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011 - Data, Culture and Impact - How McKinley drives data value at The Financial Times
200 Plays1 year ago
๐๐ก๐ ๐ก๐๐ซ๐๐๐ฌ๐ญ ๐๐ก๐๐ฅ๐ฅ๐๐ง๐ ๐ ๐๐๐๐ข๐ง๐ ๐๐๐ญ๐ ๐๐๐๐ฆ๐ฌ ๐ข๐ฌ ๐ญ๐ก๐ ๐๐๐จ๐ฉ๐ญ๐ข๐จ๐ง ๐จ๐ ๐๐ฎ๐ฌ๐ข๐ง๐๐ฌ๐ฌ-๐ฐ๐ข๐๐ ๐๐ง๐๐ฅ๐ฒ๐ญ๐ข๐๐ฌ ๐ข๐ฆ๐ฉ๐๐๐ญ!
๐ This week on the Stacked Data Podcast, I had the pleasure of sitting down with McKinley Muir Hyden, the Director of Data Value & Strategy at the Financial Times. We dive into the critical topics of data adoption and culture change in today's data-driven world.
๐ McKinley brings a wealth of experience, and we kick off the discussion by exploring her journey into the realm of data and her current unique role at the FT. McKinleyโs role revolves around bridging the chasm between data and the business. It's about ensuring alignment and, most importantly, measuring the impact of the data team's efforts as well as building the FTโs Data Academy.
๐ฬฒ๐ฬฒโฬฒ๐ฬฒ๐ฬฒ๐ฬฒ๐ฬฒ๐ฬฒ:ฬฒ
๐ How McKinley navigates the challenge of bridging the gap between data and business.
๐ค Reasons for lack of data adoption and the strategies employed to combat them.
๐ McKinley's role is unique, and we delve into the origins of this specialized position and the objectives she aims to achieve.
๐ What exactly is a data culture and why is it crucial for organizations?
๐ How McKinley measures and evaluates data culture.
๐ The challenges encountered in creating a robust data culture.
If you are looking to build and foster a data culture and inspire trust to ensure analytics impact this is not one to miss. McKinley shares her strategies and approaches as well as the common pit falls and blockers such as; Awareness, Discoverability, and Understanding.
Tune in and give us a FOLLOW, like or share!
๐ Stay tuned for more inspiring conversations, and don't forget to subscribe! hashtag
hashtag#DataCulture hashtag#DataAdoption hashtag#Podcast hashtag#DataInnovation hashtag#StackedDataPodcast
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Transcript
Introduction to Stacked Podcast
00:00:02
Speaker
Hello and welcome to the Stacked podcast brought to you by Cognify, the recruitment partner for modern data teams hosted by me, Harry Golop. Stacked with incredible content from the most influential and successful data teams, interviewing industry experts who share their invaluable journeys, groundbreaking projects, and most importantly, their key learnings. So get ready to join us as we uncover the dynamic world of modern data.
00:00:34
Speaker
This week
Guest Introduction: McKinley Haydn
00:00:35
Speaker
on the Stacked Data Podcast, I'm joined by McKinley Haydn, the Director of Analytics Business Impact at the Financial Times. Today, we're going to talk about the importance of data adoption and how to build and foster a data culture. McKinley's
Role of Data in Business Impact
00:00:51
Speaker
role revolves around bridging the chasm between data and the business. It's about ensuring alignment and most importantly, measuring the impact of the data team's effort.
00:01:02
Speaker
McKinley's energy and passion for the space is infectious. She says her strategies and insights, as well as the common pitfalls. I hope you enjoy our conversation. Hi, McKinley. Welcome to the Data Stacked podcast. It's great to have you on. We're in the Financial Times office. How are you doing today?
00:01:21
Speaker
Good. Thank you so much for having me here. I really wish we'd been able to get into the proper recording studio that I promised you. But luckily, you're prepared. So this is great. Yes. No, we found a nice little quiet room in the basement of the Financial Times office. Very glamorous. Very glamorous.
00:01:37
Speaker
But today
Creating a Data-Driven Culture
00:01:38
Speaker
we're going to be talking about the importance of data adoption and how to build and foster a data culture. In today's world, there's a huge reliance on tooling, but are they always the answer? That's really what we're going to uncover today. So I think first off, it can be great just to hear about your own journey into data and really what your role is and how your role is developed into what it is today.
00:02:04
Speaker
Yeah, so I frequently describe myself as a typical data person.
McKinley's Journey into Data
00:02:10
Speaker
This was not my destiny. In fact, I think my career comes at a big shock, as a big shock to my entire family. So my dad was an actor. My mom was an MBA lawyer, business person extraordinaire, ended her days and not literally, but I mean, her working days, her working life at McKinsey and Deloitte. So I always kind of felt like there was, you know,
00:02:32
Speaker
they were on either side of a spectrum. And as a young girl, I very much followed in my father's footsteps, spouting poetry. And so I think everybody thought that was the route I was going to take. But actually, I came out of my English literature undergraduate degree and just kind of immediately threw myself into the world of business. And I think actually that reflects
00:02:55
Speaker
an interest in trying to figure out the secrets of humankind and what motivates people. How do they work? Why do they make these kinds of decisions? And so actually, from that perspective, my career makes a lot of sense that I've wound up at an institution like the Financial Times and have created a team, Analytics Business Impact, that is exactly this. It's kind of looking at that gap between data capabilities and assets and then the actual value. Because what you really need between those two things is
00:03:25
Speaker
people, you know, decision making and people to do things. And so I think that's kind of what I bring to the table is trying to line all of those things up and actually make sure that people can do the right things in order to get value from data.
Team's Focus on Business Alignment
00:03:40
Speaker
Amazing. So it sounds like you always had that, you know, ability to want to ask questions and uncover what's going on under the under the surface. Yeah. And I'm pretty curious. And I think you need to be for in this kind of in this kind of work.
00:03:53
Speaker
Brilliant, so what is your specific title here at the Financial Times?
00:03:57
Speaker
So I'm the Director of Analytics Business Impact and I have sort of a love-hate relationship with the name of my team. It sounds terribly, you know, W1A kind of jargony on one hand, but I actually think it does reflect really the purpose of the team. So we are looking to create impact with our analytics and our data capabilities for the business. And so a lot of what my team is doing is kind of
00:04:26
Speaker
is looking at both at a more strategic level and a more tactical one. OK, where are the opportunities for data analytics to drive more value? So it could be something as large as a data democratization initiative, which would involve transforming data.
00:04:45
Speaker
changing the tooling, creating upskilling people, training, and also then looking at processes and so on and so forth. But it could be something as granular as saying, OK, we produce these 10 models. What are they really doing for us? Are they really delivering the right kind of value? And if they're not, then why not? Where's the breakdown? Were they not configured correctly? Or maybe they're just not being used appropriately. So I think a lot of what my team is doing
00:05:15
Speaker
kind of diagnosing areas of friction or opportunity. That makes perfect sense. So your role is more about bridging this gap between data and the business, making sure there's that alignment and enablement of data and ensuring that everyone at the FT is getting value from the data that is being produced and not just doing data for the sake of it.
00:05:40
Speaker
Exactly. I think data that doesn't really drive business value. What's the point? And likewise, it's also making sure that we are being really focused on business outcomes and ensuring that we know the
00:05:57
Speaker
the full context as well of the decisions that need to be made. I think this is why sometimes it's so challenging is because you've got the analysts and the data people who are in the numbers and have to do all of that complex work to extract insights and meaning. But on the other hand, there is how could they possibly know all of the context and the realities of somebody within the business.
00:06:23
Speaker
Absolutely. Even though I'm really proud of the people who work at this company in both the business side and the data side, and we have very, very strong relationships, I just think it's just unreasonable to expect a consistently deep level of
00:06:41
Speaker
knowledge exchange, but always to go swimmingly, which is why I think you need teams like mine or some way of managing that gap and trying to figure out, okay, how do you really collectively create value together in a way that is repeatable and efficient?
00:06:57
Speaker
It makes sense. Before we dive into, I suppose, the how and the what, I'd be really keen to understand what your perspective is on how mature data is at the FT because this kind of role isn't something that we see in many organizations. It's one that I'm definitely seeing pop up a lot more. What sort of level of maturity would you say you guys are at?
00:07:18
Speaker
Well, we're actually running a data and AI capability maturity assessment. I always have to think about saying that right now with some fab consultants, actually. So I'll be able to tell you a lot more in mid-November. But I think the thing is the FTS actually has a number of brands and sub companies. So I think that the maturity differs depending on which area you're talking about. For our core subscriptions business,
00:07:47
Speaker
I always joke with my peers how we're always like lamenting the state of things and how terribly behind we are. And then the moment we go to a conference or speak to people in other companies, we always kind of clap ourselves on the back with actually
00:08:02
Speaker
how great of a position we are in. So I guess I would say, you know, compared to sort of the digital natives, the Ubers, the Deliveroos, like, no, we're not on that level. But how could we be? We started in 1888. We've had a huge digital transformation. And so I think if you compare us to a lot of legacy brands and certainly legacy media companies, I'd say we're
Financial Times' Data Maturity
00:08:28
Speaker
We're doing pretty well. I think we're probably quite data mature. By the way, that does not mean that there is an area for areas for improvement. Otherwise, why would you need a team like mine, really? I actually think that this kind of function is in itself a sign of data maturity, because in the same way that an elite athlete is going to need to have much more sophisticated methods to keep at that support. Well, exactly.
00:08:58
Speaker
couch muffin who just needs to get out and walk for 20 minutes. So I think the fact that my team exists shows that we are quite data mature already, but we still have very, very high ambitions to be more.
00:09:11
Speaker
Well, that's great to hear. I couldn't agree more with you. I think, you know, the fact that you have a team focusing on what you do is a sign of that maturity and that wanting to get the most out of what you're doing. I think this role in some of the smaller startups and scale ups is maybe sort of compounded into something that the leadership has to think about, but it's not necessarily, you know, it's a bit more of an afterthought.
00:09:36
Speaker
And obviously you guys have grown into a full department, which is great to see. So now I'm really keen to understand a bit more about that sort of how, you know, how do you bridge the gap? How'd you go about doing that?
00:09:49
Speaker
Yeah,
Problem-Solving Strategies
00:09:50
Speaker
it's a good question. I mean, it actually just involves a lot of questions. So we've used kind of different ways of doing this. But one of the things I really liked doing is that kind of Toyota five whys is just keep asking why until we get to the heart of the problem. I think one of the issues that I see sometimes in this kind of area is that
00:10:12
Speaker
the root cause isn't surface and therefore you're just gonna keep putting a band-aid on a symptom without really solving the issue. So one of the things that my team really tries to do as much as possible, and it's actually a lot harder than you think, is just keep asking that, well, but then why does this happen? But so why does that happen and why does that happen? And just keep going until we feel really satisfied that we've gotten to the root cause of something and try to put our bias to one side because I guess
00:10:41
Speaker
You know, I've been at the company for 10 years. Some of the people on my team have been here for a while as well. And so sometimes it's easy to just assume that you know the answer. And so we really try as much as possible to start from a fresh perspective. Even if we're talking to somebody that we've worked with for years and years and years, just really try to treat
00:11:00
Speaker
every kind of interaction and diagnosis as like just a complete blank slate and just get to the bottom of what's actually happening. So that would be that would be kind of one of the things we do. Another thing that we a kind of framework that we use is
00:11:16
Speaker
A lot of our projects are kind of divided into sort of four categories. So there is the data itself, the tools, the people skills, and then kind of a, I hesitate to say it, but a kind of culture aspect, which encompasses beliefs and kind of feelings as well, because that's attitudes make a big difference, but also kind of how people are organized and structured.
00:11:42
Speaker
any kind of routine processes and workflows or expectations would kind of fit into that world. So we might look at, so if, for example, a business owner, maybe even a data person comes with a particular problem or opportunity, we might try to do that diagnosis using that sort of framework and kind of sense check, where do we think?
00:12:06
Speaker
Where is the issue? Now, the issue might be across all of those four things. And we've definitely seen that. But it's nice to have that kind of it helps us from automatically saying, OK, well, this is a tools problem. You need to kind of ask those why questions across all four areas to make sure that you're not leaving something actually that could be quite critical on the table.
00:12:28
Speaker
Yeah, I think the constant asking of why is such a good point.
Barriers and Solutions for Data Adoption
00:12:32
Speaker
We can sometimes be too easy to take the first answer and the easiest answer. And I think stakeholders can, you know, they want to give you the first thing that comes to their head. But that sometimes, as you say, not the root cause. So constantly digging, becoming almost that annoyance.
00:12:49
Speaker
We try to be very charming. But I suppose it's helping, you know, change their perspective and getting them to critically think, which in itself is a skill which I think many don't have initially anyway in the fast-paced lives that we live. For sure. So what are some of the reasons for, I suppose,
00:13:12
Speaker
lack of adoption and because that I suppose is then the next stage on right you understand the why now you've built something that's going to work and it's going to help them but you know what is the reason that that data product isn't going to be be used
00:13:29
Speaker
Again, it could be a number of things. I mean, again, this is where checking your bias at the door is really, really helpful. Because sometimes it's really easy to think, oh, well, they just don't get it. I've done this amazing analysis. I've done this amazing piece of work. And they just don't understand. Or some people can make it really personal. They just don't like it. They don't like data, blah, blah, blah. And sometimes that's true. Sometimes that literally is it. It's that somebody is maybe really uncomfortable.
00:13:58
Speaker
I think most of the time it's just utter lack of awareness. I mean, we really assume, for example, we've got this insights hub where we upload all the analysis we do, we've got kind of a glossary and all this stuff. To be honest, it's sort of metastasized beyond all control over the years and we're looking to redo it. But actually, we did a little bit of a survey to just find out how many people even knew about it.
00:14:26
Speaker
So sad. But without before seeing those numbers, we could have just assumed that maybe people just didn't like it. But actually, they just didn't know. And so awareness is a big thing. Tied to that is just discoverability. How easy is it to actually find something? Again, I think because we know where things are, we assume that other people know. And that is just really not true.
00:14:51
Speaker
So you start with just the basics, then you kind of go up in terms of sophistication, like, okay, so is it discoverable? Are they aware of it? But then do they understand? Do they understand what it is? Do they understand how to use it? Again, that's assuming that people just know what to do with
00:15:10
Speaker
a piece of output or is a bit of a leap. That's not always the case. Then you'd be looking at something like that. Then you'd be looking at actually it's the degree to which it can really drive action. Again, despite the immensely high quality we have in terms of our analysts and our data people,
00:15:30
Speaker
you know, sometimes just even the smallest omission of context can make a piece of data analytical output unactionable. You know, and so it's, so sometimes it's also figuring out how we can capture that information more thoroughly upfront and make sure that that can be incorporated into kind of any output that comes out. But so typically those would be the things that I was, I would be looking at. So, you know, can it, can it be understood? Can it be actioned? Um, brilliant.
00:16:00
Speaker
I think, you know, the first one is do they even know it's there, that discoverability point.
00:16:07
Speaker
There's a lot of churn within companies. People leave, people join. There's so much information to take in. Do they know that this product, this tool is there for them to utilize and how easy it is to find them? I know whenever you join somewhere new, just understanding where things are stored is such a key issue. And I think that's something that base teams need to emphasize, especially for when it's really critical or can be really critical for
00:16:32
Speaker
for their role.
Enhancing Data Discoverability
00:16:33
Speaker
So how do you combat discoverability, usability, all of the points that you mean, how are you looking to combat these challenges? Yeah, it's a good question. I think this is where
00:16:49
Speaker
I see the data industry perhaps changing and evolving and recognizing that to be really successful, it needs to incorporate other disciplines into its arsenal.
00:17:04
Speaker
You know, one thing that we've been kind of skirting around is knowledge management. You know, that's a discipline in itself. And I think that data as a discipline could really benefit from kind of a more recognized knowledge management kind of component strategy. You know, what do you do with all that insight? How do you make those into discrete products? How do you maintain them? How do you optimize for them? How do you spread them across
00:17:32
Speaker
a large organization or maybe a slightly siloed one, this all takes a certain skill set that isn't necessarily on data analysis 101. So I think a lot of, again, what my team brings to the table is sort of an ability to reach out to the right subject matter expert within the wider FT to kind of
00:17:55
Speaker
To find these opportunities where our goals and incentives are aligned so we can partner together. So for example, actually on this inside side, which is a very real example that we're currently working with, I've been working with the fabulous member of the central comms team.
00:18:11
Speaker
to create some ideas for a new kind of knowledge hub, insights hub on the intranet. And she's helped with comms plans for other kind of data related initiatives like metrics that matter. And it's just been a phenomenal partner. And I think we need to be doing that more of that kind of work to address some of the issues that we've just described.
00:18:35
Speaker
Yeah, I think that finding them in terms of stakeholders and using them to champion that, obviously the example you gave is very good around sort of that process and the longer term strategy. Other initiatives I've heard about have been sort of a data week and they have
00:18:52
Speaker
The data is put at the forefront of everyone's minds for a whole week, and they have the individual teams, not the data teams, singing about their success stories, which, again, just helps try and shift that mindset, I think, in the culture of the business and puts data on people's minds.
Providing Data-Driven Solutions
00:19:10
Speaker
It's so easy for us to get tunnel vision as data professionals that everyone cares about data as much as we do.
00:19:17
Speaker
But this this goes back to I think what we first started talking about about why it is so important to diagnose the situation and get to the heart of it because actually if you can identify you know solutions to problems that your internal stakeholders have and say hey if we just did this
00:19:35
Speaker
this way, you wouldn't have this problem if we use this kind of data, change this process so that we used data analytics in this way. I mean, who's not going to sign up to that? But it is identifying the problem correctly. And then I think it just becomes so much easier to get buy-in and get support even if you don't have big budgets, which I certainly don't.
00:20:01
Speaker
Yeah, I mean, your role, as we've already mentioned, it's relatively new and rare within the industry. You've already touched upon, I suppose, the need and the reason for why this role come into existence and what value you're driving. But how do you define success in your role? Is there a metric that you use to understand what success has been over the next six, 12 months?
00:20:31
Speaker
That's a good question and it's something we're looking at. I think, and in fact, my team is looking at trying to better quantify the value of data, which is something the entire... Yeah, this is what I've concluded. So, you know, and I think at best it's just, you know, it's just going to be... You just have to think about how you're going to use that information. So we're just...
00:20:53
Speaker
trying to do something that I think at least gives us a better sense of how to prioritize and so on. So for my team, we don't know. We don't have a God metric or an overarching number that we optimize for. It's really kind of on a case by case basis. So for example, for Data Academy, we had a certain number of signups that we were targeting, which we, by the way, smashed. For data democratization, we also had goals around the look of migration and
00:21:23
Speaker
and what kind of take up that we wanted. So it's more kind of broken down based on what we're hoping to achieve with initiatives. I think, you know, my team as of it kind of really started in kind of the tail end of 2020. So I think this this next year, my goal is to is to further refine how we think about success as a team and maybe look to start having more consistent metrics for success over the projects. So watch this space.
00:21:53
Speaker
working progress. Brilliant. Well, look, I think it'd be great to get your take on what a data culture is, because it seems like your role is very centered around, I suppose, fostering this data culture. Adding to the list of things that I have a love-hate relationship with, data culture as a phrase would be one of them. I think
00:22:13
Speaker
I do feel like data, the industry just loves a buzzword. I'm sure we're not the only industry that loves a buzzword. Well, there's data cultures on every single job ad I see. Everyone's got a data-driven organization. Yeah, I just, yeah. So, but as much as I try to deny it as a phrase,
00:22:35
Speaker
I think the concept of it is actually super important. The problem that I find with it is that it's just so, it's so kind of nonspecific. And in some ways, again, that's one of its benefits is because culture is a mixture of things. We've already talked about some of them, you know, beliefs, feelings, processes, expectations, all this kind of stuff.
00:22:55
Speaker
So I just think it would sort of behoove people to be clear about what they're kind of talking about. And that's sort of how I approach data culture, is I try to separate the components. And certainly for any particular initiative, there might be certain components that I'm looking at more than others. So for example, with something like Data Academy, emotion is a really big aspect of that. When we did a learning needs assessment,
00:23:23
Speaker
It was really interesting to see the kind of emotive language that comes up. Some people feel very freaked out by data and numbers. I've seen truly brilliant people. I mean, people who could intellectually buy and sell me three times over.
00:23:41
Speaker
crumble at like basic maths. And it's not because they can't do it. They absolutely can. There's just there's an emotion there. And so, you know, for certain projects, I think you do need to really think about, you know, what's what, what are the feelings here? You know, is there going to be a crisis of confidence that we need to take into consideration?
00:24:01
Speaker
For other projects, you might be looking actually more at workflows. So for metrics that matter, which is all about defining a framework for decision making and metrics around the organization, I'm working with our delivery department that does a lot amongst many other things, kind of project management and guiding these large scale programs. We're working together to figure out, to kind of incorporate
00:24:25
Speaker
new ways of defining KPIs and measuring success that, again, have come from the data and analytics discipline. And that's, again, a data culture initiative that's looking at workflows and expected behaviors, less so emotion. So I think it's about splitting it out and really, again, trying to target what you're looking for. Because if you just say data culture, that's
00:24:50
Speaker
I mean, that's mad. That's just, you'll just- So open. Yeah, it's just too open. So I think you just, with data culture, you just always need to really define what it is that you're looking at. As I think you can probably tell, categorization is a big thing for me. I'm always trying to break things down and just understand really what is the nature of the thing that we're looking at. What are the various components?
00:25:12
Speaker
Yeah, I suppose you could bring it back to what we were talking about previously, that I split them more than the four points around discoverability, knowledge, and they really will build out how you could define a data culture and the data culture of specific products, state of products. I think that maybe it would be an easier way of identifying the success of it, because it's then pushed into that category for that team. And as you mentioned, there are some parts of the organization that have, you know,
00:25:41
Speaker
have a better data culture, data literacy, data adoption than there are other areas and I suppose that's an easier way of quantifying it. Yeah, for sure.
00:25:51
Speaker
To McKinley, look, it'd be great to, I suppose, really get a real world example.
Looker Migration Project
00:25:57
Speaker
This is what the podcast is very much about. We were at an event hosted by Google the other week, and I know one of your other team members, Jono, was speaking about your look of migration. Seems to have been a highly successful project in terms of...
00:26:12
Speaker
you know, that data democratization. So I'd love to unpick that project with you and how your role was sort of pivotal in making that a success. Well, I first wanted to say that I think there were many roles that were pivotal to that, to the success, and I certainly don't want to take the credit. In fact, John, who you mentioned, was really kind of the bad leader, the puppet master, whatever analogy you want to use. It's just instrumental in making this happen. But I think
00:26:40
Speaker
My team skill set was a nice compliment to help land this, but essentially what we had, we had this situation where we were using Chardeo, may it rest in peace, and it just completely spiraled out of control. It's not any one person's problem, but I think this is a pretty familiar situation for a lot of companies.
00:27:02
Speaker
A BI tool running out of control. Oh, I know, I know. Totally shocking. I know we're the only ones to have had this happen. It was just such a mess. And we found ourselves in a situation where we had a very hard deadline. I mean, Charlotte was literally going to be just decommissioned on a particular day. And there was no ifs, ands, or buts about it. We were facing into the very real possibility that just all of our dashboards would just suddenly go, and that was it. Into the ether.
00:27:30
Speaker
into the ether, exactly, then you could really measure the value of that, I suppose. So what was your, I suppose, your goal then of this project? It sounds like you were maybe strong armed into a into a migration with Charcio being decommissioned. Yeah. But you know, what when you then had, what was the strategic goal of moving to look at?
00:27:51
Speaker
So, I feel like as we've been talking, I've been sort of speaking in initiatives, but I think we do have a lot of initiatives that are all leading up to the same thing, which is just advancing our data maturity. So, the whole looker thing was ultimately a part of data democratization. The purpose of which was to make, you know,
00:28:13
Speaker
was to enable data-driven decision-making. Wow, that's a lot of Ds. Easy, fast, and fun, if possible. And the belief was very much that just anybody in the organization should really be enabled to make decisions with our fabulous data. So we'd always anticipated that we would address the tools.
00:28:35
Speaker
the chart AO thing forced our hand a little bit in terms of our timeline. But I guess this is, it's important to understand that context because it shows the lens with which we were then assessing options. We didn't just want a chart AO replacement. We were really trying to think about that larger goal of what do we really need if that is our ambition, right? Exactly. And again, I'll refer back to that, the kind of the tenants I described earlier of, you know, data tools,
00:29:04
Speaker
people skills, and then the culture. That's how we were addressing data democratization. Going specifically to Looker, we did a long assessment of lots of different tools that we could use, and we had very explicit criteria that we were judging it on based on these outcomes. Now, once we'd finally decided on Looker, which definitely scored
00:29:29
Speaker
highest. Yeah, for sure. Based on what we wanted to accomplish. Now, that's not going to be true for everybody, right? And I just want to be super clear, they are the market leader, at least I think they are. And just because that's true doesn't mean that they're the right fit for you, but they were definitely the right fit for what we wanted to accomplish. And what you wanted to accomplish was this truly sort of self-serve culture? Yeah, self-serve, but also there were efficiencies on the kind of technical side. And I think what we want to do is
00:29:56
Speaker
we are still chasing one single source of the truth. I know, it's the holy grail. I appreciate it. I know, but we're still chasing that dream. But there was a lot of things about it's not just the front end functionality. Yeah, exactly. It all makes it so much more efficient and aligned with where we wanted to take our data and our BI strategy. But for the actual migration, we ended up deciding on
00:30:25
Speaker
two goals and they're going to sound really, really basic.
00:30:29
Speaker
But they're actually really hard. We wanted to maintain business continuity. So what we didn't want to have happen is that there was a dashboard that suddenly just disappeared that prevented people from doing their jobs. We absolutely do have those things. So we wanted to maintain business continuity. And we also wanted to manage expectations. And this goes back to what we were talking about before, emotions. We wanted people to be informed and aware.
00:30:55
Speaker
and this was no small feat. And so we ended up, and I think this is where, and I hope Jono would agree, I think this is where my team added a really useful skill set, is that at some point we were facing this very, very strict timeline. We had over 800 dashboards in Chardeo with
00:31:15
Speaker
literally thousands of charts. And we didn't have the time or the resource in which to fully migrate. So my team said, OK, you know what? Actually, we're looking at this wrong. Let's actually just find out what is the essential information
00:31:30
Speaker
that's really contained in some of these dashboards and create a whole workflow by which we can inform, you know, this newly designed suite of company dashboards that basically tries, you know, it's kind of an 80-20 rule, right? Try to fulfill 80% of the requirements with 20% output, right? So that's exactly what we did. And we, I mean, it literally consisted of speaking to a number of different stakeholders and having a very prescribed
00:31:56
Speaker
kind of interview process where we distilled the requirements and it ended up really enriching the final offer of what we produced. And it meant that we accomplished both of our goals. Business continuity was great, nothing fell through the cracks.
00:32:13
Speaker
And at every point the stakeholders were properly informed about the status of their thing. We also realized in the process just how much information was out there that was essentially like a nice to have and a kind of, you know, a safety blanket. I think I remember John is saying that you decommissioned 200 dashboards or something like that. It was mad. We were just not being used, not being utilized within the organization.
00:32:38
Speaker
What was so cool about this process of interviewing people and really getting to the heart of the need is we also identified things that weren't even in the dashboards that would be useful to have. A lot of what my team looks at is, what's the decision? What's the decision? What's the action? What's the decision? What's the action?
00:32:59
Speaker
That's constantly what we're trying to uncover. Once you have that, it's easy enough to design a dashboard or a chart, she says. I mean, I realize that that is also a skill set and not always easy, but you know what I mean? That's one of the biggest drivers of value is just identifying
00:33:17
Speaker
What the decision is and what the action needs to be i think there's such great advice i think for any dates professional in across any part of the life cycle whether you're an analyst a scientist working with a business user you know what is the action what's the decision how's that going to be impacting.
00:33:36
Speaker
the business, if you can understand and answer that, then it gives you your technical road back and pulling it all the way back to data engineering. You might not be working specifically with a business stakeholder, but your data science counterpart or whoever, what are they trying to answer? You can then remove the blockers and get straight to the core of the problem rather than designing something that maybe looks great and it seems great in your head.
00:34:02
Speaker
Yeah, I think that's perfectly well said. And it's just, again, I think it just shows the nature of, you know, data and analytics as a discipline that it has gotten, in many cases, so advanced that actually it is relatively easy to lose sight of the decision and the action that's going to be taken. So it's, it's by no means a silly problem. No, no, definitely not. So what was some of the biggest obstacles from your point of this look at migration?
00:34:31
Speaker
How much time do we have? Obviously the time scale was, it was challenging, just, you know, typically not having enough people, but also I just think it's just the sheer quantity. And how do you really distill requirements across, you know, Oh my gosh. Yeah. So I think that was, I mean, it was, it was just all very hard, frankly. But again, I think we ended up having a really great,
00:34:57
Speaker
team of people across, you know, a few disciplines, including, you know, we had a product manager, a delivery person, lots of people from data and analytics with complementary skill sets. And so I think, and of course, we were working very closely with the business. And that was the other thing is like we, we had, I think, a really great level of engagement from stakeholders who would really give time to say, actually, no, this is, this is really what we need. This again, this is the decision we need to make. This is the support we need.
00:35:25
Speaker
So, I think, you know, again, ultimately it was a success, but it was just the sheer amount of work was really, pretty daunting. And I think there were multiple times where we all thought, oh boy, this is just... Cry in the corner. Yeah, this is just not going to go well. And I think actually it's so interesting. I haven't thought about the look of migration for a while, because it did happen a little while ago, but it's
00:35:50
Speaker
actually really is something to be super, super proud of. We narrowed in on those two goals and really smashed that out of the park. Again, still lots to be doing to make sure that Looker can be used to its full potential and is used in the right way. But ultimately, I think we did a brilliant job.
00:36:08
Speaker
Yeah. I mean, it sounds like one of the key reasons for success from what I'm hearing anyway is that that ability and that process of you taking your stakeholders on that journey from the get-go, it wasn't just here's your final product, we've changed from Chartier to Looker, it's explaining the why, keeping them involved in that process that enables them to have buy-in and be excited about what's to come.
00:36:36
Speaker
You mentioned your migration was obviously a few years ago, I think, now. Yes. Ever since 2020, I feel like I struggled to remember five months and days, because that was such a weird... But yeah, I guess it would have been 2022, maybe? But what's been the end result? That's what I've seen keen to, you know, how have you guys looked? You know, you've said it's been a huge success. Are you able to quantify, you know, why it's been a success?
00:37:05
Speaker
So yes, and this is where I'm going to look at my little notes for my numbers. But basically, the way that we quantified success, certainly in the run-up, was so we had a way of measuring business continuity, which actually is not very scientific. It was just how many people are screaming at us? Are there any incidents? How many fires do you have to put out? Exactly. And I think we literally had
00:37:33
Speaker
I really think maybe it was one. There was one thing where there had been a little bit of a miscommunication, but again, it was something we were able to solve in a single day. This was not a big thing. Again, Jono may remember something else, but there was really hardly anything. So we felt really confident about that. In terms of managing expectations, we actually sent out a survey. We just asked people and said, hey, this is what happened.
00:37:58
Speaker
you know, how informed did you feel? Were there any, I can't remember the questions we asked, but it was maybe just a couple basically trying to determine. What supports on the tool? How was the solution? Precisely. And again, I'm not saying everybody was throwing flowers at our feet, but actually there was, it was just overwhelmingly positive more so than we actually even really expect.
00:38:20
Speaker
expected just because of the very nature of these things. People don't like change. It's disruptive. It's annoying. So we felt really great with the results there. I'm trying to think.
00:38:32
Speaker
Yeah, so we also did some things like, so training was an aspect of it. So we had created a group of power users. And so they were really instrumental into helping look or be embedded and so on and so forth. So we had like 35 power users trained over a program of four sessions. We'd had 250 viewers booked for training with about 200 already trained. So these were some of the numbers that we were using.
00:39:01
Speaker
We also started looking, of course, at adoption. So I think at the time, we had a really great adoption rate. I don't know what the numbers are looking like now. But given the fact that we're having to constantly manage our licensed users, yeah, exactly, it shows that the demand is quite high. But we had something like 75% of people who basically could log in to Looker and use it were doing so within the first week or two, which I think
00:39:31
Speaker
Amazing. Pretty awesome. So again, we were kind of tracking multiple metrics to see how it went. And yeah, it was I think we have a lot to be proud of.
00:39:41
Speaker
Amazing. Well, it sounds like it's been incredibly successful and yeah, instrumental in helping to, to do not cooperate data. And as we said at the beginning, you know, it's not all about the tools. It's, I think the processes that you put in place and your, your abilities to communicate with the business, which really enabled the tool to be a success. So yeah, definitely something for people to consider when migrating from any tool. I think, you know, it's not specific to look up.
00:40:10
Speaker
McKinley, I'm cautious of time, but there's one quick area we'd just like to understand. I understand that
Data Academy Initiative
00:40:17
Speaker
you guys, you mentioned it already, you've set up a Data Academy and I'd just like to, I suppose, understand a bit more about that. Yes, please. Let me talk about Data Academy. I'm very happy about it. So we launched it September 18th, so it's...
00:40:30
Speaker
Oh my gosh, one day away from being a month old. So it was always something that we had planned under the data democratization banner. And as I already said, we sort of did a sort of fledgling version of it with Looker, you know, this training and so on and so forth. But this is data upscaling on a whole other level. We've created 15 workshops and nine online resources in total, and we have more planned.
00:40:56
Speaker
They are all developed and run by SMEs within the business, led by a phenomenal learning and development program manager who, again, is sort of masterminding the whole project. Her name is Rosie Blamey for anybody who's listening and wants to give her some credit, but she's Brel.
00:41:13
Speaker
And again, this is another situation in which we've just had a phenomenal response. Again, this is less than one month after launch. We've had over 500 signups and that number actually could be larger. It's only held back because of capacity issues. We literally just can't.
00:41:30
Speaker
Do any more than that. The current average feedback score is 9.3 out of 10. And our average facilitator score is 9.7, also out of 10, which is just really brilliant. And we've even seen evidence of people attending multiple sessions, which again, I think shows the quality of our content.
00:41:50
Speaker
This is all brilliant. And again, it's just another way in which we are just trying to make sure that everybody at the FT who wants to make data-driven decisions has the means to do so. And skills is a huge part of that. So we're very pleased with it so far.
00:42:09
Speaker
Brilliant. It sounds like it's already been a huge success, obviously very early days, but the core goal is obviously giving the business a place which is where they can discover data. Absolutely. So, great. I mean, the metrics will really sound great, but unfortunately, I'm sure we could talk for hours. But we're running out of time, but we'd actually love to come back six, 12 months time and maybe discuss the Data Academy in more depth and see how it's progressed and where you're at.
00:42:39
Speaker
I will be taking over the world by then. Yeah, I know. That sounds great. Well, thank you so much for having me. Well, before you go. Oh, yes, that's right. There's the quick fire round of questions. So yeah, just to help essentially listeners progress in their careers. So the first question is, how do you really assess a job opportunity and how do you know it's the right move?
00:43:02
Speaker
Okay, I feel like everybody listening to this is going to just think of me as the category's queen, but I once had a really brilliant mentor way back when I was driven just by raw ambition. I still am a little bit, but I was certainly back then, and I remember I was giving her all of my
00:43:20
Speaker
my hopes and dreams, just saying about how I wanted to be some tremendous success. And she just said, what does that mean to you? Can you break that down? What does that mean? What is success? Exactly. Are you talking about money? Are you talking about prestige? Are you talking about great family life? What are the components? And I remember thinking, what is she asking me?
00:43:42
Speaker
Mind-blowing success surely that's simple enough, but she's absolutely right. You have to think about what does that really mean? Actually, you know Are you motivated by becoming super wealthy or is it that you want respect from your peers or you know larger? Absolutely, or are you just interested in growing your skill set and and learning things? I think it is not
00:44:05
Speaker
a trivial exercise to really jot these things down and have some sort of, come on, we're all data people, so some sort of weighting attached to these things and just be assessing that on a regular basis. And so I kind of do that informally in my head. And so every time there is a new opportunity, I'm reflecting on that list and saying, okay, well, what's my current position? You know, how do I feel I sort of score on these components?
00:44:28
Speaker
What are the gaps? Does this opportunity give me something that I don't have that I really value? But is it also equal on the other things that I currently have? And that's sort of my system. Brilliant. I think that's great. If you're able to understand what success is for yourself, you can then identify it in your career.
00:44:47
Speaker
So what is your best advice for people who interview? This is going to sound really basic, but I do feel like I see a lot of people not do this, but just do your research, for goodness sake. Just know something about the organization that you are applying for. And I'm just so surprised at how many people seem to waltz in and just, I mean, yeah, OK, maybe they know the basics. But just take a step back. Think about what do you think that this team is trying to achieve? What do you think?
00:45:15
Speaker
their role is in the long-term success. What are the pressures or opportunities facing the industry? I just think you don't need to come in with a thesis, but just have some idea, some interesting questions. What are their pain points and how are you going to add value to them? Yeah, and be human about it.
00:45:36
Speaker
Yeah, I think, you know, please don't look up, you know, top three questions to ask an interviewer, you know, I just, you know, really use your own creativity and think, you know, what interests you. And, and also finally, if you're not into the job, just save everybody some time and just like, don't go for it. Like, again, I feel like I have some time, not often, most people are really keen, but every once in a while you'll get somebody who just doesn't seem all that bothered. And it's like,
00:46:03
Speaker
Why are you here? Why are you here? Just don't bother. It's not a problem. Just don't bother. Couldn't agree more. Final one, McKinley. If you could recommend one resource to the audience to help them up.
00:46:17
Speaker
Okay, so
Recommended Reading
00:46:18
Speaker
I talk about this book all the time. It is the rather hilariously titled 52 Things We Wish Someone Had Told Us About Customer Analytics by Alex and Mike Sherman. And it is, forgive me, Mike and Alex, but a little bit of an unprepossessing book. It's not very big, but I think it is just the Bible for anybody who wants to
00:46:39
Speaker
use data to drive action and you could be a data practitioner, you could be in marketing, you could be in any discipline, but it's just anybody trying to use data to drive decisions. I just think this is an incredible handbook. Each chapter, you know, some chapters are only like a page and a half long because they've just really distilled the essence and just made everything really focused and actionable. I just think it's heaven and I always keep it on my table for inspiration.
00:47:07
Speaker
Amazing. Amazing. Well, we'll put a link for the book in the notes as well. Sounds like a good one. But again, McKinley, thank you so much for your time. It's been a pleasure to uncover your role and the impact that you're driving and I think really valuable for the audience and how they can enable the business with data in their own organizations. So thank you. Thank you. See you next week, everyone.
00:47:35
Speaker
Well, that's it for this week. Thank you so, so much for tuning in. I really hope you've learned something. I know I have. The Stack podcast aims to share real journeys and lessons that empower you and the entire community. Together, we aim to unlock new perspectives and overcome challenges in the ever evolving landscape of modern data.
00:47:56
Speaker
Today's episode was brought to you by Cognify, the recruitment partner for modern data teams. If you've enjoyed today's episode, hit that follow button to stay updated with our latest releases. More importantly, if you believe this episode could benefit someone you know, please share it with them. We're always on the lookout for new guests who have inspiring stories and valuable lessons to share with our community.
00:48:19
Speaker
If you or someone you know fits that pill, please don't hesitate to reach out. I've been Harry Gollop from Cognify, your host and guide on this data-driven journey. Until next time, over and out.