Episode 1: Five Years On

AI Transcription:

 

[00:00:00] Ben Culora: Hi. This podcast is brought to you by Practicus. Practicus is a recruitment, consulting and advisory business specializing in change and transformation. It’s been five years since we last sat down and chatted to Barry and Tony about the changing world of data. So where is the world of data, five years on? From all of us from Practicus, we hope you enjoy the podcast.

[00:00:24] Tony Cassin-Scott: Hello, and welcome to our brand new podcast, The Data Files brought to you by Barry and Tony. We’re gonna cover a range of topics with some exciting people. Anything from data governance and AI all the way through to what it takes to be a great data leader. I retired five years ago, I thought, but the, uh, the data vortex sucked me back into its hold again. And now I’m kind of a, a CDO whisperer, I think is what I’m sometimes called.

[00:00:49] Barry Panayi: I’d say that’s a pretty good job description. Hi, I’m Barry. I’ve worked in data, uh, for, so just over 20 years now. I had the pleasure of working with Tony when, uh, I was the head of data science at Bupa.. Uh, since then I’ve been the chief data officer at a number of places, some of which where my job was to keep us out of prison and do governance, and some of which was to really use AI and machine learning.

Hopefully you won’t have to hear too much from us. The guests are pretty exciting that we’ve got over the next podcasts. And I wanna say thank you to our sponsor, Practicus, who came up with the idea and asked us and we said yes. So we bought this on ourselves. Thank you for putting us in this beautiful data bunker. Uh, it really is true data people get chucked in the basement. If there are any photos, do check them out.

[00:01:33] Tony Cassin-Scott: Check out Practicus on LinkedIn.

Barry and I spoke five years ago in 2017, trying to guess what the world would look like in 2022 for the world of the CDO or if indeed a CDO would exist in 2022. Well, today we know that CDOs are rampant in the uk, but there are three things that we discussed. I say discussed, uh, it was mainly Barry who moaned, but I discussed and we were talking about justifying value, is what is the value of a CDO? We talked about data culture, data literacy, data fluency, and what was the role of the CDO versus the CIO and indeed the rest of the CXO board?

[00:02:12] Barry Panayi: Thank you. I was very moany uh, and, and still continued to be. Uh, I would say we got some things right and some things pretty wrong listening back. And I think it’s only fair that we fess up right now before we put the rest of our guests over the next podcast through the ringer. So, In some radical transparency, I think, uh, it’s worth saying that we definitely got some of it right in that CDOs Chief Data officers are a thing and I think they’re here to stay.

I believe that the importance of the role of data is understood as important on more boards and exec committees than ever before. That’s a tick in the box. However, a lot of the moaning around culture and literacy, which I hate. I shudder when I would listen back to it because actually it’s all about people understanding what data can do for them. Not that they’re illiterate in the use of it, especially in financial services, for example, where people run on data. And indeed some of our guests are from the insurance sector and will talk about the original data being actuaries and so on, which is another subject that I don’t necessarily agree with, but we won’t get into that now.

I, I think we did a bit of why aren’t the CIOs on the same page? Why don’t they CIOs or CTOs provision the data in a way that us poor chief data officers can use, what’s the matter with them? Frankly, if I were a CIO what would keep me up at night? I mean, you were one, Tony. I mean, you are a slight interloper in that you’ve been a CIO and in a position of a CDO. I mean, what keeps you up at night? Is it giving me the data to do my analytics models?

[00:03:53] Tony Cassin-Scott: Absolutely not. It’s keeping the lights on, which is how traditionally CIOs are managed and measured. It’s event driven production. So if you’ve got problems with your data and if it’s not affecting production, well, you can wait just like everyone else, and that is the problem, I think.

I don’t think the, the value of data has been fully understood in terms of its operational effectiveness in the way that it can carry companies forward. I think that’s changing, uh, but we’re not there yet.

[00:04:20] Barry Panayi: I totally agree with you. And, uh, I think that’s naivety that shone through, certainly from me last time. I wasn’t leaning into how can we engineer the data to service these models or analytics that we need. And why is that different from the data that’s used to surface something on a website? And so, hands up, not sure I was too helpful to my technology colleagues last time, but you’re absolutely right. You know, you don’t want the cash machines to go down, the tills to go down, the lorries not to deliver, the adverts not to play in the right slot if you are the CIO. And the CDO traditionally hasn’t had the engineering resource to take control of their own destiny. And I don’t think we saw that coming, and that’s a big one now, Data engineers are the new unicorns. I don’t know what you’ve seen in the last year or so.

[00:05:08] Tony Cassin-Scott: I, I think, well I’ve seen muddied waters on this one. There’s, as I like to, there’s no I in CIO and I think that’s even truer today than that has been in the past or the days of data processing as it used to be called. I. You’ve got data engineers, which traditionally been in the forefront of the CIO bank of skills, but I would argue going forward, they probably should be part of the CDO. But then there’s the dilemma, CIOs are operationally focused. CDOs aren’t. Why is that?

[00:05:35] Barry Panayi: Well, not for now. Maybe. I mean, if we look at ML ops, which essentially is being able to productionize data models, machine learning models, , they need to be production grade resilient platforms that those models run on.

And this is where the blurriness, I think is gonna come in. It’s a different blurriness from five years ago. So in a world where the CDO may have data engineering to enable ML ops, at some point those platforms have to be maintained and managed, and they become, run, not change. So where, where do they go then?

I think if I was smarter, I could have five years ago anticipated that the blurriness between run and change activity would increase, and that is something that’s really difficult. So then do you have two run data teams? One that’s doing operational data and one that’s doing the data science ML stuff, and then surely there’s some unintended consequences of that. So is it the CDO leaning back and getting the data engineers, or is it the CIO or CTO, whatever people call them, provisioning the right capabilities that so far they had, and I think five years ago I would’ve said, buck up, CIOs, this data’s important and you don’t get it. You idiots. But now I kind of think, I’m the idiot.

[00:06:55] Tony Cassin-Scott: Maybe that question can’t be answered succinctly and that maybe both are true depending on the organization. So for example, in the past it was about generating insight, which could, you know, could, it didn’t have to happen in real time. Now we’re talking about real time analysis use of webs that the whole load of robots and what have you. So all of a sudden we’ve gone from a kind of a it and wait type scenario to, has to happen in real time. That’s when I think the transition, certainly for the cdo, is happening. The question is, will the CIO’s role morph into supporting that, or is it something that CDO takes on? I’ve seen both in operation, but usually the CDO takes on cuz the CIO can’t fulfill that commitment.

Is that a failing of the CDO, therefore the CDO to take that on? Or is that something that the CDO should be carry forward?

[00:07:43] Barry Panayi: Or are the roles different or not? Now, I’m not sure what I believe on this, but we talked a lot about leadership last time. Can a chief data officer or equivalent come from an non data background?

I spoke a lot about, Oh, technology hate that stuff. Nothing to do with me, I’m just a data person. But what we’re talking about is the morphing of technology and data, How one relies on another because maybe 10, 15 years ago, certainly in my first data jobs, the output was a power point, that would help decision making. Now you might say 50% or some other percent of the time it’s a PowerPoint and then some other percent of the time, it’s a model that goes into production. Now, unless you’re gonna split chief analysis officers and Chief ML officers, and again, nonsense, too many chiefs, is it the case that actually you have to have both skills?

And we were rueing to kind of ask two questions back at you. We were rueing the fact that CDOs have no route to either the exec, the board, or to a CEO job. Why don’t people just get with the picture? Is it actually because the role is too narrow and there is some other leadership role which takes on data?

We were obsessed with where should the CDO sit? I dunno if you remember Tony. I I do indeed. Oh, cfo, coo, marketing bar. We kind of thought they were all crap and coo was probably the least worst. But they should, they should sit on the exec. Do, does the role need to be broader? I mean, that, that would be my question. I’m a, I’m biased, I’m a data person, but you have, you know, been the heretic that you are sat on the IT and data side. What do you think?

[00:09:18] Tony Cassin-Scott: I think it’s a broader role. I think it’s a hybrid role. It’s a bit like, uh, you can’t, you can’t divorce ’em, that people need basic data skills, I would say, and what I mean by that is to understand and comprehend and get the insight fr from the information.

I think the CDO role is about providing the tools and where required the expertise in order to interpret that information, but also to provide the services. I think it’s, if anything, I think it’s. Muddier now than it was before. And I think because, because of the nature of the hybrid is, is butting into marketing, it’s butting into sales, it’s butting into operational interest far more than it did before.

Before it was like a, a bolt on and now it’s completely intrinsic. But you mentioned something earlier about where does the CDO CDO role sit and I, I’d like to extend that as like, well, where will it go? Is it someone that could morph into a CEO, or a COO, or what does that look like? I mean, you’ve, you’ve worked in several companies. Have you seen anyone more from a CDO into any of the other C-Suite roles?

[00:10:22] Barry Panayi: No. And I don’t know if that’s, cuz we’ve got such a short shelf life that that hasn’t happened. Or if we’re so myopic that, um, we just focus on what we’re doing and don’t lift our heads up. I, we concluded this, the coo, the COO is probably the least worst, maybe because it’s the most fungible.

Uh, and it means different things in different organizations. And we’ve gotta remember, we’re talking about AI and analytics a lot here, 50% of my team currently on the data management and governance, uh, and ethics side. And I know we’re gonna have, uh, Roshan Awatar on later on in the series to talk about that. It’ll be interesting to get that lens on it because that really relies on the guts of the technology if you’re doing data management by design and very different requirements from analytics. But similarly, there’s also the productization of data, data products, and we’ve got Lydia Collett coming on to talk about that as well. And that those are the two sides that I think, would dictate where the role goes. And that might be dependent on industry. Uh, you know, if you’re in a highly regulated industry, perhaps you’d major, one would major on, it’s about staying out of jail. So data management, metadata ,governance is the most important, and morph into some sort of chief risk type thing. Maybe if you are in the retail business, the most important thing is we need a category management optimization product. We need a pricing product. We need merch ops products, and therefore the, the CDO that majors on products will end up becoming a commercial director of some kind. But one thing’s for sure. The p and l responsibility has never sat with a cdo.

[00:11:59] Tony Cassin-Scott: Well, I was gonna draw parallels with a cio, since I’ve worn both hats, it’s, I’ve only seen it once, sit with a cio, a p and L responsibility, and that was for Volvo trucks because they created like connected car and, and all of that. I think part of the issue is I think data and IT, and I’m gonna just briefly merge them together, it seems a bit of a dark art to most outsiders, whereas a lot of people can kind of get the concepts around sales and marketing and the more retail side of life. Whereas what we’ve specialized in our career is, is a black art for others.

[00:12:33] Barry Panayi: And is that because. It’s just too boring? I, I mean, I wouldn’t argue that some bits are difficult, but I would worry about going down a narrative of, Oh, it’s so hard, don’t worry your pretty little heads about it. I actually think it’s been a turnoff. IT and data’s been a turnoff. We need data, certainly, I think’s pretty glamorous and we should try and glamor it up. And I wonder, is it because people aren’t interested? Or is it because it’s difficult or is it because us data and IT folk are frankly awful at communicating?

[00:13:03] Tony Cassin-Scott: I, I think it can be confusing for others. I do think that we don’t do ourselves any favors and if you look at the sort of people that go into IT and data, they’re quite introverted in, in many ways compared to, I look at the sales of marketing population of the world. It’s not just the subject matter, it’s the culture of the individuals that data and it attracts as well.

[00:13:23] Barry Panayi: Is it, or is it the fact that data leaders or IT leaders. I’m going a bit woe is me and moaning again, I hope we don’t come back in five years and, uh, have a go at this bit. But is it because data leaders are not invested in the same as other leaders get on with the little jobs in the basement doing the, the boxes and wires. There isn’t as much leadership development. Is that why, uh, we we’re a bit crap at that leadership stuff generally, or, or am I being too harsh? You know?

[00:13:52] Tony Cassin-Scott: No, I don’t think so. I think the problem with data leaders is they’re seen as a service, service department. And the problem with that is you treated sometimes the second class citizens, and so you won’t always get the place at the top table.

[00:14:04] Barry Panayi: There’s an element of that, and certainly the squabbles earlier on in my career with it probably didn’t help either. Two geeks having a little fight about, uh, why the data’s wrong. I, I think there is a power in aligned professions coming together and putting the data forward. But you’re right, there is that perception and maybe that’s the culture we’re talking about.

I could post rationalize my comments of five years ago that I heard about, they just don’t get it is the exec summary. But that would be a lie. I mean, really what I was talking about five years ago was why is no one listening to our wonderful data driven recommendations that we put in PowerPoints and present to people and expect them to do? That was on me. That’s on, That’s on the CDOs, and maybe the culture is how can we listen more to what matters in a business. Don’t disappear up ourselves as much as perhaps we do at times. Uh, I mean, what I felt like we put the burden of culture change on the rest of the organiz. Instead of on ourselves.

[00:15:04] Tony Cassin-Scott: I think that’s right. I mean, we complain about everyone else not understanding us, but maybe our formal communication hasn’t always been the simplest. We use convoluted terms, which are kind of like blinding them with science. We didn’t adopt the right language either, I think.

[00:15:18] Barry Panayi: I think that’s right. Last time we spoke, I was a CDO and insurance company. We spoke about actuaries and pricing and risk, and their data scientists is really sort of, Um, I dunno if I agree with that, really. But now I, I’m in retail and I look at some of my commercial trading colleagues making decisions about ranges, pricing and so on. Incredibly data driven. My job is to help that as opposed to tell them what to do. And I think earlier on in my career I was telling the actuaries why their models were crap. Some of them were, but here. How can we accelerate that decision making? They, they know, they have a feel for what’s going on, so we have to help them action it. And I think it’s that pontification versus taking action culture. We need to be more on the front foot, more punchy, more commercial, and stop justifying it with 60 page decks of bubble diagrams where, you know, the bigger the bubble means this, the red it is, means this and you. I’ve done that loads and uh, it makes you feel very clever, but ultimately people do f all with it.

[00:16:27] Tony Cassin-Scott: Yeah, and I think the problem is the lack of alignment that I’m speaking generally here that we have with the business objectives, and I think if we’re far more aligned to what’s going on. So if we can link how that line of sight of value. Between what we do in the data space, and if you like the big strategic business objective to get those delivered, we’d have a lot more traction.

Yeah. And in fact, in recent years, that’s exactly what I’ve been focusing on. Forget about the data side for the time being. What is it that you want to achieve? Then how can we help you get there? Rather than you need a data governance person, you need data management. All that is true, but we need to be clear that it’s in service of creating the value that the company has identified.

[00:17:06] Barry Panayi: I, I agree. I’ve seen, with some success in my current organization, not down to me, down to, as you’re saying, the non data and IT people writing objectives, and then figuring out the squads of capabilities you need to do them, and pulling together cross-functional squads and focusing on. the product that that squad will produce. Now, five years ago, we talked about wouldn’t be wonderful where there was no big central data team. You federate it all out. But to federate it all out, we have to centralize in and, uh, center of excellence and all that. I still believe that. I think what certainly I have made mistakes on is focusing only on that center of excellence and almost becoming like a factory. People ask a question, we answer it. They go away. Maybe they’ll ask another one. If they ask the same question again, maybe we’ll do it self-service. If it’s really hard, leave us to do it and we’ll get back to you sort of thing.

The approach now, which we are with a forcing mechanism within the, my current, uh, role at the John Lewis Partnership, The objectives are shared between all of the exec. The squads are completely cross functional, so you might have a buyer, a visualization expert, a risk expert, and because it’s to do with product data. Now, how big is the washing machine? What size is that dress? What’s the nutritional content of that cereal? You have a data management product and list in there. They then form a squad with their own objectives and they deliver, and they are sitting next to each other. Now, that uncovers some very, very interesting business problems to solve. How will the washing machine fit in the hole in your kitchen? How will we comply with high fat sugar and. Legislation. How can you filter dresses on our website so that it’s always in UK dress size? It’s not European dress sizes, and it’s captured correctly. Those are questions that drive loads of incremental value. How much bread should we bake in the bakery? We get told these questions in these tasks because we’re in the squads now. The center of excellence still exists. Like five years ago, I was obsessed with the center of excellence, but now I’m obsessed with cross functional squads. Now in retail, it’s really important because retailing is hard. The concept’s pretty simple. We buy stuff and then try and sell it for more than we bought it. But the nuances around the range and how we do it and customer behavior is different from financial services. Do you think, am I barking up the wrong tree? Is that something we could have seen come.

[00:19:35] Tony Cassin-Scott: I think we should have seen it coming because it’s certainly true of other disciplines, bar, I would say the technical disciplines. So it wasn’t strictly speaking true of it unless you’re an engineering company, but generally speaking, the IT department was seen as a service provider. Give me this, gimme that. And they would do it without question.

[00:19:54] Barry Panayi: Are we fallen into that same trap?

[00:19:56] Tony Cassin-Scott: I think we have, and I think part of the problem is because there similar mindsets are involved and I, I think what you just described there is would be a utopia for most companies to get everyone to get close.

[00:20:06] Barry Panayi: Oh, we’re not nailing it. Don’t get, I don’t wanna paint the picture. We’re nailing it, but we’re certainly, I thought they told me you had, but yeah. Okay. It’s true then if I said it, it must be true. But we are, you know, we’re certainly on that road, so I disrupted you then just to put,

[00:20:17] Tony Cassin-Scott: you have what I was gonna say. I think you are the exception, not the rule . I think generally speaking, data departments are waiting for their orders and there there’s still some of that that happens. There’s a lot of order taking in my team, especially. Yeah. Well it’s the, uh, we can talk about that later, but the, the, the, the thing is about that lack of proactivity and alignment to commercial value, I still think is a, is a, is a cultural problem mainly with the data people. I would say.

[00:20:42] Barry Panayi: If I can just change tack a bit, you talked about culture, culture, culture. We’ve spoken about it a lot. There’s another thing that I recall from five years ago. We were obsessed with the culture of the organization, understanding data and what we could do to talk their language. Now, my current role, which I’m enjoying immensely, I have the pleasure of also leading the research and insight functions. Now, that’s qual and quant. Insight from our customers in the market. Now, the people that sit in those teams, the partners that work for me in those teams are different to the ones that work in the data teams. Now, there are bridges that are easy to spot between the data management governance folks and the analysts and the data scientists and the engineers. But by having this team, they bring such a wonderful diversity of thought. They’re thinking about how customers think and feel in a very scientific way. There’s some, there’s some real sight. There is sites, but not just behind market research, but how you answer the questions and closing the say do gap. I’ll credit, Dean Taylor, my director of research and insight with that one, but by having these people around and sitting them next to the data people by osmosis and slowly. Data, people are changing the way they talk about their analysis. I, in fact, five years ago I talked about when I worked for a marketing agency and I sat next to the creative guy and I learned so much cuz he was a wacky, creative, and I was a square data guy and we were forced to go to meetings together and he would come up with campaign strap lines and slogans. Nothing to do with data, but I would be there and I’d be talking about data stuff and he’d be there. There’s a fantastic, uh, anecdote about TV licensing, but maybe we’ll save that for a future episode. So I talked all about that in my smug way and then moaned about how data people don’t talk in business language, knowing that that was the answer. And now 21 years after, I think that marketing job I’m talking about, one of the biggest unlocks has been putting together people. With the different backgrounds, um, researchers and insight people, they run panels with our customers. They hear what they say, they do samples. They can tell you when people and not really doing what they say. And that’s really helped the culture change in my organization because we work together on things. Is that a thread that we should have picked up on?

[00:23:01] Tony Cassin-Scott: I think we should have and we didn’t, but I think that cross-fertilization of thought, putting different teams together is true across most disciplines anyway. Probably all disciplines. I have even, even insurance. Your actually, even, even insurance. Again, I get diversity insurance. Um, but the, But would you say that the research people, you, in your example, have picked up anything from the data people?

[00:23:21] Barry Panayi: Yes. How we can match in attitudes and thoughts to behavior, how we can shape our insight to show new propositions, for example.

So at like my current job, John Lewis partnership, We’ve put, uh, a sta out that a large portion of our business is gonna be non-retail. You know, in the foreseeable future we’ve announced our build to rent sites. That required a lot of research because we don’t have any data on. Building flats and renting them. So the research and insight people led on that. But then asked for data to understand more about how the customers are potentially living in the market. And sometimes some of the market data that has nothing to do with our customers that we can get freely available from, from third parties require some hardcore analytics. And I think previously, certainly not all teams, but some of these research and insight teams will take the pre-canned reports from your cantar, your iri. Uh, World Panel, Nielsen iq, whatever other, other data is available, and taking those pre-canned reports. Now by sitting next to the data insight and analytics team, they can go back to supplies and say, Just give us the raw data. Cuz I know, I know someone who can, uh, crunch through that and she can tell me exactly what I need to do. And they sit together and they work it out.

[00:24:41] Tony Cassin-Scott: So it’s an example where those diverse skills, it truly is that some of the parts is greater than the. Where it wasn’t before. I would say the opposite.

[00:24:48] Barry Panayi: It was an interesting factor in me taking this role because it does give you so much color both ways,

[00:24:57] Tony Cassin-Scott: but using you as the example, is that the norm or is that an outlier?

[00:25:00] Barry Panayi: It’s the first time I’ve seen. It’s the first time I’ve seen it, the research and insight teams. That I’ve come across before are either embedded in ones or twos. So you have a research person in women’s wear buying or in retail banking or whatever it is, or they sit under the chief customer officer, customer director, or equivalent.

I don’t know what the right answer is. I certainly know that having them next to the data insight and analytics teams has given immeasurable value our our output. Is so much more rounded.

[00:25:33] Tony Cassin-Scott: So what we didn’t see five years ago was the value of that hybrid working

[00:25:37] Barry Panayi: now and, and qual. Yeah, the qual researcher. I’m not gonna go into a, Oh, we can analyze video using a, That’s not my point. The point is going into customers’ homes and seeing how they live and getting them to record podcasts, accompanied shops, and what other brands they, All this stuff gives color to charts and tables.

[00:25:58] Tony Cassin-Scott: So isn’t this like an evolutionary point?

[00:26:01] Barry Panayi: This is one that I would stick my neck out on and say in five years time. We’ll see more of the qual quant. Traditionally more creative, more expansive. Thinking of those researchers along with the solid understanding of millions and billions of rows of data and products and why people are actually returning things, what reason did they give? What attitudes do people have on the brand? What does value mean? . Yeah. If you give the, uh, a data analyst or data scientist a question of are we good value for money, you’d get a pretty solid, uh, deck and answer. You ask the qual quant, you’d get a different answer. You ask ’em together, you’d get the right answer.

[00:26:44] Tony Cassin-Scott: So in five years time, will data analysis skills be seen as a secretary rather than a primary skill?

[00:26:49] Barry Panayi: Depends how deep the analytics go. I suppose the advent of self-serve probably stored a bit, but I think that that trainer has, has gone. How many cucumbers did we sell last week? Type questions do have to be answered in the business, and sometimes they still come into the center, but ultimately that self-serve. It’s gonna happen. So then your analysts will be on the more on the, Can you help me on the journey to why things happened? I, I think the conversation five years ago was, of course data. People’s job isn’t to say why anything happened. It tells you what happened in the past. Then we’ve got all excited about prediction and stuff. But now I think it’s about doing analysis and co-creating propositions and what you think is gonna happen. And I think that’s the value of the analyst analytical way of thinking. If you separate it from the data scientist whose job will be very complex modeling of what’s gonna happen in the future, output PowerPoint or very complex models. In the guts of some IT system because it’ll come up on your website. Would you like XPOs off of X item because you bought this? That’s where I think it needs to go. And actually, governance management ethics should still be done centrally and as productized as possible. That should shrink away a little bit to be only the complex cases, miles to go there or metadata and stuff. And then all of the BI stuff becomes really important. So don’t just be at buy a BI tool and. Berate the business for not logging on to look at your dashboards. That bit is so important cuz that needs to clear out of the central team. That’s democratization, isn’t it? It’s the BI stuff.

[00:28:28] Tony Cassin-Scott: It is and it becomes a commodity tool. So if you go back 20 plus years, there were sort of inf, I think they were called information specialists who using the equivalent of Google to get information from the internet or what have you. The library. Was that the library function? That’s absolutely right. Today, everyone does their own library function. They do their own searches and what have you. You don’t need any special skills because the tools are simple enough to to do it. What does the data world look like in five years time in that space?

[00:28:55] Barry Panayi: I think the tools are there now. They’ve just not been designed. With a user in mind enough, the front ends still look like, you know, business objects. That was crap. But now we just have prettier versions maybe, or maybe I’m bashing it again a bit now, but maybe the BI project in one’s organization is a less decommission tool for Tool Y. Look, there’s 10 million reports. We’re so clever cuz we’ve turned off 9 million of them and then we’ve made the remaining million prettier. Guess what? The bugger still don’t use ’em. So there’s the creation of. User centric design for these things that I think we’ve missed a trick on, but the tools are definitely there. You know, it doesn’t matter what BI two you use, they’re probably two or three if you’ve got one of them. You’re right, people have their favorites. But again, why the data people, The only one’s doing it. It’s about the squads. What does the buyer of. Ambient groceries need to see.

[00:29:51] Tony Cassin-Scott: So I, I do see the rise of decision support tools and the use of them increasing, but I think those decision support tools themselves are probably a product of better data skills for the use of AI skills and what have you to help people go through that process.

[00:30:06] Barry Panayi: I think we’ve missed another thing there, which, um, is worth bringing up the importance of community and engagement. Within a data function, we weren’t even taking the horse to water. I think in our discussion five years ago, I certainly have taken a bet on hiring a team that runs data engagement sessions, training hackathons, and so on. So that is, again, we’ve not really spoken a lot about technical stuff here. We’re talking about the stuff around it and. That’s the right balance?

[00:30:37] Tony Cassin-Scott: I think so. I mean, when there’s one assign, which I can’t mention the client, but we put in place a big culture drive to do exactly as you just described there. It was to bring the whole organization up to a level where they understood the value that they could get. And we brought in third parties, outside stories, if you like, that could tell them what they had done with the data and turn that into valuable information that that drove change. So I think there is a, there’s a big culture push to be done. But everything in parallel I think needs to happen now. Which is a hard cause you’re pressing on all fronts. Is that what you feel as well, or have you come across a different approach?

[00:31:11] Barry Panayi: Yeah. Again, and I think you, you put everyone together so they’ve got some knowledge of everything. It is all about that.

[00:31:18] Tony Cassin-Scott: So how would you summarize our last 15, 20 minutes?

[00:31:22] Barry Panayi: We’re about a third, correct? Uh, I would say not bad. I think we’ve brought out our dead. I hope this bits are rest of the guests at ease, um, that it should be a nice, relaxed series of podcasts. But just to turn up the heat a little bit, I think it’ll be fun if we ask all of our guests the same question at the end of every podcast. And given we are our own guests, I think we should ask it to ourselves.

So Tony, what’s the best advice You never took?

[00:31:53] Tony Cassin-Scott: The best advice I ever took, and I have to say it’s a function of time and therefore age, which I do take now is like take more risks and I didn’t. And the reason I didn’t because I didn’t want to fail. I don’t wanna be wrong, but actually what’s the worst that can happen? But the more you take, if anything, I would argue not taking a risk is a high risk in itself. And yours?

[00:32:17] Barry Panayi: Mine was just relax and smile more. I’ve got a pretty grumpy face and, uh, I completely underestimated the impact of being quite uptight and serious where I have on my teams, my personal life, so on. But frankly, I still forget it,

[00:32:32] Tony Cassin-Scott: Which is just as well, this is a podcast.

[00:32:34] Barry Panayi: Perfect. Face for radio, eh?

[00:32:36] Tony Cassin-Scott: Absolutely. It looks good on you Barry.

 

[00:32:38] Barry Panayi: Thank you, Tony.