What HBR’s Winter 2017 OnPoint Tells Us About Business & Data
It was a November evening that I was at the grocery store, waiting in the line to check out when I saw the latest issue of Harvard Business Review’s OnPoint publication. For those that are unaware, OnPoint is a seasonal publication that focuses on a particular topic. I typically pass these by, but this time I shelled out nearly $20 for the magazine as this volume was called The Data Driven Manager.
I bought this for a few reasons. As a data professional I think it’s important to stay up to date as to what is happening in our field. I also think it is a great idea to read articles like the ones found in publications like HBR and MIT Sloane Management Review because they can offer different perspectives on problems; and sometimes those perspectives can challenge what we believe or have seen. However, I mostly bought this copy of OnPoint because I was curious as to what is being talked about at large in academia. And by extension, what other people think is important in the world of data.
So, what did I learn?
This isn’t new news…
The first article (in both publication date and appearance in the OnPoint) was titled Competing on Analytics by Thomas H. Davenport (Mr. Davenport later went on to write a book with the same title). This was published in 2006. If Mr. Davenport is like me, it takes a while of incubation before I put it down to paper, and even longer yet that it gets self published by ways of my blog. My guess is that Mr. Davenport had been thinking and talking about the concepts covered in that article long before that article was printed.
Also of note is that the majority of these articles were originally published in 2013 or before. 1 of the 11 (9%) was published later than 2013, and as a matter of fact, it was in 2017. To me this signifies that we don’t really “get it” yet, and we’re still grasping with what data means in organizations, and what we can do with it.
…And We’re Still Talking about Big Data
Out of the 11 articles (there are additional briefings that I am not counting) 5 of them have big data in the title or in the subtitle. To me, this says that there are still many people that are unsure of what Big Data is, what it means, and how to “do it”.
Additionally, many of these articles are “entry level” conceptual propositions for how to think about big data. By no means do I suggest these articles do not have merit, but I think that this suggests that there are many people who are still trying to figure it out. And honestly, I don’t blame them for confusion. There are a lot of terms and concepts, vendors and concepts, and a many ideas of how to do it correctly. Not only are these concepts difficult to imagine, but they are incredibly hard to pull off in the real world, much less get significant value from them. Gartner believes that these types of projects fail at rates between 60–85%. This is an incredible figure; no wonder we keep talking about big data.
Analytics is still the golden egg
Along a similar train of thought, I decided to see how many articles had direct references to “analytics”. It was a surprising amount — 54% (6 of 11). Many of these articles extolled the virtues of using analytics to bring greater ROI to the business, and how using more data and algorithms will bring about these returns.
Again, I think these view points are wrong; I’ve helped plenty of clients get value through data. I think the word of caution I would give it to expect these changes and returns to be 1) linear, and 2) readily apparent. All too often the road to a data driven organization gets bumpy and the path is abandonded. This may be why I (and my colleagues at Red Pill Analytics) recommend clients to start small and grow into larger implementations and systems. And always remember: data can enhance your organization, but it will not save it.
More food for thought
There were three categories of articles: Strategy, Execution and Talent. These are all important aspects to working in a highly technical field such as data. One thing I will note about the Strategy articles is that they seemed mostly focused on greenfield strategies. Additionally, the Execution and Talentarticles were devoid of horror stories of how to claw out of the hole of high turnover, successive project failures, and botched implementations. I think these stories are perhaps the most helpful and need to be the most discussed; we do not learn most from our successes, but rather from the failures that pave the way to these glories.
A few last words
If you are unfamiliar with what Big Data or Advanced Analytics is, I would recommend getting a copy of the latest HBR OnPoint. It could serve as a useful primer as to what is happening, as it seems nothing new has happened in the last 5 years according to the publication list. However, if you are familiar with this world, I say listen to Master Yoda and “Unlearn what you have learned”. You don’t need big data or advanced analytics to be successful. What you do need is to understand what you want to know, why you want to know it, and how you are going to attain that knowledge. I think the reason we are still having the same discussions we were having 10 years ago (or 20 years ago, or…) is because the fundamental issues are the same. Spoiler alert: there is no magic bullet to answer your questions or give your data meaning. Sometimes big data and advanced analytics are the right tools for the job; sometimes they aren’t. Be sure to pick the right tools, and the right team, for the task at hand.
