Refactoring the Way you Talk About your geospatial skills: It is Costing you Money

Some of the key topics in this episode

1. Our Geospatial Skills and Marketability: There’s a realization that while our traditional geospatial skills are valuable, they might not always be marketed effectively to match the broader IT and data analysis job markets. We discuss the benefit of framing our skills in terms that are more widely recognized outside the niche of geospatial technology, such as data science or IT.

2. The Spatial Discount: We explore the concept of the spatial discount, which refers to the observation that geospatial professionals might face a disparity in compensation compared to their counterparts in more generalized IT roles, despite having highly transferable and valuable data manipulation skills.

3. Skill Development and Adaptation: The importance of continually developing skills that are not only advanced within the geospatial domain but also marketable across various sectors is emphasized. Learning and mastering technologies that have broad applications, such as SQL for spatial data manipulation, can enhance our versatility and marketability.

4. Communication and Marketing Skills: Our ability to effectively communicate and market our skills is highlighted as crucial for career advancement. We are encouraged to adopt the language and terminology that resonate with broader industries and potential employers, moving beyond the jargon of the geospatial field.

5. Finding Value in Our Geospatial Work: The discussion also touches on the importance of identifying and articulating the real-world value of our geospatial work. We should focus on how our skills can solve practical problems and address the needs of businesses and organizations, rather than solely on the technical complexity of our tasks.

6. Professional Development: Lastly, the conversation advocates for a proactive approach to our professional development, suggesting that we should seek out opportunities to learn new skills and technologies that align with market demands and personal interests.

These points collectively suggest a strategy for us, as geospatial professionals, to enhance our career prospects: by broadening our skill sets, effectively marketing our capabilities, and aligning our work with the needs and language of the wider IT and data analysis fields.

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In Conversation

A 20-Year Geospatial Career

Daniel: When I asked your title, your response was “data contractor, geospatial — it’s complicated.” Could you give us an introduction?

Brian: I’ve been a consultant, contractor, “data janitor” — as some people refer to it online in only semi-ironic tones — bumping around the geospatial world for about 20 years. Like so many people who get into geospatial, the love of geography came at a very early age — the Rand McNally road atlas that constituted the only reading material in the back seat of the family car, and the day my father brought home USGS topographic maps of upstate Pennsylvania, which blew my eight-year-old mind with a whole different visual grammar. I’m a proud member of that non-elite fraternity of liberal arts majors, always searching for a profession that’s both vocation and avocation, and I found GIS in Albuquerque, New Mexico, in 1995. The lesson for listeners: the geospatial world is very, very small — everyone is one degree of separation — so be careful which bridges you burn. I’ve dabbled across utilities, defense, local government, and federal, and the variety of those experiences is as good a qualification as any for this conversation.

The Spatial Discount

Daniel: You’ve said you make more money using your non-spatial skills than your spatial ones. Why do you think that is?

Brian: The phrase is “niches get riches,” and yet here in the geospatial niche that doesn’t seem to have been the case. My current position has very little geospatial at all — a subset of my skills — and yet the rewards exceed the vast majority of positions that really tested the limits of my geospatial skills. As I brought this up online, it’s been fascinating and depressing — people chimed in with US government consultant compensation rates where GIS-related job titles are structurally compensated about 30% less than the equivalent technical skills in a more generic systems-analyst role. Another colleague sent me the City of Albuquerque’s numbers: the starting GIS position might be $23 an hour, the starting systems analyst $32. We’re behind 30% just at the beginning of the career, and that divergence becomes more pronounced at mid and senior levels. Some people call it the “spatial discount” — a bit tongue-in-cheek, but there’s a real reason they say it.

Why GIS Got Siloed from IT

Daniel: So in the good old days it was an advantage to be known as the map magician — people left you alone?

Brian: My working theory goes back to the early days of GIS. The sector always explicitly set itself apart from the rest of IT — a parallel situation where we ran our own web servers, our own database servers; we were very self-sufficient. Where do you find the GIS people? “Down the hall, to the right, the room with the plotter.” We were never integrated into mainstream IT, and frankly a lot of us liked it that way — we liked being the priests of the plotter; no one needed to know how we did things, least of all DBAs or IT people. But that separation over time has now worked to our disadvantage. Think about map-making: 80% of the time and effort is getting the data into shape — finding it, cleaning it — and the stuff we love, the cartography and final presentation and the north arrow, is the final 15 to 20%. GIS people always had this implied responsibility of data prep and cleaning as just part of the job. Meanwhile, in the IT realm, the value of anything data-related has gone up dramatically. Those data skills — which were a means to an end for any GIS person who wanted to make a map — now stand alone, and their value in the marketplace exceeds the totality of the basket of GIS skills.

It’s a Marketing Problem, Not a Skills Problem

Daniel: So maybe we don’t need to rush out and learn new skills — we already have them. Maybe the solution is the way we talk about them?

Brian: Part of the dynamic is whether you’re acquiring skills to burnish your data-manipulation abilities and flexibility, or acquiring a vendor-driven set of skills. The debate about whether universities teach too much Esri-centric, button-pushing software has been going on for 25 years. You want your graduates to get jobs, and a lot of jobs need Esri-specific software skills — but learning spatial analysis and spatial data manipulation using SQL is a toolset with value way beyond the immediate set of GIS operations. The practitioner needs to take it upon themselves to ask: is my skill acquisition relevant outside this rather narrow niche we call GIS? But I don’t think just having those skills is enough either — the way we talk about things, that marketing aspect, is really important.

The Keyword Game

Daniel: You’ve talked about shaping and manipulating data — that’s often called data science. Are we better served framing what we do as data science?

Brian: There’s definitely a case for it — you see taglines like “geospatial data science” and the phrase “data engineering.” That janitorial data work now has a new cachet. If you say “I know how to clean, slice, and reshape data,” those phrases resonate outside the geospatial marketplace. I came across “vector enrichment” on AWS’s geospatial offering — I’m reading it thinking “what’s that?” and it’s reverse geocoding and spatial joins. If the AWS marketing people, who are a lot more skilled at creating revenue than the average geospatial person, want to call spatial joins “vector enrichment,” I’ll gladly adopt that verbiage. The job search is largely a keyword game — recruiters are looking for the same words. One of my best hiring decisions: reviewing résumés for an entry-level city GIS job, one person used the word “concatenate” in the specific context of data manipulation, and it leapt off the page — if an entry-level person knows that term, this is someone you can work with. And don’t put “PostGIS” on your résumé and expect a recruiter or manager to already know it’s the spatial extension to Postgres — making those assumptions is costing you opportunities in higher-income positions.

Two Hard-Won Lessons

Daniel: Where were you in your career when you figured this out?

Brian: Two answers. Remember, I was going to get rich through web mapping — but every web mapping project is a data management project in disguise, and people don’t want to pay for data management; it happens under the covers, and they think “what’s that guy wasting all my time for, where’s my map?” The other experience is just burned in my head. I had this crazy setup taking three-hourly hurricane tracks through the Gulf of Mexico, updating my database, with PHP scripts so people could create buffers and I’d tell them how many oil and gas platforms in the Gulf were threatened — feeding live geospatial polygons into Google Earth. I was sharing an office with a successful, wealthy guy with a PhD in geology. I showed him the buffer tool — “you can click anywhere, choose how wide the buffer is” — and he looked up at me, this PhD geologist, and said “what’s a buffer?” I did not have product-market fit. That was my target user, and I was already using the language my GIS buddies and I use every day — and to him it was foreign.

Speaking Human, Not Solutions

Daniel: A podcasting example — this podcast only survives because I figured out what people want to buy and what I’m selling. The people who approach me to sponsor are marketers, and if I don’t use their language we talk past each other.

Brian: It can be a painful and uncomfortable journey, but it’s a journey worth going on. The downfall of being technical is you want to impress other technical people, so you adopt the insider language — “look at this cool feature.” This marketing challenge isn’t just GIS people trying to broaden their career prospects — the whole data visualization and dashboard world is going through its own trough of disillusionment, with language like “actionable insights” — well, no one who takes action is looking at your dashboard. And if your website says “if you have problems, I have solutions” — I’ve got news for you, no one is Googling “solutions.” I was a solutions provider once; the phone did not ring asking for the solutions provider to please answer. And telling people their data is dirty is kind of like telling them their baby is ugly — it’s not the start of a great relationship.

What People Actually Pay For

Daniel: Is fixing all this on us, or can we wait for a knight on a white horse?

Brian: Look around and see what issues people are struggling with. We get online and argue about databases and data formats, then go back to our jobs and we’re fixing addresses, fixing numbers stored in text fields. Just because geocoding is easy to you doesn’t mean it isn’t completely bogging down somebody out there who’s dying for someone to turn addresses into coordinates and put it in a spreadsheet. You have to figure out the little pieces you do that have real value to people who don’t think in your geospatial verbiage — it’s like learning a different language, rather than broadcasting your internal professional language through a megaphone and hoping somebody responds. And don’t get into bouts of self-laceration because you don’t know spatial SQL but know a ton about imagery analysis — geospatial is too big an umbrella to be an expert at everything; take the bits that resonate most with the reason you got into the industry.

Daniel: What’s the most marketable skill you have today?

Brian: To be blunt: I save your data projects. When I started my consultancy I should have just cut to the chase — I offer middle-management safety nets for projects that, like any IT project, have a high rate of failure; I’m the insurance policy. Does “I use Protomaps tiles” impress a manager? No — he or she has no idea what that means. But because geospatial is such a black box to non-practitioners, no one has any idea what’s hard and what’s easy — and our problem as practitioners is we associate “hard and difficult” with high value to the end user, when there may be no direct correlation. Complexity does not necessarily equal higher value. What a generalist manager wants to know is that there’s a route from point A to point B, and if something comes up they’ll hear about it right away and have a workaround in hand. Risk management, risk aversion at the managerial level, and explainability — so when you unroll that beautiful map, people know the data behind those colours was collected and analyzed in an intellectually responsible fashion — those are the high-value skills, even more in demand given the technological advances.