links to futurization, no kidding
It seems as if the near decade long run of data science has met with some headwinds. Or rather one headwind called generative AI. Well, that’s not true either, because in the past few years, platform engineering, the internal development platform, site reliability engineering, and probably something involving organizational DNA and gel electrophoresis have all been sprung upon us as paths to sustainability salvation.
Liking it all, I do. I really do. There has never been more than one way to sustainability, and it was continually thwarted by, wait for it, a new shiny thing. Today we have so many shiny things that as the paths forward increase n-fold, the people to do the work do not. It took the information technology business sixty or seventy years to get to a point where there was more than one answer for reaching sustainability (other than seven ways to do the same thing, I mean). We are not going to get practitioners and expertise sufficient enough to permit any of these new paths to get enough traction to last the week. Figuratively speaking.
The introduction of computer science into university programs enabled the computer business to grow in importance. As computer science grew from being an electrical engineering sub-space to hold its own prominence, the computer business grew in indirect proportion (positively) as the students became the business.
What we hope is simple: that unlike the past two Artificial Intelligence winters, the incantations coming from generative AI can offset the missing cohorts in time enough to make it the source of a net futuring rubric. And, good for us, it seems to possess that capability now, and, the process for its learning advancing forward in time, over time, may be that engine that changes change.
The confluence of generational thermodynamics means that everything we understand is subject to change, often at times and places not of our choosing. Maybe that’s a subject for recursive access to generative AI insight compilation. Beyond this one’s ken, one might say. But on smaller topics, such as the many potential paths forward with platform engineering, the internal development platform, site reliability engineering, and doing them all with cloud native sustainability modes, we would need something like a data science foundation to help guide development. The graphic above was used before in this sequence of articles for a different purpose than we will use it here.
What we need to provide others is a way to get the current set of shiny objects organized to support the growth of the markets and drivers listed in the graphic. Several industry analyst teams have produced their vision for the matrix used here, and this one was selected because the Gartner organization has shown me some considerable respect in the past.
What differentiates this vision from some of the others of its standing is that it combines the legacy units of measure of success (on the left-hand side) with the articles of futurization organizations can choose to grow into as part of their digital transformation programs.
Which means we could, if we were so inclined, develop a succession plan for how an organization would develop, over time, without changing its entire metrics stack all at once. This is key to supporting the staff required to implement changes on behalf of the organization’s sustainability. Legacy metrics out into the future enable people to understand where the organization is going from their own perspective, not that of some generalities compiled into a mission statement.
In terms of the shiny object factor, Artificial Intelligence is mentioned explicitly in two of the futurization paths. Sustainability leads the list, so we’re good holding on to that arc. 5G, cloud platforms, and the Metaverse are all current edges for organizations. Of the remaining topics, the Digital Immune System represents the composite of devsecops and the macroeconomic climate for business solutions. Which leaves Platform Engineering, Superapps, and Applied Observability.
One might contend that without a significant investment in data science, investments in platform engineering, Superapps, and Applied Observability are pretty much doomed due to the complexity of the topics and the potential for choosing the wrong solutions to enable them to sustain a traction of helping rather than sliding backward.
So much good in all this. So very much good.
Rather than invent yet another path to the future, let’s say that Gartner permits a slight change to one of the graphics in the eBook cited above. Also let’s assume that the prognosis for the Scale, Optimize, and Pioneer allocations remains in place for five years.
To date, we have not heard back from the Gartner teams, a not unexpected situation given the stresses of today’s business landscape. If it goes another month without a response, we will offer another incantation of similar breadth and depth of futurization insight for purposes of building a working model of this kind of state change machine.
However, this evolves, it is possible to look forward with portents of good and valuable rather than ghastliness. People, resources, and hope, are too precious to waste right now, as if they ever were, and it always seems like time is short and work is long these days. We need to shorten the work and lengthen its impact. But, as usual, everybody knows that.