Defining A Good Data Strategy – Kevin Mattice, Cherre

This is Textron TV. I hope the great pleasure being joined by Kevin. Matiz Kevin.

Is she product officer with Jerry? How you doing? Kevin doing?

Well. Thanks for having me Mitch great to have you first time caller first time visitor first time text front Tennessee. First time all things first time all things great to have you on and excited to learn about Cherry.

So before well as part of doing that tell us a little bit about yourself and tell us a little bit about the company. Sure. So I I've been in real estate data and technology for about seven years.

Now, the the space goes really wide, but I'll talk a little bit about that when I talk about Cherry I have been in product management for over 10 years. Now. I have been in Cherry for about four and a half and Teresa data management platform for Real Estate.

We help our clients who are large Commercial Real Estate Investors insurance companies and lenders connect their public third party an internal data so they can make better investment management and underwriting decisions. So, you know, it's a pretty specific niche in the real estate world. But real estate data is a mess real estate data is you know, even at its best usually solidified in Excel sheets manually stitch together it comes The public domain it comes from internal systems.

It comes from providers. So we we have our work cut out for us. Information essentially is one of the questions that I had, you know oftentimes, you know technology vendors are generally just a technology.

They don't specialize in a particular, you know use case or industry or something like that. And while I've never worked in real estate having bought a few homes and researched and paid a few mortgages. I got a man.

I mean, did you talk about the public sources of data and all that? So do you do you help people with aggregating that content together and then using it in some way or is it a platform then they put whatever they want on to Cherry and you make it easier to do analytics and And whatever kind of applications they build around it sure. It's it's a lot more of the latter.

I mean some of it is Data aggregation. But our goal is to really we want to take the heavy lift of pipeline building data standardization data modeling and and stitching data together off of the plates of our clients, right if you think about our customer base, we want their focus to be on how do they make the best investment decisions, right? How do they Automate their underwriting process to reduce their risk, right so it's our goal is to help them stay focused on what they do in real estate and not have to worry about.

Say digging through tax records from a county to figure out you know, what what data is Irrelevant for the property. They're underwriting or investing in we don't want them to have to worry about how do you stitch together least comp data with sales comp data with Market Trend data, right? There's there's all all sorts of different data sets that help inform those decisions and they don't really speak to each other.

Mmm, you know, we I think people and you know who have purchased a home or think about real estate think about a property. and what what we don't often think about is the fact that you know leases are not specific to a property. They're specific to a given floor in a given building.

but if you think about a real estate transaction, it could be many buildings on many Parcels of data and the loan for those right those buildings is all grouped together. So there's a lot of entity resolution and data standardization that has to happen for data to be meaningful for our clients. Very interesting.

Yeah. Just imagine that scenarios of Can I use it for this purpose and what rights do I have to water minerals or whatever and you know, what kind of businesses how high how high can I build the building right if I want to extend it or whatever might be? Okay.

Very good. Well, so let's step back for a minute. Yeah, I can imagine and I kind of get at least from afar, you know the data management problem for in the real estate business whether you're in any one of those different company roles is pretty much Monumental and they can't all hire the best world-class data analysts to do to do all this well form so they need some help we often talk about we need a data strategy.

It used to be who the day no, or was who owns this data and that's that's where you went to to get the guidance about what we're doing why we're doing it we need to do. But I kind of get the sense of a lot of us don't really have a data strategy we might say we do but I'm not sure we really do. What do you what do you define as a good data strategy?

Yeah, it's a great question. I think even to build it into a little bit of the story especially in the real estate industry. If you go back 10 years there was a realization that data was gonna be necessary in order to compete.

So firm said we need data, right that was the the light bulb that went off and the data strategy at that point was very simply go and acquired data. Right, it was data about demographics or data about market movement. Right?

It was all different kinds of data sets and it was that was the strategy. Build a budget go buy data. and that that happened right people budgeted for it.

They they said we're gonna go buy data and then it came to what are we gonna do with that data? Mmm, right and all of a sudden it really you know, they were in these companies realize like we bought the data but we didn't necessarily include all of our end users we didn't think about How that data was going to fit into their workflow or how that data was going to get built into a process or our Master data model right that a lot of that that thinking didn't really happen. So when we think about a data strategy, especially, you know at cherry and in the real estate industry, it starts with your end users, right and it starts with what types of products and workflows Reporting.

Does that data need to go into to be mean right and then add value and that's you know, as you work back. It's obviously important to take that into account When selecting what data you are going to acquire whether it's from the public domain or you know private provider and then some of the big things are really all about. Now that you know the scope of your data, how are you going to keep it secure?

How are you going to scale it as your needs change in the business need to change? Right. How are you going to hire the right people to make sense of that data?

How are you going to make sure it it stays up if it's the quarterly reporting period end and one of your Upstream data providers has decided to add or remove or change your field. Mm-hmm. And the whole data pipeline explodes.

What are you going to do? Right? How are you going to know that that happened?

How are you going to alert your team? How are you going to make sure that you know, the the analysts who have been online all weekend are banging down the doors of the you know, the it or the data department and say why is it all Brooke? Well, I don't know that folks outside of the teams working on this relies.

When we say integration, right we talked about connecting things together. It's a moving object constantly changing sort of like a cellular organism and every cell is changing on its own path and time and you know, hey the API just changed. How did we know that?

Well, they just did it. They didn't even send us an email and now we got to go fix that. You know from security to all those Integrations it it's an effort to keep up with that.

And you know the other thing you haven't mentioned yet, or maybe you did was also just where is our data, right? What is in what cloud what are we pulling into a platform like yours? You know the the currency of it the relevancy of it.

Yeah, that's part of the whole thing too. Because often times you don't know where all that data is because it's being created generated especially in a world where storage is massively available. It's not quite free always but you know, you can store a lot of content a lot of data.

Definitely. I we I mean there's a reason that there are many v2p businesses out there who are strictly data lineage providers right? Does everybody wants to know what changed when did it change who touched it last and It all goes back to the point of how does.

How does that impact your your business process? right, and I think that's one of the biggest missing pieces is monitoring and observability very simply how do you know, you know, if if you rely on data that was manually entered into a system at a given point in time and you need it in a report in the hands of Executives at another point in time. What are all of the different Transformations that happened to that data as it went through your process and if something fails How do you know how do you react?

And how do you triage that problem? And you know, it's not it's not a problem that can be solved just by technology either right a huge part of data strategy, especially if you're looking at build versus buy is to just ask the right questions. Right, I think.

A lot of people if they're they're going and they're gonna buy a technology that's going to interact with their data. They're not thinking necessarily. What is the incident response process if something fails.

Mm-hmm. So that's a big missing piece that we call midnight before the quarterly report comes out right and says, oh we got a problem. Absolutely.

Yeah, and it happens all the time and it usually happens on Saturday at six o'clock, but only happens on weekends and after 9pm so exactly exactly during normal hours. Yeah. I'm curious.

You know, I just it's great to talk to someone who's a data expert and working in this field. You know, we talked about databases and more contents is stored and whatever technology that might be. What do you see is how do you define a data platform?

What that mean to you as someone in in that part of the industry? Sure, and I think it's in it's an important question to ask because oftentimes if you're speaking to someone who doesn't really have a fully Big Data strategy you say you know, what's your strategy for for your data? And they go well, we purchase snowflake.

We're good exactly done, right we're finished and I think that's you know going back to my last point. It. It doesn't answer a lot of the the questions right?

There are database Technologies. There are orchestration Technologies. There are right Technologies of how and when to run your code to make sure that data flows through your pipelines, there are Telemetry right.

So how do you know when things are going wrong and your pipelines and get logs out to your users there are data delivery, right and API stitching technology. I mean, it's all there's so many different Technologies, but it's all about how you Stitch them together to solve your business needs right? And one thing we see all the time is the evolution of I know I need a bunch of data.

So I'm purchased it and then I you know, I purchased a database technology and I put all the data in it. But now what happens? right and you think you think about what's needed to make data meaningful and I talked a little bit about some of the challenges and real estate data and that's a specially where.

hiring people and product management hiring data analysts involving subject matter experts to understand How does that data stitch together? right is a meaningful piece and then That extends to data governance strategy and if you're dealing with as often as true in real estate. financials information Right.

The accounting team has made a change. in the accounting system and that needs to flow down into Downstream reports How do you manage those changes, right what people should be allowed to make changes to the data? And at what points in the process should they be allowed to make those changes?

Right. It's interesting too because The not only the uses but also the the meaning of the data itself, right what one one source of data might call a customer might need to be a customer you or might be a customer but we defined it differently of where customers are so you can't just take here's all these sources map them into a nice data platform input whatever Tableau or whatever on top of it and great now start writing apps and reports, right? It's there's replicate, you know, duplicate data you has to be due to you know, Coral hated to sign out what's wrong and what to use or not.

It's a pretty complex problem. You really jump into it at least to me it it's fascinating, but absolutely well and the point you just made is actually such a good one you talk about client access. and security and I think a lot of times if you're if you're a firm who's going to Outsource right to a data platform.

You say are you sock too compliant? and a lot of companies will say, you know, well, we're on Google or AWS and they're sought to compliant and I think for a lot of customers who haven't gone through the process of thinking that through that's those are not equivalent, you know, sock two compliance doesn't transfer from one company to another and understanding who has access to your data is critical. Battle and knowing where where it is, you know, it's 10 pm.

Do you know where your date is? And all right. Go back to I'm gonna start using that I like it.

You know that our parents used to say, you know where your kids are. Oh very good. This has been awesome enjoy talking with you about this working folks find out more and start to work with some of the capabilities that you offer through cherry.

com if you're a developer. If you're a data analyst if you're anyone who works with data in real estate when you're in Cherry, you can take a look at our our marketplace with third-party data providers. You can take a look at our API documentation.

It's all free trial access. com sign up group and and with with the sign up with some you have free account. You can start to do some of this.

Let me try make some connections use some of these services or at least imagine how might go from my spreadsheets and my SQL databases to you know, a data platform really really targeted at real estate kinds of data. Yeah, absolutely. Very cool.

com CH e r r e that I got it like the fruit like the fruit just to make sure everybody's on the same page there what's been fascinating talking with you Kevin and you know keeps up to date. This thing's move along and You know new challenges pop up or new capabilities are coming out from Cherry. We'll talk to you again.

Absolutely and thanks for having me much. Okay, great to have you on we'll see you again soon. com.

CH e r r e and Kevin it's been a pleasure. We'll see you. Thanks.