The first prototype Fleming valves.
The first prototype Fleming valves. (source: Wikimedia Commons).

Evangelos Simoudis has spent many years interacting with entrepreneurs and executives at major global corporations. Most recently, he's been advising companies interested in developing long-term strategies pertaining to big data, data science, cloud computing, and innovation. He began his career as a data mining researcher and practitioner, and is counted among the pioneers who helped data mining technologies get adopted in industry. In this episode of the O'Reilly Data Show, I sat down with Simoudis and we talked about his thoughts on investing, data applications and products, and corporate innovation.

Open source software companies

I very much appreciate open source. I encourage my portfolio companies to use open source components as appropriate, but I've never seen the business model as being one that is particularly easy to really build the companies around them. Everybody points to Red Hat, and that may be the exception, but I have not seen companies that have, on the one hand, remained true to the open source principles and become big and successful companies that do not require constant investment. ... The revenue streams never prove to be sufficient for building big companies. I think the companies that get started from open source in order to become big and successful ... [are] ones that, at some point, decided to become far more proprietary in their model and in the services that they deliver. Or they become pure professional services companies as opposed to support services companies. Then they reach the necessary levels of success.

Bringing data science closer to the line of business

You need to bring the analytics and the ability to make data-driven decisions much closer to the decision point, which in this case is the business user rather than the data scientist. Again, one of the trends that I'm seeing more recently is that even corporations that have been blessed with the ability to have large cadres of data scientists ... are no longer keeping them as independent groups, but they are dispersing them and bringing them much closer to the business. Or they are trying to train some of the business users on how to feel much more comfortable working with data. This is a big problem that, particularly, corporations face and ... why applications and tools that provide insight as a service can be particularly important.

In the end, it's all about actionable insights

I think you need three ingredients. You need data, you need the right ways to combine the data and extract features from that data, and then the third ingredient is the ability to analyze the data and bring together the analysis results in a way that provides these insights and these measurable actions. I have several executives now through my advisory work who have told me that just giving analytics helps, but not as much. [They] need to be able to know what actions [they] need to execute in response to these analytics, to the results of these analytics.

... I've been writing about this concept that I call insight-as-a-service, which is the ability of systems to not only identify correlations, but to identify a variety of relations that they can then link to a set of actions, to plans whose effectiveness can be evaluated through a set of key performance indicators.

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