Are Datasets the New Server Rooms?

Roger Peng

Josh Nussbaum has an interesting post over at Medium about whether massive datasets are the new server rooms of tech business.

The analogy comes from the “old days” where in order to start an Internet business, you had to buy racks and servers, rent server space, buy network bandwidth, license expensive server software, backups, and on and on. In order to do all that up front, it required a substantial amount of capital just to get off the ground. As inconvenient as this might have been, it provided an immediate barrier to entry for any other competitors who weren’t able to raise similar capital.

Of course,

…the emergence of open source software and cloud computing completely eviscerated the costs and barriers to starting a company, leading to deflationary economics where one or two people could start their company without the large upfront costs that were historically the hallmark of the VC industry.

So if startups don’t have huge capital costs in the beginning, what costs do they have? Well, for many new companies that rely on machine learning, they need to collect data.

As a startup collects the data necessary to feed their ML algorithms, the value the product/service provides improves, allowing them to access more customers/users that provide more data and so on and so forth.

Collecting huge datasets ultimately costs money. The sooner a startup can raise money to get that data, the sooner they can defend themselves from competitors who may not yet have collected the huge datasets for training their algorithms.

I’m not sure the analogy between datasets and server rooms quite works. Even back when you had to pay a lot of up front costs to setup servers and racks, a lot of that technology was already a commodity, and anyone could have access to it for a price.

I see massive datasets used to train machine learning algorithms as more like the new proprietary software. The startups of yore spent a lot of time writing custom software for what we might now consider mundane tasks. This was a time-consuming activity but the software that was developed had value and was a differentiator for the company. Today, many companies write complex machine learning algorithms, but those algorithms and their implmentations are quickly becoming commodities. So the only thing that separates one company from another is the amount and quality of data that they have to train those algorithms.

Going forward, it will be interesting see what these companies will do with those massive datasets once they no longer need them. Will they “open source” them and make them available to everyone? Could there be an open data movement analogous to the open source movement?

For the most part, I doubt it. While I think many today would perhaps sympathize with the sentiment that software shouldn’t have owners, those same people I think would argue vociferously that data most certainly do have owners. I’m not sure how I’d feel if Facebook made all their data available to anyone. That said, many datasets are made available by various businesses, and as these datasets grow in number and in usefulness, we may see a day where the collection of data is not a key barrier to entry, and that you can train your machine learning algorithm on whatever is out there.