(upbeat techno music) >> Live from Las Vegas. Its theCUBE, covering Informatica World 2019. Brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World 2019 here in Sin City Nevada. I'm your host Rebecca Knight, along with my co-host John Furrier. We have two guests for this segment: we have Pinkrose Hamilton, she is the VP Business Intelligence at Hackensack Meridian Health. Thanks for coming on the show. >> Thank you for having me. >> And we have Andy Crago, he is the Managing Consultant at Infoverity, thanks so much Andy. >> Thanks for having me. >> So tell us a little bit about this partnership between Hackensack and Infoverity. >> Well we were looking for an implementation partner, we were looking for the skills to come in and help us really implement MDM specifically, we're also implementing a few other technologies that we can probably speak about, but that's how we got connected. >> So tell us a little bit about what life was like before MDM. What were sort of the obstacles, the challenges that you were wrestling with? >> So Hackensack Meridian Health is the largest health system in New Jersey, and we are a very fast-growing, we like to consider ourselves disruptive, health industry in New Jersey, and so because of that we were growing and acquiring mergers acquisitions, and many different EMRs, many different physician credentialing systems were involved in this so we had to make a decision of do we wait 'til we're all on one system, which we all know will never happen, or never happen in time sometimes, so we decided to do the MDM approach which makes the most sense to us. >> One of the things that's interesting we talked, we go to hundreds of events, we talk to a lot of experts and practitioners, and everyone buys into cloud at some level, cloud natives, certainly born in the cloud, great benefits. Data's critical because in SAS, data's great if you have it because you can feed machine learning, you can take more risks, be agile, and more risk more reward. And the apps, it's all good, right? On the enterprise side, on premises, legacy kind of kicks in. If data can't feed machine learning or can't feed the app, AI really can't be enabled. This becomes a key challenge in the industry. How do you guys look at that? Because as you lay out, it's not a simple answer go to the cloud, just do on prem, you got to think about architecture. What do you guys doing with regards to where the data's stored, how do you think about it, what's some advice, best practice can you share? >> Well, I consider data storage being more like a house you're living in, right? So we buy our starter homes and we start our families. And then we outgrow this house, and then we have to say okay, I need a bigger house and we start growing. And so data's run pretty much the same way. We start outgrowing our on prem houses, and so now we're moving out, and we're moving to bigger and better things, which is cloud. And so I think hybrid is where we start, right? We can't start with okay, everybody move out and move into this new house, it's let's go build this new house somewhere else, let's test it out and see if we like it. So that's my thought process around it. >> So you've got the addition, that's got to work with all the plumbing, right? >> Right! >> So it's the same thing And then you got more track homes, and you got electronic cars that go in between. >> Exactly. >> Automation. So this is more of a systems view? >> Yes. Take care of the operational piece. >> Absolutely. >> Then think about developer angle, what's that, how does that architecture look? >> So in terms of what we're trying to do right now, I mean, it has to be kind of short-term vision with kind of a larger scale architecture, so you know as Pink was saying in terms of the hybrid architecture, if we are able to develop reusable cleanse functions such as the address doctor funtionality, we're were reaching out to a third party service, bringing in more enriched information, we have that in an on prem model right now. But in the future, that configuration and work will easily transition into that cloud architecture, so we're trying to keep our eye on the future and make sure that things are reusable as we move forward. >> And how do you two work together? I mean, this is such an interest, in this age of co-opetition, you're not necessarily competitors of course, but how do you work together to come up with the right solutions? What does that look like, the partnership? >> Well, we totally hate each other. >> That's right. (laughs) >> It's the first we've talked in a while. >> No, the partnership, I think, we hit it off right from the beginning. It was just a matter of you know, when we acquire new technologies and that decision of how much time and effort is it going to take for me to train my team and to identify the right folks on my team and what work am I going to take away from them in order to give them this additional work and this learning curve that needs to go into place. So I think we have to augment our teams with experts like Infoverity to come in and say, this is how this tool functions, and sometimes we bring in the technologies and we kind of just crack it open, but we don't really get the full use of it to understand exactly every bell and whistle we can take advantage of, and these guys are the experts that help us do that. >> And it's always a challenge, I mean, I think data's been center of the version for many many years, it's kind of mainstream now, and you can't look at the headlines these days without hearing one year anniversary of GDPR, privacy, so there's always been that risk management compliance stuff that's been around, certainly you guys know that. But everyday there's a new thing. Oh, you've got cloud, you got georegions, you're in this country, you're in that country. So as more regulatory things creep up, who knows, maybe blockchain's out there. So again, all these things are circling around complexity, which constrains data, not necessarily frees it so much. Well maybe build software. Do how does Informatica and customer deal with this, because I'd imagine you have to build an extraction layer, has to be some tooling around it, monitoring. >> Yeah. >> What's your take on this complexity? >> So in terms of an architecture perspective, we consolidate all of the different silos of patient data into a centralized repository. Historically, you would build a lot of point to point feeds based on a certain application. We built some custom work and we ship them off some data. But really what we want to do is be able to master once and publish to a canonical model that's more self-service and hub and spoke so as consumers and customers of the data need to come and get it, they can come to a centralized place, we can augment what data's available there, and kind of scale that with the architecture across real time capabilities, cloud, and other use cases that we come across. >> Do you feel good, data's frictionless, it's out there, it's addressable. >> In terms of the vision that we're on? So I mean, it's a couple steps at a time. But in terms of; >> It's that addition to the house. The journey and set of tools that we have, that's definitely where we're going, so. >> I want to ask you about the skills gap. One of the things that has emerged is that in the healthcare industry, it is much more evolved in the sense of there's an understanding of how to work with data. And perhaps because you've just always worked with more data than say a retail company or a consumer products company. So first of all, how big a problem is this for Hackensack Meridian Health? Is it as bad as the headlines suggest? And also what are you doing to combat it? >> So our main goal is to take care of the patient, right? So when a patient is introduced to our system, we want to be able to take care of that patient and their family members in the best possible way that we can. So if we're working with a very disparate organization, where we're on multiple EMRs specifically, it's hard for us to identify that episode of care for that patient. So the MDM piece particularly, with the patient domain allows us to do that. It allows us to view the entire episode of care for that patient, to see you went to these doctor's offices, you had these things done, you went to this lab, you had these tests done, you went to the hospital, you had this procedure, and this is what your follow-up looked like. So from a; and we're also conscious of the patient's expense in all of this as well as you know what's the provider's expense, what's the payer's expense, so you want to make it cost-effective. You want to make it accessible so that are there services that a certain zip code or patient population needs that we're not providing? That we can provide? And so this is the whole entire continuity of care. To take care of our patients the best way we can. >> My daughter just graduated college this week in Cal, the first ever data analysis college class, inaugural class so it shows how early it is. Cal's a great school, been doing data for a while. Data's a huge opportunity. Whether it's women in tech, new service area comes up. You don't need to be a hardcore programmer to get into the data business. But there's certain patterns we're seeing emerge, that you don't have to have a certain degree, because the jobs that are open, there's no degree for. There's only the first class has graduated from Berkeley. So I got to ask you for the folks in high school, or parents out there or anyone looking to reskill, what specific foundational and/or advanced skill sets should people be looking at if they really want to get into data? It could be anything. So I'd love to get your take on what you think those skills are for people out there that they want to learn something new and ride the wave. >> I'll start a little bit. I think a lot of people get really technical with data, but I think you really have to understand data within business contexts. I mean, if you're looking at a physician record, understanding the type of physician, maybe where the care was administered. You have to really think about okay, what am I trying to solve, what pain point am I looking at. So it's not about relational databases and writing sequel, you really have to understand the functional purpose of data within the business problem that you're considering. >> So machine learning's hot, the nerds go there, the geeks go there, but there's a bigger picture than just coding. >> Exactly. There's a whole data strategy that you need to consider and kind of plug and play as you go along and really understanding the data within the business context is key. >> I'm so glad you asked that question, because I'm going to give a different viewpoint from this. I have a daughter who's a junior in high school, and she's preparing her career path, and so she wants to follow mom's career path and wants to do data science, so it's very exciting for me, you know? I'm actually a role model, which you never expect your children to think of you as one. >> Congratulations. >> But yeah, she picked up a few sequel classes early on in high school. And I think that the underlining foundation of coding is probably a little bit important to get that piece of it, because when you're leading the function, and definitely knowing the business knowledge. When we start any project, we go in and we start with discovery, right? What is it that you do, how do you do it, what are your workflows, what do they look like? So that's definitely key. But adding in that technical piece makes you that perfect data science human that I would look for as an employer. >> It's certainly evolving. There's no one yet playbook, 'cause there's so many diverse opportunities to take in from visualization to ethics to coding to business value, unbelievable. >> Yeah. >> Great. Well Pink and Andy thank you both so much for coming on the show. >> Thank you so much for having me. >> Lots of great advice for newly minted graduates! >> That's right >> Yes. >> Thank you. >> I'm Rebecca Knight, for John Furrier, you are watching theCUBE. (upbeat techno music).