Comment by tinco | Hacker News Reader

Comment by tinco | Hacker News Reader

So, I don't have time to read the entire paper, but I thought it would be interesting to share my experience from the other side. I lead the engineering for a company that does the "data work", we gather terabytes of data, mostly photography and lidar, every day.

So far it's been a crappy industry to be in, it seems the only way to make money is to provide the entire value chain, and this is the business model we're pivoting to.

Just a bunch of photography and lidar scans isn't worth much to anyone, and it's very costly to establish a quality data stream. We had enormous capex on sensor gear, training personnel, running the operations. All this to save a couple bucks on sending an inspector physically to the site.

If you do the entire value chain, you gather the data, you process it into information, and then you turn that information into actionable reports. So that means that besides running our operations, we're also doing the modeling work, and we're providing the industry specific expertise for the actual inspection. So our customers pay for knowing what is broken, where it's broken, and what the impact of that defect is.

In the end, I believe we'll be very successful, but it will be because our investors were willing to take us all the way, where many other companies have simply given up on the concept.

That's an insightful experience, thank you for sharing. To give a counter-counter point, in my experience working with LIDAR (terrestrial in my case, not airborne) there's a wealth of tools that claim to process large amounts of unstructured pointcloud data and make all kinds of fancy visualizations and 3d models (Pix4D comes to my mind). The "catch" was often to find high quality data to feed these tools (other than the sample data provided by them). I'm convinced that having high-quality proprietary data is the moat the investors are looking for, not the algorithms. After all, universities are cranking improved algorithms all the time for free.

Oh for sure, I suppose that's also why the investors are still interested in us. But high-quality proprietary data is not what the customers are interested in. They just want to see a report that says how much m2 of brickwork needs to be repointed in the next 5 years. And they don't care if it's a machine learning model that deduced that information from high quality drone imagery, or if it was a dude on a ladder.

We're also working with terrestrial lidar btw. When we started out we thought we were going to do everything using photogrammetry, but right now we're deriving most of our geometry from the lidar.

I work in legal tech. The money is in identifying, gathering, processing and letting people search though terabytes of emails, spreadsheets, slack messages, etc. Corporations and law firms pay handsomely for eDiscovery. Ingesting and making sense of millions of emails for example ( think reply, reply to reply ) is a interesting problem.

We’ve found something very similar in a totally different industry. We started with data collection and people were willing to pay for the data, but not enough to create any kind of long-term sustainable business. We’ve slowly moved up the chain to provide operations support and now “actionable intelligence” alongside industry expertise and people are finally excited and willing to pay us real money for a full solution.

Now the challenge is keeping the team from falling back into the easy habit of just selling the data or trying to design product that are nothing more than glorified dashboards.

Does that mean you develop more individual solutions for every customer instead of a one size fits all product?

Inspections are generally standardised over entire industries. This is nice for the companies because it means inspectors can be trained anywhere, and directly hired to perform their industry standard inspection methods.

What I meant was that we had to actually hire or partner with industry inspectors, so we could sell a package deal to the customer, we had to go outside of our comfort zone and sell inspections, when all we really wanted was to sell data.

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