Gravy welcomes Armando Escalate to the Advisory Board!
Armando sat down with us over dinner recently to share a bit about his background and what interests him about Gravy.
Gravy: Tell us about yourself…
Escalate: I spent more than 25 years in software and engineering management at companies like TransUnion, LexisNexus, and Unisys. Most recently, I was SVP and CTO for TransUnion (TLO Division). I’m also a startup guy, and was at Seisint in the early days. It was purchased by LexisNexis in 2004.
In my free time, I’m building an experimental aircraft. It’s a fun challenge and nearly complete. I’m also finishing my private pilot license.
Gravy: What brings you to Gravy’s Advisory Board?
Escalate: Over the last few years, I’ve joined the boards or invested in a few promising technology startups. I enjoy being at the stage in my career where I can share my engineering and technology management know-how with others, helping them to build great products and gain traction in their respective markets.
Gravy: Why is location intelligence the next big thing?
Escalate: Location intelligence is a powerful new source of consumer data – and data is what makes the difference between a good product and a great one, a good company and a great one. Five years from now, data will be the difference between a company that thrives and one that fails. So there is a huge emerging market for data that companies can use to enrich their own 1st party data stores, and location intelligence is one of the big ones.
Gravy: What is your favorite Gravy product, and why?
Escalate: Gravy DaaS. After all, I spent much of my career working with the original ‘big data’ giants.
Gravy: How do you see your industry using location intelligence in the next 5 years? 10 years?
Escalate: Today location data is being used for ad targeting, for competitive intelligence, but I think the bigger opportunity is in using it with other datasets – demographic, purchase, social network activity, other qualitative data. It’s a rich layer providing better place and consumer understanding, fueling things like predictive analytics, machine learning, AI. We’re going to see many, much more complex, use cases emerge.