MapBrief™

What gets measured gets managed.

I made a pilgrimage to the ruins of the birthplace of “what gets measured gets managed.”

The Midvale Steelworks in North Philadelphia is where Frederick Taylor and his stopwatch laid the foundation for “scientific management” at the turn of the 20th century. Notable too was his assistant there, Henry Gantt, who developed the eponymous project schedule chart. The location stopped producing steel over a half century ago, but the dream of precise productivity measurement continues to haunt our 21st century workplaces.

We in tech never really put away Taylor’s stopwatch, whatever the acknowledgements of its shortcomings when it comes to “knowledge work”. Because management all too often falls back on countable things: hours (preferably billable, or tax deductible), lines of code, pull requests, etc. And we all play along with the kabuki theater of KPIs during performance review season, pretending we all aren’t falling victim to Goodhart’s Law in the meantime.

And now AI has come along and proven very adept at producing countable things–lines of code and pull requests–around the clock(!). And it even spawned its own new countable thing–tokenmaxxing–initially embraced eagerly by management that alas, in record time, fallen victim to its own incentives.

Danger for Those Specializing in the Easily Measurable

Among the recent wave of tech layoffs, consider Cloudflare’s CEO on laying off 20% of its workforce despite profitability:

AI isn’t coming for builders or sellers, but it is coming for measurers. Tireless, independent, efficient and available, AI systems can now measure an organization with a level of objective detail and precision that was previously impossible even for the best employees.
…
The vast majority of those we laid off last week were measurers. We cut middle managers across the organization because AI allows us to have more direct reports per manager while still measuring and mentoring our teams effectively.

ClickUp CEO Zeb Evans had even more detailed and interesting thoughts about his own company’s 22% cut in headcount:

We must reward people that create productivity accordingly. This aligns incentives on both sides. Plus, in a world where your best people create 100x impact, you can’t afford to lose them.
…
Compensation bands of today should be thrown out the door. We’re introducing $1 million cash/year salary bands with a path available to nearly everyone in the company if they produce 100x impact by creating or managing AI systems.

Million-dollar salaries–sign me up! But a quick question here: I’m seeing this phrasing “100x impact”–what, precisely, is “x“?

Triumph of the Qualitative?

If AI has taken over the old metrics–hours worked, lines of code written, et al–how are we humans to be evaluated in the workplace?

The early consensus seems to be centering on “taste” and “judgment“!

Given my experience in technical/engineering teams for over two decades, I can confidently state that our management structures are wholly unprepared to evaluate tech workers on the basis of such qualitative characteristics such as “taste” and “judgment”. I mean, have you ever heard these would-be arbiters of taste discuss their favorite films? Is this really the “x” we’ll dutifully build a new set of KPIs around?

No, my guess is we’re still a far way off from replacing Taylor’s stopwatch because the ‘objectively’ quantitative is the comfort food of our world of zeroes and ones. We will permit its hollow ruins to stand if only as a monument to our own uncertainty of what to replace it with.

— Brian Timoney

Last year I ran into the always-incisive Will Cadell and we immediately started discussing a favorite hallway-track topic:  how to effectively sell Geo.

ME:  “If I had to do it all over again, I wouldn’t even bother with the web but just sell PDF maps to the Oil & Gas industry with big red arrows that said ‘Drill Here’.  At least with a PDF they know exactly what they are buying.”

WILL:   “They are not buying the PDF; they are buying the arrow.”

Cue An Epiphany.

We in tech love latching on to metrics: map a billion points in your browser, access petabytes of imagery using a shiny new platform, etc. etc.

But so little conversation about the arrows our customers actually want. 

Instead, we jam a billion points into their browser and wish them luck in finding whatever answer we thought they were looking for.  Sure, it is better than the old days when we actually handed customers hard drives of data with an invoice (!), but not by much.

To torture a different analogy, the customer is looking for a needle and we respond by trying to sell them a bigger haystack.

I get it: each client’s arrows and needles are too unique to build a business that can truly “scale”.   Or maybe AI will soon bequeath us an All-Purpose Answer Machine.  In the meantime, perhaps our time is best spent not heads-down grinding on the latest GPU-accelerated ways to push more data at our end-users but maybe circling back in a quiet moment and asking “wait, what was your original question?”

— Brian Timoney 

I saw some of the most talented Spatial Minds of my generation…take non-spatial jobs for better money.

Recently LinkedIn brought me tidings that two people in my network took new positions with no obvious link to the Geospatial industry. In their spatial endeavors, these folks were technically adept, innovative, and happy to volunteer to speak about their work at industry get-togethers.

And now they’re gone.

And I don’t blame them.

A Supply Problem?

This came to mind as I watched a program titled  The Geospatial Workforce Crisis: A Diversity of Pathways Forward put on last month by the National Academies of Sciences, Engineering, and Medicine. Industry veterans from academia, government agencies, and the private sector came together for four hours of presentations and panel discussions addressing the current and projected shortage of workers in the Geospatial industry.

In the main the content was thoughtful and interesting, but one thing bothered me:  over the four hours there was little to no conversation about compensation.

Look, 21st century Late Market Capitalism has its flaws, but the basic dynamics of Supply and Demand haven’t been suspended: if there is a shortage of something you want and need, well, there’s a price for everything.

The Consequences of the GIS Silo

What’s the GIS Silo? It’s the decades-long insistence that GIS was so magically distinct that it was to exist apart from mainstream IT.

The GIS Silo was great for vendors: define a niche market and insist your customers’ niche technical requirements couldn’t possibly be understood, let alone subsumed, by a normal IT department.

The GIS Silo was okay-ish for the training/education industry. Following the Vendor lead, they could teach some theory and some software and there were usually some OK jobs waiting for industrious students.

The GIS Silo is no longer working for its Practitioners.

Every day you fiddle with interfaces that promise to GUI the complexity away, you’re costing yourself money.

The larger IT industry is rewarding SQL skills.
The larger IT industry is rewarding Programming skills.
The larger IT industry is rewarding Data Pipeline management.
The larger IT industry is rewarding Data Visualization chops.

Inside the GIS Silo the most lucrative credential is now…a Security Clearance.

Sure, few of us got into this industry for the money, but also few of us can avoid the 21st century middle-class conundrum of School Loans/Mortgage/Kids – Pick Two and ignore the discrepancies in compensation.

It’s All About Distributions

The compensation issue simply isn’t about a single median figure, it’s about the distribution.  Let’s compare GIS Analyst vs Business Analyst courtesy of Zip Recruiter:

Even discounting the vagaries of job titles, the skew in the distribution of GIS Analyst salaries is notable because it implies a stagnant middle grinding away while effectively blocking the ability of new entrants to rapidly ascend the wage scale as you’d find in more “normal” distributions (check out the distribution of Data Scientist to see what even greener grass looks like).

If your most skilled practitioners are leaving and the stagnant middle can’t advance technically, then we see the all-too-common GIS Manager solution:  a job posting for entry-level positions with a blizzard of acronyms that reads like a Rosetta Stone of Vendor-Foisted Technical Debt.  I’m no expert on the hopes and dreams of Gen Z, but maintaining your early 2000s  SOAP web service for fry-cook wages is not a future that galvanizes the imagination.

* * * * * * * *

For all of the hand-wringing about labor prospects in the “AI Future” I’m confident that anyone working with Data will be fine:  data quality issues will be huge and presenting clear visuals of increasingly complex relationships in data will only increase in demand.  Markets for Geospatial know-how will grow,  from a dynamic Remote Sensing industry to anything touching climate issues.  For practitioners there’s never been more opportunities to independently enhance their skill sets.  And maybe, just maybe, the real “crisis” in the industry is the insistence on holding on to a Status Quo from the Past that is no longer serviceable for its Practitioners.

— Brian Timoney