We took the verticals that FounderDating Network Cofounder members (those members who have indicated that they are interested in finding cofounders) selected as markets they are interested in started a company in and compared the last six months with the same six months one year ago…
– via Founder Dating
It comes as little surprise to me that the verticals that seem to remain pretty stable include commerce, small business, advertising, cloud services, and enterprise. To my mind, this is reflective of how our economy intersects with technology in a fairly general sense. Of course mobile is still big, and I believe most investment in mobile is driven by commerce (including advertising) and business needs, with cloud services serving a supporting role. It is interesting though that mobile startup investment seems to be reaching a plateau rather than growing or declining.
It’s also no surprise to me that the wearable and smart home verticals are on the rise, given the buzz around “Internet of Things”, health-data scenarios, and clean energy over the last few years. Interest in these verticals has existed for a long time, but investment is happening now for two reasons: maturing new technologies are finally enabling them, and our social norms are changing. It of course remains to be seen whether there will be a bubble in either wearables or smart home startups, but for the moment there’s a scramble to deliver new products and services in both spaces, and there’s a lot of room for growth over the next few years.
The consumer electronics rise is probably related in part to wearables and smart home, though it’s interesting to contemplate what might be happening if some portion of that rise is independent. (I’m not going to do that here though.)
To me, the most interesting stand-out in the Founder Dating verticals report is an apparent decline in interest doing startups in the data & analytics space.
Is Big Data investment waning?
I see more and more job listings these days, in all sorts of technology disciplines that call for “a passion for big data” or “proven ability to analyze data for customer insights”. In part at least [big] data analytics seems to be getting absorbed into the broader technology toolbox—that more an more “Big Data” is seen as a core competency, or from another point of view just another part of the “cost of doing business.”
Simultaneously, the idea of Big Data driving markets in-and-of itself seems to be dwindling. And I think this is a good thing.
Data by itself is just data even if it’s Big
I’ve felt for a few years now that there’s been an over-emphasis on data for its own sake, at least the way it’s been marketed so far: More data, more types of data, more sources of data, more users contributing data, etc.
There’s certainly been a huge rise in data warehousing and reporting capability across the many industries touched by high-tech. And many companies have made at times extravagant claims about how Big Data will revolutionize all aspects of your business (technology or otherwise).
It’s true that we can now store, search, and retrieve information with a capacity and speed that was unimaginable even two or three years ago. But for the most part, availability and cost-effectiveness of data collection and reporting by itself has not (so far) revolutionized our lives or our businesses, except in a few niches—web search and social networks being two of the most visible.
It’s the analysis, stupid!
Take Facebook and Twitter in the social space, Google in search, or 23andMe in the consumer DNA analysis space. For at least these verticals there’s also been a correspondingly large investment in data analysis—probably in nearly all cases a much larger investment.
We need to understand that good data analysis requires a lot of creativity, long-term investment in tools and algorithms, and an iterative development process—all of which is far from free. The data by itself is just bits on a disk somewhere.
Access to vast amounts of data has indeed been a fantastic aid that has driven broad, albeit often incremental improvements in decision making, product design, and operational efficiency. More rarely it’s enabled completely new product spaces, though without a real data analysis component, most of the new markets that have opened up have been related to data warehousing. The mere availability of lots of data has not so far been a panacea. And it may never be.
It’s certainly true that we take for granted today that we have comprehensive map data at our fingertips.
Ultimately though, the most interesting Big Data scenarios require that we aggregate and correlate vast data-sets in ways that ask specifically designed questions, and which report results that can be interpreted as effective, meaningful, actionable answers to those questions. (Remember Douglas Adams’ 42?)
And so far asking the right questions is still nearly completely in the domain of human beings.
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