Episode 36: What does a Chief Data Officer do?

About this episode

If data is the new oil, does that mean the Chief Data Officer is the new baron? Not exactly, but it is one of the fastest growing and most critical executive roles in the enterprise. Not all CDO roles, or organizations are created equal. In fact, in many ways this position is still being defined. 

In this episode, Tim and Juan welcome special guest Mohammed Aaser, CDO at McKinsey & Company, the world’s largest management consulting firm. We’ll discuss the unique responsibilities and challenges for a CDO in an increasingly data-driven economy. Plus, we’ll learn how the firm builds and maintains its thriving data culture.

This episode features
  • How a CDO drives critical value for the business
  • Future trends and potential disruptions in the data space
  • Who would make a better CDO, Tim or Juan?
Key takeaways
  • Be a data entrepreneur, and consider innovation, technology, and enablement
  • You must evangelize a data-driven culture, and build a cross-functional data community
  • Take a persona-based approach, and empathize with the different needs that exist

Special Guests:

Mohammed Aaser

Mohammed Aaser

Chief Data Officer, McKinsey & Company


Tim Gasper 0:10 Hello, everyone, welcome. It is Wednesday, February 24. And it’s time for cataloguing cocktails it’s your honest, no BS non salesy conversation about enterprise data management, with tasty beverages in hand. I’m Tim Gasper longtime data near nerd and product guy at data dot world and joined by Juan

Juan Sequeda 0:31 I’m once again I’m the principal scientist here at data dot world and as always, it is a pleasure to take a break during the week and have a good chat with my my partner in crime Tim and I am super excited today. I’m always excited. Always Wednesday catalog and cocktails is always my big thing. But with my really good friend, Muhammad, Muhammad Auster. How are you Muhammad? doing?

Tim Gasper 0:55 how’re you How’s everyone doing in Austin? I know you’ve been through a lot over the last couple of days, or last week at least. Yeah, well,

Mohammed Aesar 1:03 we’ll get to that we were. Were warm and we’re dry and everything’s good. So Well, I mean, water flowing.

Mohammed Aesar 1:11 I’m in Minneapolis, and we were in a deep freeze. And today, it’s like 45. And so this is like, amazing weather people are probably out in shorts and short sleeves. It’s like it feels like spring is almost arrived. All right,

Unknown Speaker 1:23 well, just a reminder, here at Calvin cocktails we are being we’re recording right now. Feel free to you can keep your camera on and keep your camera off. As ever, you want to please be muted. But the first 30 minutes we’re going to be having this discussion and then after 30 minutes, we will stop the recording and we will have kind of open live discussion with Mohammed. Check out our new website at data dot world slash podcasts. You can follow us on Twitter. We have a new Twitter handle the honest no BS data. You can also follow us on LinkedIn. Give us a review on Apple podcasts and follow us on Spotify. And again, for folks who are just listening on the podcast. We record callin cocktails live every Wednesday at 4pm Central. Everyone’s invited we stopped the recording at 430. And we also have our slack community slack dot data dot world. Anyways, that’s kind of organizational here. But let’s start with cheers, toasting. What is everybody drinking today? How about you Tim? You go first? Unknown Speaker 2:19 Well, to your comment, Mo I’m glad to have water flowing and energy keeping my lights on that’s that that’s got me happy. It’s back to basics for me.

Unknown Speaker 2:31 Yeah, we had a had a tough week. And I’m just enjoying I actually made something interesting. Um, I love Waterloo. Well, I think they’re here in Austin have Waterloo blueberry water. And I mixed it up with some pasture fruit syrup. And I also got some Angostura bitters. That’s my drink right now. And it’s a nice it’s actually pretty nice hot day here in Austin. How about you mom? What are you drinking?

Unknown Speaker 2:51 Well, I’ve got a little bit of passion food as well. It’s there’s a there’s a kind of coconut mixed with bubbly water, coconut water thing, and I love it. So I’m enjoying that. Unknown Speaker 3:01 Alright, so cheers for, for having water for having energy, and just realizing that there’s a lot of things that we take for granted. That’s what we’re sharing here for me. Fancy water. Fancy. Unknown Speaker 3:13 Fancy water, I got some passion for going to that’s the popular thing today.

Unknown Speaker 3:18 So we did we got the funny question or icebreaker question. So we were asked who would make a better CEO? Tim, or one or me? I had to vote for Tim because Tim is a better Tim knows how to go deal board more better with customers? And me, I think I don’t know, what do we Mohamed, we’ve known each other for a while What do you think?

Unknown Speaker 3:38 You know, I think you you both complement each other well, right. So where Tim has strengths. One has other strengths. Right? And so it together, I think you guys make a a dynamic duo. So Tim, I think your ability to work and build, you know, these sort of cross functional type relationships and kind of go do the commercial type of work, if you will. And one you on the deep technical side, right. And so I think you need both for any data transformation to be successful. You need to get your business stakeholders all on board. And you need to build the right technical architecture and bring in the right tools. And so I think combined, you’re you’re definitely more powerful than separate.

Unknown Speaker 4:22 I’m already blushing. But let’s let’s kick this off my personal post in the chat. Tell us where you’re coming from. What are you drinking? Or what do you Towson for and and who’s your favorite CEO? And with that, all right, Muhammad, you’re I’m super excited to talk to you because you’re the chief data officer of McKenzie. Right this gigantic, amazing organization. And and this hole in the spirit of being honest and no BS. What’s your honest, no BS definition of a CDO? And what should they be doing for yours? Unknown Speaker 4:53 One? It’s a great question. You probably get a lot of different answers based on who you ask. actually like it was probably a years ago, there was an HBr article on, like, you know, the roles of a CTO, and I have to admit, like, I think I got forwarded from, like 10 people, and I think every CTO posted it. And it covered, you know, four or five different types of archetypes, folks who are truly just doing data governance, other folks who are doing data, as well as analytics are playing that Chief Data Analytics officer, other folks who do more of the data monetization, you know, some some data ethics types of roles as well. And so and also data defender around like, information security. And, you know, I be, you know, I think both CEOs will get questions across all of these dimensions, but ultimately, they do have to select and choose areas to focus on to be successful. So I’ll just define it based on what I do. I see, I see being a chief data officer as being the data entrepreneur within the organization. And a big part of that, for me, it’s it’s around three areas, it’s around innovation, it’s around understanding, you know, what are the data assets that you have? It’s about partnering with the business and your colleagues to understand how you can deploy them ultimately, for us at McKinsey. That means, you know, how do we deliver client impact through the use of data? And so data innovation is a big part of that all the external data that’s out there, how we build data assets, and so on, so forth. The second thing I think about is data engineering and architecture, how do you actually have the right tooling, technology and people to bring that data together and support your organization. And as you know, most organizations are large and are have federated model we have that here at McKinsey, as well. We have data leaders and tech teams also in our different practices or divisions, if you will. And then the third area is data enablement. And this is around building the data culture within the organization, getting folks excited about what data is available, making sure that they’re aware of it, building a data community, and so on, so forth. So those are the three areas that I focus on. And again, as I highlighted, it’s really been the data entrepreneur, helping people see the potential and the value in the data, and then ultimately driving impact from and for me, it’s leveraging in our clients, For others, it may be having, you know, your business stakeholders, leveraging data more effectively to transform the way your organization works.

Unknown Speaker 7:22 I’m loving this whole data entrepreneurship. And that’s like the first question now I’m like, we should start asking within organizations, like who’s the entrepreneur within your organization that’s doing stuff with data? And I think that’s, that’s,

Unknown Speaker 7:34 yeah, who’s really spearheading the initiatives and the innovation, right?

Unknown Speaker 7:38 So So from there, it’s part of the beam, creating this entrepreneurship, or this whole spirit is goes into driving culture. And this has always been a hard, hot, very hot topic. Again, it always comes back, right? We’re talking about different roles, the, how do you build a culture of data? And what does that even mean? Right? What does the data culture mean? I mean, you guys are just a gigantic organization with I mean, I mean, you have so many people, like how do you build a culture on data?

Unknown Speaker 8:09 You know, I think it all starts with knowing and knowing people and building your network. So my background is, I grew up within the firm. You know, I started out as an analyst. And, you know, I, for vast majority, my career, I’ve been at the firm, I did have a stint where I lead marketing analytics and data strategy for a large financial services firm as well. And the same thing is true, when in that experience, it’s ultimately data is almost like the lifeblood of an organization. And it connects you with so many different people. And I think the success of leveraging data is also the people relationships that you have. So let me just share with you a couple of things that I’ve taken away. And one of the things I’ll just reflect on, too, is that like, I helped also get the analytics division at McKinsey going McKinsey analytics. And so during that process, I got to know so many of the different data scientists at the firm, I got to know many of our consultants, many of our leaders across the firm. And one of the key things now for me from a data culture standpoint is like it’s on any given day, I could be approached by five teams, 10 teams, you know, many different individuals who all have questions, hey, I have this problem. What data can I use who, you know, who can I support, and I do make it a point to build relationships with everybody, even if that means me, talking to them on zoom for five minutes, or talking to them on slack for a couple minutes and guiding them and getting to know them and the problem that they’re solving, etc. So I do believe that building those relationships are at the core of starting and getting your data community going and showing that no matter what level you are in the organization, we’re here to be your support. So that’s one, but you know, I’m one person. So how do you actually build the true culture around the organization and that brings me the second point, which is one of the early efforts we took was to Say, we were a large organization, we have data champions or data experts across the firm in different geographies, in different in different practices. How do we build a map? Actually, of all the data experts we have within the firm? How do we get them together and build a data community such that if any consultant or any person at the firm has a question related to data, they know that they can turn to this community. And we started it, we call it the Find the data community, and we started putting up all of the laptops, all of the lunch, kind of like their lunch rooms, and you know, folks would go into the office, etc. And even emails, etc, we’d go on, say, Hey, we’re launching this community, here are all the experts on it. And all of a sudden, we started getting all you know, a bunch of traffic, people asking about, you know, questions, whether like, what are the total Evie sales in a, you know, by by city over time, right, and just all these different types. And you’d be surprised all different data experts from around the firm had answers. And all of a sudden, this community has grown to be huge, we moved to slack, it actually became the foundation for us to start then institutionalizing some of that knowledge. And so then we started formalizing that knowledge into a data catalog, we started building an overview of all the data assets, those experts there that’s integrated with our search engines internally. And we get 1000s and 1000s of users every single month, who are now using and understanding who are the experts and getting access to data. And as you know, data itself is not the end, it’s actually solving the problem. And so the experts, plus the knowledge of the data itself, are what is needed to solve many of our client problems. And so that’s the approach we’ve taken. So far, it’s been, it’s been great. But we’ve really started I think, with a human centered approach, versus a technology centered approach

Unknown Speaker 11:47 that makes a lot of sense, you’re really putting the relationships in the people front and center. And, you know, just to follow up on that, Mo, you know, how much of that was really bottoms up versus, you know, you and your team, and the programs that you’re pushing, sort of had to do a little bit more of a top down, what’s the sort of balance there that makes all that happen?

Unknown Speaker 12:10 You know, I think you do need to have in set up orchestration of it all, you really do need to galvanize that community, you do need to help paint a vision forward around what’s possible and get them excited. And so a big part was getting those, you know, while there are 300 data experts, about 60 of them, were really, really, really closely involved, as we were getting this going, where we were understanding the data that they dealt with, really kind of building an Excel version of kind of what are the data assets we have as a firm etc. And those 60, you know, started, you know, getting the other 300 on board. And as soon as the community started, and as soon as our you know, and we are very client driven firm, ultimately, we are client driven firm. And when client questions come up, I think everyone is really eager to see how can we help support? And so when, when questions come in, and people are asking about all these different types of data that are available. And I should be clear, these are all data sets, that are like external data sets, kind of market data sets, right? When people have questions around a lot of these market types of datasets, you know, experts can come in and help inform them, here’s what you should be thinking about, here’s the types of data that are relevant. And so it’s a combo. And ultimately, I think my success in a central role is only there if if the different groups have a problem that they’re looking to solve, and I can help accelerate them on their journey. So you know, it can’t just be driven centrally, it has to be it has to be

Unknown Speaker 13:42 mutual. So So this is one of the things that we hear all the time is let’s go democratize data, right? Yeah, we got to do that. And we want to give data access to everybody. Do you ultimately give data access tip? Is that the end goal or or, or? Because at the end of the day, it’s like, well, getting access to the data is not what you need, you really need to be able to go answer your business questions, get the insights out of that, and maybe just getting the access to the data by itself is probably not the best path for that. I mean, how do you guys do in balancing the centralization and decentralization that you were talking about?

Unknown Speaker 14:18 So let me I’m gonna say two things here. They’re gonna probably sound at odds with each other. But so are so many things in the world, right, that you have to balance together. So the first is that ultimately, I think we all start with this opinion that, hey, let’s let’s get all the data together. Let’s like provide access to the data. And then you know, boom, we solved the problem. And in reality, what you find out is, even if you made the data available to the user who thought they would use the data, you know, a consultant, for example, like, you know, they don’t have let’s say, they don’t know exactly how all the fields have the data, and it’s going to take them 10 times longer than someone who actually knows the data and can say And dice it. And ultimately what you learn is people want insights, you know, and they want less actually around the data, they want the insights. And so, so that’s one thing to keep in mind. But the second thing to keep in mind, too, is that you do also want to make data available at the same time. What I mean by that is, so the fact that people want insight doesn’t mean you shouldn’t do that, you know, make the data available and create a streamlined process. But I would argue that for those, that there are different profiles of data users across an organization, there are likely folks who are the insight consumers, there are the folks who are the analysts who are likely working in creating insights and sharing those. And then you’ve got like the data engineers, and in the data scientists who are maybe building data sets, and they’re doing more advanced analytics, predictive models, and then deploying those, let’s say, there’s three. Now, the reality is even for our data engineers, and our data scientists and our data analysts, wow, do they have to jump through so many hoops to work with data, because if you actually think about salmon kind of swimming up river, right, and how many only how few make it all the way, it’s kind of like that with data, first, you don’t know where the data is, then you need to get access to it. And then once you get access to it, you realize you don’t understand what it is, and then you need to get the expertise to do it. And then you realize that you’re, you need like a Spark cluster to run anything by the time there’s so much friction there. So what I what I consider is take a persona based approach, understand who are the different customers and start first, with a mindset of building, I would say momentum, using and delivering the most highest impact, impact insights to the consumers, which are your largest group, your largest persona within the organization are the consumers. And they’re going to should you

Unknown Speaker 16:57 start with the biggest Chris with the highest quantity of personas or goobie, a smaller group that’s more influential?

Unknown Speaker 17:05 Well, it I think, I think it’s ultimately though See, the, it will depend ultimately will depend on your organization, if you have a bunch of analytics use cases, you need to go deploy, then focus on the data scientists and engineers, if you’re focused on, you know, for us, typically, it’s around the client impact piece, the insight generation is, is is a huge part of that, and ensuring that many of our clients are, are getting access these insights, etc, that becomes pour over. But it ultimately depends on your business, but it’s good to prioritize, who is that that consumer that you’re focusing on? And then be like laser focus in terms of how do you improve that journey for them, and then continue to like, what I say is, momentum is really important early on, I think many of us are probably familiar with efforts where people have built, you know, spent many years building architectures, and then haven’t really seen all the value coming from it, or it’s taken a long time to get there. And so momentum means like, you know, make sure that business consumers have that analytics of those analytics, where that data are really seeing some essence of our getting really excited, that builds the case to invest further in building out your architecture. And then now, when you build out that architecture, that technology, you’ve now created a higher metabolism. So build that momentum, and it will get you this metabolism to now take data, used it more efficiently and effectively and scale it through your organization.

Unknown Speaker 18:31 That makes sense, you know, and in your comments about momentum, have me thinking not just about sort of, organizationally, how you’re empowering these things, but also, you know, technology momentum and sort of leveraging the trends that are going on in the space. I know, Mohamed, you’ve been thinking a lot about, sort of what are the right technologies? What are the big trends? You know, just to transition a little bit? What do you what do you see going on in the space? What do you see as the, you know, the big, impactful things that are sort of changing how we’re doing things around data?

Unknown Speaker 19:01 I gotta say, I’m really envious of your position, because you probably get to chat with so many people. So many companies big and small startups like, Yeah, what’s next? What are we What are you excited about? What should we get? Yeah.

Unknown Speaker 19:13 Yeah, I mean, I think there, there are three areas that I’m really excited about. And you know, I’m sure to many others. But these are the ones that are kind of near and dear to me. The first one is thinking about data as a product. I’m happy to dive into that. The second is external data, and how I think most organizations are really familiar. At the firm. We work with a lot of external data. But and I’ve been also, you know, lead analytics at a large enterprise, and you have a lot of internal data. But often you don’t have a view in terms of what’s happening with your customers outside. You don’t have a view on what’s happening with the market. But you know, how can that create a whole new perspective on how you run your organization. And then the third area is really humanizing data. In many ways today, data is it’s it’s technology. It’s art. To texture, it’s like, you know, getting data to flow from 10 different places to 10 different places, it honestly feels a bit, and you have, you know, it’s just not a great user experience. It’s really not. And so how the the next, when we think about the next phase of data, how can tech companies, platforms, etc, really think about making it easier for these different personas to interact and work with data, so it becomes a seamless experience. So those are the three areas I’m gonna,

Unknown Speaker 20:32 let’s, let’s unpack those. Let’s unpack those. And I think we’re all seeing the chat question in the chat. Let’s unpack data as a product. What do we what do we mean by this?

Unknown Speaker 20:40 Yeah, I think it is an emerging space. And I think you had a few guests on and you talk about data mesh, and Data Fabric, and maybe this is like a cousin, relative of one of those concepts. But the idea is that, you know, in many organizations, I think, organizations have gone in, they’ve invested in building data lakes, right over the last, I’d say, five, six years, that’s been the biggest piece. And we’ve seen the transition now from more Hadoop to more kind of, you know, using more of a object based storage, and then, you know, having a querying layer on top, etc. And so we’re seeing, we’re seeing even how the data lakes changing. But in practice, what do you have, you have an organization that’s taking a lot of their different data from, from the organization from their systems, and they’re putting it in one place. And, you know, they’re asking a lot of their data engineers, to go figure out what all that data is, what it means how the business operates it, and then, and then create, you know, analysis reports on top of it. And I think it’s worked. I think there have been some great results from it. But I think we’re still just scratching the surface, I think there’s a huge opportunity to truly scale data and analytics, in order to do that, you have to start thinking about data as a product, instead of throwing into Lake having the data engineers kind of learn about a document things on the fly, organizations can almost take a page from, like, you know, some of the famous Bezos memo that said, Hey, every service needs to be exposed as an API, or you’re not going to be working here anymore, right? It’s almost like every organ, every part of the company has a data Product Manager, and they understand the business, they understand kind of the business concept, what a customer is, what he active customer is, what an inactive customer is, you know, define what an order is, is and people said, well, that’s Business Glossary, etc. And to a certain extent, that’s true, it is, but I actually think of it less from a Business Glossary standpoint, but I think about it is, you know, what we’re doing within these organizations is that in these groups, we’re starting to connect our data, create data models, and, and really expose a refined set of data that is agreed upon by the business that represents how the business operates. And you can envision now, different parts of an organization having these data product managers, and they’re, they’re connecting their data, you know, they could be using a knowledge graph, for example, I think that may be one of the leading ways. So that may be one of the emerging ways to do that, when they create these data models. And then they’re linking it, in fact, to the the data, you know, the relational data that might be underneath them. And they’re now creating this platform where everybody in the organization can pick up and query the data and build on top of it. And so that’s where I think data that was good. And we also find is that the more complex and complicated analytics use cases, do require your data to be connected. And the last thing you want to do is have a team reconnecting connecting and defining different terms. And not really knowing the context, I think these need to happen for the enterprise way, you will need some transformational analytics use cases to power the development of some of these data products. Hopefully, this is,

Unknown Speaker 24:02 yeah, no, you don’t want to keep reinventing the wheel over and over and, and defining things slightly different in different ways. And you really want to get aligned to get connected and get repeatable about that kind of stuff.

Unknown Speaker 24:13 Yeah, and what you what you’re seeing actually in the marketplace, is that companies that are truly data companies, and what I mean data companies, I mean, like truly they like, they live and breathe and sell and create data, many of them are turning to this model to manage their own data, whether it be like, you know, for folks who who are companies that focus on real estate data, or on web extracted data, or on private business data, they’re there they’re actually building these kind of data models that they can integrate data from so many different sources and then have a common you know, a common data model that they use and it maker they make available to their customers. And so they’re they’re truly their data product companies. Now I’m also I’m kind of making the point that hey, organizations if they want to steal their data You know, just like the product companies, but they also need to start thinking about their data as a product, you need to have this data Product Manager, they need to start using some of these tools. It’s a journey. And so anyways, that’s like we’re really seeing this happen. This is

Unknown Speaker 25:12 a reminder of our last episode with Sam bail, right from great expectations. Here’s like, well, there’s a reason why we have subject matter experts because they know more than you about a particular subject more than the data engineer, right? And you go talk to these folks. And it goes back to people who listen to listen, listen to my toxic stuff, everything I bring up this whole knowledge scientist, I think I’m so bullish about this is the role that we need. The way we’re going to go treat data as a product is the same way we do software, right? Why don’t we treat data the same way we treat software, I’m not saying we do exactly the same thing. But I’ve been having these conversations over and over again, reminding people, we would never push code into a master branch without it being documented comments, people do peer reviewed, there’s tests, it goes through ci CD, but we do that for data all the time. I mean, I think just going through that mindset and making that making data first class citizen, that’s we need to go do and it’s not just about the technology did the Kelly’s there. It’s about it’s a it’s a bigger people and processes. Challenge, I think goes back to the whole culture of data that we’re starting to talk about

Unknown Speaker 26:19 it. Yeah, in one, I think ultimately that, you know, it does have to be something that the organization strategically from sort of the executive level down, they say, Well, look, we’re getting good value of our data. But if we truly believe we have 50 analytics use cases, 100 analytics use cases that we want to power, can we really, truly do that? If we haven’t invested in building some of these products? And I think they’ll quickly say like, hey, it’s worth the investment. But instead of us going in and spending, you know, a ton right upfront to build a perfect architecture, why don’t we go in one of our divisions, and one of our groups and start building this out for our customers, or build this out for our suppliers or build it out in a domain and have a data Product Manager and then start using some of these tools and get going, and then prove the value again, build the momentum to build the moment. Right,

Unknown Speaker 27:12 one of the things you’re mentioning on what’s next is humanizing data. And just seeing here in the chat, Josh is saying about ROI. Should the data engineering be separate from software engineering? Or should that be considered a specialty of it? And like you think about even in software, you have user experience, right? You have user interfaces, user experience, what is the user experience of data? I mean, that’s something we need to think about. For me, it’s always been like, I want, I want, I want a data data consumer, to look at the data. And by looking at the data, I mean, like I look at a data model, it should be simple. And they should be able to say I want to, they can follow their finger on a graph, when order is placed by a customer customers in that address, I want to have a report that gives me this thing and they follow it on the graph. Give me that data. And it should be as simple as Okay, here’s the query that follows that model. Super simple. It I don’t, I mean, we’ve, we’ve gone into this whole world of managing data, to support the applications and not to support the actual consumers of data. And I think this is the big disconnect. So we really need to think about what is the user experience for data?

Unknown Speaker 28:18 And yeah, and again, I think it also varies on the different personas, right? The, you know, who are the insight, to who are developing insights, who are actually building out the data products, and who are maybe even building analytics, like, one of the things this is kind of like, a dream of mine is to see a world where you have your data as a product, you have these entities that people you know, may have agreed on us expose them, you’ve created almost like an NLP engine on top of it. And then you can go and ask questions, you can truly ask questions of your data and get results. You don’t have to write SQL to do it. You can have business people ask questions, you can. And you know, look at the end of the day, I do think domain expertise is so important when it comes to data, in the sense that it’s really important that when I talk about humanizing data, is it’s you know, for those who are familiar with bi, right, there’s a lot of reports out there, people look at some operating reports are great, you see some metrics change time to time. But the reality is, I think data producers have a responsibility to a certain extent to work with their business partners, to ask the questions on what are what like to put themselves and be empathetic to the consumers and say, What is it that you want to drive and change in your business? What are the hypotheses? How can I actually create analysis that will truly change the way you make decisions? And that requires, that requires fundamentally that not just producing a nice BI dashboard or producing an analysis, it actually understand requires understanding the problem and so you think You’ll see a world of two things happening. One is that more and more, the more successful data practitioners are going to be spending tons of time with their business partners. And they’re really going to be understand the problems and they’re gonna actually help their business partners think about new problems, they can go solve the maybe the business problem the business partner didn’t think about. And the second thing you’ll probably be seeing happening, is it the business partners who understand the data and understand the potential of it will increasingly be looking for tools that help them answer questions, you know, in a faster and more expedient way. And I, again, I think data products will help with that.

Unknown Speaker 30:39 Yeah, that makes total sense. And I think that’s super exciting. And, and then the last thing you said was external data. Right? And how does that quickly, how does that come into play?

Unknown Speaker 30:49 Yeah. So you know, as many of us have been, you know, having our own challenges getting our internal data together, the external data universe has been expanding, you know, very quickly, some sources say there’s over 5000, new data sources that are available everything from, you know, foot traffic data, to spending data to social media, comments and reviews, and you start to find out that there’s so many powerful use cases on customer analysis on also prospecting, on competitive benchmarking understanding your organization’s health relative to others, that you can use external data to help inform. And I think organizations that really start thinking about where are their use cases where either strategic analyses or analytics use cases where external data could give light, or cook could shine, some additional insight will have an edge, they will have an edge, but it does require it doesn’t require an you know, I’m familiar with this, because I’ve gone through this journey is having these sort of data scouts or these data experts who are partnering with your, with your your business, and your practices, if you will, and helping them understand helping, helping kind of understand what their problems are. And then understanding sort of the availability of data out there to then start problem solving, playing this translator role, to say, Here’s data that you could use, to change the way your group makes decisions, or how your organization make decisions. And again, it comes back to this point, still, the actual data is there, you still need to humanize it, you still need those translator roles, to make it to make it happen.

Unknown Speaker 32:31 told you 30 minutes fly by, we can keep talking. And here’s the cool thing. For folks who are listening to this podcast, if you join live, you can actually join part be part of the after party. So we always want to wrap up, we always kind of sum up with our takeaways, Tim, take it away with your takeaways, my takeaway of takeaways, so

Unknown Speaker 32:50 first of all, I really liked your comment mo about sort of data culture and how you know, you may be approached by five or 10 teams. And, you know, the key is that you’re trying to build relationships with everyone, right? And, and I love that and tying it back to momentum, right momentum starts with one person pushing what needs to be pushed and evangelizing and then and then growing it out from there. So I love that and the personal touch that’s associated with that. And I feel like that that feels like a really key ingredient to being an excellent CTO. And then secondly, I loved your comment about changing the paradigm where we’re really trying to make it so the data is accessible, that people are enabled across the organization. And that time to insight is as fast as possible, you know, and you mentioned things like even natural language and other types of new approaches that hopefully can start to lower the bar and make it so that data literacy isn’t having a stats degree or something like that. It’s really making it so that everyone can be a part of that.

Unknown Speaker 33:49 Well, I got I might I got a handful here. One, your chief data officers really good deed entrepreneur. And and I really love that who’s So ask yourself, who’s the entrepreneur of data within organization who’s focusing on innovation, doing new things with data? Yeah, what’s the next thing on engineering and architecture and I love to connect with Tim said, the data enablement, you’re building a culture. So those are the kind of the key things of a CTO, also ask yourself, who are the consumers of your data and how to avoid have to say it, boiling the ocean, right? Take a persona based approach, right? build momentum around that for those personas. And and what’s next. Data is a product, external data. I think this whole notion of data Scouter data hunters are a data concierge service, right? Those types of the business to help you solve problems, and humanize data. And I think the big another big takeaway is what’s our user experience for data? So mama to wrap up two questions. Well file two questions. Number one, what’s your advice? Just be out very broad open question. What’s your advice? And second, Who should we invite next?

Unknown Speaker 34:53 And you’re muted.

Unknown Speaker 34:55 Sorry, yes. So on the on the advice question. Um, I. So I guess one of one of my friends share this piece with me that, you know, there’s the 8020 rule, the preto principle, you know, 20% or 20% of things account for 80%. Right? And you can, you can kind of be efficient that way, I kind of believe in the 92 rule, which is spend 2% of your energy, building the right friends, and surrounding yourself with the right people and 90% of your life gets solved for you. Nice, I love that. And we invite next. And then, you know, I think it would be cool, you know, I’ve been avidly reading a lot around the Knowledge Graph space. And I think Dan McCreary has published some great insights. It’d be great to to invite him and learn from him on his experiences. All right, well, Unknown Speaker 35:55 definitely Dan, hopefully you’re listening. Otherwise, we’re gonna go reach out and get you invited. Muhammad. Pleasure, Tim. As always love our Wednesday’s chat here. Cheers. Happy winter. Thank you.

Start first with a mindset of building momentum, using and delivering the highest impact insights to the consumers, which are your largest persona within the organization.


See the catalog for data discovery, governance, access, and analysis.

Request a demo