How can a business intelligence and analytics body prove its legitimacy to the world?_o
I have used the word “legitimacy” for two reasons. Adjective – as a child born to parents because business analytics, intelligence and functions are related in that manner, and verb – to make lawful or justify (its presence).
In most traditional organizations (non-tech, non-startup) today, adoption of business intelligence and analytics as well as data science is a big elephant in the room and we need to talk about it.
The common gap that I see in many organizations is the absence of a translator. It could either be a data translator or an analytics translator – we need both. You may think – “Why is this a gap in the first place?” or “I believe we do have translators.” If you didn’t think either, then you acknowledge the gap and can read on.
Talking about the role of such a translator, Bernard Marr (author of “Data Strategy”) thinks that[1] “A data translator is a conduit between data scientists and executive decision-makers. They are specifically skilled at understanding the business needs of an organization and are data savvy enough to be able to talk tech and distil it to others in the organization in an easy-to-understand manner.”
The position needs to be created with a strong intention to drive adoption and not to fit someone into a role. That said, organizations shouldn’t just blindly hire a translator, they also need to understand the skillsets required to be one:
- Understanding what business intelligence and analytics means and what it can do – This doesn’t require someone to have done hands-on analytics for years together. This needs awareness.
- Ability to think using first principles – This will allow the translator to question the status quo (the way the business/end user works today or the beliefs they have) and engage with them to come up with innovative solutions
- A strong passion to unlearn & learn (as a repeatable loop) and share the learnings – A lot of what this person does will change over time and the translator needs to embrace this chaos and complexity but with a mental model in mind
- A person driven by empathy – This is critical because only if you’re able to put yourselves into the other person’s shoes will you truly understand the pain points/challenges and design a solution that gets accepted and works.
To support this translator, we also need an equally boosted team. Tim Urban’s[2] mind (with a few different mental models) is an epitome of the kind of people we need in this team. In the article, He speaks about the different types of complexities one should embrace:
- Complexity as gathering – Having a researcher’s mindset and gather all the relevant content and material out there
- Complexity as dusting – Having an archeologist’s mindset to dig out and unravel something
- Complexity as pattern matching – This is a mix of the above two where we need to understand the interconnectedness of the unraveled idea with a more complex string of ideas
You would have realized by now that finding a perfect fit for the translator role is hard. We can probably start by looking at a few key characteristics that will help us identify the right person for the role.
What are sales people good at (Apart from selling of course)? They are good at articulation through story telling. Articulation needs to be done by being in a specific limbo – in a way a layman (or layperson to be gender-neutral) would understand, and this can only be done if we understand what it is to be a layperson. One thing that the translator needs to keep in mind is to not fake it. If we cannot find these people, we can create them. A HBR research here[3] talks about how learning a little about something makes us overconfident.
Being an analytics/data translator doesn’t mean that we need to apply AI or ML (or any buzzwords) to everything you’re working on. A HBR article here[4] talks about how problems can be broken down into automation problems and learning problems. There are certain types of straightforward problems for which you can automate without a learning approach. For all the other problems where automation requires learning, we need to delve into machine learning (or deep learning or some other kind of learning that will come up in the future). A quick one on Machine learning – At its core, it is a set of statistical methods meant to find patterns of predictability in data sets. These methods are great at determining how certain features of the data are related to the outcomes you are interested in. Some business intelligence and analytics tools are coming up with machine learning features to support data science consulting.
The key to surviving in a world of automation and learning is to identify newer sources of value creation. Like it is mentioned here[5], any organization or even parts of organization will feel threatened by the onset of robots (or automation or AI). One of main questions that we will need to sit together and answer is “How can we use technology to extend the skills of humans in ways that aren’t immediately clear, but will seem obvious a decade from now?”
Take this specific story for example: Lynda Chin, who co-developed the Oncology Expert Advisor powered by IBM’s Watson, believes that automating cognitive tasks in medicine can help physicians focus more on patients. “Instead of spending 12 minutes searching for information and three with the patient, imagine the doctor getting prepared in three minutes and spending 12 with the patient,” she says. “This will change how doctors will interact with patients.” she continues. “When doctors have the world’s medical knowledge at their fingertips, they can devote more of their mental energy to understanding the patient as a person, not just a medical diagnosis. This will help them take lifestyle, family situation and other factors into account when prescribing care.”
Here, the value is created on a very different level. Instead of spending time on gathering information, what if we could spend time on giving it a human touch and using technology to answer/validate specific questions/hypothesis. All that said, automation will also ultimately remove jobs off people – leaders will need to handle that separately by either helping them upskill themselves or using them in other parts of the organization.
Elon Musk has said the machine-over-mankind was humanity’s “biggest existential threat.” In one of the projects I was working on, the end users felt a threat to their existence when we put an algorithm in front of them. Whatever said and done about the threat automation poses, it all lies in the way people think about it (Read more here[6]). Work that requires a high degree of imagination, creative analysis, and strategic thinking is harder to automate. As leaders we need to ensure that our people are taken care of and enough opportunities are given for them to upskill themselves and become more resilient. Something to ponder – a quote from an actual study: “While ATMs took over a lot of the tasks these tellers were doing, it gave existing workers the opportunity to upskill and sell a wider range of financial services.”
There is an undeniable need to have a data sciences group within data analytics companies but does the group have all the support it needs? The write of the article poses 4 key questions to organizations who think they need to hire a data scientists. While the focus of the article is in startups or traditional organizations, the questions are good for any organization to re-think what they’re doing: How much data do you have?
- Do you have established key performance indicators (KPIs) and regular business intelligence reporting?
- What do you imagine this data scientist will do once hired?
- What support networks are available to your data scientist(s)?
There is lots of talk around work-life harmony vs. work-life balance – people believe in whatever works for them. What we need to acknowledge is that we can learn from the way we “manage” either of them (Work or life) and apply it on the other interchangeably. This article[8] talks about a mental model to acknowledge and learn from failures in terms of 3 stages: Stage 1 (Failure of tactics – the “how” mistakes), Stage 2 (Failure of strategy – the “what” mistakes) and Stage 3 (Failure of vision – the “why” mistakes). The key to re-dreaming or re-defining is to figure out the right tactics and strategy — clear the dust from the air — and you’ll find that the vision often reveals itself.
The words strategy, execution, road-map, etc. have been used very frequently by everyone in the recent past but not everyone knows what this really means. As leaders, you often hear from your first line reports that “I’m spending all my time managing tactical problems and no time to think long-term strategic things” and if someone tells them “Ok, I hear you. If I clear your calendar for an entire day to do more strategic work, what will you actually do?” Most people will not have an answer to that and this article[9] probes this a bit more. Quoting a very powerful statement from the article – Some people assume that thinking strategically is a function of thinking up “big thoughts” or reading scholarly research on business trends. Others assume that watching TED talks or lectures by futurists will help them think more strategically. Harvard Business School professor David Collis says, “It’s a dirty little secret: Most executives cannot articulate the objective, scope, and advantage of their business in a simple statement. If they can’t, neither can anyone else.” And it’s true, if the executives/leaders can’t do it, no one can do it. Every leader needs to have strategic thinking as part of their jobs.
If each person in our team is able to inculcate one/few of the characteristics that we expect out of our translator then we would have uplifted our team to a newer level.
Let’s think a bit more about teams and organizations in greater terms…
All organizations will need to do a re-dreaming or reinventing exercise and through the process answer these questions – Who are we? What is our purpose? How do we deliver value (to our organization and in turn our customers)? What business are we really in?
This[10] is one example of a Finland based paper company that reinvented itself to do bigger things by asking a lot of questions to itself (more on the questioning to follow). The story of Netflix is another great one to learn from. From being a VHS rental to a DVD rental to online streaming to now developing their own content, Netflix has shown that it is a more resilient organization that is fit to survive any challenges. Amazon and Netflix have become healthy competitors and have found a way of working with each other. (Netflix uses Amazon’s servers to host their content, read here[11])
The first step in pursuing this for any organization or team or individual is to develop the skin for challenging the existing convention and biases and thereby questioning the status quo. Many people call this first principles thinking and I can’t emphasize the power of such thinking enough. If we can somehow inculcate this in our teams, any problem (even a muddy/fuzzy problem) can be solved using a structured thinking approach. (Read more here[12]). Quoting from the article – “While it’s easier on your brain to reason by analogy, you’re more likely to come up with better answers when you reason by first principles. This is what makes it one of the best sources of creative thinking. Thinking in first principles allows you to adapt to a changing environment, deal with reality, and seize opportunities that others can’t see.” This is the true meaning of becoming future-proof.
Once the power of first principles has been imbibed, the next step for organizations or teams would be to ask the question “What business are we really in?”[13] This question challenges the basic assumptions that we have about our own organization. I found this line very powerful (As quoted in the article) – “To question the reason for one’s existence is a sign of intelligence and maturity. If leaders of all levels shy away from inquiring into their organization’s vision and mission they condemn their business to short-terminism, leaving it to the whim of markets.”
Any new idea will face challenges. You will face them either in your own minds (and hence need to sell the idea to yourself), or you will face challenges from your leaders and team members who will also have to be sold on the idea. Satya Nadella talks about his journey[14] to rediscover (Yes! Re-discover, Re-invent, Re-dream – Different words leading to the same cause “change”) Microsoft in his book “Hit Refresh: The Quest to Rediscover Microsoft’s Soul and Imagine a Better Future for Everyone”. I haven’t read the book but in this podcast with McKinsey he talks about turning artificial intelligence (which is one of his core focus areas for Microsoft) into value among other things (like culture & innovation, metrics & compensation, empathy & purpose). Following what the industry is doing is not enough, we need to focus on things that add real value and articulate that to our wider organization. For podcast lovers, please listen to this episode here[15]. This episode is a pleasant one to hear with so many ideas to ponder upon. Another article here[16] from McKinsey focuses on linking talent to value.
Making a commitment to reinvention before the need is glaringly obvious doesn’t come naturally. Things often look rosiest just before a company heads into decline: Revenues from the current business model are surging, profits are robust, and the company stock commands a hefty premium. But that’s exactly when managers need to act.
To make reinvention possible, companies must supplement their traditional approaches with a parallel strategy process that brings the edges of the market and the edges of the organization to the center. In this “edge-centric” approach, strategy making becomes a permanent activity without permanent structures or processes. An edge-centric strategy allows companies to continually scan the periphery of the market for untapped customer needs or unsolved problems.
Lots of organizations still consider the analytics consulting team and business/end user team as two different teams. When this is the case, there is enough a gap created for either team to not trust the other. Trust (purely in this context) comes when both teams have enough skin in the game. While it is difficult to create “people” who can master both the business side and the analytics side, an easier path would be to create an environment within our organization to promote it. We all know the reasons why exchange programs are common in the academic world. It presents a multitude of opportunities for the learner. Why can’t we have an exchange program between our analytics teams and our business teams? I’ll leave you with this thought.