Q So if you think of the arbitrage between technology and, I guess, almost a social engineering that's required for people to, Ask the right questions that machine learning and deep learning can solve in the enterprise. What, what, what's your, what's your take on this? What's the, when you go and sell to a company, do you feel that people know how to harness the power of what you're offering?
A Um, I, I, I guess I understand that question as, um, the, the way we approach business users at Aremo is that we actually respect them a lot. Uh, some people build software that say, you know, let's dumb them down. Uh, in fact, business users are very sophisticated people. They just speak a different language. Um, so earlier we had a discussion about the, the challenge of the business user having a lot of business rules in their head, and yet they can't translate them to SQL. So one of the things we do, like, you can imagine we do a lot of things under the hood in terms of technology. We actually split the ETL process both cognitively as well as technologically into two parts. One we call EETL, and the other we call BETL. EETL is Enterprise ETL, and BETL is Business ETL. And essentially we provide tools for people to, the EETL is essentially responsible for data structures. Converting a timestamp into Monday, you know, Tuesday, and so on. Whereas the business user should expect that the data set is already come, already well structured, but they will then easily from that impose business rules on it. And by, by separating these variables, you can essentially have that collaboration between data engineering and, and, and business quite, done quite smoothly. So you saw some examples of the ability to do that, because when I do that demo, if you think a little bit, you say, well, …
AI assessment note: “by separating these variables, you can essentially have that collaboration between data engineering and”