Entrepreneurship Podcast post

Data Use and Interpretation with Brett Hansen

George Grombacher August 25, 2022

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Data Use and Interpretation with Brett Hansen

LifeBlood: We talked about data use and interpretation, the value of data, the many types, how successful companies think about it, and how to create a plan of action, with Brett Hansen, CMO of Semarchy. 

Listen to learn how to overcome frustration when it comes to keeping your data organized!

You can learn more about Brett at Semarchy.com, Facebook, Twitter, YouTube and LinkedIn.

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Our Guests

George Grombacher


Brett Hansen

Episode Transcript

george grombacher 0:00
Come on Bob Leffler. This is George G. And the time is right to welcome today’s guest strong and powerful. Brett Hansen. Brett, you ready to do this?

Unknown Speaker 0:18
I think I am.

george grombacher 0:19
Let’s go. Brett is the Chief Marketing Officer with SmartKey. They’re a leader in data integration and Master Data Management, Marketing, enabling organizations to create immediate value from their data. Brett, tell us a little about your personal life more about your work and why you do what you do?

Unknown Speaker 0:37
Well, a bit of SmartKey, for seven months, previously had worked at another data company for a couple of years. And before that, I was at some big companies that you might have heard of Dell, and IBM. What attracted me to some marquee is a company that was truly changing the way organization use data. And, you know, at IBM at Dell, at the other small company that worked out, you know, we talked a lot about data and how important it is and how it’s can be used all these sorts of things. And it certainly we made progress at all those organizations, but it never felt like we kind of got it over the edge, right? How do I really empower a decision maker, whether that is someone like myself in marketing, a doctor, someone in finance, to make better decisions with data. And in some marquee just has a solution that kind of cuts through it all. And we’ll talk more about all the details later. But there’s something to be said about actually making that final bit of difference that can enable companies to actually realize the value of all their data. What

george grombacher 1:54
do you have a sense of how many companies out there that have an appreciation or an understanding of the value of the information they have?

Unknown Speaker 2:04
Well, it’s fascinating. It’s a huge market, right? So people are spending billions and billions and billions of dollars on, I’m collecting data, I’m storing it, I’m moving it from point A to point B. But then how many of them are actually realizing the full value? Right. And according to Gartner, you know, good source, you know, eight out of 10 projects that are associated with trying to extract business value fail, which is a pretty, pretty bad, right? I mean, that’s a 20% In my role, that’s an F F minus minus, right. So, you know, I think companies and organizations know that there’s something there. Right, and then they wouldn’t be spending the money, they wouldn’t be making the investment. They wouldn’t be storing all this data. If there wasn’t something there. It’s it’s that final step on All right, I have it in, you know, a database, I have it in Sioux flake, I had an application, I haven’t always please, how do I bring it together? So that I can, as a business user, make better decisions on how to engage my customers, as a doctor make better patient decisions, as an engineer make better decisions around structural elements, those sort of things? There’s all these different pieces. We haven’t brought it all together for customers yet.

george grombacher 3:16
Why is that? Just from lack of the ability to do it, the lack of will to do it, haven’t thought about it? It’s really hard. Right? So

Unknown Speaker 3:27
I mean, I’m sure you’ve had other guests on your show, talk about data and just think about the last 10 years, right, you’ve just seen such an explosion and what we can collect, obviously, the concept of Internet of Things came up, you also had in parallel a transformation of how we stored data, both from a concept of where it was stored, going from local to virtual environments, as well as the concept of, you know, I have these clouds and snowflake and I my own cloud, and I have a stone local elements. So all of these things created more complexity. And so now the opportunity is, how do you start to look at all these different sources, and bring them together, so you have one master data record. And then from there, I can actually extract the business value, which I’m hoping to receive. Otherwise, I’m looking at my CRM data here. I’m looking at customer information over here, I’m looking at under Billings and payments someplace else. And then then my brain, which is never going to be sufficient here is trying to piece all those things together. And that’s what we have to overcome. And that’s, that’s what’s the next evolution of data is all about.

george grombacher 4:40
You’ve said snowflake a couple of times, what is that?

Unknown Speaker 4:44
Snowflake is a unicorn slash Pegasus came out of nowhere. They’re now one of the world’s leading cloud based stores of data. Right. So as you think about, you know, the massive amounts of flexion where do I store it? Right? And 25 years ago, when I started my career in IBM, it was related to databases, right? You had DB two and Oracle and Microsoft SQL, you know, you actually had an on prem server that had a database. Now, there’s these newer formats for how we store data, which allow companies to be much more cost efficient, they’re actually more secure. So they’ve taken these giant leaps. Now it’s again about Alright, now that I have all this data. How do I make use of it?

george grombacher 5:30
Got it. And couple of steps back, sorry, when we’re talking about data that can really be is that a name and an email address?

Unknown Speaker 5:41
It could be? Alright, that’s a great question. So and that’s an I, you know, you just also hit on another key point here, which is the complexity of data has increased, right? So again, 20 years ago, it was rows and tables, right? It was it was think of him as like a spreadsheet. Now you have obviously still, things like that. But you even have more data, which is in what we call unstructured videos, this podcast, you know, schematics, engineering documents, all of those things now, just increase the complexity. And then we also can further increase the complexity as you have data that just is stagnant. You know, it’s, you know, once I create an email record with the name and an email, and email, you’re fine. But what about stuff that has to be pulled in real time. So I mentioned health care providers, obviously, they can’t just look at data that’s six months old, it would be probably pretty important to know if you’re in some sort of critical event, the latest information you have, and then obviously, bring that in with the legacy information. So as a health care provider, I can see a complete view of who you are. But that way, you need both legacy data that’s been sitting around, but you also need that real time element as well.

george grombacher 6:58
Fascinating, right? I was just, you mentioned medicine. And if I were a healthcare provider of some kind, I would want to need as much real time data as possible. But now that I have it, okay, great. It made sense that I should get it all like, Ah, now, how in the world? Do I interpret this? How do I make it actually usable? And that’s where you come in?

Unknown Speaker 7:22
Exactly. So there’s there’s two things that we think about one is integration. Right? So how do I move data from point A to point B? Right? And that’s really a storyline of being as efficient as possible. Right? How do I make sure that you know, with very limited resources, and as we talked about lots lots of data needs to be moved around? How do I provide tools that can efficiently securely move data from one point to another point so we can access it. The second piece, which is a little more complicated is the concept of Master Data Management. So going back to sort of opening remarks, you have data, all these other locations. So let’s just let’s just use a chief marketing officer example. I obviously have a CRM system like a Salesforce or HubSpot. And that’s super important to me. But I’m also probably using maybe some billing information, because I’m looking at existing customers. I would like to know, before I market to those customers, also, are they happy with me. So there might be some customer value information, there could be tickets that would have pulled into this so that they had some sort of issue that pull into this. So you want to be able to look at a company and organization a person and have a holistic view. And that means bringing data in for different locations. It can also mean enriching that data. So using outside information, so I might use a tool that can provide me additional contacts that I don’t have, or more information about location, email addresses, phone numbers, political information, all those sorts of things. So the first step is an aggregation concept. Right? So rather than just looking at each piece in a silo, how can I get a holistic understanding of what is happening? What is that, that customers that prospects 360 review. The second part of that is not all data is created the same. So there might be some data that is going to be athlete bulletproof. Others, well, there might be quality issues, and I need to know that so I can make better informed decisions. And then the final piece of this, of course, is being able to visualize it. So obviously, a single record is all about being able to see all the different sources, those sort of things. But if I aggregate that up, I’m going to look for trends and that sort of things as reviewed dashboards and reporting. So it’s Moving from, again, the world of, you know, a data set. So I’m looking at my serum data which and by the way I use, you know, lots of graphs and charts just with that. But if I really want to understand what’s happening with a customer or market segment or anything like that, I’m gonna want to look at lots of different datasets, and be able to pull that together in a comprehensive and cohesive manage, look at it from a governance perspective, as well as also for quality.

george grombacher 10:27
What is the secret sauce of of what you’re doing? Is it that you can identify? When you’re looking at this massive amount and different kinds of data, you can identify it, that you can officially move it or like,

Unknown Speaker 10:45
I have a question. No, I hear your question. And I wish I could say it’s just do this one thing, right. And that’s the challenge. A lot of companies have gone on the path of, we’re really focused on just moving the data, or we’re really focused on governing the data, which means who can have access to it, which that’s, that’s great. That’s a portion of the storyline. But if you’re not thinking about all these pieces, in a comprehensive cohesive way, all you’re gonna do is just create a bunch of roadblocks, right? And you’re not going to actually get that 360 degree. So your question is very valid, like, what is the secret sauce, the secret sauce is, you know, in order to make the recipe perfect, you need the Cayenne, you need the paprika, you need the chili powder, and like, no one spice can be left out, or otherwise, you’re getting a less than ideal deal.

george grombacher 11:32
So what is the typical engagement then look like? You, you you go into an organization that has their their an ideal client for your customer, for you got all these different things? How to say, Okay, what is it that in a perfect world? What would how would you use all this information? How do you How does that start?

Unknown Speaker 11:52
It’s, and this is what makes us different? We start with the business question, what are you trying to achieve? Right, so there’s all this technology stuff. And we can go and talk about ELT and, you know, the governance and all that stuff. And that’s important, don’t me wrong. That’s part of what makes you know, our product, great. But what’s really important is having the conversation about the business objectives, I in trying to solve x. And by the way, one of the things we frequently hear from customers who maybe have tried this in the past, and you know, a year later are still pounding their head against the wall, is we try to solve XYZ CQRS. Eventually, we can get there, like, don’t get me wrong, like we want to solve everything. But let’s start with a problem. So let me actually give you a real customer example, to illustrate how this ideally works. So there’s a great restaurant culture full day, they make wonderful burritos, redoubles. A lot of my calories were consumed using to fulfill their initial challenge was location. So they have stores, obviously, across the country, where are those stores? What status? Are they in? You know, are they open, closed? Within what time? You know, what’s their staffing, just the basics of location? A very solvable problem, right. But it’s still required, pulling different data sources from different systems, they actually enriched it with Google Maps information. So we actually had an actual map location on the store. So that was a starting point. And we were able to execute that very, very quickly, as in less than two months. They said, Great, we got our store information. Well, what are we really important is understanding the menus, because not all stores have the same menus. That’s what makes Chipotle great is a diversified based upon their local, local location region. So the next thing we worked on was menus. Okay, now we had pulled information from different sources, we had an accurate view of the menus. And then from there, it went to ingredients. And from ingredients, it went suppliers and from suppliers. It went health and management. So ultimately, you can start with one project, solve a problem, and then move next one. And the great thing is, is with our solution forces, we can scale across those different domains, we can scale in terms of amount of data consumed. But most importantly, we keep solving problems. Because even if you think you know exactly what you want to achieve, likely over time, those objectives are going to change and evolve. And that’s super important with data. Because if you try to solve a data project and say, Hey, I know exactly what I want, I’m gonna solve it completely in this this way. And this is what it’s gonna look like. That’s not how business works. Things are going to occur, changes are going to happen, acquisitions are going to be made, divestitures are going to occur, you’re going to go with new product lines, and you’re always going to be looking to change what you need, but it needs to start with what is my business objective? What am I trying to achieve here?

george grombacher 14:57
Yeah, that makes a ton of sense. Verse is just going and doing the whack a mole where a problem is popping up, I’m just going to figure out how to solve this. And it may not ever be an integral solution. So the next problem that solves up, I’m going to fix it. But it just could be an apple and an orange versus your approach is making sure that as you solve a problem, any new problems will be able to communicate and theoretically speaking, seamlessly integrate with one another.

Unknown Speaker 15:28
Absolutely. Another piece of that, again, is, is so often because again, this is a technical challenge. And ultimately, we work with technical team members. There’s this natural routing of oh, let me go solve my technical issue, I have a governance issue, or I have a discovery issue, right? They there’s a natural, just human motion of given the fact that I’m a technical person, I’m going to solve this with a technical solution. And ultimately, we are doing wrong. But we see again, so many projects fail, because you haven’t done the diligence upfront to understand what am I trying to achieve from the business objectives? What is the outcome we want to have and having as much specificity as possible on the requirements, and then we can engineer that solution based upon your needs? Right? And that’s what ultimately will drive success for the business and make it look like heroes?

george grombacher 16:22
Makes sense? Love it. Well, Brett, people are ready for that difference making tip? What do you have for them?

Unknown Speaker 16:30
It’s just gonna play off what I just spoke about, right? I think that when I talk to customers early, or prospects, I should say early in the cycle, a lot of them are frustrated. A lot of them are are, you know, we’ve spent money and we know we have this data, I know it’s there. I just can’t get at it. Right? It’s just like, it’s just out of reach. And so what I we always counsel them on is, understand what you’re trying to achieve. Write it down? What is the outcome you’re looking to achieve? Whether it’s again, understanding your customers better understand patients better, it could be something very simple, like what I started with Chipotle is, which is where the heck are our restaurants, right? And then from there, we can grow and expand that sort of thing. And so if you can start by understanding your business objective, and documenting that, and making sure that it is an achievable play, then we can grow and expand and really get more and more value out of your day.

george grombacher 17:33
I think that that is great stuff that definitely gets come up. But thank you so much for coming on. Where can people learn more about you? How can they engage with SmartKey?

Unknown Speaker 17:41
I would assume to our website, it’s not difficult is some market.com semarchy.com. If you want to talk to us, we have forums out there we have chat, so you can just drop us a line and we’ll be in touch to help you out.

george grombacher 17:54
You’ll find the data that they enter and use it correctly and appropriately. Amen. Your job as much as I did show your appreciation and share today’s show. The friend also appreciates good ideas go to some marquee.com It’s sem archy.com. And, you know, find out if it’s the solution that you’ve been looking for Thanksgiving, Brett. Thank you, George. Appreciate the time, and until next time, fighting the good fight. We’re all in this together.

Transcribed by https://otter.ai

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