The following story of “Ruby” reverse engineering a startup based on market conditions, industry trends, and nascent investor fads is taken from Throwing Rocks at the Google Bus by Douglas Rushkoff.
One of the smartest technologists I know, a young woman from the West Coast I’ll call Ruby, decided to launch a company on a whim. Ruby did exhaustive research on emerging interests and keywords in the technology and business press, as well as conference topics and TED subjects. What were venture capitalists getting interested in? Moreover, what sorts of technical skills would be valuable to those industries? For instance, if she concluded that big data was in ascendance, then she would not only launch a startup related to big data but also make sure she created competencies that big data firms required, such as data visualization or factor analysis. This way, even if her company’s primary offering failed, it would still be valuable as an acquisition—for either its skills or its talent, which would be in high demand if her bet on the growing sector proved correct.
She ultimately chose geolocation services as the growing field. She assembled teams to build a few apps that depended on geolocation—less because the apps themselves were terrific (though she wouldn’t complain if one became a hit) than because of the capabilities those apps could offer to potential acquirers. Working on them also forced her team to develop marketable competencies as well as a handful of patentable solutions in a growing field with many problems to solve. The company was purchased, for a whole lot, by a much larger technology player looking to incorporate geolocation into its software and platforms. The employees, founder, and inventors who believed in her are now all wealthy people.
E-commerce using Elasticsearch
And Machine Learing
Last night, I attended an Elastic meetup hosted by HBC Digital. The Hudson’s Bay Company (HBC) owns Saks Fifth Avenue and Lord & Taylor. This meetup provided one of those “aha” moments. It is possible to build a local search engine using elasticsearch. This search engine would incorporate e-commerce and machine learning.