Transforming Data Culture: A Conversation with Malcolm Hawker

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Hi. Welcome to another episode of How I Met Your Data. Sandy Estrada here.

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Myself and my co-host Anjali Bansal are thrilled to have Malcolm Hawker with us on this episode.

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Malcolm has over 25 years of experience in data and analytics.

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He is a recognized thought leader and advisor in all things data,

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but particularly master data management and data governance.

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He has played a pivotal role in helping some of the world's largest businesses

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improve their enterprise information management strategies.

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He is currently the chief data officer for Prophecy, where he leads a global

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field of master data management and data governance practices at the firm.

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He also hosts the CDO Matters podcast, where he interviews senior data leaders

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from various industries and domains.

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Now, if any of you have ever heard Malcolm Hawker speak, you would know he's

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an extremely passionate data leader.

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We dive into a lot lot of different topics. But first and foremost,

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this episode is recorded right after Anjali and Malcolm returned from their

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CDOIQ conference in Boston, where they co-hosted a session.

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And we're going to talk about that session. We're also going to talk about a

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number of other topics that I think were little pet peeves of Malcolm and mine and Anjali's.

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So we get into that discussion as well.

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He's got a lot of experience, so

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it's great just having these types of conversations with someone like him.

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So without further ado, let's get the show going.

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Music.

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We'll be right back. so welcome malcolm and anjali fresh from cdo iq so anjali,

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How did you end up at CDOIQ with Malcolm?

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I had met Malcolm last year through a number of different conferences that we

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just happened to be at. At one point, we were on a closing panel.

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I think we probably agreed and disagreed much at the same time.

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We just struck up a friendship, tended to run into each other at other conferences

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as well. And one of the things that Malcolm had kind of offered as advice to

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me a number of times was get to CDOIQ.

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You're going to meet a lot of great data leaders.

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It's in Boston, easy to get to. And so kind of at the beginning of the year,

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when CDOIQ put out their call for speakers,

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I ended up putting in a paper that was actually inspired by one of the conversations

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Malcolm and I had had in London about data culture. So I put this paper in.

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I had initially envisioned it as a panel.

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As it got approved, I went, you know, we've done panels before.

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They're met with, you know, kind of different levels of success.

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So I wanted to do something a little bit differently and kind of couldn't get

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away from this good cop, bad cop idea. yet.

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And so just kind of thinking about that, I was like, you know who would be a really good bad cop?

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Because they are very, you know, very passionate about telling you what is wrong

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with the world today, but also offering,

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not just telling you what's wrong, but also offering you some tangible outcomes to strive towards.

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So, you know, so I, you know, we ran into Malcolm at Data Universe.

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I had a Canadian chocolate bar with me specifically for Malcolm.

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And I made my ask. I said, hey, I had this talk that's been approved.

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It was actually inspired by our conversation.

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And would you like to partner with me on this and kind of do something a little bit different, but fun?

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And he said yes, which was a little surprising knowing that he actually ended

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I ended up presenting Monday through Thursday.

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Our talk was on the last day. So I'm like, oh my God, by the time he got to

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Thursday, how his voice wasn't completely hoarse and gone is beyond me.

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Now, Malcolm, when she made the pitch to you, did she inform you that you had to play the bad cop?

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Or is that a surprise? It was inferred. Okay.

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She she told she told me about the format and

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kind of what we were going for right you know i don't know

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good cop bad cop but more like buddy movie maybe but

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but either way i mean i'd kind of assume when it when

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it came to some of these things that it would would it be inferred that

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i would be the provocateur and that that angela would be would be the ambassador

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because because she is so i just kind of naturally figured that out and it's

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a role that i relish in that i i am I'm happy to embody at conferences or anywhere else because I'm,

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as said, I am passionate.

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Nobody's ever going to accuse me of not being passionate.

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And I think we can do a lot better as data leaders.

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And I may not have all the answers, but I do know that the things we've been

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trying just aren't working that well.

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So anytime I have an opportunity to kind of poke a little bit and to talk about

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doing things differently, I pounce. So thank you for including me.

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Oh, no, thank you for joining me. It was fun. It was a really fun talk.

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Yeah. And I will say we were on the last day of the conference where you can

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kind of expect a mixed set of results in terms of participation, in terms of audience.

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We actually had a lot of people join us. We did. It was good.

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Yeah. And they were engaged. One of the things that I really enjoyed about our

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format was the fact that we were able to leverage the conference's app to poll the audience.

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So not only poll the folks that were in the room, but there's a virtual component

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to the conference as well.

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And the virtual attendees could also kind of live enter in responses.

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Oh, that's fantastic. Yeah, I mean, there were 3x the number of virtual attendees

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that there were actual attendees, which I think is great.

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Kind of, you know, it's broadened the tent. And that Hova, Hova,

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Hova, whatever, W-H-O-V-A app is really good.

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I think it's actually one of the best. Usually at these conferences,

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when the conference organizer makes me download the app, I'm like, oh, God, another app.

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I need another app. But that one actually has utility. It works and it's just easy.

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So yeah, I was glad that we had the poll. I'm glad that we had really good turnout.

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The topic was good. The discussion was good. It was awesome.

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So let's cover the topic. What was the topic? So the title of the talk was Transforming

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Your Organization to Embed DNA in Your DNA.

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So, you know, as we kind of started shaping the content, it was really talking

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about how data leadership has been kind of operating in the same mode for the last decade plus.

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And things haven't really changed. We haven't really moved the needle.

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We've got like great opportunities ahead of us, but we're not really capitalizing

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on them in the way that we would have expected or hoped or, you know,

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have the potential to do.

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And so part of that or a big part of that is really rooted in the data leadership

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and the culture that exists in the organization.

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Yeah, I mean, a major takeaway of our presentation was that this idea that culture

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is not a deliverable, right? Culture is actually an outcome.

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If you deliver value, if you embody leadership characteristics,

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and this is something that we stress in the presentation as well,

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if you kind of embody these four attributes of a good leader,

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of a good data leader, if you do those things, if you deliver value to your customer,

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then culture will follow. it necessarily has to follow.

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So the idea that culture is just some sort of deliverable, like a checkbox on

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a requirements doc, right?

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It's like, okay, well, I got to do this. I got to do this. I got to deliver data culture.

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And then when I do those things, I can deliver value to my customers.

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I used to hear this as a Gartner analyst all the time.

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We see it in the actual data. We see it in Gartner's CDO survey where CDOs are

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asked, what are your biggest impediments to delivering value to your organization?

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What are your biggest roadblocks? And the answer consistently,

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top three, one of the top three is always there's a lack of a data culture.

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And to me, that perspective is completely upside down, right?

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The idea that it's a deliverable, that's kind of upside down because you're

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talking about a three, four, five, 10-year journey, an ongoing journey that

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never really kind of ends.

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As Anjali rightly said in our presentations, like who isn't using data? We're all using data.

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Everybody uses data all day, every day. So even the idea that there isn't a

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culture that wants to use data or wants to be fact driven is probably untrue.

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You kind of put all these things together.

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And the main takeaway, the main thing that I was trying to say is like,

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hey, double down on value, find ways to deliver value, then the culture will follow you.

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But any idea where culture is some sort of dependency is probably hindering

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you more than it's helping you. Hmm.

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That's a really good and thoughtful point out. It's so thoughtful because the

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reality is it's not, you know, it's not something that you go create a team around and focus on.

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And I think they're also, there's a lot of competing factors in terms of conflating

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culture with data literacy as well.

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I've seen that a lot where, you know, data, what they really mean is data literacy,

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but they're saying it's culture.

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Those are two very different things. And I do find that people People conflate

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those two sometimes as well.

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So you're absolutely right. You deliver value, people will become more literate

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because you're delivering value that they want to leverage.

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Therefore, one begets the other. Yeah, this idea of culture as a dependency,

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I think, has some rather perverting effects in what we do as leaders.

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A focus on data literacy, I think, is one of them.

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To me a focus on literacy without focusing

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on the delivery of value with without focusing on having a really easy to use

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product without focusing on user-centric design principles without doing the

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things that we know are necessary to build great products that people get value

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from if you're just focused on literacy because you see it as as a dependent

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action to drive the data culture,

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and you're not doing those other things, well, you're going to alienate the

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very customers that you're there to serve because you're basically telling them,

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hey, you need to figure out all this stuff.

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You need to up your game. You need to do all of these things.

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And what do I need to do as a data leader?

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Well, I need you to get you to see the value of data.

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And again, I would see this all the time when I was an analyst.

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I would ask my customers, okay, well, what are you doing around this idea of

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data product management?

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Or what are you doing to drive value? What are you doing to measure value?

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Practically nothing is usually the answer in that case. You know,

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what are you doing to understand your customer needs?

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What sort of feedback mechanisms do you have with your customers around their

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dating? And a lot of the answers were very, very, very light.

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And then I would ask, okay, what else are you doing? Well, I'm focusing on data

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literacy as a means to deliver on my data culture mandate.

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Oh my goodness. I think you're approaching it backwards.

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Yeah. Yeah, absolutely. So what was the response in the room or even online

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related to that messaging?

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You know, there was a lot of it. Like I said earlier, there's a lot of engagement, right?

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There's a lot of head nodding. I think that the message very much did resonate

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because we had a number of folks come up to us after the talk was completed

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with comments, with questions, with their own reflections.

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So, you know, I think the message really landed and

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gave people give people food for thought yeah i

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i agree that that that tends to

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be the response to a lot of the things that i share

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because often i'm sharing perspectives that

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i i don't want this to sound egocentric but but

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i'm sharing perspectives often that i believe they probably

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haven't heard before right the like the idea that

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data literacy is is problematic right the

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idea that you're you're you're forcing people to get

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trained on things they may not like using

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right right or have difficulty using or

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lack confidence to use the idea that you're forcing them to get training on

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that could be doing more harm than good like just that that as as a as an assertion

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for a lot of people would be like what do you mean data literacy is good everybody's

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telling me data literacy is good it has to be good because everybody's telling

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me it's good gardener's is telling me it's good.

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My consultants are telling me it's good. Everybody's saying it's good.

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What do you mean it's bad?

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So in a lot of the presentations that I give, there's a time needed for people

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to kind of absorb the message.

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But as Angeli said, there were many that had said, hey, listen,

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this is great. I really appreciate it. So I think all in the response was actually quite good.

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Because on the flip side, I didn't hear anybody tell me, and maybe they're just

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avoiding conflict, I don't know. I didn't hear anybody tell me,

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hey, you're crazy, right?

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Like, you know, go back to the drawing board because you completely missed the

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mark. I didn't hear any of that. So that's good.

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I want to dig into what you said because I haven't heard anybody say that before.

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And I have, over the last few years, I've kind of jumped on that bandwagon as well.

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You mentioned the whole fact that you're trying to train somebody on something

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that may not be ready to use or they may not like to use or they're just never

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going to use is a problem in itself.

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That message is, I feel, is new.

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I feel a lot of people still have the blinders on in terms of,

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I need to do this fancy dashboard and that's how I'm going to deliver the information

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versus understanding who the user is.

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What would you say is kind of a tactic there in terms of helping individuals hear that message, one?

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And two, what should they do with that message? Well, to me,

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the overarching tactic that we as data leaders need to be thinking more about,

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I will broadly encompass in the in the notion of integrating product management

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as a discipline into data management, right? Like that, to me,

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that's the broader wrapper.

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And if we did that, if we hired product managers,

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if we were rabidly focused on understanding how our customers and I use that

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word purposefully, customers, not users, not stakeholders, not business.

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Business people, customers.

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If we hired product managers who all they did all day, every day was understand

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customer needs, understand how customers were using our products,

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literally standing over people's shoulders, watching them use a dashboard.

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What are they clicking on? What are they not clicking on? Where are they getting,

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where are they stopping?

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Those types of those types of activities if

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we're doing that if we were polling our customers asking them

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are you getting value from this even even

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going so far as is having conversations maybe even around pricing what would

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you be willing to pay for this if you had to pay for it right like think of

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this as a real product right if we're serious about data products products have

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price tags the idea that you can walk into a retail store and have everything

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on the shelf have no price tag on it, that means it's not a product.

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It's kind of like saying I play poker and then asking, okay,

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well, what do you usually wager? Well, we don't wager anything. Well, it's not poker.

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You got to be betting to play poker. And if you're talking about products,

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there's got to be a price tag.

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So it's these types of things, Sandy, that we need to be focused on.

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And if we do all of those things, we understand what customers want,

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what they need, what their challenges are, how data is used to overcome those challenges.

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Well, that's a very different enterprise than saying, okay, I've got some training

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that you're going to need to get on our data products in order for you to get value out of it.

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I would argue if you do all those other things and take a product management

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approach, you're going to build products that people want to use.

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Will there be a training aspect to it? All products have some go-to-market function. They all do.

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Training, marketing, there's all sorts of things that we need to do as product

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people to make sure that the products are successful, including lifecycle management,

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archiving, right, sunsetting products.

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But if we do all those things, maybe there's some training required,

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maybe, but it won't be mandatory necessarily, or it will be received very positively

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because the product will be seen as something that is driving value to the organization.

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So it's a totally different mindset. It's a different model.

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That's great. That's great. Love it.

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So how long was this session? Was it your standard bare 20 minutes or was it

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like a 45 minutes? 45 minutes. Yeah.

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So what other ahas did you have on this 45 minute journey?

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Let's see. I mean, we dug into just a couple of the areas of failure, right?

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And one of them that we continue to hear, especially from a governance perspective,

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is our data is garbage. It's, you know, of such poor quality.

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And, you know, that really is something that we, you know, we kind of anchored

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in and said, that's not the right message.

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You know, like, if we really want to drive a culture of data within your organization,

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we need to assume positive intent, you know, in terms of your data,

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people's behavior. Nobody's trying to make your life's difficult.

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So if you're going around saying that your data is garbage, what message are

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you communicating to your people?

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Like look at that as an opportunity to either think about your data differently,

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maybe look at the context of your data so you can allow for two things to be true at the same time.

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But like really take on that model of behavior around positive intent and collaborate

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with your people to understand what's most important to them.

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I like that. Flip the script. Yeah.

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Yeah, I love it. I mean, this kind of traces back to this idea of seeing culture

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as a deliverable, having perverting effects on what we do as data leaders.

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Positioning data quality as a burden and not an opportunity is a classic example of that.

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When I was at Gartner and I had conversations around culture day in and day

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out, What I came to see often was that when people said there's a lack of a data culture,

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what they really meant is my customers aren't doing what I want them to do.

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Literally. They're not going to governance committees. They're not owning data,

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whatever the heck that means.

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They don't care about data quality. All they care about is their business processes

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and they don't care about my data. So I'm going to make them care about my data.

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And when they don't, I'm going to express my frustration using words like garbage in, garbage out.

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You can lead a horse to water. You can't make them drink.

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They just don't get it. They just don't get it, Sandy. They don't see the value of data.

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So when we approach everything like, okay, I'm going to make them see the value of data.

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I'm going to make them understand why it's important to understand systems and

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processes sitting underneath the data.

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When I'm going to tell them over and over again that they're making my job harder

285
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and harder and harder, out of a frustration because I can't get them to get it.

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I can't get them to see the value. I can't get them to go to governance committee

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meetings. And so I'm frustrated.

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And I'm going to use words like, you need to own.

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And you need to stop throwing garbage over the fence.

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I just see it just add all this stuff up. And it's like, wow,

291
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we're really disempowering ourselves here.

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We're really disempowering ourselves. If we think that the pithy metaphors or

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analogs that I use here is like,

294
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just imagine if the chief operating officer of Frito-Lay said their corn was

295
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garbage all the time, or if the

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COO of Anheuser-Busch said their water was garbage all the time, right?

297
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That's one end of the spectrum. The other end of the spectrum is we're sitting

298
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on this mountain of unrefined gold.

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We're sitting on a mountain of unrefined gold that everybody, everybody,

300
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it doesn't matter, everybody is saying is the new gold and everybody is saying

301
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is going to be the thing that helps my company deliver transformative value

302
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and I'm responsible for that?

303
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Holy cow, what an opportunity. This is incredible.

304
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Instead, it's garbage in, it's garbage out. What can you do?

305
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What can you do? Yeah, they dropped the responsibility and handed it off to somebody else.

306
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You need to fix this. This is your doing and I can't run bad data.

307
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The trope that they don't understand blows my mind because the reality of the

308
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situation is every single operational team on the planet is knee deep in Excel,

309
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punching data every day. That is what they're doing.

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They all have analysts. They're all looking at data all the time.

311
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They understand the value of it.

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So it's not a matter of understanding the value. It's a matter of helping them

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understand the process that is required to refine and ensure that value is there.

314
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But it's even in the words, it's the perspectives, but it's also the words that we choose to use.

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And it's the way we choose to express things. I'll give you an example.

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There's soundbites, plenty of soundbites floating around saying that 80% of

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data scientists' time is lost due to data quality issues.

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Issues like what a ridiculous statement but we hold on to those things and we

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use them we we use them to validate the fact that our jobs are so hard so hard

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and we're and we're and we're inefficient,

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and we're not as efficient as we could be and we're not driving as much

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value as we could be because you make it hard because look at

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this stat 80 of a data scientist time just gets

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thrown out the window do you know how much we're paying for those data scientists and you're

325
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making them them wrangled all of this data while the fact remains

326
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the data is of different structure different

327
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formats different definitions by definition by

328
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design crm systems function differently than erp systems by design and if you

329
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want to blame somebody blame henry ford don't blame the people that actually

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coded those those applications right because they're they're i mean it's how

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it is by design these things are different because our business processes are different.

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The applications mirror the business process.

333
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The data mirrors the application. So that's it. And it's not some sort of flaw

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in the system. It's actually by design.

335
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Sorry. See, I'm passionate. I told you I was passionate. No, I love that.

336
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All right. So we have deliver value, change your language. Is there a third one?

337
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So, I mean, we went with, you know, create the right mindset,

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prioritize collaboration.

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It was continuous engagement. So, you know, instead of sitting in the ivory

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tower saying you must do things this way, work with your people.

341
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Like get out there, get in the muck and actually understand what people are

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doing. What are truly their pain points?

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What do they need in order to be successful and thereby make the organization

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successful, deliver that value.

345
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And then we can start to build the right products for that. Malcolm had a great

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example around a baker baking bread.

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And they're the only bakery in town and nobody's buying the bread.

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And the baker's standing there going, well, you just don't understand the value

349
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of my bread. You don't understand how hard it is to make the bread.

350
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Well, maybe they're all celiac and they can't eat the gluten.

351
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Did you ask? That's a really good metaphor.

352
00:23:47,500 --> 00:23:53,060
Yeah, yeah. To me, the shifts here are actually kind of subtle, right?

353
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Like the really good news here is that, you know, we're not necessarily talking

354
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about fundamental shifts in technology.

355
00:24:01,560 --> 00:24:06,280
It's not like we need to like pull out all the technology and put new technology in, right?

356
00:24:06,560 --> 00:24:11,880
I think a lot, we can make a lot of positive differences here with just subtle

357
00:24:11,880 --> 00:24:15,800
shifts in how we manage our team. subtle shifts in how we approach problems.

358
00:24:16,580 --> 00:24:21,420
There are already kind of roles that kind of align to this idea of product management,

359
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although I would hire a product manager because it is a unique skill set.

360
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The idea that a business analyst could be a product manager,

361
00:24:27,020 --> 00:24:30,120
but the roles are pretty close, right?

362
00:24:30,180 --> 00:24:34,240
The idea of I need to understand and document requirements, I mean, that's fairly close.

363
00:24:34,780 --> 00:24:38,560
I would argue from the perspective of what do you need to do?

364
00:24:38,840 --> 00:24:42,000
And Angela just touched on some some of the characteristics that you need to

365
00:24:42,000 --> 00:24:45,300
embody. When it comes to brass tacks and like, what does the organization look

366
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like? Who do I need to hire?

367
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What I would recommend is one, a product manager, two is something called the value engineer.

368
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I would argue that at a certain size of company, well, this is all sizes,

369
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but it depends whether it's an FTE or not.

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But at a certain size of company in a data analytics function,

371
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you could easily justify an FTE to be a value engineer consigliere,

372
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to be the right-hand person of a VP of data analytics to help do things like

373
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budgeting and forecasting and planning.

374
00:25:16,305 --> 00:25:20,265
You're probably already doing that already, but take that role one step further

375
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and actually get it into the realm of starting to understand and build models

376
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of how does improving data quality actually drive business outcomes?

377
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How do our key business drivers, how are they influenced by better data?

378
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Start measuring that thing out.

379
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With a north star of getting to the point where your store of data products,

380
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actually each product has a price tag on it.

381
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Now, we could get into interesting conversations about, well,

382
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data is not actually a balance sheet asset and fall down that rabbit hole if

383
00:25:50,025 --> 00:25:52,565
we wanted to. I don't think that that's a useful conversation.

384
00:25:53,065 --> 00:25:56,985
I'm talking really about a conceptual framework here, some sort of thought exercise

385
00:25:56,985 --> 00:26:00,345
maybe even to say, if you had to put price tags on everything, could you?

386
00:26:00,925 --> 00:26:04,065
That's different than saying, well, my CFO doesn't think it's a balance sheet

387
00:26:04,065 --> 00:26:06,485
item, so that means I'm just going to throw up my hands and not care.

388
00:26:06,925 --> 00:26:10,745
Yeah. No, and you only improve what you track, right? So if you're tracking

389
00:26:10,745 --> 00:26:12,785
the value of it, then you could improve on it.

390
00:26:13,245 --> 00:26:16,385
So yeah, that makes absolute sense.

391
00:26:16,545 --> 00:26:20,725
I think I think it's challenging when it's considered a cost group.

392
00:26:20,805 --> 00:26:25,845
It becomes a very challenging issue for a VP of data, for example, or a CDO.

393
00:26:26,165 --> 00:26:30,625
Speaking of which, I heard that you got a new role at Prophecy. Congratulations.

394
00:26:31,545 --> 00:26:38,785
Thank you. Yeah. CDO of Prophecy. Are you working to implement some of these ideas there?

395
00:26:39,165 --> 00:26:42,085
My role is an externally facing one.

396
00:26:42,325 --> 00:26:46,865
So I would best be called a field CDO. There are a number of companies that

397
00:26:46,865 --> 00:26:51,485
do what we do, including Microsoft as a CDO who's more externally facing.

398
00:26:51,905 --> 00:26:56,705
So my role, my daily responsibilities largely have not changed.

399
00:26:56,965 --> 00:27:01,045
I support our clients, I support our prospects, I support our field,

400
00:27:01,185 --> 00:27:03,425
and I evangelize in the market, right?

401
00:27:03,505 --> 00:27:07,185
And I do things like this to help other CDOs understand how to best optimize

402
00:27:07,185 --> 00:27:10,025
their organizations, how to structure their organizations, how to approach governance,

403
00:27:10,065 --> 00:27:13,185
management, you name it, from having done this for 30 years.

404
00:27:13,305 --> 00:27:17,765
So new title, largely same job.

405
00:27:17,845 --> 00:27:24,085
Now that said, from the perspective of implementing what I suggest and following

406
00:27:24,085 --> 00:27:27,805
my recommendations, I do come at my knowledge honestly, right?

407
00:27:27,885 --> 00:27:30,125
I have managed data and analytics groups.

408
00:27:30,245 --> 00:27:34,005
I've managed governance efforts. I've implemented MD. I have been the person

409
00:27:34,005 --> 00:27:37,685
making decisions about budgets, about org structures, about all of these things.

410
00:27:38,625 --> 00:27:42,925
I feel like I could live on either side. I know I could live on either side

411
00:27:42,925 --> 00:27:45,625
of the equation, whether I was internally facing only or externally facing.

412
00:27:45,885 --> 00:27:51,285
But given my passion, given my knowledge, given what appears to be rather kind

413
00:27:51,285 --> 00:27:56,105
of decent skills for sharing knowledge, if this dog is learning some old,

414
00:27:56,125 --> 00:27:57,825
this old dog is learning some new tricks.

415
00:27:58,105 --> 00:28:01,245
I actually think I'm best suited.

416
00:28:01,385 --> 00:28:05,665
I think I'm, from a value extraction perspective, doing what I'm doing and the

417
00:28:05,665 --> 00:28:08,705
role that I've got, I think is a perfect word for me. So am I hearing like a

418
00:28:08,705 --> 00:28:13,825
2025 Studio IQ, a year as a studio reflection piece?

419
00:28:14,225 --> 00:28:16,945
Oh, that sounds great. These stories write themselves. I mean,

420
00:28:17,005 --> 00:28:21,405
they write themselves. I think that would be great.

421
00:28:21,665 --> 00:28:24,645
Maybe put my money where my mouth is and measure my own value.

422
00:28:24,645 --> 00:28:27,885
We can frame it up and create the right mindset, prioritize collaboration.

423
00:28:28,865 --> 00:28:29,825
Continuous engagement.

424
00:28:30,225 --> 00:28:32,085
And what was the last one? Drive change.

425
00:28:32,505 --> 00:28:38,045
So yes. Yes. I'm all about driving change. we desperately need change.

426
00:28:38,365 --> 00:28:42,685
So I find it interesting, just a fun little aside, that in my presentation,

427
00:28:42,965 --> 00:28:45,625
my individual presentation, not the one that I did with my learned colleague

428
00:28:45,625 --> 00:28:52,625
here, I included a graphic of the Albert Einstein doing the same thing over

429
00:28:52,625 --> 00:28:55,825
and over again, expecting different results. I included a graphic like that.

430
00:28:56,225 --> 00:29:00,365
And the very next presentation I went to was my friend Juan Cicada from Data.World,

431
00:29:00,525 --> 00:29:03,325
and he had the exact same graphic in his presentation.

432
00:29:03,645 --> 00:29:07,665
So I was like, Oh, I wonder. Yeah. And we had, and we had Angela,

433
00:29:07,745 --> 00:29:10,305
we had gone to dinner the night before with Juan having this conversation.

434
00:29:10,365 --> 00:29:14,585
And he said, I'm going to alter my presentation because of the things that we just talked about.

435
00:29:14,905 --> 00:29:18,145
I didn't actually get confirmation from him that, that he added that graphic

436
00:29:18,145 --> 00:29:20,405
because of our conversation, but who knows?

437
00:29:20,525 --> 00:29:25,245
I had an early, I had an early preview of his, of his deck that was already in there.

438
00:29:25,565 --> 00:29:28,905
So he was already thinking about that. Sure.

439
00:29:29,005 --> 00:29:33,785
The clock track around it was altered based on some of the things that we were

440
00:29:33,785 --> 00:29:35,485
talking about. At some point.

441
00:29:36,267 --> 00:29:39,907
Do we, as data advisors, at the end of the day, we're all advisors.

442
00:29:40,047 --> 00:29:41,847
We're helping executives.

443
00:29:42,967 --> 00:29:47,047
We are. At one point, it feels like an uphill battle, doesn't it?

444
00:29:47,127 --> 00:29:51,247
With those messages, like, hey, you guys keep doing the same things,

445
00:29:51,287 --> 00:29:54,247
expecting different results. We keep telling you the same things.

446
00:29:54,667 --> 00:29:59,567
It feels daunting, doesn't it? It does, and it doesn't.

447
00:30:01,287 --> 00:30:05,687
There's a consulting answer for you. Define daunting. I mean,

448
00:30:05,747 --> 00:30:09,807
from a top-down perspective, if you look at this from a global kind of macro

449
00:30:09,807 --> 00:30:11,307
perspective, yeah, it's daunting.

450
00:30:11,707 --> 00:30:15,287
But if you take more of a bottoms-up, iterative, kind of agile approach and

451
00:30:15,287 --> 00:30:18,767
just take some baby steps, and if a lot of people started to do them,

452
00:30:18,847 --> 00:30:20,087
I think it would be less daunting.

453
00:30:20,087 --> 00:30:26,767
But to your point, Sandy, I mean, yes, we live in a very complex ecosystem of

454
00:30:26,767 --> 00:30:33,047
reinforcing behaviors that are reinforcing that where a lot of the players in

455
00:30:33,047 --> 00:30:34,967
the market are reinforcing negative behaviors.

456
00:30:35,087 --> 00:30:37,527
They're not enforcing positive behaviors.

457
00:30:37,647 --> 00:30:42,027
I actually came up with a little graphic and a little theory to explain all

458
00:30:42,027 --> 00:30:44,947
of this. I called it, what did I call it?

459
00:30:45,347 --> 00:30:50,067
The semantic pedanticism feedback loop.

460
00:30:51,387 --> 00:30:56,267
The semantic pedanticism feedback loop. It was a riff on why we keep inventing

461
00:30:56,267 --> 00:30:59,587
new words for things, because we do.

462
00:30:59,587 --> 00:31:04,447
However, it actually explains, when I look at this model that I built,

463
00:31:04,527 --> 00:31:11,127
and now I'm going into crazy town, this complex interaction between software vendors, consultants,

464
00:31:11,527 --> 00:31:18,707
and analysts, and customers, and users, people signing the SOWs.

465
00:31:18,707 --> 00:31:25,747
There's a complex web that exists there where once an idea is birthed,

466
00:31:25,747 --> 00:31:29,687
once something is created. I'll pick on data literacy.

467
00:31:31,028 --> 00:31:36,528
Data literacy starts to bubble up, I would argue, six, seven years ago.

468
00:31:37,148 --> 00:31:38,668
It's bubbling a little bit.

469
00:31:39,348 --> 00:31:43,408
Then in 2019, Jordan Morrow, while he was working for Click,

470
00:31:43,628 --> 00:31:47,228
commissioned a study. It's a vendor commissioned study.

471
00:31:47,668 --> 00:31:51,208
Take that with a grain of salt. All vendors do studies to say,

472
00:31:51,308 --> 00:31:54,808
hey, generally those studies already have the answers and they're just looking

473
00:31:54,808 --> 00:31:56,688
for the study to validate the answers.

474
00:31:56,688 --> 00:32:02,528
But that aside, that aside, Jordan commissions a study for Click that finds,

475
00:32:02,768 --> 00:32:10,048
in essence, that finds that people aren't getting value from data because they

476
00:32:10,048 --> 00:32:10,948
don't understand the data.

477
00:32:11,108 --> 00:32:15,088
The underlying premise is, is what Jordan would say, is there's a skills gap,

478
00:32:15,288 --> 00:32:22,028
a skills gap on users when it comes to data and the way that we deliver value is to close the gap.

479
00:32:22,428 --> 00:32:26,708
This was the assertion coming out of this. So this report gets a little bit of steam.

480
00:32:26,928 --> 00:32:31,068
People at Gartner start to hold onto this and start to smell something here.

481
00:32:31,408 --> 00:32:34,128
Well, interestingly, analyst firms, I won't pick on Gartner,

482
00:32:34,168 --> 00:32:35,348
I will just say analyst firms.

483
00:32:36,048 --> 00:32:42,808
These are annual subscription businesses that need people to re-up every year.

484
00:32:43,488 --> 00:32:48,048
And if everything was evergreen, if everything we did didn't really change that

485
00:32:48,048 --> 00:32:51,128
much, governance isn't changing that much, MDM isn't changing that much,

486
00:32:51,128 --> 00:32:52,368
much. Data quality isn't changing that much.

487
00:32:52,468 --> 00:32:56,228
If things didn't change that much, those annual subscriptions would be,

488
00:32:56,288 --> 00:33:01,228
I would argue, less compelling because you don't need to re-up your annual subscription.

489
00:33:01,648 --> 00:33:04,948
How do you get people to re-up an annual subscription? Well, you create new things.

490
00:33:05,688 --> 00:33:08,748
You make new things. Hey, flash, flash, flash.

491
00:33:08,968 --> 00:33:11,428
Hey, there's this new thing. Have you heard of it? It's the data mesh,

492
00:33:11,548 --> 00:33:13,928
or it's the data fabric, or it's data literacy.

493
00:33:14,248 --> 00:33:16,368
And there's all these things that you need to learn about. out.

494
00:33:16,948 --> 00:33:21,348
And they start pushing and pushing and pushing and pushing. And then all of

495
00:33:21,348 --> 00:33:25,168
a sudden, people start calling asking, hey, what's this data literacy thing? Tell me more.

496
00:33:25,628 --> 00:33:31,868
And then Gartner has data to show, and analyst firms have data to show that

497
00:33:31,868 --> 00:33:33,708
people really care about data literacy.

498
00:33:34,068 --> 00:33:36,768
It becomes this self-fulfilling prophecy that all of a sudden,

499
00:33:36,788 --> 00:33:39,508
vendors jump into the fray and say, oh, wow, this thing must be big.

500
00:33:39,928 --> 00:33:41,668
People are asking about data literacy.

501
00:33:42,488 --> 00:33:45,428
Consultants jump into the fray because their customers are saying,

502
00:33:45,528 --> 00:33:49,428
hey, tell Hey, Sandy, tell me about data literacy. I'm hearing this data literacy thing.

503
00:33:49,768 --> 00:33:54,228
And all of a sudden, this gets spun up. It's the hype cycle, right?

504
00:33:54,328 --> 00:33:57,228
It's the hype cycle. In the case of data literacy, I would argue it came from

505
00:33:57,228 --> 00:34:02,708
one study from one vendor where all it took was a few analysts to jump on that pile.

506
00:34:03,208 --> 00:34:05,208
And lo and behold, a movement is born.

507
00:34:06,317 --> 00:34:11,937
I mean, that's usually the case, either a study or a white paper from somebody.

508
00:34:12,177 --> 00:34:17,657
I mean, data mesh blew up overnight, it felt like. And people just took the

509
00:34:17,657 --> 00:34:18,957
highlights of it, right?

510
00:34:19,697 --> 00:34:23,257
It just, yeah, it's one thing after another like that, for sure.

511
00:34:23,257 --> 00:34:26,877
But your question was, hey, you're looking at things a whole different way.

512
00:34:27,517 --> 00:34:32,877
You're suggesting that we need to dispense of some sacred cows.

513
00:34:33,377 --> 00:34:37,537
If true, that seems daunting. And I would say, yes, it is daunting because there's

514
00:34:37,537 --> 00:34:43,537
these other forces that continue to reinforce things that, in the case of data mesh, my goodness.

515
00:34:44,237 --> 00:34:49,077
Did anybody stop to ask, wow, okay, conceptually, this is interesting.

516
00:34:49,357 --> 00:34:53,097
Full decentralization, that's interesting. Having domain autonomy,

517
00:34:53,377 --> 00:34:54,077
that seems interesting.

518
00:34:54,857 --> 00:34:57,077
Federated computational governance, I don't know what that means,

519
00:34:57,137 --> 00:34:57,977
but it sounds interesting.

520
00:34:58,437 --> 00:35:02,917
All of these things, okay, this data products, ooh, that really sounds interesting

521
00:35:02,917 --> 00:35:04,537
because nobody's using my products today.

522
00:35:04,677 --> 00:35:08,497
And it seems like this thing can maybe help with that. So all these things are interesting.

523
00:35:08,777 --> 00:35:11,797
But did anybody ask the question, it was like, okay, wow, wow,

524
00:35:11,837 --> 00:35:17,117
when you go from a hub and spoke to a, to a spaghetti bowl, what is the cost?

525
00:35:18,797 --> 00:35:22,877
Right. And did anybody ask, wow, well, hub and spokes as inefficient as they are.

526
00:35:22,917 --> 00:35:26,257
And sometimes, you know, maybe not the best for our end users and maybe not

527
00:35:26,257 --> 00:35:29,317
the best. And maybe, maybe we have to sacrifice some, some things at the domain

528
00:35:29,317 --> 00:35:33,557
level, you know, airlines are a hub and spoke for a reason.

529
00:35:33,897 --> 00:35:38,397
Network typologies are hub and spoke for a reason, but did anybody stop to ask,

530
00:35:38,477 --> 00:35:40,597
okay, well, what would the cost be to go from that to something?

531
00:35:40,597 --> 00:35:43,857
It is this basically the spaghetti bowl, like a spider web of connection,

532
00:35:44,077 --> 00:35:45,377
because that's what it advocates.

533
00:35:45,517 --> 00:35:49,177
It advocates domain to domain, this organic domain to domain sharing,

534
00:35:49,437 --> 00:35:53,797
where it's quite okay to have the same report repeated 15 times over,

535
00:35:53,857 --> 00:35:56,537
as long as you're sharing between domains. Did anybody ask?

536
00:35:57,057 --> 00:36:01,337
No, nobody asked. But the hype gets created, everybody pumps it up.

537
00:36:01,857 --> 00:36:05,537
Three years later, it's the biggest thing and the greatest thing since sliced bread.

538
00:36:06,461 --> 00:36:10,041
And I'm talking to companies that are like, yeah, we just turned off our data

539
00:36:10,041 --> 00:36:11,421
warehouse. I was like, what do you mean?

540
00:36:12,481 --> 00:36:14,661
Yeah, we're following the mesh. We're implementing a data mesh.

541
00:36:14,741 --> 00:36:15,601
We turned off our data warehouse.

542
00:36:16,401 --> 00:36:20,101
Do you want, okay. How are you going to support cross-functional use cases?

543
00:36:20,641 --> 00:36:21,681
Well, we're figuring that out.

544
00:36:22,421 --> 00:36:26,361
How are you going to give the CFO the one report that says how many customers we have?

545
00:36:27,521 --> 00:36:34,201
Anyway, sorry, I'm ranting. But we live in a complex ecosystem where contrarian

546
00:36:34,201 --> 00:36:37,381
ideas don't tend to get inflated.

547
00:36:37,401 --> 00:36:42,281
The ideas that reinforce the status quo tend to get inflated or hypey things that are unproven.

548
00:36:42,461 --> 00:36:44,881
Hypey things. Hypey things. What's new?

549
00:36:45,121 --> 00:36:47,201
And I fall prey to that. I was going to say, one of the other things that we

550
00:36:47,201 --> 00:36:52,061
touched on a little bit during the presentation was the fact that just as consultants,

551
00:36:52,221 --> 00:36:57,001
a lot of times our clients are coming to us saying, how do we benchmark against

552
00:36:57,001 --> 00:36:58,621
our competitors? competitors, right?

553
00:36:58,661 --> 00:37:01,261
Like how are we performing against everybody else?

554
00:37:01,501 --> 00:37:04,261
And I just sit there going, why do you want to know?

555
00:37:04,461 --> 00:37:08,681
Like, let's deal with your problems and figure out like what we need to do to

556
00:37:08,681 --> 00:37:12,601
get you moving forward as opposed to comparing yourself to somebody else.

557
00:37:12,661 --> 00:37:16,741
Because guess what? They have the same issues, right? Like we're all solving it together.

558
00:37:16,941 --> 00:37:20,801
They're no further ahead than you are. They're not doing anything differently.

559
00:37:21,261 --> 00:37:24,021
So why do you want to know? you know and is it

560
00:37:24,021 --> 00:37:28,541
just that safety in numbers is it just therapy to know that maybe others are

561
00:37:28,541 --> 00:37:33,321
suffering in the same way it just it's kind of an interesting interesting quandary

562
00:37:33,321 --> 00:37:36,981
in terms of like why do you want to know and like like take a risk like let's

563
00:37:36,981 --> 00:37:42,041
just do something differently to move forward if i had a dollar for every time

564
00:37:42,041 --> 00:37:43,401
i was asked when i was an analyst.

565
00:37:44,061 --> 00:37:49,381
What does good look like or where do i stack up in the industry i'd be a rich

566
00:37:49,381 --> 00:37:54,061
man even if they were Canadian dollars, I would be a rich man.

567
00:37:54,181 --> 00:37:59,561
So completely concur, Adelaide. I mean, it's, it's, but I see that again,

568
00:37:59,621 --> 00:38:02,861
getting back to this idea of mindset and how we approach what we do,

569
00:38:02,901 --> 00:38:04,921
I see that being a fairly defensive posture.

570
00:38:05,841 --> 00:38:10,761
Meaning if I'm trying to define a strategy, or if I'm trying to execute on something,

571
00:38:10,901 --> 00:38:16,941
and I take the perspective of, I need to do just slightly better than the other guy or gal, right?

572
00:38:17,321 --> 00:38:22,901
And if a good enough is good enough, and if I can check that box and say that

573
00:38:22,901 --> 00:38:27,021
Gartner has approved this plan, or that I know that I'm doing things the same

574
00:38:27,021 --> 00:38:30,021
as everybody else, that's a defensive posture.

575
00:38:30,201 --> 00:38:33,581
Because it's not innovative. It's heard. H-E-R-D.

576
00:38:34,819 --> 00:38:40,119
Right? Instead, to your point, well, okay, do you want to be in the herd?

577
00:38:40,439 --> 00:38:43,579
I think some people want to go down proven pastures, right?

578
00:38:43,719 --> 00:38:46,559
They want to walk the path others have walked.

579
00:38:46,679 --> 00:38:50,999
They want to know that if they take those steps, it's going to work for them,

580
00:38:51,079 --> 00:38:52,619
which isn't always the case.

581
00:38:53,439 --> 00:38:57,819
Nike's culture is completely different than an older company,

582
00:38:57,939 --> 00:38:58,999
an older manufacturing firm.

583
00:38:59,259 --> 00:39:05,479
So it's hard for, I do not like those conversations. organizations and we have the data, right?

584
00:39:05,599 --> 00:39:08,259
I mean, we work for one of the largest consulting firms.

585
00:39:08,379 --> 00:39:13,039
We have the data, but it hurts. It hurts to present that because no matter what

586
00:39:13,039 --> 00:39:18,779
we say, there are so many other dimensions to the success of that factor that

587
00:39:18,779 --> 00:39:20,879
may not be present at that organization.

588
00:39:20,919 --> 00:39:24,439
And they don't have control over some of those things, whether it's budget,

589
00:39:24,619 --> 00:39:28,439
you know, how the organization's outlined, like structured, et cetera.

590
00:39:29,339 --> 00:39:33,139
There's things there you you don't have control over that are outside of the

591
00:39:33,139 --> 00:39:36,739
scope of your domain. So let's stay focused on what you can control.

592
00:39:37,039 --> 00:39:40,779
I mean, those are hard messages to deliver because people are not ready to listen to those.

593
00:39:40,939 --> 00:39:45,199
And maybe just kind of following the pack is good enough, right?

594
00:39:45,259 --> 00:39:50,859
Maybe that's okay, but that's not the mandate most CDOs have been given over

595
00:39:50,859 --> 00:39:57,479
the last three years, right? I think the last data that I saw was nearly 30%

596
00:39:57,479 --> 00:39:59,859
have some sort of change mandate.

597
00:40:00,519 --> 00:40:04,819
Right? So if you've been given a change mandate, if you're in charge of a digital

598
00:40:04,819 --> 00:40:09,979
transformation or a digital acceleration, then you necessarily can't be following the herd.

599
00:40:10,559 --> 00:40:14,439
I mean, Nike, what a great example, right? And there are others out there,

600
00:40:14,479 --> 00:40:18,399
Procter & Gamble, McDonald's, Nike, it goes from 15% direct-to-consumer sales

601
00:40:18,399 --> 00:40:20,879
to over 50% direct-to-consumer sales in five years.

602
00:40:22,659 --> 00:40:29,239
That's crazy, right? And in the middle of a pandemic, Right.

603
00:40:29,359 --> 00:40:33,959
So there are examples out there of companies doing this stuff and getting and getting it right.

604
00:40:34,792 --> 00:40:41,152
But I want to, at least for me, how I approach this is I do want to deliver change.

605
00:40:41,312 --> 00:40:46,112
I do want to deliver transformation because there's untapped value in our pile

606
00:40:46,112 --> 00:40:50,132
of unmined gold or unrefined oil. It's there.

607
00:40:50,672 --> 00:40:55,512
It's absolutely there. And I don't ascribe to the herd mentality,

608
00:40:55,752 --> 00:40:57,832
but maybe for your organization, it's okay.

609
00:40:58,032 --> 00:41:01,112
Maybe it's good enough. And I think for some, that may be okay.

610
00:41:01,192 --> 00:41:04,532
But I think for many others, it's not. Depends where they are, for sure.

611
00:41:05,632 --> 00:41:09,812
The question I need to ask you, Malcolm, since we have you for another few minutes,

612
00:41:10,332 --> 00:41:13,372
Gen AI, obviously, forefront of everybody's minds.

613
00:41:13,572 --> 00:41:17,212
Let's skip that for a second, because that was the next new thing. It's been here.

614
00:41:17,572 --> 00:41:22,572
We're all trying to deal with it, good or bad or ugly. What do you think is the thing after?

615
00:41:22,752 --> 00:41:26,692
I think I know your answer, but I am very curious where you're going to take

616
00:41:26,692 --> 00:41:29,432
that. Oh my gosh. Well, what's after?

617
00:41:31,452 --> 00:41:38,872
Depends on how after you mean by after, because there's an after out there that frankly perturbs me.

618
00:41:39,032 --> 00:41:46,492
I read a book recently titled Our Last Invention by a guy named James Barrett, B-A-R-R-A-T.

619
00:41:47,092 --> 00:41:53,432
His after is after AGI, generalized intelligence, where the machines have become

620
00:41:53,432 --> 00:41:57,832
smarter than us, where they start solving novel problems in ways that we couldn't even have imagined.

621
00:41:58,352 --> 00:42:03,912
That after starts to think, I think starts to look a little dystopian and we

622
00:42:03,912 --> 00:42:04,952
don't need to talk about that after.

623
00:42:06,012 --> 00:42:08,852
But the after between AGI and...

624
00:42:09,631 --> 00:42:14,331
Where we are today, I think is really exciting, right?

625
00:42:14,431 --> 00:42:19,351
There may be some sort of, you know, Skynet future out there. Who knows?

626
00:42:19,651 --> 00:42:22,711
Hopefully it's a long time in the future and hopefully we get our you-know-what

627
00:42:22,711 --> 00:42:26,291
together societally and together as a people.

628
00:42:27,071 --> 00:42:31,851
But between now and then, wow, exciting. And in the data and analytics space,

629
00:42:32,071 --> 00:42:36,931
I am a huge believer in what I would loosely call the data fabric.

630
00:42:37,571 --> 00:42:43,151
I don't see this necessarily as being hypey, although if you said that I was

631
00:42:43,151 --> 00:42:44,911
hyping it, I think you wouldn't be incorrect.

632
00:42:45,691 --> 00:42:50,511
But I see a data fabric being far more than what it is today.

633
00:42:50,951 --> 00:42:56,311
So today, V1 of a fabric is this hyper-virtualization layer that allow you to

634
00:42:56,311 --> 00:42:57,771
connect across multiple sources,

635
00:42:57,951 --> 00:43:01,651
do a SQL query against a graph database or a Cassandra database it didn't it

636
00:43:01,651 --> 00:43:07,691
doesn't matter it's all this kind of virtualized access layer which is really

637
00:43:07,691 --> 00:43:15,711
really cool because to me what that has done is is basically functionally eliminated,

638
00:43:16,211 --> 00:43:21,611
the differences in many ways between a lake and a warehouse right and and if

639
00:43:21,611 --> 00:43:24,651
you and if you can do that you're you know some of the vendors that are focusing

640
00:43:24,651 --> 00:43:28,831
on fabric like microsoft or to me are kind of pulling the rug out from the data

641
00:43:28,831 --> 00:43:30,671
bricks and snowflakes of the world but that's a separate issue.

642
00:43:31,391 --> 00:43:34,731
V1 of the fabric, I think pretty exciting.

643
00:43:34,811 --> 00:43:40,451
V2 of the fabric will be when we start to what Gartner would loosely call activate metadata.

644
00:43:41,562 --> 00:43:48,602
Where we start to kind of farm and deeply analyze the metadata of our organizations

645
00:43:48,602 --> 00:43:54,422
to get to a point where the data can start to classify and govern itself, okay?

646
00:43:54,802 --> 00:44:00,322
And if you look at kind of artificial intelligence, it's a spectrum, right?

647
00:44:00,382 --> 00:44:03,442
Over on here is completely manual and over here is completely automated.

648
00:44:03,902 --> 00:44:07,442
Somewhere over here is augmentation. And that's kind of where we're starting.

649
00:44:07,522 --> 00:44:10,102
We're starting on this road to augmentation where the machines help us make

650
00:44:10,102 --> 00:44:13,942
decisions. The machines can help us to better model our data.

651
00:44:14,042 --> 00:44:17,302
They can help us to better govern our data or define quality rules.

652
00:44:18,222 --> 00:44:23,362
Because there's data out there to tell us when data is high quality and when it's low quality.

653
00:44:23,702 --> 00:44:29,382
We won't need humans to tell us when data is fit for purpose because the metadata

654
00:44:29,382 --> 00:44:31,182
and the transactional data is going to tell us that.

655
00:44:31,742 --> 00:44:35,002
We will know when transactions were successful. We will know when they were

656
00:44:35,002 --> 00:44:36,622
quick. We will know when they were slow. low.

657
00:44:36,862 --> 00:44:40,222
We will know when there is an error in a transaction, quote to cash,

658
00:44:40,342 --> 00:44:42,802
procure to pay, pick any business process you want.

659
00:44:43,502 --> 00:44:46,642
There's data that is going to tell us when things are working efficiently and

660
00:44:46,642 --> 00:44:50,122
when they're not working efficiently, what data is needed to fuel those processes.

661
00:44:50,842 --> 00:44:55,122
That's what I view as metadata activation. You layer in artificial intelligence

662
00:44:55,122 --> 00:45:00,722
on top of that, where there is some sort of recommendation engine that is starting

663
00:45:00,722 --> 00:45:03,442
to make recommendations on how we manage and govern data.

664
00:45:03,962 --> 00:45:08,902
This includes some idea of what could loosely be called a semantic layer to

665
00:45:08,902 --> 00:45:14,222
allow us to start querying that metadata in very natural language processes.

666
00:45:15,482 --> 00:45:21,542
So a semantic layer also today is what we've got is v1, v2, v3s of semantic

667
00:45:21,542 --> 00:45:25,722
layers start to change how we interact with data. We won't do it through dashboards anymore.

668
00:45:26,142 --> 00:45:30,222
We'll all do it through whatever application we're using at that given moment.

669
00:45:31,122 --> 00:45:36,002
And we won't need in the future, things like a common language will be mitigated

670
00:45:36,002 --> 00:45:41,482
through AI because AI seems to be pretty good at language, seems to be pretty good at it.

671
00:45:41,542 --> 00:45:44,422
So this future state where these things start to come together,

672
00:45:44,522 --> 00:45:49,742
incredible, like just game changing how we interact with data,

673
00:45:49,842 --> 00:45:51,462
how we manage data, how we govern data.

674
00:45:52,022 --> 00:45:56,462
And companies like yourself, I think are uniquely positioned to help their clients.

675
00:45:57,478 --> 00:46:03,358
Map out that roadmap, right? And slowly start to figure out what does my strategy need to look like?

676
00:46:03,378 --> 00:46:05,778
What does my operating model need to look like? How do I need to adapt these

677
00:46:05,778 --> 00:46:07,958
things and get to where we want to get to?

678
00:46:08,318 --> 00:46:11,898
Because the value here is absolutely incredible. The companies that figure it

679
00:46:11,898 --> 00:46:15,458
out will outdistance themselves in their competition. I have no doubt about it.

680
00:46:15,698 --> 00:46:17,838
Absolutely. I love that perspective.

681
00:46:18,578 --> 00:46:23,718
And I do agree with you. I think that the fabric, Rick, I'm seeing hints everywhere.

682
00:46:24,418 --> 00:46:28,778
Snowflake and Salesforce are joining forces. Microsoft's getting included and

683
00:46:28,778 --> 00:46:33,178
they're sharing data across their platforms so that the consumer or the customer

684
00:46:33,178 --> 00:46:36,378
can activate all of it, regardless of where it's sitting, for example.

685
00:46:36,638 --> 00:46:41,298
So they're virtualizing themselves, which is going to help with that V2 model that is coming.

686
00:46:41,478 --> 00:46:45,538
So I'm very excited about that. I remember when IBM came out with,

687
00:46:45,578 --> 00:46:50,798
I think it was IBM came out with something, must have been 16 years ago,

688
00:46:50,798 --> 00:46:54,338
where they started to virtualize access to tables, for example,

689
00:46:54,338 --> 00:46:55,638
across your organization.

690
00:46:55,878 --> 00:46:59,718
I hated that thing. I remember my company bought it. They were like, here, Sandy, enjoy.

691
00:47:00,138 --> 00:47:03,858
And I'm just like, I'm not taking responsibility for this. It's not going to function.

692
00:47:03,958 --> 00:47:07,138
It's not going to work. But I think we're getting further and further into where

693
00:47:07,138 --> 00:47:09,878
it should be. I'm excited to see the innovation continue.

694
00:47:10,198 --> 00:47:14,698
And semantic layers, true semantic layers is everyone's hope and prayer.

695
00:47:14,858 --> 00:47:20,358
Yeah, I agree. I think, you know, vendors are, some vendors,

696
00:47:20,398 --> 00:47:21,998
I think, are very uniquely positioned

697
00:47:21,998 --> 00:47:25,798
here to start looking at their businesses in very different ways.

698
00:47:26,318 --> 00:47:33,478
I remember a time, this was 2008, Salesforce 2008 release, where they released

699
00:47:33,478 --> 00:47:38,378
capabilities that they called Salesforce to Salesforce, which was basically

700
00:47:38,378 --> 00:47:40,698
inter-instance sharing of data.

701
00:47:41,724 --> 00:47:46,584
It blew up. They actually released the capability, but their model for it,

702
00:47:46,584 --> 00:47:50,704
their vision for it was to allow people to share contact data across Salesforce

703
00:47:50,704 --> 00:47:53,564
instances, because everybody complains about contact data.

704
00:47:53,644 --> 00:47:57,564
People data is just notoriously high velocity and low quality.

705
00:47:57,924 --> 00:48:01,924
And can we start to get to a point where people, basically it's commodity, right?

706
00:48:02,984 --> 00:48:07,784
It's reference data if we can figure it out. Can we start to actually pool our

707
00:48:07,784 --> 00:48:12,424
resources and pool our data and pool our management of that data,

708
00:48:12,504 --> 00:48:17,384
aka stewardship, aka governance, to create a shared data set that everybody benefits from.

709
00:48:17,584 --> 00:48:21,684
And if we do that, we'll lower your Salesforce annual fees. That was the original

710
00:48:21,684 --> 00:48:23,984
model of what they wanted to do.

711
00:48:24,324 --> 00:48:28,284
What they ended up doing was just basically to enable cross-instance sharing

712
00:48:28,284 --> 00:48:32,944
for complex value chains and complex supply chains or partner networks.

713
00:48:33,204 --> 00:48:38,064
And if you want to expose your leads to your partners for or lead sharing, that kind of thing.

714
00:48:38,464 --> 00:48:42,964
But they never carried through with their, kind of what I saw as their vision

715
00:48:42,964 --> 00:48:45,004
at the time. And nobody has.

716
00:48:47,044 --> 00:48:52,464
2024, nobody has. What I'm talking about here is governance as a service,

717
00:48:52,784 --> 00:48:56,824
stewardship as a service, data quality as a service.

718
00:48:56,984 --> 00:49:01,524
Because if you're Salesforce, Oracle, IBM, doesn't matter, you look horizontally

719
00:49:01,524 --> 00:49:06,744
across your customers, all of your customers, assuming your user agreements

720
00:49:06,744 --> 00:49:09,084
allow you to do this, lawyers, cover your ears,

721
00:49:09,284 --> 00:49:12,084
assuming you have the right to do that,

722
00:49:12,304 --> 00:49:16,144
you've got a record for Acme, you've got a record for Acme, you've got a record

723
00:49:16,144 --> 00:49:17,884
for Acme, and you've got a record for Acme.

724
00:49:18,104 --> 00:49:20,364
And it's all managed largely the same.

725
00:49:20,904 --> 00:49:24,404
It's all managed largely the same. And can you expose using AI,

726
00:49:24,544 --> 00:49:30,044
using semantic layers, using data fabrics, can you expose data management as a service.

727
00:49:30,604 --> 00:49:33,884
We'll manage your quality for you. And by the way, we know what good looks like

728
00:49:33,884 --> 00:49:38,404
because we can look across all marketing organizations for your industry.

729
00:49:38,984 --> 00:49:42,064
Because it's not company by company, it's division by division.

730
00:49:42,564 --> 00:49:47,024
The marketing division of company A looks more like the marketing division of

731
00:49:47,024 --> 00:49:49,504
company B than the finance division of company A.

732
00:49:49,984 --> 00:49:54,384
So it's horizontally and can vendors start to figure this stuff out?

733
00:49:54,484 --> 00:49:58,144
I was talking about this at Gartner four years ago with MDM vendors.

734
00:49:58,244 --> 00:50:03,464
I said, hey, could you maybe start enabling data quality as a service for your

735
00:50:03,464 --> 00:50:05,104
customers by looking across your customer sets?

736
00:50:05,484 --> 00:50:09,064
I talked about it with so many different vendors and it's like, oh, that sounds hard.

737
00:50:09,384 --> 00:50:13,464
Our user agreements, user agreements, user agreements. Lawyers are never gonna let us do that.

738
00:50:13,764 --> 00:50:18,324
But that's kind of where I see things going for vendors that are brave and wanna

739
00:50:18,324 --> 00:50:19,964
take those steps. Love that.

740
00:50:20,684 --> 00:50:24,404
Well, you have it here, folks. folks, the future, the next step,

741
00:50:24,524 --> 00:50:28,064
what we are planning to see and hope to see organizations deliver.

742
00:50:28,544 --> 00:50:32,184
Malcolm, thank you so much for your time today. And Anjali, for the recap,

743
00:50:32,444 --> 00:50:35,464
I am just so happy to see your smiling faces.

744
00:50:36,400 --> 00:50:45,658
Music.