Thought Leaders in Big Data: Interview with Sasha Gilenson, CEO of Evolven (Part 4)

Sramana Mitra: Let’s do another couple of use cases of your technology.

Sasha Gilenson: Another use case we have is about proactive prevention of problems. One of the frequent activities in IT and the development of agile methods is change transition – the release of a change into a productive environment. When you have a complex environment that is affected at multiple points all the time, one of the big questions is, “How do we ensure that these transitions are implemented accurately and correctly?” Part of the use case, what we call release validation, is ensuring that the transition between environments leaves those environments consistent. When a change is introduced into a productive environment, it shall essentially perform as it was planned and tested. This way you maintain visibility and control after the changes done to the environments when you have automated processes.

SM: How do you ensure that in this particular use case?

SG: All environments are different. If you speak about IT, you have different types of environments: Preproduction environments, production environments, etc. Essentially, the configuration could be different across those. You have millions of attributes. If you compare these environments, you get huge numbers of differences between them. If you look at them on a granular level, you will see that. We need to apply analytics to see which of the changes resulting from the transition could be relevant or could impact the performance of the environment, which ones are irrelevant, and which ones are expected. This must happen in order for them to function successfully. It takes massive amounts of data, and we are reducing them to the ones that really matter.

SM: How far are we from self-correcting systems? If you can figure out where there might be problems, and if you can manually diagnose based on certain heuristics, conceivably you can put in a layer of machine learning on top of that and start being able to self-correct or prevent problems. How far are we from that?

SG: What we see on the market is that there are tools that make the first steps for self-healing. One of the things you need to have for successful self-healing is to know what is wrong. That is something we are providing. To answer your question: not so far. Things are still very complex for automatic remediation, but we do see that a significant number of issues can be self-healed in the near future.

SM: I can imagine that the future of this industry is in that self-healing system, right?

SG: It is making a complete loop. Essentially it starts with automating change innovation, change transition, change analysis, and then remediation or healing.

SM: Tell me a bit about how you built your company. Are you the founder?

SG: I am.

SM: And where are you located?

SG: I am located half the time in Israel, where our development center is, and half the time in Jersey City, where our headquarters are.

This segment is part 4 in the series : Thought Leaders in Big Data: Interview with Sasha Gilenson, CEO of Evolven
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