This week at South by Southwest, I met with a cool startup called DueDil, which is trying to provide a Lexis-Nexis-meets-Google-meets-LinkedIn style of service that aggregates public data on companies from a variety of databases, then uses that to create new metrics to indicate how solvent or successful the business is and will continue to be. In talking to founder Damian Kimmelman, I realized that the aggregated data his company has access to could help remake popular ratios and metrics used today for risk analysis.
The company, which is based in the U.K., has raised £250,000 ($401,855 USD) of angel funding and currently has data available on 7.7 million British companies. Eventually, however, Kimmelman wants to bring the service to the U.S. if all goes well in Britain. Obviously, such a service would be useful for investors, especially as the secondary market for shares in tech firms picks up speed, but I became fascinated by the idea that by aggregating different sources of information from financial databases, LinkedIn, Twitter and public records, DueDil is making correlations between data sets and solvency that people may never realize exist. For example, there’s a relationship between lawsuits and a company’s ability to repay loans, according to Kimmelman.
It’s been difficult to track multiple lawsuits against a company in one place, but DueDil wants to make it easy, then automate the algorithm to determine the correlation between such lawsuits and loan repayments. Other predictive attributes will likely emerge as more information is gathered, aggregated, then tested for correlations. It brings me back to the idea that what’s really powerful about the coming wave of big data isn’t that tons of information is in one place, but that from seeing all that data, we can see correlations and links that help us ask better questions to determine success.
After all, statistics may not lie, but they can be manipulated. Asking the right questions and seeing new relationships is more important than merely running the same queries faster or against more data. For more on the topic of big data, check out our Structure: Big Data event on Wednesday in New York City, or watch it streamed via our site. We’ll be dealing with how to turn data into actual wisdom, and, from there, into revenue.