Analytics 2023: Where to from here?

Analytics 2023: Where to from here?

11 Years ago, a Line Manager shared the Harvard Business Review Article title "Data Scientist: The Sexiest Job of the 21st Century". When I read it, I knew immediately that I was in the correct discipline for the next decade or so, and that eventually every other career would intersect with it too. That is exactly how it played out. Data, in its variety, dissection, consumption and distribution have inextricably enriched each and every facet of our lives. Generationally, we are now smarter, safer, stronger, healthier, and wealthier all because of it. No longer is it gatekept behind the adage: the 'IT Guy'. Now we have champions of industry across all regions, races, cultures, religious preferences and gender spectrums because of it. Learning programs, degrees and jobs have all changed; and become altered forever because of it. Go back 2 decades or so and the data analyst role was locked behind the Chartered Accountant paywall and confined to excel spreadsheets and experts who were not too keen on showing others how they created pivot tables. Okay, so tell us something we don't know, right?

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Even the technology stacks available to analytics professionals- in all their various flavours, have made what was once the impossible, possible. Ask anyone how they could transform a billion lines in time for the next ExCo meeting and they would have likely had some unkind words for you. It is becoming easier and faster to do more and more with data. So, data availability and size is exponentially bigger? Got it. The tech used is faster, cheaper and more efficient? Got it. So, what now?

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Well now we have the ‘missing middle.’ The people component. Generally the skills gap between your seasoned data analyst veteran, who has been using SAS to write out their data set to Excel and update their report; and the radical data scientist using R and Python to predict when you need to swipe your card for maximum loyalty points; is extremely large. You could probably fit a 1Gb Hard Drive from the 60s between the ocean-like skills gap that exists right now. The problem therein is that we need a diversity of roles and functions. Not everyone can be all-star end-to-end analytics professional. In that also lies inefficiency, for instance Henry Ford helped introduce the production line for exactly this purpose, the segregation of duties. This creates efficiency. The challenge now is that so many seasoned data analysts find it challenging to grow into more complex roles within the same field such as Data Science or Modelling. The inverse is also true- so many Data Scientists and Modellers don’t know how to transform data to get it in such a way that their algorithms and models can consume it effectively enough to scale efficiently. This juxtaposition has paved the way for many Learning & Development companies to rebrand their offering to help diversify the skills and create an even playing field. This is largely still in it’s infancy as companies are only beginning to realize that it is better to upskill it’s internal workforce than to externally employ and have to embark on contextual training so that new staff are adequately equipped to tackle the organizations pressing issues.

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Elaborating on this, the hiring process has become ever so tedious for organizations. To put it simply there isn’t a definitive framework on which to assess whether or not you have a good analytics hire or a not-so-good one. Recruitment is challenging. We quickly frame the role requirement based on the nearest available degree compatibility and go from there. Unfortunately, it is not enough. Staff churn quickly becomes a problem and organizations quickly get branded as ‘No-Go’ zones for the analytics profession. It also leaves room for skilled professionals to milk-the-market and get fat off the salaries set by people in HR who might or might not have an idea how to benchmark the skillset. So they typically use the incumbent’s existing salary as the benchmark. The double-edged sword there for analytics professionals is that they can quickly price themselves out of the game and then find it hard to move on into new roles or disciplines because they are simply too expensive. Also, hiring managers who qualified in their disciplines, ten plus years ago, might be ill-prepared to be able to accurately access a candidates skill level. Now I do not mean that in a condescending manner. However what we understand regarding the doubling of computing power according to Moore’s Law is also true for the rate at which information becomes stale and invaluable. So from behind my keyboard- I am saying that if you are not constantly learning and adapting to new tech and methodologies even in Tech & Analytics, soon your qualification and certification will become stale and obsolete. So, keep on learning, darn-it! The tech is however catching up to address both ends of this spectrum- things are becoming easier. Platforms like Alteryx, other market leaders and new-fighters are entering the bout promising to streamline processes and day to day activities to soon make traditional analytics professionals redundant too. Don’t fear, this will take some time however as these platforms are not widely understood or disseminated in all circles, yet!

Another spanner-in-the-work are Executive mandate owners who attend conferences and accept LinkedIn messages from all the tech-solutioners-sellers-come-expensive-buzz-word-capitalizers, who are keen on showing them all the endless possibilities. On Clean Data! Louder for those in the back- CLEAN DATA! After RFP’s and necessary NDA’s have all been signed and organizations are locked in 10 year contracts, the lowly data analyst has now choice but to use this tech that might or might not make their lives easier. More often than not, these deployments and transitions are tedious and fall-flat because many professionals simply do not want to. They are change averse. That is normal. Albert Einstein famously said: “If you can’t explain it simply, you don’t understand it well enough.” How are you going to explain that any software analytics platform to a 30+ Year veteran will make their lives easier until you have accurately understood the root of their problem. More often than not, it is not the software that saves you but rather how well you have optimized the process. If and only then if you are still sitting at an impasse should any tech solution be considered. Ok we get it, we need to keep learning new tech because we need our jobs? And stop bombarding us with new tech because we understand our jobs better than anyone else!

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Analytics and data in general are on the precipice of its next golden age. After the dust settles around the Executives who procure the Gartner quadrants that sound the most fancy and all the ‘business-people’ finally understand why your dashboard isn’t updating; or why the same business people realize that they don’t need insights until they understand to ask the right questions first- then what. Well then we move onto the next phase. Qualitative measurement. Turning the numbers and models and algorithms into lay-speak. Make it make sense darn-it! I for one predict behavioural science to be the next frontier. Hell, that idea might even be stale as there are so many real-life applications and teams shaping what that looks like and how to capitalize on it to its maximum. Yes, I am looking at you Cambridge Analytica. In closing, this year should be a year of discernment, readjustment and realigning. No matter where you are on the analytics spectrum, focus on: How do I make myself the most valuable for the place I work? How do I use data to shape the community I come from? How do I stay at the bleeding edge of the next data revolution?

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Note at the time of writing this article the information available was as fresh as an avocado. If I hit send it might not be as fresh, and it does not serve as any endorsement or disfavour for any platform, service or company. Use whatever gets you the most accurate answer, the quickest.

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Warm Regards

Marcellino Carlo

+27 (0) 78?603 9042

[email protected]

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