With Malcolm Hawker
Malcolm Hawker is a thought leader in the field of MDM and Data Governance and has consulted some of the largest businesses in the world on their enterprise information management strategies. Malcolm is a frequent public speaker on data and analytics best practices, and cherishes the opportunity to share practical and actionable insights on how companies can achieve their strategic imperatives by improving their approach to data management.
As the Head of Data Strategy for Profisee, Malcolm’s mission is to raise the awareness of the value of MDM and Data Governance to senior data leaders at companies across the globe. MDM and Data Governance remain a significant challenge to most companies, but through the application of practical and outcome-driven data management best practices, Malcolm’s goal is to empower companies with the confidence and knowledge needed to become truly data driven.
Full Transcript
Lauren:
Hi, everyone. My name is Lauren Hawker Zafer. This is Redefining AI, a tech podcast that focuses on key narratives that drive digital innovation, and help people understand artificial intelligence, machine learning, insight engines, and the inside era.
In this episode, I've been joined by Malcolm Hawker. Malcolm is the Head of Data Strategy at Profisee, a contributing writer at Forbes, and a former senior Gartner analyst.
Now, today's session, it's called, What is the Difference Between the Insight Engine and Master Data Management. And it is a title that indicates the direction that we do intend to go today with our guest and data expert, Malcolm.
So, Malcolm, it's really lovely to have you here. Welcome to Redefining AI.
Malcolm:
Same, Lauren. It's a pleasure to be here.
Lauren:
Let's hear a little bit about you Malcolm.
Malcolm:
Oh, gosh, well, you captured a bit of it in the intro. I'm a former Gartner data analytics. So, my name appears on the last three MDM, Magic Quadrant documents. I have contributed to a number of other research pieces while at Gartner. My time at Gartner ended in April and I was there almost three years. So, in that time, I tracked over 1,500 conversations with companies, probably over 2,000 data and analytics leaders, during that time in those conversations. So, I've got a pretty good sense for what I think works, what doesn't work, the direction of data management, the direction of MDM, and certainly depth of knowledge in and around governance.
Prior to joining Gartner, I was with a little data provider called Bradstreet, a fairly well-known provider in this space. And really my focus there was to present as a data evangelist. I was out literally touring companies located around the globe and extolling the virtues of MDM and master data needs of a third party, data provider to full and to fortify MDM use cases.
I've been a consultant. I have been an IT leader. I ran an IT organization with a $2 billion, publicly traded company, here in the US. So, I've been a seller. I have been a buyer. I have been a consultant in the last 25 years, actually closer to 30 years; that's scary. I have seen it all from a data and analytics perspective. So, I am looking forward to the conversation today.
Lauren:
Okay. So, you seem to have worn many guises and hats and been involved in different areas of the whole space of data management, and as you mentioned, data governance.
Malcolm:
Indeed. I actually started my career more on the product side of the house. I was on a product management track. I actually was the Chief Product Officer with a small software startup based out of Austin, Texas, that was doing project management software. And it's interesting now that I'm in the data space and have been for 20-plus years that there is a lot of talk in the data space around data as a product. I come from a product background. And it's interesting to hear some of the conversations of these maybe revolutionary perspectives of treating data as a product. And I'm like, I've kind of always thought of it as a product because I come from that background. And then, you are building a solution. You are building things, whether it is a software, piece of software, or whether that's a report or a model, you are doing it through the lens of meeting a user need and buyer, or a customer need, right? So, I have always put data, or anything at that matter that I've been involved in, building, creating, defining, or architecting it as a product. I think that makes sense for all to look at what we do through the lens of a product. Whether that is a data product, or whether that is a package good, something you'd use day in and day out. Maybe you're a project manager and you're building a schedule; well, that's a product, right?
So, I have seen some interesting and overlapping now with what was kind of old is now new again from the data is a product perspective. But yeah, I certainly comment on my knowledge base. I think rather honestly, I have been an analyst, but I haven't always been an analyst. The software buyer, but haven't always been a software buyer. Been a consultant, but I haven't only been a consultant. So, I've seen these issues from multiple angles.
Lauren:
Yes, it's definitely a relatable angle. And I think that obviously today we're going to be talking about all things data. And before we say all, I think it is important to narrow down the scope of what we're looking at and where we can really draw a parameter. As you've mentioned, I mean, you've just taken it from the angle of data as a product and everyone's talking about data. They're talking about data fabric, data mesh, data integration, data orchestration, data activation.
In your opinion, Malcolm, what are the differences? And if any overlaps? And where does master data management fit into this whole data mandala?
Malcolm:
Oh, boy. Mandala, what a wonderful word to describe it. The minute you said that, I have this vision of creating this unbelievable painting in sand, and then destroying it all. That would be like the zen thing to do, right? So, you can start all over again.
There are interesting metaphors in the data world. Because we certainly seem to have this habit of recycling things, renaming things, and repackaging things. I think we are certainly seeing that a little bit now. I mean, when your product is data, or when your product is insight, or when your product is analysis, or your product is supporting things that really don't change that much, I think it's a natural tendency to want to repackage things and call them different names in order to create demand. I think that may be a portion of what we are seeing in both the data mesh and the data fabric. Particularly, I think more so on the data mesh. Probably a different podcast to deep dive into how to define data mesh and how to define data fabric.
To the question, where does MDM fit? You know, I believe MDM, master data management, as a discipline, a foundation of a data architecture, or the foundation of all of your operating models and supporting data management. There are people who believe that a single source of truth is unattainable. But I actually disagree. A single source of truth, with different versions, I think you can actually have different versions. I would not say that there is a single version of the truth. That is not very accurate anymore. Because you can have use-case-centric versions of the truth. We know this and it has always been intuitively true, right? The version of the truth for marketing is different from the version of truth for accounting and finance and compliance.
The MDM literally sits at the foundational elements, sitting underneath any data management architecture, whether that is a data fabric architecture, whether that is data mesh architecture, and the ability to apply consistent business rules to shared data. I stress the importance of the word shared because that's really where MDM lives and breathes. Data that needs to be shared widely across the organization and have consistent definitions, consistent structure, consistent quality, consistent governance. That is what MDM really, really does. It enables widespread sharing data because at certain levels of the organization, natural differences that exist in the operating level need to go away. Meaning, a version of the truth with marketing may be different than the version of the truth for finance and compliance, that is true at an operating level. If you are sending an invoice or you are shipping some goods, that is what I mean at the operating level. But at the higher level of the organization, at a C-level, a lot of those differences need to go away. If your C-level person asks, how many customers do we have? There can only really be one answer. You are not going to say, well, CEO, what do you mean by customer? How do you, Mr. and Mrs. CEO, define customer? That is not a conversation that you want to have. Trust me, I have tried it. It doesn't work very well.
So, MDM helped resolve those differences and allow for widespread sharing and data through the creation of shared people processing technology and then in management of data.
Lauren:
Okay, yes. So, that's enablement of really interconnected data and processes on a basis of widely shared data. At the start of conversations normally on Redefining AI, I like to do a quick fire contextualization event with other guests. And this is an opportunity - because sometimes when we ask guests or you know we're talking in conversation about definitions, it's quite difficult to narrow it down to one or a couple of sentences.
So, we like to present you with a little bit of a challenge. And the challenge would be that I want you to define these things in just one sentence, if you can? All of the concepts pertain to your expertise and your knowledge in MDM. The start of this sentence is - the definition of MDM is?
Malcolm:
The people, processes, and technologies needed to enable widespread sharing of data across an enterprise.
Lauren:
Okay. MDM is not?
Malcolm:
The management of data within a specific application, system, or process.
Lauren:
Interesting. MDM gets mixed up with?
Malcolm:
AI and analytics, data integration, data quality, and data science.
Lauren:
MDM is the key to?
Malcolm:
Widespread sharing of data and the realization of business value through database.
Lauren:
Interesting. I like that one. And the last one, mainstream adoption for MDM is?
Malcolm:
On the upswing, but still lagging.
Lauren:
Okay. So, when you say that, on the upstream but still lacking. I mean, you've been in the realm of master data management for a long time now. I think at the start, obviously, you've mentioned coming up to 30-years?
Malcolm:
Oh, boy. A long time. Yeah.
Lauren:
So, what lens are you seeing MDM through at the moment?
Malcolm:
Well, the reason why I say that it is still lagging is because I really feel that it is lagging in its ability to deliver the potential value. The value that I know is there because I've seen companies leverage the value of MDM.
So, when I was at Gartner, we did a Magic Quadrant a couple of years ago, where a part of that we had done a user survey that showed that MDM, at least as a discipline, was still highly compartmentalized within most organizations. Meaning there was MDM happening within a specific application or a specific system, within MDM, but we knew that MDM was highly siloed within the organization. Meaning what happened in one department, one system, or one application, upwards to nearly 50% of the time, right? So, that is the indicator that MDM is an enterprise way of managing data. Most companies are not using MDM enterprise-wide. It happens sparingly. This is all for a specific use case. And that's okay. Because I would recommend that the company is going to start slow and start with specific use cases, specific business problems, and I shudder to say, a specific domain. I don't recommend domain-centric approaches to MDM, really. It is hard to track the value. But it's okay to start small and grow over time. I just know, given the data, given my experience and my conversations, MDM has a ton of room to grow within most very, very large organizations. That is why I say it is lagging. It is most certainly on the upstream. And the reason why it is on the upstream is because of the focus on digital transformation. Companies see that MDM is a must-have, not a nice to have. That's something I actually wrote for the Magic Quadrant.
Lauren:
Okay. I mean, at Squirro we focus on the core problems around data, this whole silo-ness of data. And our key focus is unstructured data. You mentioned the challenges that pose in data silos. Is that the biggest challenge? Obviously, you've highlighted that there is a certain complexity aligned with carrying out master data management and you don't recommend starting small. Is siloed data the main problem? Or what are the other ones that are blocking this progression?
Malcolm:
Siloed data is not a problem if that architecture supports your business strategy, with the exception of a handful of extremely large holding companies. I will just use the phrase, holding company. Where by design, they are highly, highly decentralized. And at that holding company level, they actually want silos to be present. Because maybe you own a railroad and maybe you own a company that makes pizza. And there's very little overlap between a railroad and a pizza company. And you want them to operate differently. Well, then silos make sense. And if that's your business strategy, then we can have as many silos as you want because it totally supports your business strategy.
But silos for most organizations, at least at a master data level, meaning things like customers, products, locations, assets, employees - that data needs to be widely shared, having data silos is a real problem there. It significantly hinders our scale. And it significantly hinders operating efficiencies utilizing that data.
So, yes, data silos are a problem. But I would argue, really the root here, data silo is really, in many ways a symptom. The underlying disease is, I would argue, generally a lack of governance. Which is also itself a system of a lack of, shall we say, an organizational fortitude when it comes to executing on anything that requires a broad horizontal focus in the organization, right? When you have to break silos, look at things horizontally, this is a huge challenge. It requires a focus from a strategy level. It requires really looking at data as a strategic asset. I would argue it requires some focus on data as its own organization, maybe with its Chief Data Officer.
So yes, silos are a problem. Governance is another massive problem. But often, the root of the problem is organizational structure, lack of focus, and organization around lack of strategy around data as an asset. A lot of companies use that, provide a lot of lip service here, that will say, yes, we care about our data. Data is an asset. They don't manage it that way.
Lauren:
But you also see there is an upcoming trend. I mean, there are recent statistics that show in the last couple of months, the last year that there has been an ever-increasing search and desire for Chief Data Officers. There seems to be an interest in ensuring that there is this governance, especially across this horizontal level that you've mentioned.
Do you think it pertains to - we've been talking a lot about larger organizations, what about smaller organizations?
Malcolm:
Yeah. I have seen more and more focus on that kind of data organization within smaller and smaller companies. I am just returning from the Gartner Data and Analytics Summit in Orlando last week and I was struck by the number of conversations that I was having with relatively small companies. And by small, I would say 100-million, 200-million, 300-million. 10-years ago, I wouldn't have had those conversations. They would not have been talking about better data management. They wouldn't have been talking about data quality and MDM and data integration 10-years ago. Because everything was fine 10-years ago, they had not hit some of the barriers that companies have inevitably hit with the lack of data management.
So, I actually do see more and more focus on relatively small, and what I mean by small again is sub $500-million companies that are focused on some of these solutions. Because again, it comes back to digital transformation. What we see, over and over again, is that these companies want to have better customer experiences. They want to optimize their supply chains. They want to automate some of their customer support processes; whatever those use cases are, the list is long. But even with these relatively small companies, they are trying to do these things. They are embarking on new business strategies. And then they find out, we can't do this because our data is a mess, right? I don't know how many passwords we have because our customer data is really, really siloed, and on, and on.
I am seeing more of a focus on this with relatively smaller companies. This is another reason why I think MDM is still kind of lagging. I know that it can be used for a specific business value with companies historically, which were considered too small for it.
Lauren:
And obviously in conversation, not only with, you've also just given us an example of a smaller organization, larger organizations, you surely must have noticed an ignorance, perhaps? And maybe an organization thinking that they are governing their data well and then you've identified, that is an obvious ignorance. Can you give us examples of any of these ignorances where you see that people are not able to identify a lack of optimal governance?
Malcolm:
Well, I think that's one way that you can express it. I really think that for most smaller companies, it just comes down to a matter of priority, right? If given the choice of driving what appears to be a dollar of revenue or a Euro of revenue, or pound of revenue, whatever, pick your unit of choice. When it comes to driving a unit of revenue versus a unit of cost savings, they will almost always choose the revenue. And for a lot of us, I guess where ignorance does come into play, a lot of these companies don't see the overlap between how their data can actually drive revenue. And that's where I am seeing some change. I think this is partially a result of and an increased focus on GPA offices or people that hold those types of roles. Meaning Gartner produced a metric last year, as a part of their 6th Annual CDO Survey that said, upwards of 25% of digital transformation initiatives were being driven and owned by the Chief Data Officer, right? That means that people historically that have been very data-centered, looking at data through the lens of operating efficiencies are now actually responsible for driving revenue and figuring out how to use data to do that. So, I think that there is a shift happening. I think that shift is a focus of business strategies on digital transformations. It is a function of more and more CDOs involved to your point, where a lot of the ignorance of the past as you would view data only as an optimizing data management only as a way to drive cost savings, I think you are seeing more and more businesses understanding that yes, you can actually drive top line revenue through better data management, right?
The classic example here, that I have always given to most of my clients that have been challenging me around this notion, business enablement through better data, was cross-sell and upsell. That's an easy one, right? If you can drive or create one more sales qualified lead through better data management, maybe that's by exposing a customer data hierarchy that you didn't expose before. Maybe that's why showing where company A is buying product A, but they are not buying product B. You may not have known it before because that company was named two different things, but it was the same entity. If you can highlight that sales opportunity, it will prove there is the revenue and that's a function of better data management.
So, I think a lot of the perceived potential ignorance of the past and we are starting to see more and more and more focus on data as a business enabler instead of simply just a cost item or an expense item. And that is what is really driving the ship.
Lauren:
Yeah, that's interesting. I remember as well, listening to another conversation that you had, you said that the future of MDM is bright. And that it has to be.
Malcolm:
Yes.
Lauren:
And if we bring technology into the equation, what types of technology provide optimism to help keep the future bright?
Malcolm:
Yep. So, here's where we can dovetail into the notion of AI. And what many, including myself and previous analysts would have called, augmented data management. And you know, the future of MDM is bright. Because it has to be. That's a little pity. I get it. But in a world where we are widely sharing data, we know that data sharing enables scale, right? And whether that sharing happens within one company, within one department, we know that widespread sharing of data enables scale. It creates network effects that simply do not exist if you are not sharing that data. This is true whether you are printing out a physical report from one department and handing it to someone in a different department. That is sharing of data. If that is true within a single department, a division, or application, it is also true when you look across companies. So, company A and B, look at everything that is happening these days around global supply chains, look at everything that is happening around the interdependencies from an energy perspective, particularly in Europe, right? So, the list of potential use cases here is incredibly long, where I would argue that highly siloed data, and highly siloed insights is limiting your ability to scale. And in many places, it is actually creating business risk in the case of energy in Europe right now. It is also creating national security risks. I would argue that the more data that we are sharing, whether that is supply chain related data, whether that is customer data, of course that has to be done in an ethical way. It has to be done in a regulatory-compliant way. I am not talking about just going and sharing data and shotgunning and spraying customer data all over the place. Of course, not. This has to be done within regulatory frameworks, often that exist at a national or country-specific level.
But we know that sharing the data enables scale, right? It enables flexibility, enables adaptability. It enables better forecasting. Enables better modeling of the data. The more data that you have that can feed into the model, in theory, the more accurate it is going to be.
So, if you view MDM as a critical enabler of sharing, which I do, then we can talk a little bit more about that. Particularly sharing beyond your four walls in an individual company, then this is why the future of MDM is bright. It has to be bright. In a globalized world where we have interconnected supply chains, interconnected nations, and interconnected consumer demand and experiences to optimize those are going to require some form of widespread shared analytics today. This is why I believe the future is very bright.
But technologies will inevitably play a massive role here. Because, you know, the historical knock on MDM was that it was very rigid, required very specific rules, very human-driven when it came to the perspective updates, particularly data stewardship, and some human reviews of data. And those things I suspect in the short term will remain true, right? The algorithms are only so smart from the perspective of what we call entity resolution. Acme, Acme, Incorporated, Acme and Son, is that one thing or is that three things? Increasingly, the algorithms are pretty smart and can be trained, based on input from humans. But there's still a human element there. So, the technologies will have to improve in order to enable that scale. The technologies will certainly have to improve because there's more data than there ever has been before. I know that sounds kind of trite. But it's true, right?
Particularly if you start looking at sharing data across corporate boundaries, there is going to be more and more data. This is where AI can most certainly play a very, very important role here, in helping understand relationships that would naturally exist within data. Helping understand how to profile the data, and lineage the data, and helping build common data models that may need to exist to facilitate a physical level of sharing the data from company A to company B. So, technology is going to play a critical role here.
That I would argue, like right now, I would say that the technology typically kind of exceeds the governance maturity of most of the organizations using it. So, meaning the AI capabilities where a lot of the platforms are really, really phenomenal. And for a lot of the organizations, the governance models are lagging, meaning you could conceivably use some form of ML to train better, create models to create better matching, for example. But do you have the underlying governance model there to kind of facilitate that? Do you even have a consistent customer definition today, right? I think it's a little unfeasible. Just assume that you can go from governance to governance, maturity of a zero or a one. So, typically, the maturity framework is almost always the same. It is zero to five, whether it's Gartner framework or any other consultancy. But if you're at a zero or one or two, and you go by technology running at a four or a five, well, that's a gap, right? And just turning on a technology is not automatically going to bring up your governance maturity. Like if you don't have the rules defined, right? If you don't have a governance committee, if you don't have the people and processes in place to understand the value of governance or to operationalize data governance within your organization, simply having technology in place is not going to do much. It's like a Ferrari just sitting in the garage.
Lauren:
Yes, it's an interesting point. I think it pertains or falls back to the whole argument or the fundamentals around the importance, as you mentioned at the start, the augmentation to the human and the human necessity to contribute to that governance.
I asked you the question, because I think that in terms of master data management, there is an opportunity with the related capabilities of the insight engine around integration, processing, and as you've mentioned, AI, especially in the unstructured data space. Is it something where you can see as well? Is technology strengthening each other?
Malcolm:
Absolutely. So, you had asked me very early on and I didn't address it. Not because I was avoiding the question, just because I could end up on a rant; sometimes you'd think I was paid by the word. But yes, absolutely. I can see MDM and insight engines sitting right next to each other, right? Or particularly if those insight engines are doing interesting things like finding other more advanced data management capabilities to data to allow for things like, for providing insights around relationships, for example.
So, when I was a Gartner analyst, I wrote the research you know, that talked about this paradox that exists when there is an incentive to try to bring a lot of data into an MDM hub. Meaning, I could run some graphs in my MDM hub and then I can understand the complex relationships that exist within data. Well, to do that, there's an overhead, right? If you're going to manage and persist and maintain data in an MDM, there is a governance overhead to that, right? We have long argued, and I would argue that MDM is inherently kind of an MVD-type process, minimum viable data, right? What is the true shared data within the organization? The way that I visualized it for my own clients was a three-ring Venn diagram. One, two, three rings. The middle of that ring is the master data, the data that needs consistent structure, governance, quality, and on and on. But there are insights that exist outside of that middle ring, outside of that overlap. There are insights that exist within transactional data for example, web logs. Take a web log for example, what are clients clicking on and what are they not clicking on? There are insights that exist there that could inform your master data. So, could you use an insight? Or could you use some other advanced analytical tool outside of an MDM hub to inform how you manage master data? The answer is absolutely, yes, you can. And I would argue, you should, right? Because maybe you could learn things about a relationship in the data that you didn't know about that is highly relevant.
Lauren:
Yeah, definitely.
Malcolm:
A great example is like a household, right? Where you find a relationship between people who are related, maybe a family level, or a household level, that you would have never known of if you were just looking at that individual. In my house, we have a modern family, with people with three different names. We are not family related but we are household related. Could you run into something else to understand the complex relationships and then start to potentially manage those relationships in an MDM, yes you could.
Lauren:
Yes, definitely. There is this knowledge organization, like making the semantics explicit. That is common for what people are talking about. We ourselves, as well, are focusing quite heavily on the investment into knowledge graphs and the importance of those. And so, I certainly agree in that sense that there is an opportunity to really understand the semantics of the relationships within the data.
Malcolm:
Yeah. And graphs are just one way of doing it. I would just throw it out there as one example. But you know, there is most certainly a need to wrangle, very technical word. Wrangle unstructured data outside of an MDM in a way that is scalable and flexible and extensible. Because MDM has historically not been very, very good at that because typically they're running on more probational forms of data. Because you need the structure, because you need the quality, because you need those governance rules applied in a specific way, that doesn't lend itself as well in an unstructured data world. So, I kind of look at this more like a kind of waterfall. Like, back in the old school, kind of waterfall requirements, I could actually look at this data management through a type of waterfall if you apply it through some sort of filter on unstructured data to help better understand, okay, what are some of the relationships with this? What are some of the semantics of the data? How is it defined here? How is it defined there? Can I use some of those insights to take a deeper dive, potentially? Or a different way of managing that data within an MDM hub. I think you actually can.
Lauren:
And you've obviously spoken as well in that alignment, of data and IT practitioners. What are their priorities in terms of dealing with structured and unstructured data? And what sort of trends are you seeing here?
Malcolm:
Yeah, it's a great question. And, you know, obviously, IT practitioners have been dealing with unstructured data for a long time, through the lens of analytics, right? And the ability to kind of, you know, this is what leads to this conversation. So, data swaps, and data lakes, and data warehouses, and what lives where, and no unknowns. And through the lens of analytics, you know, IT people have been dealing with, for lack of a better word, figuring out how to kind of optimize and solve for structure versus unstructured. And that's the way to manage that data to enable analytics. But where I am seeing things is really on operational use of that data, which is MDM lives and breathes. Meaning, can it create an actual customer record? And then use that within operation systems. If that overlaps, what are some of the insights from an analytic perspective - that I can take that unstructured data to use to improve how I operate? So, can I drive some of the insights in that nature, but then actually take the next step, jump into the operational realm? Which is okay, connect and push that information into a CRM, or into a digital marketing platform, or into a customer service platform, or you name it where that data is being used to make decisions and fuel operational workloads and business processes.
So, I think a great example of this is in the realm of CDPs, customer data platforms, where you are seeing the overlap there where there is a lot of unstructured data that lives within a marketing world or within you know, customer support world, or service world. And marketing organizations are trying to figure out how to make operational decisions based on that insight, right? To what ad do I serve? What offer do I get? What funnel do I put them in from a sales perspective? So, it's really where the unstructured meets structured. And it is where the analytics meets operations where the overlaps exist. I would argue that is a perfect fit for where MDM should most certainly live and breathe.
Lauren:
Yeah, I also see an opportunity as well. On top of that, you've got the full insights opportunity of bringing it out from the sort of package that you're preparing, through master data management.
Malcolm:
Yup.
Lauren:
We're going to run out of time soon, which is a shame. So, I want to ask you one last question. What entices you to live in a world of MDM? What do you enjoy about it?
Malcolm:
What entices me? What enticed me from the very beginning was this notion of something sounding incredibly easy, right? Which is, how many customers do I have? That seems like a really simple question, right? How many products do we have? How many employees do we have? These seem like very, very easy questions to answer. However, for most very large companies, particularly on the customer side, for very large companies, those are really, really difficult questions to answer. So, there's something about my personality, something about my psyche, that is attracted to this notion of something sounding simple, but has really been very, very hard. Because for a lot of people, when they see that, and when they start to kind of learn more and more about data management and MDM and some of the things that we see everyday within the data governance space, there are a lot of people that will look at that and say, oh, that sounds awfully complicated. I don't really want to deal with that. Because it involves people, involves processes, and involves technology. There are a lot of tentacles to it. I'd rather just avoid it.
For me, whatever this odd thing is in my brain, I want to help figure it out. So, that's what I find fascinating here. It is this paradox, the seemingly very, very simple but actually, in reality, very difficult. And that's what draws me to MGM. That is what draws me to this space and keeps me coming back. Because it is getting hard. It is not getting any easier. It is only getting harder. So, for people like myself, and companies like Squirro, and others that are in this space, there are tons of opportunities.
Lauren:
Yeah, excellent. I mean, you have proven anyways that you are consistently engaged in the field and obviously very passionate about it. And you've passionately contributed to our conversation today, Malcolm, and I thank you for that. I'm sure that everyone has learned just as much as I have and enjoyed the conversation as much as I have. So, thank you very much.
Malcolm:
Thank you, Lauren. It's been a joy to be here. I look forward to having more conversations in the future.
Lauren:
Perfect. I want to thank everyone for listening as well. It's been insightful. And if you want to learn more about AI, machine learning, and search, then come and take one of our free courses at Learn.Squirro.com.
