Fighting the hype by understanding the basics.
This is probably the most insightful post for CEOs. It helps them confidently support the ventures from the right Digital senior managers while challenging the wrong ones. Because, wrong efforts can only lead to wrong legacies. And these, to the strangle of the business via strangle.
The rabbit hole and the elephant in the room.
December, 4th, 2022
A simple formula that says it all.
Y is the Impact of Digitalization. It is the result, in terms of value-add, of the digital transformation.
As it turns out, after years investing in Digitalization (the X) there is not a significant Y in the organisations but a myriad of data-driven projects that barely talk to each other and hence, are expensive to maintain (let alone to evolve). These are becoming a concern for the senior management who starts to struggle to back them up comfortably at a board level.
And the fact is that they are uncontrolled, indeed, which is frustrating. But, do you know what is even more frustrating? Typically, there is no one to blame. The data-driven legacies (both tech and culture) settled within the companies for years now leave little room for impact... by design.
And that triggers a common wonder, of course: how have we reached this point? It feels weird because there are all sorts of university masters out there, and an army of consultants, holding your hand through the "right path" towards an optimal transformation. As such, you started with your data swamp, followed by a data lake, which was subsequently complemented by a data warehouse or, smarter yet, a data lakehouse. You even hired some semi-cool weirdos from San Francisco who sold Forbes-level culture transformation - yet with a flavor of marketing instead (and, honestly, too much of it seemed their own personal branding rather than ad-hoc value add).
Could it be the governance of the whole thing that went wrong, then? First, you were told to centralize it all in the Digital teams. Now, you are being told that the impact is unlocked if you federate data ownership here and there and share it through APIs as data products.
It sounds like an opportunity to explore. But also too much like potayto, potatoh. It could easily be a movement along the Pareto-Front rather than a Pareto superior. As such, there is a good part of the senior management that doesn't buy into anymore. They challenge its real capacity to unlock impact at last as they are afraid they could merely keep going down the rabbit hole of data without significant results.
And it is at this point that they wish they could be able to zoom-out a little. There seems to be a lack of structure in their digital transformation that they are missing. And they can feel it, guess it, but they can't state it clearly.
And why is it so difficult to zoom-out? Why the overall lack of judgement in the industry? Two sources of pressure; one common reason.
The sources are external and internal. We, external consultants, want to sell. And the easier way to sell is by creating a trend that pushes for FOMO at the industry. On top, we cannot sell disruption but just incremental innovation (interesting point by the way) hence, there is a honey pot at messing around with the X (which is already known hence, incremental). More interestingly, many internal mid managers are eager to buy into it. They are easily lured around the X. Why? Because companies have previously created all sorts of data-focused roles, all along, that have lead to a myriad of data-related professionals that are now comfortable with, pretty much, the status quo. Not all - lucky us - but not negligible, either.
And, obviously, this cannot lead to anything else but to steady, negligible impact.
So, we've seen why exaggerating the X was a sad yet steady equilibrium underpinned by many dimensions. Now, why is the absence of the F(·) also an equilibrium? Well, easy: understanding the F(·) , the Algorithmization, takes many years (combining deep academia and industry) and a massive personal investment (in my own case, I took 3 periods of 7 years each - moving from econometrics to ML models to ML machines). Furthermore, once you grasp the right skills you still need judgement and creativity to apply them as it remains a greenfield - advanced academia has some of the tools but not a close-form theory combining them.
This is, there are forces magnifying the X and forces leaving the F(·) aside. This is the backbone of what we always call the Digital Dis-Economy. The Y = X.
Whether shifts of the demand, of the supply or both.
To the light of above senior management worries, the shift of the digital transformation demand, i.e. the shift from demanding the X to demanding the F(·), is already happening at large companies - e.g. last week we were at four different listed companies discussing very sensitive transformation challenges demanded by their C-suites.
How about the supply's shift? Who is going to help organisations fulfill at last an E2E algorithmic transformation? Who is going to help them become on-platform organisations? Well, for that we have open-sourced #DataMAPs, to my knowledge the deepest experimentation on Algorithmization, hoping for a new trigger of gradual efficiencies, globally. A very especial one underpinned by Augmented Machines so that we merely transform roles instead of making them redundant. Nevertheless, in competition, the aim should be to do more with the same resources rather than doing the same with less resources.
Else, there will be massive redundancies down the road that won't help society. This is one of the reasons for this post - we need to be more responsible with the innovation budgets as the consequences can be either very positive or very negative. Note that also tech-related roles are on the line this time as, in equilibrium, salary equals the value of the marginal product - where value involves impact. Which is in part why there are currently massive lay-offs at big techs that cannot subsidize lack of impact anymore.
Only then will we be able to start triggering the real impact while tuning current roles towards value-add before too late.
Credit: I must confess I've learned it all, along with #DataMAPs, at www.algorithmization.com
Now you understand why we always advice clients to do nothing rather than doing transformation wrong.
You either go for transformation the right way or you don't move at all. You can't go half way since any little tech advance means setting out legacies - which include culture. And wrong tech projects are not only expensive now but, more importantly, expensive in the future given their implications. Not only in terms of economic costs but also of change resistance. Nevertheless, the resistance to a change of a change, compounds.
Thanks once more for reading!
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