Malcolm Hawker 

Hi, I’m Malcolm Hawker, and this is the CDO Matters podcast. The show where I dig deep into the strategic insights, best practices and practical recommendations that modern data leaders need to help their organizations become truly data-driven. TuneIn for thought provoking discussions with data. IT and business leaders to learn about the CDO matters that are top of mind for today’s chief data officers. 

Rishabh Dhingra 

Welcome everyone to the latest episode of inspired. I am super thrilled today to welcome Malcolm Hawker to the inspired episode. Malcolm is head of data, strategy and prophecy, a thought leader in data strategy, digital transformation, transformation, master data management. Data governance is also contributor writer and at Forbes and member of Forbes Technology Council and was previously a senior director at Gartner, providing thought leadership and strategic guidance to C level and senior business executives. Malcolm Super thrilled to have your inspired and welcome. Thank you rich. 

Malcolm Hawker 

It’s exciting to be here. I look forward to our conversation. 

Rishabh Dhingra 

Yeah, there’s so many things I need to talk to you about and learn from you, but we’ll start off with your journey first, so I was looking at your journey and if I’m not wrong, you started into product management and leadership in product management at AOL. And then there was a transition that you made. From, you know typical core product management into data governance and MDM. So I was intrigued to understand and know about your journey, like what led to that transition being a core product manager or core product management guy to the data world specifically in data governance and MDM. Yeah, it it all kind of happened a. 

Malcolm Hawker 

Little bit by half instead, so I was on a product management track for sure. All of my experience was mostly in in product management. I had kind of risen to the point of actually being a chief product officer. I was I was working at a. Small startup in in Austin. Texas I I had a a great career over 10 years at AOL and I had another three to four years at a company called Hoovers which was bought up by that at Bradstreet. And each of those situations I was in product management roles where I had been a product manager was was writing requirements, building software building. Tools both for customer facing tools and and internally facing kind of back office IT type tool. And work my way up to kind of team lead and then. Chief product officer. And it was it. Was a great ride and I learned so much about the the art of product management and product leadership and what makes for good product management and product development as a whole. So when you’re when you’re in that. You’re not only particularly at. AOL, so wearing so many hats, this. Was a 10. Year run at AOL during the kind of the explosion of the Internet, so we were. We were we. Were doing everything by the seat of our. Pants and and. We were doing agile before it was kind of even agile because like the product managers were like sitting right next to the engineers. 

Rishabh Dhingra 

OK hmm. 

Malcolm Hawker 

And we were. Writing requirements and and feeding the requirements and the engineers as fast as they could as they could digest them, but. The great point. The great part about that is that I got. The test actually lowered engineering and SDLC that the everything that goes into writing good code and and and creating good software and actually and and and during my my tenure in that kind. 

Rishabh Dhingra 

Right? 

Of that product management track I. Actually got for two years. 

Malcolm Hawker 

And this was. Kind of part of my my my professional development and a part of my plan for two years. I actually got to manage a team of engineers like ******** Java engineers that were building large portions of their wells advertising infrastructure that was. That was just so. Worthwhile and so valuable as a. Product person to. 

Rishabh Dhingra 

Right? 

Malcolm Hawker 

To learn how to manage engineers and learn how they think and learn how they’re motivated and and be able to kind of speak in engineering and and understand the the core tenets. Checking in code. Checking out code. 

Speaker 3 

QA processes you you you. 

Malcolm Hawker 

Name it that. That was fantastic. 

Rishabh Dhingra 

Right? 

Malcolm Hawker 

I made a transition into data and analytics really. Just kind of by. By necessity, I was working for this small startup as as a chief product officer. And that company ended up. Getting bought out. We were building. Product Manager project management software, resource management software and we ended up getting bought out by. Suite and my position was eliminated and most of the staff positions were eliminated. They ended up buying, you know it was pretty much an acquisition for the customer base and less so at the technology and less of people, and right on that time I had a significant illness. 

Rishabh Dhingra 

Malcolm Hawker 

It wasn’t me, but a family member had a significant illness, so I had been recently laid off I had. 

Speaker 4 

It was a family member with significant illness. 

Malcolm Hawker 

And I thought to myself, OK? It’s a great time to try something different. A great time to. Get into consulting. Which is exactly what I did and started to. 

Speaker 4 

Consult back to some of the. Executives that I previously worked. 

Malcolm Hawker 

With at AOL. And that led me. To data and analytics so. I I did have. What I was doing, product management. I was doing product management for internally facing tools. Some of the things that I had product manage in the past. Like BI tools like reporting tools for internal. Users so I. Had a bit of an it and. That foundation, but I at that. Point if this kind. Of transition away from. Product management and into IT and into data analytics can really just as a result of of necessity and just life changes it. Just things that just happened, but as it turned out it was. It was a. It was a fantastic transition for me. I love being in product management but. 

Speaker 3 

I love the. 

Malcolm Hawker 

Data analytics space as well. Because what I found was that some of the problems in in the data space what really attracted me to them was was this this duality that this paradox meaning? There were things in the data world that seemed really easy, right? And on the. Surface like heavy and accurate count of your customers, right? That that’s that. That was my first job out of product management. As a consultant, I got hired as a consultant. To answer what I. Thought was just like this infinitely simple. Question of how many customers does this very large company publicly traded company? 

Speaker 

Have I thought? 

Malcolm Hawker 

This is gonna be slam dunk. This is going to be TS Consulting gig I’ve ever had and and. Because, you know? I’ll set up Tableau and run, you know, a few reports and and proofs. You know, yeah, and? I realized that data was in. Multiple places. OK, well that’s Not uncommon to have data in a few different. 

Speaker 4 

Marks multiple data warehouses. 

Malcolm Hawker 

I figured OK, this is going to be pretty. Easy and then I figured. 

Speaker 4 

Out what is like Oh no. 

Malcolm Hawker 

This is going to be really hard. And and and that combination of what? Appeared to be easy, but was actually quite hard. Was like was was was that combination to me was incredibly attractive and and and to this day to this very day that combination of it seems easy, but it’s. Actually really, really. Hard and a lot of people kind of avoid data analytics. For that reason. They avoid data management and DM data governance for that reason. 

Speaker 4 

Because, again, sounds easy. 

Malcolm Hawker 

But doing it is is really, really hard, and that’s what’s kind of kept me passionate and kept me excited to be in the space that. 

Rishabh Dhingra 

Right I I think, uh, even the more most established players do find it hard to do data management and data governance and how the data world has explored. It has become even more important to have this data governance. You also mentioned about. Being the and whoever you remember from my consultancy gigs, I was first exposed to who were data doing a consultancy project for a client in the US. Yeah, so we were looking at, you know the spending of different customers, their headquarters, headcounts and then finding the potential customers for a particular service of a client who could be the potential you know, buyer of that service or a product. That’s when we grab data from who? And B&B and started doing an analytics or. 

Malcolm Hawker 

Yeah again something that seemed really simple, right? Information about companies or information about people there’s. There were there was a. Who just did have a bit of a contact database? 

Speaker 4 

That kind of. 

Malcolm Hawker 

A B2B sales and. Marketing type contact database. And they could sell. You like mailing lists and emails. And that kind of thing. But the but the core. Of it was this database of information. About people and again, well, that should be easy. 

Rishabh Dhingra 

Right? 

Malcolm Hawker 

I could just go on Google and find. 

Speaker 4 

All of this stuff that’s out there. 

Malcolm Hawker 

It’s on the Internet. How hard? Can it be well having? It consistent and accurate and curated and constantly got. With a consistent set of business rules being. Applied to that so that you know that. The same rule that went into creating. Acme Incorporated was the. Same rule that beta incorporated used in every other. Not to mention the complex. Hierarchies that. Exist between companies and the parent. That exist? That’s that’s that’s that’s. Hard, right like? 

Rishabh Dhingra 

Many of that stuff is really, really hard. 

Malcolm Hawker 

The companies were like OK, can you do this? For us can you set? Up this up for us, yes. One of the funniest things that I’ve. Been at hoovers and we we managing a product management team. And and we. We built the first integration in the sales. Dot com where you could be in Salesforce and click on an account in Salesforce. 

Rishabh Dhingra 

Malcolm Hawker 

It would automatically pull in data through through an API connection from the from the Hoovers database in into Salesforce so. I I got in that. In that world I got to. To attend a lot of. The very early dream forces through. A lot of which. Were a lot of. After a lot of fun and like you know. Going to see Metallica in the Moscone, said. There with your company paying for it so. 

Rishabh Dhingra 

Transcript yeah, and also like the product management to data shift, I think the product managers their role has become so data-driven as well. So I think they are also the they have to be champions of data and understanding data and doing analytics as well in order to understand the product finding opportunities. You know, I’ll learn. More about their product as well. And one great thing you mentioned you were doing is. I’ll just no one knew what did you. 

Speaker 4 

Use technologies like no. 

Rishabh Dhingra 

It called. 

Speaker 4 

No, we didn’t. 

Malcolm Hawker 

We didn’t call it. Agile I mean like but AOL that. That was the cloud before we called. It the cloud. It was a multi tenant envy. Yeah, and and and. We didn’t. We didn’t call. It the cloud, but is this service that everybody used and everybody had the same databases and. Everybody had the same tools. But now we don’t calling it agile. We were just calling. How fast, how fast? Can we move? In our in our in our SDLC and. You know, in some of the bigger projects. Some of the kind of more sensitive initiatives we were following. More of a waterfall pattern, but most certainly we were working on smaller stuff. I mean it was the product managers were working hand hand in hand with the engineer. 

Rishabh Dhingra 

Right? 

Malcolm Hawker 

So, but that that that whole that whole. Experience of like you know, working with engineers and and you know understanding kind of how they think versus a product management mindset was just just so valuable. To me and. And and you know, you kind of mentioned, you know. Product managers having to. Think in more kind of data centric ways. Couldn’t agree more, you know. I think we’ll we’ll talk more about. Kind of. You know data as a product. 

Speaker 4 

That got a lot of a lot. 

Malcolm Hawker 

Of a lot. Of perspectives there but but I think I think product managers make for fantastic. Leaders in the data analytics space because of their rooting in in understanding customer problems. And because of the because. Of of of to me. The best product managers are fantastic problem solvers. Like like like you you have this need you have this problem you have. This market need. Your customers are trying to do XYZ and what do I need to do as a product manager in order to address that need? What are the pieces, the functionalities, the capabilities that user experience? The design what do I need to put together? I don’t how do I put all of those pieces together to solve a very. Complex problem to. 

Speaker 4 

Me that that. 

Malcolm Hawker 

Is the kind of the core of of product. Management and we can talk about P and. L and we. Can talk about other. Other software skills, like you know. Project management type skills and communication. And interfacing with people but problem solving like. Being very good at problem solving that to me that is the core DNA of. A good product manager. 

Rishabh Dhingra 

I couldn’t agree more with that. I’m definitely we’ll delve more into data as a product, but there are couple of of other topics that I wanted to discuss with you and before getting into that, it would be great if you can expand more about your role at prophecy and what prophecy is doing right now. So it will be great if you can. Expand on that. 

Malcolm Hawker 

Yeah, thank you so so prophecy. Is a vendor of MDM Master data management software. We are on the Gartner Magic wand. We’re going to use this space around master data management. My will is is. Head of data strategy. But but really, what that means is primarily I’m focused on evangelism for back, for lack of a better word. It is my job to raise the awareness in the in the market of the importance. Of data and analytics. The importance of master data management. How MDM can drive value for organizations? How MDM can be used as a foundational element of a digital transformation? We firmly believe. 

Speaker 4 

That prophecy that. 

Malcolm Hawker 

A rising tide will lift all boats, meaning if I’m out there on LinkedIn, if I’m on your podcast, if I’m on other podcasts or on that industry events, and I’m talking about how important every game is and I’m talking about best practices. I’m talking about industry trends. I’m talking about business value. That when people see that on LinkedIn or they see this on this awesome podcast, or they see this. Maybe I’m giving a presentation at at an industry event. 

Rishabh Dhingra 

They’ll say, aha. 

Malcolm Hawker 

I need this MDM. Stuff right we we need to figure out how to have accurate and consistent and trustworthy and high quality customer data or product data or supplier data. And I heard that guy that Malcolm guy talking about it. Maybe I should. Go talk to him a little bit more about you know where, what, where. This should be on. The road map. And so that’s really my role is is it is evangelism in in the space around MDM. I I do work for a vendor of course, and I’m I’m obviously a little bit biased, but my primary role is to build awareness around the value of MDM and then the value of. My my company solution in this space, secondarily. 

Rishabh Dhingra 

Great, this is a very important topic because I think all of the business decisions today are as opposed to, you know, say a decade back now everything is reliant on data. You know everyone is looking for data hungry for data and then they take in Paul and our key business decisions which impacts the bottom line of the business. Now if that data is in trustworthy. There’s two columns around it. That means you might end up taking decisions which are not right for the business and ultimately not right. For your consumers. Yeah, which ultimately means you’re going in the wrong direction or trajectory so. If you bundle this all together, it tells you the importance of MDM and data governance and have criticality of having the right data and the. Governance around it. 

Malcolm Hawker 

You’re absolutely right. 

Speaker 

What you just? 

Malcolm Hawker 

Said is is bang on but wish you would be amazed. 

Rishabh Dhingra 

How many how? 

Malcolm Hawker 

Many organizations out there still really kind. Of treat this as. As a technology problem or really kind of skip over governance and MDM and data quality. And think that just I can deploy. A data warehouse right? I can go, you know, implement Snowflake, which is fantastic software by the way. I’m not talking Snowflake, but or. Any data warehouse type infrastructure doesn’t. Really matter I could. Just go drop a bunch of data on this Amazon. Or Google or Azure so. Many companies think that that. If you just put all the the data in one place, then you’ve then you’ve solved for data quality, right? That you’ve solved for for having a single source of truth that you’ve solved for having you know consistent data governance and. And it that couldn’t be further from the. Right all you’ve done is put your data into one bucket. Right you you you may. Have limited the number of queries that that you that you have to make or the. Number of sources that you have to go into. You may have made it a. Little easier to centralized permissions. And access to that. Data, most certainly because you could single place. And you may have even. Put some cool. Analytics and visualization. Tools on top of it those. Things are great too, I’ve used. Click first table business objects, you name it. I’ve used them all, those are. Great tools but but if you have two records. One is Joe Smith and the other is Joseph Smith. And they’re the exact same. Person, but you’ve got 2. Separate ID’s. For them, which one are you supposed? 

Rishabh Dhingra 

To trust right? 

Malcolm Hawker 

And putting it into one place. Is not solved for that issue. It hasn’t solved for the issue of of your record. For Acme incorporated, missing an address or having the address be incomplete or incorrect. So yeah, I’m I’m also using data warehouses and I’m also using. You know you. Know cloud based solutions for. For for housing data, but if you. Don’t address some of these data quality is. You’re going to have. A lot of problems, right? And I see the same thing. I mean I see. That day in and day out. I see a lot of companies out there marketing around. I’ll just just slap it into it with us and. 

Speaker 4 

Got your problem solved. 

Malcolm Hawker 

Or just slapping AI layer over the top? 

Speaker 4 

Of it, and then your problems are going. 

Malcolm Hawker 

To be solved too, right? Well no. We we saw this. We saw this 12 years ago with with Hadoop. Right and companies went and. Spent a lot of money. On the data infrastructures, a lot and. It didn’t solve for the data quality issues. So executives were still running reports. Looking at it. Is like I I. I can’t make any sense of this or I see the same record four times or or I have an agent in my call center who’s logging into my application and seeing the same customer record five times. So that’s that’s the core issue that that. That prophecy is trying to. Solve, that’s the core issue that I’m. Trying to bring attention to. In the market. And by saying, like hey, there are some. Great technologies out there, don’t get. Me wrong. Fantastic stuff out there. Amazing tools, but MDM first and foremost is a discipline, it’s a. Way of managing data. Secondarily, it’s it’s a software, right? And that same is true with data quality data quality, their software, but data quality. As a program being. 

Speaker 3 

It’s it’s it’s. It’s inherently disciplined first, so. 

Malcolm Hawker 

Those are some of the things that I’m trying to kind. Of insights that I’m trying to bring to the market. 

Rishabh Dhingra 

Yeah, and especially like now. We are in the era of acquisitions modules, right? So we are bound to have different organizations bringing their customer base or database databases together. And then there are challenges of data. When do all those kind of modular acquisition of databases also happen? And you may not got. Pretty sure you were heavily involved in. You know, providing this guidance to execs, particularly in MDM and the other one is facing, which would have involved a lot of resource and client consulting. So as you explained, the problems that we see in the industry are about the this discipline. Understanding the discipline rather than the. Technology using this is where the organizations fail or succeed today or there are other things. On top of that where the organization could succeed in implementing their MDL data governance solutions. 

Malcolm Hawker 

Yeah, there’s good news here. There’s some bad news. As well, you know we. Broadly speaking, you, you, you could look at all data management but but let’s let’s focus on MDM because that that’s a comfort zone for me. Not my only. One is people processing. Technology, right? It’s it’s. It’s simple, there’s no, there’s no new. Math there that these have has been well known for a long time. 

Rishabh Dhingra 

When I look. 

Malcolm Hawker 

At a lot of the organizations and and and you mentioned my time at Gartner while I was at Gartner for nearly three years. I I had 1500 conversations with different companies, like so 1500 companies. People probably close. To about 2000 people at those companies about. What’s working and about what’s not working from the perspective of? IBM and governance and data management as. 

Speaker 3 

A whole. 

Rishabh Dhingra 

If you were. 

Malcolm Hawker 

The bigger challenges that I see is is 1. Most certainly is a lack of data governance, right and not investing enough in data governance in the form of both people and in the form of policies and procedures and managing a a data governance process that would include things like creating a data governance committee. Having having roles defined for data stewardship, having having been business rules and the policies and procedures defined that say things like Group A is responsible for this task and Group B is responsible for another task. Policies and procedures related to data definitions. Data structures I mean. I mean the your average data governance framework can contain a lot of things right? The the D AMA. The Donna has a framework, Gartner had a framework. There’s a lot of frameworks out there, but includes things like inequality, MDM, security and access. I would argue that. 

Speaker 4 

That is it. 

Malcolm Hawker 

That is important. To to a data governance function, even things like archival retention. Ethics of data and on and on so number one problem that I see number one challenges companies just not investing enough in in data governance. The number two problem. Maybe I should have said this. First, because I think it’s probably. Closer than #1 in terms of impact, is companies really really? Really struggle with understanding and quantifying the connection between investments in data and business outcomes. Meaning do you? Have a business case like product measures. We have to create business cases. And we don’t get funded exactly right. Yeah, right right but but in the data world. And in the. IT world, you know, business cases are are are fairly rare and and it’s very rare in the MDM space. As a matter of fact when. 

Speaker 4 

I was at Gartner. We have research that. 

Malcolm Hawker 

90% of data analytics leaders could not quantify the the the business benefit that that that the MDM program had on their org. 

Rishabh Dhingra 

So that is true. That is true, and from my experience as well, it has mostly been at a very superficial level. Nothing in detail as a product manager would go through. 

Malcolm Hawker 

Right, right? Because again, if we’re in a product grade, we didn’t justify, we wouldn’t get. The investment right? So so. What I would see is. When I was a guard, I would ask. My clients well do. You have a business case for investments in MDM. Or data quality. You won’t go. Buy a tool what what’s your business case? Well, our data will be better. And I’m like well. OK, but that’s not really the business case. Right, can you quantify increased? Revenue, reduced costs and reduced business risk. Or some combination of those 3. Well, no, now can you can’t do that. That’s impossible that. 

Speaker 4 

You can’t do that because the connection between. 

Malcolm Hawker 

Data and outcomes is is. Is indirect right? There’s not a one to one correlation between better. Customer data and more sales. And I would say, well, time out they’re actually can be there. Most certainly can be because I guarantee you that your sales organization has metrics around things like if I have one, one more qualified lead. If I get a. Sales lead what? Will there will be metrics that will say that there’s you know what chance will it convert? And if it does convert. How much money will that? Bring to the company and. How and how long will? 

Speaker 4 

That company customer stick around what’s the? 

Malcolm Hawker 

Average life cycle of of a customer so. If you could deliver one more qualified lead, or if you could do that faster, or if you could. Get a product. On the shelf faster, whether it’s product data, customer. Data employee data I would. Argue yes. Absolutely you can build business cases here, but most most I will say most data analytics, they just are not doing that. That means that they struggle. Scope, right again. Product managers. They know if you only, you’ve only got time, people and money right? And and and and. And if if one of those has to go well, you have to limit your scope and the data. Analytics will, I would ask all the time OK, what’s your scope? All right, well, that’s customer data. Well, that’s everywhere. That’s not really a limitation on scope, so if you don’t have a business case, it’s. Really hard for you. To to to have to limit. Your scope it’s really hard for you to prioritize. Like it’s really hard for you to understand. Where the biggest benefits? Are going to be right. 

Speaker 4 

So every effort, then at that point. All efforts become the same, right? 

Malcolm Hawker 

Because you can’t differentiate whether doing A or B or C. 

Speaker 4 

Is going to. 

Malcolm Hawker 

Drive more value for the business, right? So it inevitably leads to scope creep, and it never really leads to situations where data living leaders can’t justify the things that they’re doing. They can’t prioritize. They have a hard time getting stakeholder involvement and engagement and stakeholder buy in, because inevitably, if you if you were looking at MDM inevitably where you are getting towards is a place that requires business process changes. Right, because today chances are your onboarding processes are broken and your. Supplier processes are. Suboptimal, or you allow salespeople too much latitude in the CRM to. Do whatever they want. And if you want to fix. The data problems you have to. Fix some of the business processes that that. Are involved in creating that data so. You will have to have. Difficult conversations with business. Leaders that will say things like well. We need to make this. Field a required. Field now it hasn’t been. It’s been a free text field that. Salespeople fill it out with whatever they want. Well, we. Need to change that then that business. Leader is going to say why do I need to change that? I’m hitting my targets my. My SLA’s are all fine, I’m hitting my. Revenue targets but. You’re telling me everything’s broken, right? Well, that’s That’s that makes. For a very odd. And difficult conversation if you don’t have a business case, you need to be able to. Say hey if we. Do this, we make this required. Field, we know that it will yield $1,000,000 to this. So that’s channel. #2A A lack of the business case number 3 is a lack of senior executive support, right? So we see this all all the time. You know people kind of managing these as IT projects without going to sales leaders or finance leaders or the CEO or the. 

CFO and and getting. The right levels. 

Malcolm Hawker 

Of support for MDM initiatives. I mentioned scope which is a which. Is a definite. A function of of the lack of. That business case and then then finally. 

You know the the biggest challenge that I see is still to this. 

Malcolm Hawker 

Day data data. People treat these initiatives as technology initiatives. They don’t treat them as as as business process initiatives or organizational effectiveness initiatives, or even revenue generating initiatives. They just treat them as like an IT project. Where you deploy software, the software is deployed and. 

You’re done, yeah? 

Rishabh Dhingra 

You put it so well, I was thinking about one of the issue. I would say that I had faced in one of my assignment as well at some at one of the companies where I also feel we are more reactive. You talked about business case right? So when we require that. Yeah, that’s when we, uh, our processes are broken. I don’t have this data in the form and that I need or in the way I eat, right? So we are being dragged and then realize, oh, we are missing out on the opportunity. But if you do a business case saying that we don’t have the data that is required in my case, it was a synchronization data acquisition problem and we didn’t invest at the, you know, in the initial phases because we didn’t build a business case. Now we had we built a business case. We would have said this is what the data would be used for. This is how. Marketing could reach out more customers and that’s your business proposition. Whereas you know we were being more reactive, so it’s also about when you build a business case. That’s what product managers do. They are more proactive in understanding where the opportunities are and same applies in the data world as well. 

Malcolm Hawker 

Yep, hang on but but. 

Rishabh Dhingra 

It’s it this is this is. 

Malcolm Hawker 

Another reason why I think product managers make make for fantastic program leads in the data and analytics world because they know how to build business cases. They product managers. Know how to have to. Sit down with business people and say. OK, if we change a or if we change B, we make it faster. We make it easier. We reduce some friction in the other. What do you think that that would? Mean to your business. How would it? Would you run faster? Would you would you? Would you be able to? 

Hit your sales. 

Malcolm Hawker 

Targets and those. Those are the. 

Conversations that. 

Malcolm Hawker 

Need to happen. Build the. Business case and. And yeah sometime. There’s going to be a little bit of art. 

Rishabh Dhingra 

Right exactly this. 

Malcolm Hawker 

Is a modeling. Exercise, it’s a. Modeling, exercise, making, making, making a, a quantification of the impacts of better data inherently is a modeling exercise. It always is right? But that’s OK, right? If you treat it iteratively. 

If you say. 

Malcolm Hawker 

OK, we will monitor this. We’ll create some reports. We’re going to monitor this right and and we’ll. Keep an eye on. It the model will get better as. We go, we’ll refine as we go. But you know, getting away people involved is. 

So important, right? 

Malcolm Hawker 

Within any business unit, there’s always two or three people who are the the people kind of do the annual budgets. They do the planning, they do the forecasting. They’re like the Excel people for whatever VP. Of of of. 

You know, supply. 

Malcolm Hawker 

Chain or VP of product manager, whatever. Find those people who work with them. Those are the people you. 

Rishabh Dhingra 

Right? You you have some rise in few bullet points, I know the the strategy or the steps the organization should take in that and and we’ve been talking about product having the product lens as well, so I wanted to get. I think it’s a great segue to discuss more about the as a product and you have. Specialization specialization in both products? And data, so if you can share your experience with us on data as a product, the concept if you have to define it, how would you define it? Maybe some examples would be great and I know sometimes it gets confused with data products. So like. Yeah, some people would use it synonymously, so if you can share some insights in that that. 

Malcolm Hawker 

My experience with data as a. Product is kind of frustrating. 

Rishabh Dhingra 

Yeah, it it it. It’s kind of pushing because I. 

Think I I? I, I think what I see, what is data and analytics people right? Which which are IT people right? 

Malcolm Hawker 

These are. 

These are the. 

Malcolm Hawker 

People that are managing the data quality program, the MDM program or the data. It you know the the. The tableau first or maybe. 

Even a data science program that’s all cool. Stuff and these. 

Malcolm Hawker 

Are these? Are really good. Inspire people. Don’t get me wrong. But this notion of data as a product, what I see is that those people like. My people IT people. Tend to look. At the world from the bottom up. Right where where you when you say data? Is a product that. Inevitably leads people to look at. That things at the most minute level. Like the most. Atomic level possible. And then what I’m hearing and I see. On on LinkedIn and conversations at at, I was at a conference a couple of weeks ago at the CEO conference in Boston, where people were presenting about data as products and. 

They start talking about. 

Malcolm Hawker 

Well, like that’s a field. 

Or it could even be an. 

Attribute of data that could be a product. 

Malcolm Hawker 

And and I’m like, well, I guess it could be a little chunk of metadata or an. Attribute or field could could be a. Certainly you could. You could productize that, but. What happened in looking at this through the lens of the problem that is solving? Right? Like like product managers tend to look at the role from the top down, yeah, right? And and they. They they go from the need. Into the component parts that are required to. Fulfill the need. And then most people tend. To look at the bottom for the bottom up. Where they will start at a attribute to a field. To an ontology to a set of tables. To, to, and and and and. I think that that. Does kind of this notion of. Data as a product, a bit of a disservice. Right, if you start with. 

The top and. 

Malcolm Hawker 

Started with the need right and my clients have the need for reporting is the is is the best example right? Like they have they have a. They have a need for building report. They have a need for insight act so they have. A need for. A A model that. That predicts that’s another good example. A model that predicts future demand. Right, that’s a very that’s. A very specific need. That you can put your hand around you can. Build a business case. Around it, start there. And then work your way into the fields or attributes of data. That you would need. In order to fulfill that. And just like building a car right? My customers need a car. I don’t hear anybody. You know? I would imagine I haven’t built. Car manufacturing, yeah recently but I. Can’t imagine that the product manager sitting around. Thinking goes, no, it’s that. It’s that individual nut or that the bolt is. That a product. 

I think we’re going. 

Malcolm Hawker 

To call it a bolt, a bolt, a bolt is a. Product and that’s a product that. Individual washer is. A product too. And ohh yeah, over there this. Area that brake pad. Is a product and that actually? Might be a product and you could. It’s a it’s a unit of sale so so so. My point here, which is is that in. The data Linux world I love. Don’t you love what what I love? Data management as product management. The application of product management disciplines to data management. That I love. I love that if we. Could do more. And more of that. The application of product management type disciplines into the world of data management data management would be a much, much, much better place. Would be more. 

I would argue. 

Malcolm Hawker 

Would be more far more. Customer centric, it would be far more effective. It would be far more productive. We’d be able to quantify the the the. Business benefits that we were driving we would. Be able to prioritize our efforts. We would be able. To spend money more efficiently. But what we do? Instead, is we get in these. Arguments about what is a data product. And is it a field? Is it an attribute and and and and that just it just it’s not helping, right? Because it’s backwards, right? Start from the need and then you may be able. You may be able to have a unit of sale all the way down to that individual field or that attribute, and that’s fine, but starting with the need and then figuring out what goes into that. I think it’s I think it’s very we should. Be starting so you know a lot. Of this has to do. You know data is a product. A lot of the current kind of. Mania there is is thanks. To a a big infatuation right now with the. Mesh and which is really kind of the a core tenet of. The data mesh is. This is this notion of data products right? And and you should treat. Your data like a product. And but OK, again, fine, right? You know, treating the data as a product and and if you were to sell it, you know what the customers want. Would they not want? What would the attributes of it? 

Be what would. 

Malcolm Hawker 

You charge for it. What would you even given? Things like your. Your your life cycle and managing for the. 

The the product life cycle OK? 

Malcolm Hawker 

The data life cycle you can get into things like archival. And sunset of products. Obviously that stuff is great like I love it, but what I see is a lot of energy being wasted with debating online about whether a field. Of data is a product. 

Rishabh Dhingra 

Right? You said it nicely and it’s all about having that holistic view or a bigger picture and understanding the problem that your data product is going to solve for the consumer. Yeah, and that consumer could be internal, could be external, anyone but you. Working with and sometimes it could. I mean there’s a new other concept as well, which is being spoken about when we talk about data as a product is also. That’s right, which is data as a service. So how do you think like these two differ from each other? Or is it go through maturity curve from data servers or to DAP or from DAP to? 

Malcolm Hawker 

That’s a great question, so you know I’ve tinkered with the idea of of data as a service, right? I worked for Dun and Bradstreet for a number of years, and that’s data as a service. You buy a subscription to data. At its most certainly you know it’s API driven and you use it as you need it, and you don’t actually technically own it, you just subscribe to access to the data in. In theory, if that address you. Want their data back? They they they. 

Under the contract. They they could pull it. 

Malcolm Hawker 

Back because you don’t own that data, but. 

Rishabh Dhingra 

The data service could be. 

Malcolm Hawker 

A lot of different things. Right, it could be a subscription to data I, I think when I hear data as a service that’s really kind of what I think it’s like. You’re you’re, you’re subscribing to some sort. Of of data. Which is a product as well. So, so if you’re selling data as a service. I mean I. Would argue that that’s a. That’s a data product. And there’s there’s. A lot of Gray area there between the two. But yeah, yeah, I mean, I I I think I. Think data as a service. 

Is is like it’s just just like you were subscribing to to software you could be. 

Subscribing to the. 

Rishabh Dhingra 

Data right right? I’m talking about data as a product. You mentioned few things about it and what I also wanted to. What if organizations are not thinking in that mindset of data as a product? Why do you think it’s important and the benefits this mindset is going to bring for them? 

Malcolm Hawker 

Yeah, well I think. If if the company is thinking. Of data management as product management right if they are applying product management principles to data management. I I honestly think that that will give them a competitive advantage. I I I, I truly believe that if they were, if they were looking at. Data ads. Product I, I think it would drastically change a lot of internal processes, right? I think it would bring more maturity to things like building business cases more maturity to understanding customer needs, to defining customer needs, like managing products, managing whole suites. Of product. Not just an individual. One but a a family of. Products right so? And again, I I think that the companies that do. That I I. Think if they’re successful at it could most. Certainly drive competitive. You know, differentiation with companies that don’t? Right because. For all the things that. I talked about before if. If you are out there and. You can’t prioritize. One data, right? If you can’t say this. Data is more. Important than that data if. You just create all, treat all data as the exact same. 

With the exact same value. 

Malcolm Hawker 

With the exact same benefits. You’re going to have the really, really, really. Difficult time managing data efficiently, not to. Mention the fact that the amount. 

Of data, right? The 3V’s of. 

Malcolm Hawker 

Data are exploding. 

Right? 

Malcolm Hawker 

So if you can’t prioritize data if you don’t understand how data. Is being used within your organization? Or how it’s being used externally you’re. Gonna have an awfully hard. Time, right, right? Product managers that’s What they do? How is this product being? Used, I mean I can. Remember when I when I? Was at a product management organization. We we would hire. Like these, these testing companies and put people. And and you know, we’ve been building. And and and put people into. These kind of this testing scenarios and watch them. Interact with the product, how? Are they actually interacting with it? What’s working what’s not working, where the buttons working when it’s not working right and and we started to do more of that in in the data world, right where we had data as a product. I mean this this could be immensely valuable. I think to do in analytics organizations and to CDO’s. Yeah, right. 

Rishabh Dhingra 

Yeah, I mean there there is. 

Malcolm Hawker 

Some aspect of of selling data like data. Monetization, I mean. I think that that’s where if you were to. Look at this from. Kind of like a Venn diagram. That’s where there is that between product management and data management. Like data monetization is the kind of the closest thing to to like. You know, building and selling. 

But honestly, not a lot of companies. 

Malcolm Hawker 

Are doing it. 

Unless it’s unless it’s the business, right? Unless it’s. 

Malcolm Hawker 

Like a done advisor, it’s the business to sell data. So few companies I forget the exact number but but I what I was a gardener. I know we had done some. 

Research around this. 

Malcolm Hawker 

And it’s it’s. It’s a very, very small fraction of companies I I want. I want to say fewer than 20% of companies and. 

Probably even less than that. 

Malcolm Hawker 

Don’t put me on that number, but a very. Very small number of companies are actually monetizing their data right. Selling data for money. But if you treated data management as product management, integrated product management disciplines into data management, I think it would be an easy stretch to move into data. Yeah, right and. 

Rishabh Dhingra 

Good yeah. Well, I think we’re thinking of some scenarios. I do think applying these principles sometimes also help in channelizing your resources, your company, your people in the right direction in terms of data as well because. Thing that we could build a fancy model to every problem in data science is say it’s going to solve it, but maybe that’s not a consumer problem at all. Consumer problem is something else. So having a product lens doesn’t look it from the technology perspective as to whether you are just putting an analytic solution or a reporting or a jazzy model, it’s about solving the problem so. I’ll actually channelizing your resources in that way, which also talks about then the ROI on your on your data as a product, right? That’s your ROI, basically. 

Malcolm Hawker 

Yeah, I I saw this with big data right? And I saw this with a lot of companies investments in Hadoop which for many companies many companies turned out to be. An exercise of. Creating a lot of very, very very. Interesting answers to questions nobody was asking. 

Like like, oh? 

Malcolm Hawker 

Look at this coronation I found right. I I I I I found that a causal relationship between eye color and employee tenure. People who realize. And to stay employed longer. 

What he’s asking? Yeah, right. Like how? 

Malcolm Hawker 

Am I gonna? Action that and but, but besides that like, hey. 

Speaker 

How do they care? 

Did you know that people with blue eyes? 

Malcolm Hawker 

Stay around longer than than and finally Guy. Says I don’t. Know that I’m being I’m. Being glib, but that’s the. Kind of stuff. I’m talking about these these interesting causal. Relationships that nobody really was asking about and couldn’t. 

Figure out how to operationalize anyway. Right so. 

Malcolm Hawker 

So to point rush yeah, if if if you first start with the need like the need here is we need we need to find out we need data that will help us predict employee tenure. OK, that’s great, and maybe maybe down. The road we’ll. Find out that you know. 

Their eye color has something. To do with it now of. Course but but. 

Malcolm Hawker 

But start with the problem 1st and and then come up with the answers, but instead so often what I see in the data and then what? 

What was they come up with these answers and. 

Malcolm Hawker 

Figure and say OK well. Can you use this? 

Is this is this worthwhile? Yeah, I found this cool stuff. I’ve built this amazing AI model that that is is it any? Is it worth anything to you like that? That’s that, could be. 

Malcolm Hawker 

An awfully big waste of money. 

Rishabh Dhingra 

Right right, we have talked about like one of the pillars of a typical consumer product, which is like understanding the need or business case, right? But a product a consumer product has lot more different pillars that are applicable like usage, support, usability, reviews. Identification, specification, even security and documentation. All of these are different pillars that you can apply to your typical consumer product that product managers. Would think about. Using all of this also could be applied to data when we send data as a product. 

Malcolm Hawker 

Without a doubt. Yep, without a doubt. 

Rishabh Dhingra 

Maybe if we can help us unpack maybe one of these pillars that you think we would not think before. 

Malcolm Hawker 

Usability, yeah, usability? Right? That that’s. That’s a that’s a way that that’s. A way of describing what in the data world? The phrase we use is fit for purpose. Right it is. Is this data fit for the purpose? For how it’s going to be used right, that’s. To me, that’s usability. Is it usable? Yeah, right, like it’s it’s. 

Rishabh Dhingra 

Malcolm Hawker 

It’s a slightly different twist of the word, but. But the the but the the intent is. The exact same security and access right? Who who? Who should be able to see the. Or interface with this data. Or what are your security protocols to make sure that. That you are staying. You know regularly compliant to to regulations or I mean all the things that you would mention in terms of kind of like the core tenants of of product management I think will easily be applied. In the data management world, and we’re. Not actually doing it, you just apply. Different, slightly different words, right? And and another. Example is in. In, you know marketing. Somebody in data analytics would say. Well, we can you know can. You operationalize the data, can you use the? 

Data as it fit for. 

Malcolm Hawker 

Purpose the marketer may actually say. Well, can I activate that data? Thank you. 

Rishabh Dhingra 

Right like? 

Malcolm Hawker 

We we see this all the time, it’s just for whatever reason we like to use different words to say the exact same thing, maybe to make things more complicated. I don’t know, but. But yeah, he’s ability is, is is. The data fit. For purpose is it? Is it trustworthy? Is it? Is it accurate? Is it consistent? 

Rishabh Dhingra 

Yeah, right, and I can think of product support. Could also be because what we do is when products is long we look at how consumers connect they face any issues, how we can support them and same could be applied to data once we expose this data there are issues. How can we support? So yeah, I could also see like all these pillars being applicable to data. 

I don’t like. 

Rishabh Dhingra 

World as well. 

Malcolm Hawker 

Yeah, and and and the support if. You, if you’re building let’s, let’s assume. You’re building a A. Right? 

Rishabh Dhingra 

You’re going, you’re going to have. 

Malcolm Hawker 

Some idea of what? The defect rate. Is going to be right you you? 

You should. 

Malcolm Hawker 

You should have some idea of what what that. Defect rate will. Be from the manufacturing perspective from a. Software perspective from usability perspective and. If you don’t, well, you’re you’re. You’re either way. You’re going to drive support right? Because there will be defects. There will be some optimal usability. There’s going to be things that are going to drive you S. And then the data row exact same fit data. 

Will be incorrect. It will be inaccurate. 

Malcolm Hawker 

It will be. It will be outdated, right? That will that will drive queries that would otherwise be going to a customer support line, but instead they go to. Help desk right? Right, so instead of going. To your 800, you know. Your call center. They’re they’re going to the help desk. But it’s the exact same thing. Help desk what’s wrong? Well. My report doesn’t make any sense because. The data it is is wrong. Right where it’s missing an address? So I looked at this address and it was for Domino’s and it was. Supposed to be for something. So yeah, all of those tenants, I think could be easily easily overlaid into the data analytics world. But again, we just love. To use different words. 

Rishabh Dhingra 

Yeah yeah, and we have briefly discussed about where like data governance is also one of the aspects that fits into this overall data strategy. Now, thinking of the future of the analytics or the other one is what do you think? What are you most excited for in this world of data analytics or data governance in the future? 

Malcolm Hawker 

Well, I think there’s a few different things so. So one I’m. I’m excited about the future in general, just. Because we we. Know that there will be continued to be more and more data. That’s going to continue to be. 

A need to figure out all. 

Malcolm Hawker 

Of this stuff, right so? I like paying my mortgage and I like having a. Job so so that in and of itself is is. It is inspiring to me, but in terms of the future, well there are some things that we know are are are are here and will continue to be here that continue to to expand. And what I would have called when I was a gardener, augmented data management what what that what that means is the application of AI ML other cool new technologies graph, other stuff like that to provide added layers of of automation in in the world of data management. I would I would put. The creation and measurement of data fabrics into. That bucket as well. Slightly separate topic as. The data fabric is an architectural pattern. Whereas these other things are unique discrete technologies. But there’s more and more more. And more and more data. And we are more competitive than we’ve ever been. Customers and users. They’re desperate for insights, right? They’re desperate for competitive separation. They are. Desperate to fulfill, and they did. The idea of digital. That means that more data is needed to drive the insights that will drive the differentiation. That means that with. Limited numbers of. People we need more and more and more automation. In the data space. 

Right? 

Malcolm Hawker 

That will come through the lens of AI that will come through smart robots that are able to do things like. Fix for data quality issues or kind of heal fixed data in real time or more basic basic form is. You know training ML. Models to to to to steward. Data in more automated ways or to learn off of what people are clicking on and not clicking on. And what data stewards are doing and not doing and? And and and enhanced. Models to to help with data stewardship and and data quality management graph is pretty cool. More and more graph applications of data from the perspective of. Relationships this graph is very good at managing and and showing. They can be really powerful to understand context and data. Yeah, right where the for data management and data modeling was this very structured exercise. Now we’ve. Got graph that will. Say hey listen, you know there’s other things here that are relevant to a customer or product or an asset you may. Not have known about, so it’s it’s actually practical. Graph is an inherently practical application of unknown knowns to me right, which is pretty cool. Other feature things. Well, I honestly believe that blockchain and distributed Ledger technology will be transformational. I I. Just I have a hard time seeing a future where distributed ledgers. Do not play a role in data management. 

I just I just it. 

Malcolm Hawker 

The the purpose built there are some use cases. Not all not. All don’t don’t get me wrong I. Can already hear people out there. 

Saying wait a minute. 

Malcolm Hawker 

If they’re immutable, how come that? Immutable does does not mean not. Changing right Ledger. Ledgers are are are just, you know, you know, read and write, not update right. You can’t change ledgers, right? It’s not just because you you. You put a word into a Ledger that it’s that. I mean it. Is there forever that’s? 

Rishabh Dhingra 

The immutable part, but. 

Malcolm Hawker 

Things like data lineage data. Right supply chain management like understanding where things. Are touched in the supply chain I I. Happen to think that the consumers are going to demand more and more insights around the provenance of things like food right there. If if I’m eating a. Head of Lettuce who touched it? Where did it? Come from was it ethically sourced right? 

Rishabh Dhingra 

That’s a great point here. 

Malcolm Hawker 

Like like and and I want to be able to. See the entire lineage of this this head of cabbage. This is already done. There’s there there. Are some very expensive restaurants in the world that I know of 1 in. San Francisco that that. Has meat with a pedigree. 

Rishabh Dhingra 

Malcolm Hawker 

Right? Like where did the meat come from? It’s like Kobe beef, like insanely expensive Kobe beef but but we’ll show you where it came from. Where was the cow raised? What was the cow eating? These are perfect use cases for leather technology. Yeah, that’s kind of step one. Step 2 is I do actually see blockchains playing. A role here. In in this notion of what I. What I would? Call the creation of share shared data networks. Right, right now MDM is is a centralized pattern of data management where every company every large company has their own MDM sitting within those databases is information about people and about companies that is. Being replicated over and over and over and over and over. Acme incorporated appears. In, in in. One company’s MDM, it appears in another company’s M it. Appears in hundreds. And thousands of companies of MDM. And that data is being stewarded and managed and governed. Largely not entirely, but largely the same way with a whole bunch of centralized points of management and stewardship and governance. Why can’t we put MDM over top of those MDM’s? Why can’t we start sharing some of that? And why couldn’t we even implement some notion of a token? Or dare I say, a cryptocurrency, to reward people for stewarding, stewarding and managing the quality of the data. In that shared asset. Right, right, this is this is this is the business model of third party data services like experience like Donna Bradstreet like others that create and manage data and centralized. It will they get the value of curating of managing. 

Speaker 

Of of of of. 

Malcolm Hawker 

Managing the business rules for those databases. But I could easily see a world where in. The future, we decentralize that, and the management is. Put into individual loans. And we create through Dows decentralized autonomous organizations the governance rules. And even the, and even the the financial incentives. To manage that data in more of a shared. Way so I I think that’s cool stuff. 

Rishabh Dhingra 

Right? Yeah, there was some interesting use cases, but yeah. 

Malcolm Hawker 

Yeah, so hey, I can talk to this. Stuff for another hour. If we wanted to. 

Rishabh Dhingra 

I wish you had more time, but those are some interesting technologies as well as of course blockchain. I definitely agree with, but you’ve opened my eyes to different use cases as well. Obviously I’ve seen use cases that it will be helpful for, but thanks for sharing that. I wish we had more time as I said, but before letting you go, I wanted to know. From you, what has been your biggest life lesson or learning that you could share with our community? 

Malcolm Hawker 

My biggest life lesson. My goodness, you number one is you. Are never finished learning. Never, never. This is a constant, constant evolution. It’s a constantly learning and the more you are learning and the more excited you are to learn. I, I think that the. Better you will. Be as a as. A business person or as a person in general. So I I I I love learning and and I think that’s been a. 

Speaker 3 

Sing for me. 

Rishabh Dhingra 

And it was in agreed well, thank you so much Malcolm for being with us. It was such an insightful conversation and having you all inspired. I wish I had more time, but we can definitely next time chat more about it and Many thanks everyone for tuning in today. If you know someone who you think we should interview or if you would like. To connect with me, drop me a line on LinkedIn, Instagram, Twitter or Facebook. I hope you have an amazing day. Stay happy, stay healthy and be inspired. Thank you.