The other reason why analytics initiatives fail.
There’s been a lot of debate around the reason for why analytics struggle to add value within companies, generating several strategies or “tips” on how to avoid the mistakes when trying to use analytics to solve business problems.
The result is a mythical character called data scientist, that has to evolve constantly to match the expectations of the business side, with attributes, knowledge, and abilities like coding, statistics, visualization skills, communication skills and business savvy. The problem with this is that we got used to these requirements that almost everyone thinks this is the way it should be, but inreality is not. So the next step was to come up with an army of professionals with deeper, but fewer skills, so the data engineer, data analyst, data architect, and analytics or data translator were born (I’m sure there are a lot more, but this looked crazy enough). I find especially intriguing the last one, the analytics translator. There is an article that defines the analytics translator as “neither data architects nor data engineers. They are not even necessarily dedicated analytics professionals, and they don’t possess deep technical expertise in programming or modeling”, my reaction was “Wait, What?!”. Basically, analytics translators are supposed to close the gap between data science and business by connecting analytics insights and business objectives. My opinion is that this is a little extreme, don’t you think?.
So far, I’ve said things that probably everyone has already read before, so, you might be wondering why the title of this article is “The other reason why analytics initiatives fail”. I’m not going to give you new reasons why data scientists need to develop another skill (or superpower at this point) so the business side can understand the value of analytics or data science initiatives. I come from the business side, but years ago I started to develop data science capabilities to leverage my business knowledge for decision making, and in my experience, the other reason why this relationship (data science and business) is not progressing as fast as is supposed to be is because for business executives or decision makers is very hard to change from “business-experience decision making” to “data-driven decision making”. Yes, it’s the lack of interest to really pay attention and try to understand what data scientists are trying to say, only because business experience is “more significant” than any insight obtained through data analysis. I’m not saying that all business executives think the same way, but many of them feel this way. I’ve participated in meetings where the opening line is “Tell me something I don’t know already”, in other words, “my experience is far more valuable than any analysis you are going to show me” like it’s something they have to challenge or be against.
Business experience and analytics capabilities are a winning combination for decision making and therefore value creation. Analytics shouldn’t keep trying to move closer to the business side, it is the business side that should try to move closer to analytics and data science, building capabilities and embracing analytics insights for decision making and value creation. Every C-Level executive should learn at least the basic Machine Learning algorithms and how to apply them in Human Resources, Sales, Operations, Finance or anything of their interest. Also, a little knowledge on Deep Learning can open endless opportunities for anyone with a business background.
My advice to anyone on the business side:
- Pay attention to what the data scientist has to say on meetings, they will tell you things that you probably already know, but now you can back your experience with data. They will also tell you things that you don’t know, dig deeper into that, ask questions, don’t pretend that you already know because you don’t want to look bad in front of others.
- Work together with the data scientist through the analysis, you will develop analytics capabilities faster, but most importantly, you can add the business perspective and get to what really matters faster or even discover key insights along the way.
- Start developing those analytics capabilities now, there are many resources online for beginners to advanced. Coursera, Udemy, DataCamp among others. I recommend Business Science University, it will walk you through an entire data science project from the business perspective and with business objectives all the way.
The same way, every data scientist is expected to have some business skills, everyone in business should be expected to have some data science skills. Hopefully, if you take my advice, we won’t need translators to understand each other in the future.