Data Management Done Right with Malcom Hawker from Profisee - Data Forward Podcast
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Ramon Chen:Hi, everyone, and welcome to another episode of data forward. I'm very excited today to have with me Malcolm Hawker, chief data officer at Prophesy. Hi there, Malcolm.
Malcom Hawker:Good morning, afternoon, evening. I guess this could be any time. Good to see you, Ramon.
Ramon Chen:Yeah. Great to have you on. So today, we're gonna talk about how to get data management done right. And what is that? It's gonna encompass a whole load of different topics from cataloging to governance, data products, little bit of master data management, and a little bit of data observability too.
Ramon Chen:So but before we get started, Malcolm, everybody really knows you, but would love for you to give you, the listeners your background.
Malcom Hawker:Oh, gosh. You know, I I won't bore everybody with the entire 30 year history. I'll I'll I'll do I'll do that maybe the CliffsNotes. Yeah. I've I've for my entire 30 years in the workforce, it's kinda crazy to say 30 years, but it's it's a fact.
Malcom Hawker:It's actually even more than that, which is a little crazy to say.
Ramon Chen:Yeah. You need better.
Malcom Hawker:Yeah. Yeah. I started I've always been in technology. Always. Always been in software, always been in IT, and I really started my career in a product management track.
Malcom Hawker:I I got to kind of work my way up the ladder. I became a chief product officer of a, of a software startup based out of Austin, Texas that was eventually sold off to a company called NetSuite. Mhmm. But lead product teams, designing good products, doing product market fit, doing pricing, doing design, you name it. Building great software was something that I was really, really passionate about.
Malcom Hawker:And I learned a ton in that process. I actually had a chance to manage engineers. I've managed, you know, the the the systems that are creating all of the data that I'm now in love with. So that was a great learning opportunity. And I made this pivot in into more on the IT side of the house and and the infrastructure and operations and managing IT and and and managing data, managing databases.
Malcom Hawker:I had the chance kind of working my way back up the ladder again, had a chance to lead an IT function at a $2,000,000,000 publicly traded company out of DC. Decided that that leadership was was something I was really good at, but but, something I didn't necessarily wasn't passionate about, and and made a pivot to become this is a conscious decision actually in my in my forties to say, okay. Well, where do I wanna be in 10, 15 years? And and I decided to become more of well, let's just call it a thought leader. Because what what what I'm passionate about is sharing what I know.
Malcom Hawker:What I'm passionate about is sharing all the experience that I've I've I've developed and communicating that whether that it's in written form or in video form. Obviously, I'm doing this today. That led me to a path of working for a company called Dun and Bradstreet where they gave me this lofty title of distinguished architect. It led me to a path to be a Gartner analyst, which was a fantastic experience, and I could never ever replace everything that I learned over 3 years.
Ramon Chen:And that's where we met, at Dun and Bradstreet and then later at Gartner.
Malcom Hawker:Yep. Yep. Yep. And, and I've parlayed that now into a CDO role with with prophecy where I could best be described as a field CDO where I am very active in evangelism. I'm very active in our field.
Malcom Hawker:I'm very active in working with our clients and helping them understand why MDM, why data catalogs, why governance, why data strategy, why should we be doing these things, and the and the best way to approach some of these these difficult challenges. So that's a lot about about about me. That's my story.
Ramon Chen:That's fantastic. And then you're the host of CDO Matters, which is a very popular podcast. And, tell us a little bit about how that's going.
Malcom Hawker:Yeah. Well, you're you're very kind. Thank you. Yes. I have a podcast.
Malcom Hawker:We're under our 62nd or 63rd episode, which is crazy. For over 2 years now, we've been we've been publishing heaven missed a beat every 2 weeks. I have a mix of of thought leaders. I have a mix of CDOs. My goal with that podcast is is to extend the tenure of CDOs.
Malcom Hawker:A hard stop. That's the goal. My my mission is to extend the 10 years of CDOs. We know CDO tenures are still half of CIOs. We know a lot of CDOs struggle struggle to deliver value, and my goal is just to share what I learned.
Malcom Hawker:I'm not selling anything. I'm not trying to push a specific agenda other than some of the opinions that I have that are fairly well formed. But, yeah, we've got a lot of great guests. I've got I I provide a lot of interesting insights. I I am I'm really focused on trying to be as provocative as I can because I think the status quo is kind of broken, and there's a better way to do things.
Malcom Hawker:There are different ways to do things. And the way that we've been approaching things for a while is is not driving the value that that we know data can bring to an organization.
Ramon Chen:So that's
Malcom Hawker:what I focus on on the podcast.
Ramon Chen:Yeah. 100%. And the controversy, if you wanna call it that, but I call it sort of more pragmatic, well thought out approach, this is what we're gonna dive into. So one of the first topics, we we recently, had a conversation, and we were talking about sort of a outcome driven approach. Right?
Ramon Chen:You were putting forth a really great perspective how a lot of people are just taking inventory, doing cataloging, doing governance, and a lot of large consulting firms are prescribing that.
Malcom Hawker:Yeah.
Ramon Chen:But it's not really working. Can you can you describe why?
Malcom Hawker:Yeah. Pardon me. There's there's a few things that that I noticed while I was a Gartner analyst where I was where I was given the honor, and and I consider it that. Speaking to no fewer than 1500 CDOs and CIOs about what's working and what's not working. And some of the the themes that I saw around things that aren't working are what I would call this very kind of obsessive bottoms up inventory driven approach to managing data.
Malcom Hawker:Right? Where, you know, an excessive focus on data catalogs. Now don't get me wrong. I love data catalogs. They are foundational.
Malcom Hawker:You need a data catalog. You need a place to you need you need 1. Metadata management is absolutely positively critical, and everybody will agree with that and everybody gets it. But what I saw time and time again, Rowan, was a new CDO would come into place, and she or he would be given this mandate often a transit or some sort of transformational mandate, be data driven, you know, digital transformation. We're gonna change how we operate.
Malcom Hawker:We're gonna put data at the core of it. Yeah. Here you go, CDO. Go. And so many of them would would start by doing this kind of this inventory.
Malcom Hawker:They try to glossary everything. They try to lineage everything. They would they would try to understand what's out there and get their hands around it. They would go about trying to assign owners, of of everything, of all of their data. And 7 or 8 months after they started, they would call me up at Gartner, and I'd say, hey.
Malcom Hawker:How's it going? And the answer was, well, I've got half our data inventoried. We've got, you know, a quarter of it glossary. And I'm still struggling to get access to a few key databases over here because nobody wants to really kinda play nice. And I'm having struggles with governance.
Malcom Hawker:Nobody's showing up to the committee meetings. Nobody wants to own the data. Everything's going sideways. Help.
Ramon Chen:Yeah. What do I do?
Malcom Hawker:And and so what you suggested there, the exact opposite of that, instead of a bottoms up, kind of be a little more top down. Figure out what the business priorities are. Figure out the challenges are. Go talk to people on the business side of the house to to understand how data could be leveraged to solve some really difficult problems. That's what I mean by outcome driven or or business driven.
Malcom Hawker:Now it doesn't mean that you don't need to do a data catalog or a glossary or a lineage. You need to do those things, but you own and governance especially. Yep. But you only need to do those things insofar as you've limited your scope to a very specific business benefit.
Ramon Chen:Exactly.
Malcom Hawker:You you can say, okay. I'm gonna do a customer 360 with a goal of 5 percent improvement of cross sell of this line of products. That's a very, very specific goal where you can put a fence around that and say, okay. What governance do I need to do that? What data do I need to catalog to do that?
Malcom Hawker:What what what do I need to do from a data quality perspective? What are the rules that I need to do? What about MDM? All those things. But you put a fence around it, and it and it it doesn't turn into this, like, thing that has no end where you can't even really define.
Malcom Hawker:Okay. Well, I gotta do the entire governance framework because I really don't know what success looks like. That's what I mean by outcome driven.
Ramon Chen:No. That's exactly exactly what's, what we're seeing as well, where people are, tired of just cataloging everything and just doing governance for governance sake. And they also want governance to be operationalized, right, and not just sitting on the sidelines looking pretty. It's all nice and neat and so catalogued. So one of the things that, that I was, indexing when I read your your post was the value.
Ramon Chen:Right? The value you're gonna get from the outcome. And then working working backwards, forwards, shifting left, shifting right, whichever the way you describe it, is something key because then you're only investing the effort that drive the outcomes.
Malcom Hawker:Yep. So so so value, the the the concept of value is is acknowledge I can acknowledge that to be slightly squishy, but dollars and cents most certainly are not. For sure. Money is not. Yeah.
Malcom Hawker:I mean, the amount of money we put in the bank or the amount of money that we're saving or indirectly the amount of risk that we're mitigating also translates into a financial benefit. So when I talk about value, what I'm actually talking about are dollars and cents. The most widely held well, maybe not the most widely held. One of the most widely held myths in CDO ranks is that we cannot quantify the business value of improvements that we make with data. It's a myth.
Malcom Hawker:We continue to tell it. I don't know why we continue to tell it. Probably a separate podcast in and of itself of of of why so many people cling to this. I do understand that the benefits are one degree removed. Right?
Malcom Hawker:They're indirect. Can can you say that if I spend a dollar on data quality that it's gonna be a dollar in the bank? No. Because there are 2nd and third degree relationships here. Can you build caught, like, hard and fast causal models?
Malcom Hawker:Maybe, but those are pretty expensive. But that doesn't mean we can't measure impacts. The entire marketing function is built on something called attribution. Can you say that $1 spent on marketing is gonna equal $1 in the bank? Of course, we can't.
Malcom Hawker:There's all sorts of touch points. There's all sorts of influence that we're giving. There's all sorts of opportunities to to improve things like how we sell, the products we build, and on and on. But we can still build models to measure these things. We can do this.
Malcom Hawker:The the joke I tell is we're in the modeling business.
Ramon Chen:Right. Right?
Malcom Hawker:And I'm I'm not talking about, like like, Blue Steel, like like modeling modeling. If if we're telling our business that we, data professionals, cannot measure something, then then then then as those senior leaders who who the c c suite CFO CEO, if they're hearing that from the data leader, they've got every right to question your capabilities as
Ramon Chen:a data leader. Yeah. And one one of the things that you brought up, I think it was a quote that said that people who manage data don't have much data, right, to measure themselves against, which is a is a huge irony in my mind. So are you seeing that being more progressive relative to trying to capture the data and trying to link, right, with lineage with things like data observability being able to see. These are the sort of sources and attributes that are being curated, and a lot of processing is improving the data quality.
Ramon Chen:And these are tied to the business outcomes Yes. That actually are being delivered.
Malcom Hawker:Yeah. Absolutely. The the little pithy quip that that I've given some of my presentations, which honestly, Ramon, kinda lands a little flat. Like, most people look at me and they they it it's like gonna take a couple minutes to think about it, and then after the presentation, they'll come up and say, oh, okay. Yeah.
Malcom Hawker:The pithy quote quote I I I say is that data people have no data on the value of data.
Ramon Chen:That's right.
Malcom Hawker:And that's a lot of datas, but it's absolutely positively true. We we know it to be true. Survey after survey finds that it that it is not the truth. Mhmm. So then how do we actually do some of these measurements?
Malcom Hawker:You just touched on some of the to me, the low hanging fruit, which is on one imagine this may be like a ledger, you know, two sides of a ledger, debit credit. On one side of the ledger, you've got business metrics, KPIs. Right? You've you've you've got cost of goods sold. You've got days days in inventory, days outstanding on an invoice.
Malcom Hawker:On the on the other side of the ledger, you've got data quality metrics. You've got, you know, completeness. You you you've got accuracy. You've got all of the usual dimensions of data quality, whether there's 4, 6, 12, you pick. Yep.
Malcom Hawker:It's fine. They're all good. You've got the dimensions of data quality. You most certainly can link them together. You most certainly can.
Malcom Hawker:If I improve the accuracy of my customer data, remove a duplicate record, what impact would that help have on a specific business KPI? That is the easiest approach to this, and you most certainly can can build these relationships and you can measure the value of data quality for sure.
Ramon Chen:Yeah. Well, jumping forward, I was gonna ask this a little later in our conversation and dialogue. But since we're on the topic of data quality, about a year ago, you wrote another very interesting, you could call it controversial post about master data management not requiring, a huge amount of high quality data. Can you expound on that?
Malcom Hawker:Well, so let's let's imagine a world where a data professional actually can measure the business impacts of a data management practice like MDM. Let's imagine a world where you say, okay. If I improve your data quality, your customer data by 5%, what business what will that drive? The business comes back and says, okay. Well, I've I've maybe you sit down with people in your FBNA group, financial planning and analysis.
Malcom Hawker:Those are really generally good people to work with on the business side. Sit down with them and say, hey. If I did this, what do you think would happen over here? If you came back if they came back and said, you know what? I think that we could drive a 3% increase in our debt profitability next year or 1% or a half a percent.
Malcom Hawker:Doesn't matter. There'd be a number associated to that. Right. Based off of a 5 a fairly conservative 5% improvement. Right?
Malcom Hawker:The challenge we run into in the world of data and analytics across the board, whether this is MDM or data quality or governance, Oddly, we tend to look at the world through very deterministic lenses. We tend to look at the world through these all or nothing propositions that don't serve us very, very well. I've spoken to literally hundreds of data professionals who think that to get any value out of MDM, you must clean up your data first. But if you built a model that said, hey. You know what?
Malcom Hawker:With a 5% improvement, I can drive a $1,000,000 in revenue. You don't need to get to a 100%. You don't need to clean up all the data. You can price in. The price I use is you can price in the fact that maybe some of your data is not good.
Malcom Hawker:Mhmm. But if you still drive 5%, will it still be worth it to the business? Absolutely. It's still worth it to the business. So Yeah.
Malcom Hawker:I'm not saying we need to forget data quality. I'm not suggesting that data data cleanups are always a bad idea. I'm just saying proceed with caution, and let's start with driving value. Let's start with giving up the idea of perfection because it'll never it never happened. Let's put a bias to action and have that that bias be driving value and not
Ramon Chen:data clean
Malcom Hawker:not and not data cleanup.
Ramon Chen:Exactly. Well, perfect is the enemy of good. Right? Well, as as with all things. So in order to achieve this, you've also written a lot about a customer centric data organization and a product data centric organization.
Ramon Chen:Can you describe what you mean by that?
Malcom Hawker:Well, a lot of this is very much a reaction to the hype these days around data products. Mhmm. And which I think is the the only leftover the surviving leftover of the data mesh Yep. Is is is data products. And and I'm not trying to disparage data products because I think it's it's a good idea with one caveat.
Malcom Hawker:And that caveat is data products, comma, created under a discipline of product management. If we are serious about products, then we must necessarily be serious about product management. I mentioned I've got I've got almost 20 years of experience in product leadership. I know the value of good product management. I know the value of good product design.
Malcom Hawker:We all do. We're probably carrying it around in our pockets. That's good product design. Absolutely. I mean I mean, literally revolutionize how we I'm holding up a cell phone, by the way, everybody who's listening.
Malcom Hawker:It's like, what is he doing? I'm holding up my my touch screen on my cell phone. That's good product design. Right? So so product management is well established and it's established a discipline, and there is so much we can learn in the world of data and analytics around product management, and it starts with putting the customer at the center of everything you do.
Malcom Hawker:Customer, and I use that word purposefully, not stakeholder, not end user, not end just customer. Start there.
Ramon Chen:Exactly. And then you also describe sort of in this continuum, and you have some beautiful organizational diagrams, which, if you're willing to, we can share as part of this podcast. We'll weave them in. But the, shipping left and shipping right. Right?
Ramon Chen:You described sort of this notion. Tell us a little bit about that. And then also as, sort of an adjunct to your comments around having product management or data, which I love, Are there any unique skills that these product managers need to have?
Malcom Hawker:Yes. But let's start with this. Let's start with the shift left and shift right. So Yeah. There's a lot of debate these days on what is a data product.
Ramon Chen:Yeah.
Malcom Hawker:And there isn't a single definition. I have one of mine, and I'll I'll I'll share that in a bit. But the idea of data products, at least to data professionals, exist in this spectrum. On the left hand side, data products, this is what I call shift left, are closely aligned to business applications. They are the kind of the lowest atomic unit of something that potentially could be delivering value.
Malcom Hawker:It could be a field. It could be an attribute. It could it could be who even a joint on a table. Who knows? But but the lowest kind of atomic level of what I would call is really a raw material
Ramon Chen:Mhmm.
Malcom Hawker:Where a lot of the people on the left hand side of the spectrum see data products as things that are used as materials that go into building some sort of analytical product.
Ramon Chen:Right? The concept of your data supply chain.
Malcom Hawker:Exactly. Exactly. That's a great that's a great metaphor. It's just kind of a it's more like a raw material like a supply Yeah. Would be.
Malcom Hawker:A lot of people view data products through that lens, and that kind of aligns to the way that the data mesh looks at the data products. On the right hand side of the spectrum is a finished product that is good to go, that is consumer ready, that is trustworthy and accurate. And here's the key part of my definition of a product is is solving a specific customer problem or a specific customer need where and that customer would be otherwise willing to pay for that product.
Ramon Chen:Correct.
Malcom Hawker:I don't wanna get into a whole bunch of back and forth on, like, internal accounting and gap, and it's not really an asset. And this we fall down these rabbit holes way too easily. But conceptually, imagine yourself as a CDO. You're running a store, and your storefront maybe is even your data catalog, and you've got stuff of you've got things that you're trying to sell in a perfect world. If you had to run that store as a p and l, profit and loss, could you?
Malcom Hawker:Yeah. Like, it's an interesting thought exercise, and I think that's a a wonderful north star for CDOs to just kinda keep in the back of their mind, which is if people had to pay for this, would they? And if you take that mindset, it it drastically changes everything. You're you're not an overhead. You're not you're not a service desk.
Malcom Hawker:You you you are out there to solve customer needs. That's the right hand side of this what I'm calling this kind of this data product spectrum. That's where I think we should aspire to be. Now it doesn't mean that you can't also be in the left hand side because you could argue you need those raw materials to produce good data products. So one is not exclusive of the other.
Malcom Hawker:They can coexist. But I would argue you need to do both as a CDO. Sometimes you're producing raw materials. Maybe you've got data scientists that don't need training that that can take that data and do something with it, and they they don't need a lot of handholding and self-service is totally fine for them.
Ramon Chen:But for
Malcom Hawker:other customers, maybe they need a little bit more of a bespoke customized product experience and both are correct. Your question was, are there skills that are unique? Absolutely. Product management is a unique skill set. It's a unique skill set.
Malcom Hawker:And I would argue, you know, I've had this discussion, you know, people ask, can I train my data stewards to be data product owners? Well, you could try, but it's gonna be a major investment. And I would argue there's a very, very different mindset. Product managers are unique skill with a unique mindset with a unique way of looking at things. And all else being equal, I would go out and hire a skilled product manager because training a product manager on data will be way easier than taking a data person and training them a product management.
Ramon Chen:That is that is really, really great insight. So relative to to data products, right, how do you see these playing with the traditional way data governance is viewed? Right? Because, you know, we we started the beginning of this this conversation around people are over indexing on data governance, really sort of, you know, making sure that everything's locked down secure. Yeah.
Ramon Chen:You know, there's there's a lot of reasons for that in terms of accessibility to the data and and sort of permissions and so forth and privacy. But how does data products and data governance come together in your mind?
Malcom Hawker:Yeah. I just I just had a post on this earlier this week on LinkedIn. Thank you, Ramon, kind of calling that out. We have 20 years of data, give or take 20 years. We've got about 20 years of data to suggest that the status quo of data governance is not working.
Malcom Hawker:It's it's not working. And Einstein would would tell us that if we keep doubling down on the status quo and expecting different results, well, then that's on us. Yeah. So I think we need to be looking at data governance in a very, very different way. I think we need to pivot data governance away from what most people believe to be a control function to more of an enablement and business success and customer success function.
Malcom Hawker:Easier said than done. Right? There's there's a lot of work to be done here, but it does start with putting the customer at the center. It also involves product management. It involves shunning these kind of all or nothing highly deterministic ways of thinking about data quality and embracing more probabilistic ways of looking at things.
Malcom Hawker:It involves looking at things through the lens of context and acknowledging what is true to one may not be true to everybody. Getting away from these monolithic framework driven approaches, don't get me wrong, I love DAMA. I love the DM BOC. It's a wonderful framework. You will never find one that is is more exhaustive and and more and more inclusive.
Malcom Hawker:It's fantastic. But when we take a framework driven approach, when we take a control driven approach, the end result necessarily of that the end result will be the minimum the organization will do the minimum required simply to keep the regulators happy.
Ramon Chen:Right?
Malcom Hawker:If you if this if this is all about control, then what you will find and what we have found this over and over again is you'll get the minimum. We won't get sued. We won't break the law. We'll maintain PII. We'll we'll we'll be HIPAA compliant and GDPR compliant.
Malcom Hawker:That's that's the minimum bar, but we won't get much above that. And that's strictly a function of us approaching this through control and not enablement. A lot of things we can do. I'm working on a book on this right now. We'll publish it next year.
Malcom Hawker:But there there's it's an uphill battle, but we need to revisit how we think about governance.
Ramon Chen:Yeah. For sure. I mean, for the longest time, a lot of governance was sold to for people to stay out of jail. I call that the 3rd leg of the stool. Right?
Ramon Chen:You know, you either wanna make money, save money, or stay out of jail. Those are the three things that kinda drive people's incentives in business. Right? Yeah. So so this is super interesting.
Ramon Chen:Let's so let's bring it all together and some of the newer technologies. Right? So as you know, Excel data is in the business of data observability. It's a new topic. Some of your former colleagues of Gartner have Yeah.
Ramon Chen:Created a category. It's not quite to a magic quadrant status. The 1st market guy came out. What's your, you know, putting your sort of sort of hat on and industry lens and what you're seeing about data observability, its relationship to master data management, to prophecy. What are you seeing?
Malcom Hawker:I so I tend to look at data observability a little more philosophically. And this maybe isn't where you wanted to take the conversation, but I I I mean, to me, from from a kind of a nuts and bolts perspective, yeah, check. I mean, data observability just makes complete and total sense. Right? You don't want your pipelines failing in the middle of the night for all sorts of different reasons.
Malcom Hawker:And the more you can be proactive on that instead of reactive, the better you're gonna be at the you know, the better your organization's gonna be. The the happier your customers are gonna be and you're gonna get more sleep as as, as as somebody who's running a data function. So Yes. To to me, that's that's kind of, like, data observability 101. Value prop is their value prop is is clear.
Malcom Hawker:Totally get it. But where I get into the philosophical is I've I kind of view the things that that you're doing in the world of a data observability as as a little bit of a trial balloon as to where I think the data management function needs to go. Exactly. Higher data management function. And and what what do I mean by that?
Malcom Hawker:If if we can build whether it's ML or whether it's AI based solutions, I I don't know when you don't need to talk about your your proprietary IP. But if we can build technology that can predict when a data pipeline is very likely to fail Exactly. We most certainly can do the exact same thing to predict when a business process will fail. Quote to cash, procure to pay. We can we can build process to understand when data quality is good and when data quality is bad, when data quality is fit for purpose in mind and may not be fit for purpose.
Malcom Hawker:We can start to be consultative to our business customers and say, hey. Listen. Yes. I understand that that this process may be running, but at you run the risk of another process actually not running because we have deployed this this this solution that tells us this data is problematic to a downstream process. So and we can start to predict some of these things, and we can start to measure some of these things.
Malcom Hawker:So I love what's happening at data ver observability because I think that is where we need to go with large where Yep. It starts it starts to look a little bit what like some of the original, and I use the word original, vision that Gartner had behind the data fabric. Yes. So I I would argue that in the last year or so, maybe that vision has been pasteurized a a little bit. Maybe folks in Gardner would would disagree with me, particularly Mark Beyer, Anderson Mzadi, and a few others.
Malcom Hawker:But Yeah. But I think the vision has been pasteurized a little bit. But but Mark's Mark Beyer, his original vision for for the fabric, I would argue And if he's listening, it means that he's probably perking was was a world where we get to a place where data can start to inform its own classification and use where transactional data, metadata, alls every data that we've got when we run AI on it, not just Gen AI, but all sorts of other AI, can we get to the point where the data can start to tell us, vis a vis what Gartner would call some form of recommendation engine, probably Gen AI, by the way, Some form of recommendation engine where data the data can start to tell us, hey, here's what a good doc data model looks like. Hey, here's what some data quality rules look like. Exactly.
Malcom Hawker:And to me, that feature state, the closest thing that we have today to that is data observability.
Ramon Chen:And that's exactly it. I just spoke to Mark actually a couple of months ago. Actually, I've spoken to him about 3 times over the last 6 months. But, his sort of drive and the entire Gartner sort of, research team are indexing on this on active metadata. I know you you like that word.
Ramon Chen:Right? Yeah. But this the active metadata. Right? But data observability is increasingly not just being used to enforce data quality and then alert when pipelines are breaking, but it's gathering all that active metadata to see when things are anomalous.
Ramon Chen:And then actually spot patterns to identify data products. What would be a good data product candidate based on the consumption usage? Who cares about it? What's the value? All of that good stuff.
Ramon Chen:Right? And then from that, it takes a secondary role post development to enforce the SLAs on the data contract. Yeah. All of these things are kinda coming to roost, but I love the way you describe it in terms of, you know, how we should think about it.
Malcom Hawker:The data contracts, that's interesting. I was at a conference last week in Canada and and, Chad Sanderson kind of data contracts guru spoke at that. I I I've long been a believer of data contracts going back to when we used to call them Wisdalls. And I'm I'm being a little bit glib here. But but I I I love that concept as well, for external uses of of data, particularly kind of data monetization and external uses of data.
Malcom Hawker:I actually happen to think that blockchain potentially could play a role here down down down the road, but every time I mention blockchain
Ramon Chen:sort of Doug Laney used to write a lot about monetization. Yeah.
Malcom Hawker:Interesting. Well yeah. So, you know, people kind of look at me like I'm crazy when I start talking about blockchain and data management. But but yeah. You know, Gartner, active metadata totally and completely agree, and we are fast approaching a world where this is not necessarily pie in the sky.
Malcom Hawker:Right? It's not it's not necessarily pie in the sky. There are there are groups that are out there that are kind of doing these things, but in very very kind of in very small, limited scopes where they are solving 1 business problem or 2 business problems, where they're doing it through kind of services engagement where they are running knowledge graphs. They're building what I would kind of call what starts to look like more of a next generation semantic layer, which would include necessarily some idea of the capabilities of data observability. So we are fast approaching this world.
Malcom Hawker:It is future state, but it is most certainly, I I think, where we are going because we have to go there. Right? We we we we we have to go there. Our businesses are demanding it, and AI demands it. So it's a win win.
Ramon Chen:Fantastic, Malcolm. It's been an absolute pleasure. Time flies when you're having fun, and, I really, really appreciate you coming on and hope you'll come on again. And, you know, my my goal is to be 1 100th as successful with this podcast series as CDO Matters is. You're a gold standard, obviously.
Ramon Chen:And, it's just a pleasure, and I hope to speak to you soon.
Malcom Hawker:Likewise. It's been my honor to be here, and I hope you'll invite me back. Thanks for
Ramon Chen:having me. Thanks a lot, Malcolm. Take care.
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