I said so !
More than two years ago, I wrote a Sci-Fi short story on this very blog site. The link to that article is below. While the entire scenario that plays out in that story has not happened yet, since I set the story in year 2029 and I am still confident that the capabilities and tools mentioned in my story will be in place around 2030, a key aspects of my story was:
” There was now one specific area where the demand peaked. Companies now needed to train their new workforce-The Digital Workforce. This was about “Reskilling”. Resources like Alice-who were not true advanced analytics professionals but needed skills and expertise to leverage the machines optimally as well as train these machines using a best in class approach. With more and more functional managers taking ownership of these advanced analytics tools, there was this huge demand in the market for people who could help train these functional managers beyond rudimentary analytics.”
And the Guys with “those skills” are here
Flying under the radar, there has recently been an influx of many niche companies that focus on areas that some traditional analytics consulting companies have either ignored, have not invested enough in or struggled with. And they all revolve around the skills and scenarios indicated in my story above and in some of my other posts. They are:
(1) First, “reskill” the managers in such a way that they can tie Data Science to Business and Operations strategy
(2) Once they have that skill, in collaboration with them, help them develop an optimal Data Science strategy for their functions
(3) Help them understand what kind of Data Science talent they need, how to hire and retain them
(4) Collaborate with them to select best off the shelf Data Science tools
(5) Guide the newly minted Data Science cognizant managers in selecting use case projects where customized algorithms need to be built.
Below is a screengrab from the offerings page of one such company. My interest in finding such companies have already led me to explore at least 15-20 such companies.
What is different about these companies you ask ?
The most important aspects are:
(1) They don’t focus on what has become the most commoditized part: Actually building/coding a Data Science model. Sorry to say that but with thousands of companies doing that now, in all price ranges, there is no value remaining in that area. Yes, you can help with that too, but that should not be your unique value proposition.
(2) They are cashing on the scarcity that will soon become bigger than the “Unicorn Data Scientist” scarcity – The scarcity of Analytics translators who can bridge business and Data Science worlds. They are essentially focussing on developing Analytics translators within your organization- helping you build the most competitive manpower edge of this decade.
(3) They help you hire. As I have mentioned in a multitude of my posts, a significant aspect of what is wrong with Data Science in my opinion today is because of the way we hire. Many of these companies help you develop a hiring strategy focussed around your business, not just imitate what others are doing. The best of AI strategy will fail if you walk out without helping the managers understand what they need to hire for.
(4) The world of off the shelf Data Science and automation solutions has become extremely crowded and confusing because of a tsunami of new players entering the market on a daily basis and being flooded with funds. Helping companies navigate those troubled waters is a service that is at premium and these companies are proving that as well.
But aren’t established players already providing these services ?
Not exactly !
Very few are.
First, the rush has been so much on “building algorithms” that many of these established players lost the focus that the real value was not there. Anything that ties directly to leveraging technology will (if not already is) become commodity.
The focus was not on first helping the workforce understand AI. Strategy was focussed mostly on churning Data and AI strategy decks that are shared with senior folks. A couple of workshops to “educate” some functional managers did happen before a “use case” was finalized but the fact is, educating the managers first should have been the core element of the project. If you ask my candid opinion on why AI and Advanced Analytics projects are failing, it is because we are not focusing on developing managers into Analytics translators first.
“Building internal capability” is focussed on building Data Science teams, whereas the fact is that the foundation of that capability is to build an army of translators. THEN, develop Advanced analytics teams.
There are many other aspects that many current players are not doing right. I can go on and on but if I were to summarize it, the two point summary will be:
(1) They did not realize that the real opportunity was not in building Data Science tools but in Data Science strategy. And then they did not articulate what exactly encompasses Data Science strategy.
(2) Data Science strategy is not just about Data strategy or use case selections and executions. All important strategic aspects, like the ones mentioned above, fall between these two.
So what can established players do ?
Well this is simple, expand the current offering to align with what will soon become a big demand area as disappointments from current approach escalate (which is leading to these extremely niche companies cropping up)
Hunt for some of these companies and accquire them. A couple of them are well scaled and very attractive (btw, the one in the screenshot above is NOT one of them). And then, expand the team rapidly. Execute some projects, and showcase them. Take my word, there will be so much demand, you will be busy for the next decade- and without getting involved in building vanilla commoditized algorithms. This whole decade will be about disappointed organizations, recovering from failures of intial hype driven projects, looking desperately for experts that can provide them all the services mentioned above.
Views expressed are my own and may not represent the views of my employer.