Finding The Balance In Data Analytics
Recently, the Editor of Management Today, Adam Gale, has published a brilliant article — “The dangers of data analytics”. I’ve been involved with Artificial Intelligence for over 30 years and wrote extensively about the impact of AI and all forms of its analytics on decision making in business. It fascinates me — plain and simple. It always did. And I’m convinced that the best way I can help companies to face such dangers — is for me to join their AI Advisory Boards (ABs).
As one of the most experienced BusinessAI™ veterans on the planet w/ 30-yr hands-on AI expertise I show CEOs the importance of striking the balance between relying on a Silicon Brain vs. their own experiences that are stored in a hundred of billions of individual’s neurons and synapses. And 30 years later, I still see the same analytical challenges in front of C-level executives — regardless of how deep is the learning they embraced. The fundamental questions remain unanswered and are quite similar to the business complexities I bravely faced so long ago…
So, when I read the following paragraphs, it clearly reminds me of why I had to write the book: “AI BOOGEYMAN — Dispelling Fake News About Job Losses”. Simply put, pushing the data analytics pendulum to the extreme creates a false sense of job-insecurity linked to machine superiority…
“Ten or 15 years ago, I had to keep promoting the idea of using data analysis and statistical optimisation techniques, because many managers thought their intuition was good enough. There’s been a drastic change in perception in the last few years that’s almost turned it upside down. Many managers now think that decisions can be made only by algorithms — Oded Koenigsberg, professor of marketing and deputy dean of degree education at London Business School”
Despite the hype, job-losses attributed to AI data analytics are mostly fiction and scare tactics of unscrupulous “experts”. I often draw an analogy between the AI and the … spreadsheets. Both are TOOLS! Spreadsheets didn’t eliminate accountants, and AI’s innovation is not going to eliminate many of the jobs we currently hold.
For years, I’ve been advising CEOs to build their own Personal Advisory Boards. My message is equally important to CEOs and to their financial backers: BOD members are not your friends — they are your investors! The only impartial advice to CEOs may come from… the Advisers they choose! In fact, those who are truly open to guidance end up solving their problems much better than they would have on their own. And even by offering a sounding board, it may help CEOs clarify and sharpen their own thinking. It also couldn’t hurt if the advisor also brings a solid data mining experience with him, or her to the table.
“The most important thing that bosses can do, in a world awash with algorithms, is to recognise that while data is very powerful and very useful, so too are intuition, experience, leadership, open-mindedness and judgement, the strengths on which they were hired. After all, even the most proudly and successfully data-led firms in the world are still run by and for human beings”
Digitization and Artificial Intelligence are causing the greatest disruption of global markets and their utmost technological transformation of the last 50 years. I recently published several LinkedIn posts recommending innovative strategies for eliminating trading fraud, credit card fraud, and luxury cars’ high-tech relay thefts. In all cases, solving the problems and finding the best solutions involves as much domain-specific knowledge and experience as it does data analysis.
“CEOs and senior executives will need to understand data at least well enough to appreciate its value and its limitations, rather than just placing blind faith in the experts. The danger otherwise is that you will have CEOs who don’t understand data, making business-critical decisions on the advice of data scientists who often don’t understand the inner workings of business”
For example, I’m convinced that fintech companies face a “once-in-a-lifetime” opportunity to build a “trusting banking brand” — offering safety and security. It would uniquely position innovative fintech players ahead of even the largest competitors — for many years! Please see:
And yet, AI technology is only one part of a much bigger picture. In some cases, a number of perfectly working solutions are offered by established IOT players — at the fraction of the costs. After all: the graveyards of AI innovation are full of brilliant ideas with a lousy business model — no matter the industry.
The more you dig inside the training and testing sets — the more you realize the difficulty of creating adequately balanced representations. It needs to be fully considered at all time — before, during and after the analysis is complete:
“Two truths about data: not everything of value can be quantified and not everything worth quantifying is necessarily available”
And unless a big business is fully transparent and ready to reveal its trade secrets — the veil of secrecy is making any meaningful analysis of the results — pointless:
“Science allows hypotheses to be tested, to be proved or disproved by experiment. Data science in business does not. The findings of corporate analytics departments can’t be independently verified, replicated, scrutinised or challenged, because the data sets and algorithms are almost always proprietary. No one can peer-review Google’s insights from its own data sets, without being granted access to them”
My advice: ask not only of what has been incorporated inside the training and testing sets — but also what is missing. You’ll be glad you did…
“Being data literate means that at least you know how to ask the right questions of the data specialists, especially as the science becomes more complex. “Rather than obsessing about what’s going on in the ‘black box’, ask how I trained the model, what datasets I used, how did I know they were complete, how did I test and validate the model. Commercial and data people speaking a common language requires work from both sides — it’s equally important that data teams understand the business context”
As I asked so many times before: who are you kidding? No matter how good, or deep your neural networks are — you’re not going to send your family on a plane without a pilot in a cockpit, nor are you going to let your kid climb a school bus without a driver. And do you really think that Elon Musk couldn’t afford the best data analyst when he promised Level 5 Autonomous Driving 5 years ago?
Now, I’m not Robert De Niro or Billy Crystal, but starring at a lopsided approach to a decision-making process often leaves me astonished and dumbfounded. Oh, well — “Analyze This”…
Oleg Feldgajer is President & CEO of Canada Green ESCO Inc. Oleg is positioning the company to become a leader in financing AI-enhanced green energy projects and ventures. CGE’s mission is to guide DISRUPTIVE businesses in ENERGY & TRANSPORTATION toward profitable business models. Oleg is passionate about such a mission and firmly believes that without AI-based innovation, we will all prematurely choke on polluted air and dirty water. CGE delivers 100% financing (levered and unlevered) to its clients — and utilizes large equity pools, and non-recourse debt. Oleg offers creative, fresh ideas to open-minded businesses — that embrace both: logic AND opportunistic intuition. CGE stands against mediocrity & its modus operandi is quite simple: If CGE is not invited to join your BOD or Advisory Board — we failed!