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How to Innovate and Manage Large Data Sets

At the Baylor Angel Network Analyst dinner, the student analysts asked the Angel members how they managed large, complex data sets.

In my response, I explained that the solution to the large complex data set problem is to build models. In the startup world, there are many types of startups, but if you categorize them by type and stage you can create a manageable number.  This allows you to contrast and compare new businesses against the models.

Each model has certain characteristics, dynamics, and risks. For example, a B2B enterprise software model has a CAC:LTV ratio which measures the strength of the business model. A consumer product goods company has a category growth rate and a gross margin which determines how much funding you will need to grow it. By organizing startups into a discrete number of models, you can manage large volumes of data.

Another question focused on how to develop new innovations. In response, I described how applying convergence to the models can generate new innovations.  By merging two models together, you can create a third model that is new and unique. For instance, if you take B2B SaaS and merge it with the music industry, you’ll create a streaming category exemplified by startups such as Spotify.

It is important to note that not all convergences make sense and lead to productive innovation. A key factor in achieving productive innovation is that it must solve a real problem.

This dovetailed with another topic of discussion—solving a real problem.  It’s important to work on solving real problems and not “nice to haves.” It takes time and funding to bring innovation to the market, and that effort should be directed toward solving an actual need.

Hall T. Martin

Hall T. Martin is the founder of TEN Capital and a builder of entrepreneur ecosystems by startup funding through angel networks, funding portals, syndicates, and more. Connect with him about fundraising, business growth, and emerging technologies

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