes of this arrogance. A survey by Splunk found that among executives at large non-digital native firms “92 percent of them are ‘willing’ to learn new data skills, only 57 percent are ‘extremely’ or ‘very’ enthusiastic to work more with data — and half say they’re ‘too old.’” If you’re not too old to make decisions that decide the future of your company, you’re not too old to learn how data can make those decisions better. Fear of failing to grasp new concepts is understandable. It’s, however, just something to get past. And if want to fear something, fear the lack of opportunity that comes with a lack of data skills. After all, one “survey found that well over 80% of executives say that data skills are required for promotion in their companies.” Again, not trying to be unkind, rather living by that Dutch maxim.
There Are a High Volume of Various Ways to Learn about Data at High Velocity (this is a data joke that you can use as an icebreaker if you choose method number 5).
1. Learn or relearn essential bits of knowledge that complement your business skills and support a better analytics process. For example, if you have experience with elements of the scientific method, you can leverage that to ask better questions. Similarly, product development, design thinking, and other frameworks can support analytics projects to deliver more usable outcomes.
2. Learn at a conceptual level some of the most common algorithms and what business processes they tend to work best on. This overview is a fairly light introduction to some of the most common ones with easy to relate to business applications.
3. There are a lot of great MOOC courses to improve your understanding even more, including these. And I personally have completed Eric Siegel’s ML for Everyone so can attest to the quality of that one. The content and his delivery make this course fly by. None of these courses will make you a data scientist, but they will make you a better busines partner and help you make better decisions.
4. With this basic foundation in place, you can learn as you work with your analytics partners day to day. Flipping from an attitude of “I don’t care about your algorithm to, tell me more.” In each analytics project, be inquisitive and ask questions like: “Why did your analytics partners choose the model they chose and what other methods did they consider and why?” Not only will you learn more about how the different analytical models can benefit your business and the types of tradeoffs that come with any given model, but the exchange with your analytics partner will also reveal how well they understand your business and create opportunities for you to deepen that understanding, which will benefit you in the end. Other good questions include: “What data would you have liked to have access to and how would that have made the result of the analytics better?”
5. Take a geek to regular lunches. You can supplement your learning in a more informal way and find someone in the analytics team who you find is good at explaining analytical concepts and have regular lunches. In exchange for their analytics knowledge, you can share your hard-earned business knowledge. Not only do these exchanges support personal development, but they can also lead to real positive outcomes. One retail executive I know credits substantial improvement to their inventory carrying costs to her lunch dates. Through a series of lunches with a talented analyst, the senior executive painted the consequences of too much or too little stock, while learning a bit about the simpler analytics techniques. Meanwhile the analyst began experimenting with a deep neural network for machine learning (DNN), as she learned more and more that the problem centered around what’s known as a loss function and homed in on this. And if you want to know more about that technique you can read this, or take a geek to lunch. They will be over the moon to explain it to you.
Data is at the heart of the transformative change in business today and while analytics teams should be the leaders in this effort, business leaders have a role to play in bridging the gap. Bridges are, after all, built on from both sides.
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.
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