Jonas Christensen 2:32
John K. Thompson, welcome back to Leaders of Analytics. It is such a pleasure to have you back. We spoke a few weeks ago and we had such a good time that we ran out of time and then we had many more questions to go through. A much longer conversation to complete. So we're back for more. Welcome back to the show.
John Thompson 2:50
Thanks, Jonas. So glad to be here and you're right. We had a great time the first time around and there were so many more things to talk about. And I'm glad we could find time in our shared calendars to get together.
Jonas Christensen 2:59
Yeah, absolutely. Listeners, you might hear that my voice is a bit different. I did say to John, that in the southern hemisphere at the moment it's winter and we have lots of colds going around and I'm a lucky recipient of it. So, you'll be able to hear throughout the next few shows, whether I recover or get worse. But other than that, John, let's get straight to the questions. Because last time, we talked about types of skill sets and habits that analytics teams and the leaders should possess. Now it's time to talk about how we set those teams up for success. So let's talk about how we structure the overall organisation to take advantage of the unique and differentiating potential that analytics can actually bring. So if we start with structure, where should analytics teams sit in the organisational structure or perhaps where should they not sit and why?
John Thompson 3:49
Yeah, this is a very interesting topic and discussion. And I've had it now for years with people in Europe, Australia, the United States, Canada, all over the world really. And it really comes down to the fact that the analytics team needs to be as close to the strategic decision makers as possible and hopefully as close to the CEO and COO as possible. I've seen the analytics team in the IT department and I think that's a bad idea. I've seen it in the CFOs organisation in finance and that's a bad idea for a whole different set of reasons. And then you've seen it in all sorts of different places: marketing and supply chain and things of that nature. And I do hear from people that ''Oh, I have an exceptional IT leader and they get data and analytics and we're gonna put the analytics team there''. And you know, I don't argue with people. I said ''That's fine. If you have an exceptional leader and they know, you know, how to manage an analytics team and get the most out of it and focus on cross enterprise strategic orientation, then great. That should work''. But when I see over and over and over again is that the IT department or the finance department or the marketing department has an agenda. Every organisation has an agenda. Analytics really should have a cross functional strategic agenda. And if you're working for IT, I can guarantee you, your analytics team is going to end up being bent to the will of the IT department. And when you sit and talk to IT staff members, they want to talk about servers and tools and vendors and outsourcing and things like that. They don't want to talk about data and strategic change and transformation, which is what the analytics team should be focused on. So where not to put it? In the line of business functional areas. Where to put it? As close to the CEO and COO as you can.
Jonas Christensen 5:46
Very good. And I have also seen all those things play out in my career. I really do concur with you that it can become, not necessarily political, because it's not out of any sort of political malice or anything, but it's human nature to be focused on your own little patch. That is what ensures, of course. That analytics gets pulled that way. And you do mention the exceptional leader. I think, when you do have one of those, structure matters less in any organisation because these leaders are so exceptional. I've also seen that. Unfortunately, they do leave and don't stick around. So once you have that happening, then you back to structure and you can't get it wrong there. Why should it not sit in a finance department necessarily? Why are you against a finance department?
John Thompson 6:31
Well, you know, finance is a wonderful function and the people in finance are numerous. They have to be. That's their job: Numbers. But finance has a very interesting and prescribed cadence, especially in a public company. They have the monthly close, the quarterly closed. Have their reporting to the street, whether that's the Australian market, or the US markets, or the European markets, or all of them if you're a global company. So what happens is the finance people - you know, it's kind of like the fish in the water. What's water? They don't know that they're acting like this, because that's just the way they act. You know, so you go in there and every project to them, it's the old metaphor of, you know, ''If you have a hammer, everything looks like a nail''. So when you have a conversation, the first thing that comes out of their mouth is ''Can it be done for the quarterly close?'' Well, probably not. They're asking for some really interesting and fun things to work on. But they usually span, you know, financial reporting periods. And the finance people, as you said earlier, Jonas, very well said, you know, everybody focuses on their patch. So if you have a strategic analytics team, and they're working for the CFO, I can guarantee you 80 - 90% of their work is going to be financial analytics and trying to predict what's going to happen in the finance department. It's not going to be a cross functional, strategic, transformational organisation, which it can be. It's just human nature.
Jonas Christensen 8:01
Yes, I agree with you, John. I have tried for many years, but it's still not possible for me and my teams to make the machine learning model deliver all the results in the financial year. It typically spans a little bit over that. So again, agree and what you're really saying is the analytics functions should stand on its own and be its own function, that is an objective reviewer of the organisation without the influence of any patch steps. John, where do organisations typically make mistakes, when it comes to designing first analytics teams, but also then embedding them in the organisation?
John Thompson 8:37
You know, in the book ''Building Analytics Teams'', you know, it's probably the most quoted section of the book. Maybe because of the subheading. I call it ''The original sin''. And, you know, it's one of those things that when you talk to the CEO and CFO and the COO, you know, they all look at it and say ''Oh, it's a technology function. You know, it deals with data and servers and computers. So, it should go under IT'' or ''The finance person is really smart with numbers, so let's put it under them''. It is a lack of understanding of what an analytics team really should be and could be and is. As we've talked about, an analytics team, just to say it explicitly, should be a cross functional, data driven innovation engine that drives the transformation of the business. And you're not going to get that from the CIO or the CFO or the supply chain head. You're only gonna get that if it has a top down mandate from the CEO or the COO. So that's the focus of what an analytics team should be doing. If you want strategic change and strategic improvement from your analytics team, you need to support them with an executive mandate. And that executive mandate has to be attached to every programme and every project that you do.
Jonas Christensen 9:54
Yeah, and I think if you relate it to an RnD department, then besides businesses that traditionally have an RnD department like medical firms and so on, analytics teams are actually, for a lot of consumer businesses at least, the closest they get to research and development department, in a sense that this is where you experiment with the business and figure out where the dynamics profit and loss are. Where you can experiment with your consumer and client experience and to see how you can actually improve those things through iterations. A little bit like in a big medical firm, you build your products like that, you experiment, you measure scientifically what happens. You make this pill. Does save the patient? Yes or no? It's the same sort of thing, really. And the techniques are also the same, funnily enough. So you wouldn't put your RnD department at Johnson & Johnson on the marketing department or the finance department. You would make it stand alone. So this is similar to that in my world. So you starting here to talk about, John, the executive presence of analytics as well, which is something we're going to get back to later because we're going to be talking about chief analytics officers, chief data officers, chief data and analytics officers. So I have lots of questions on that. But I might save them to a little bit later. Because before we get to that, it's, of course, not enough just to have a great analytics team that is capable of producing high quality work. We also need an organisation that's capable of consuming all this output that we create, however advanced it might be. How do we plant that seed of advanced analytics and build a data-driven culture and organisation? And perhaps you could give some examples of how you've approached this problem in your career?
John Thompson 11:39
Yeah, great question Jonas. And it's true. You know, we can do fabulous things with data and analytics. And you know, if the organisation is not ready to consume it or use it or even in some cases believe it, then it's fun and it's interesting, but it's not going to drive business value. You're not going to have value realisation. So in some cases, I talk about projects and programmes and people say ''Well, they're the same thing'' and they're definitely not the same thing in my world. A project is: You go in. You work with an executive or a subject matter expert. You work on something for a week or a few days and you give them a number. And that's a project and you're done. A programme, you know: You're going to partner with them. They're going to partner with you. You're going to work on many different data sources. You're going to do all sorts of different trial and error on your models. You're going to come up with something that has a strategic value to the business. You're going to change price. You're going to change your messaging. You're going to change how you do business on an ongoing basis. Now, if you're an analytics leader, you must understand this fact. That if you go into an organisation that is not data-driven and analytics driven, you will probably have to do many years of projects before you get a chance to do a programme. Because you're going to have to convince many different executives and many different managers and many different subject matter experts that you're doing work that is a high quality enough level, that they can trust you that you're going to change their business. That they can trust you to change their business. So you know, in the job that I have right now, I'm just about to cross over my four year mark and we've done some programmes. But I would say the great majority of things we've done in those past four years have been projects. And now we've gotten to a point where we have enough people through the organisation from the top down to frontline managers. They're coming to us and saying ''You know, you've been talking about projects and programmes and now I get it. I'm ready to partner with you and jump in and grab the oars with both hands and do an ongoing programme, where we're going to analytically change price or we're going to analytically change how we market''. So, it's not something that happens with one project. It's not something that happens with one conversation. This is something that happens over a year's duration and getting people to understand that they can trust you and your team and the data and the analytics that you're bringing to their business. So, it's in some cases disheartening for people to hear that message. But I want you to to understand that this is reality. That's how change happens in enterprise class corporations around the world. And I've seen this happen many different times, where I've gone talk to someone who's a manufacturing expert or the head of supply chain or the head of marketing and we've done projects and I've explained the difference between a project and programme. They're very nice about it. They're like ''Look, I just want the number, please''. So we do that. And then it happens somewhere between like three and six months, usually the people will come back, that same person will come back and say ''You know, can we have another conversation about an analytics programme that helps us change and improve on a continuous basis?'' And I say ''Absolutely''. And those conversations usually end up with us partnering and initiating a programme that's going to make a difference for the business at a strategic level. So it's one of those things that as an analytics leader, you have to have patience. Many of us do not. You know, we have a hard time being patient, but you need to win them over with results. You need the word of mouth to permeate the organisation. And then once you've gained that trust and they've had those conversations and they know that you're on their side and you're not going to drop the ball, then you can start engaging in programmes that really do the things that you yearn to do.
Jonas Christensen 13:06
So it's a really interesting concept: This ''Projects versus Programmes''. When i hear you talk about it, it's a very underutilised concept. How does that typically play out in practice in terms of where your team then gets deployed? Because necessarily, a project is more ongoing and more continuous and therefore, the analytics team almost becomes an embedded team within the function that you're partnering with, to some extent at least around this programme. And instead of some months, it might take a year or years, I'm guessing. How does that affect the way that the team works and the team members within that analytics team? It's, sort of, almost the embedded resources at the end of it into your marketing team or what have you.
John Thompson 16:14
It's true and it does go that way in some cases. But we and my team worked very hard to automate the backend. All the data feeds all the data ingestion. We worked really hard to make that stuff bulletproof. So, we don't have to keep turning the crank by hand. So you know, once people say ''Hey, we like this. We really want this to happen'', you know, we have automated the backend away. So we don't have to do anything there. Most of the time, you know, the data is accessed, ingested, integrated, run into the models and the models push the information out into the dashboards or the reports or the production systems or whatever it is and it all happens behind the scenes. So you know, often people do think that analytics is magic and if it's done right, it kind of looks like magic. But it's not. It's just hard work. And then you know, when the time comes, you know, we are monitoring the models and watching when they're going out of range of tolerance. And then we'll come back in and talk to the people and we'll say ''Hey, you know, we're gonna update the models with the new data'' and they don't really care. They trust us at that point. But what then happens beyond if you do all that good work, which is a lot of work and you need to do it, then your team actually becomes a trusted consultant and embedded within the marketing department, the supply chain department, the pricing department, and they invite my team to their strategic reviews and their annual planning and all sorts of different things and ask us questions throughout the year. So we do end up being an adjunct to these departments. And I have some people that are almost on full time loan to different parts of our business all over the place.
Jonas Christensen 17:50
And I think really that typically is the ultimate measure of success, that that happens. I've seen in my career where my staff members have been - can I call it poached? - by other departments in the organisation, and it kind of is the ultimate flattery, even though at the time it ruins our plans. It's very nice when people can be seen as high performance and having really delivered value that that someone else actually would like to offer them a role in their function. And I'm not saying that that's exactly what's happening here. But when we do get invited to strategic meetings and all that stuff, that's a really healthy sign that analytics is being used, I think strategically. Thank you for that, John. I think that was really helpful. And I'm gonna use myself, this idea of projects versus programmes. Now, a question that's connected to that. How do we select and prioritise the right projects or programmes to work on in the first place?
John Thompson 18:45
I have a lot of people come to me with this question and ask me on LinkedIn and different places: ''How do I select the right projects?'' The answer is that the projects have pretty much already been selected. You know, organisations have strict strategies. They have goals and objectives they're working on towards their annual targets. And those are the things you should align with. You know, we last year had a real problem in pricing and the management team was really tying themselves up in knots, as they should. It was a real problem. So I raised my hand and said ''Hey, our team can help with this. You know, this is a strategic problem for us at the highest levels and we can help''. So wherever there's pain in the organisation is where you should be focused. You know, if your company has no problems, well, then good for you. That's great. But I've never heard of any companies that don't have any problems. It's not that you have to make things up or you have to invent problems. You have to find projects. The projects are out there. Just align yourself with some of the biggest challenges the organisation is trying to solve.
Jonas Christensen 19:51
Yes, you attach yourself to these sort of strategic challenges or strategic imperatives. One thing that I've also always found is that there's a need for you to train the organisation to ask better questions. So typically, when you start out the questions are - they seem basic to me - they're often structured in a reporting format. ''Can I get five years of sales data for this part of the business?'' You can, but what do you need it for? '' I'd like to understand how pricing has affected my revenue''. Okay, well, let's answer that question rather than give you five years of sales data. How does an analytics team go through that journey with the business? And what are the ways that we can mature the questions that are posed upfront by stakeholders in the first place?
John Thompson 20:36
That's a great point, you know, because many of our stakeholders are not data and analytics professionals, and nor should they be. And we're not asking them to understand different algorithmic techniques or data integration techniques and if we're asking them to do that, then we're asking them to do the wrong things. What we want them to do is to ask us questions, just like you said. ''Hey, can I get the five years of sales data?'' Well, the real question is: Why do you want that? And then they're like ''Well, I'm trying to understand what is the correlative or the causative factors that are causing our demand in our high frequency users to drop?'' It's like okay, great. Now we're getting somewhere. Then we take them to the next level. ''Would it be good if we can help you understand that and then build a model that actually predicted what was going to happen with your high frequency users in the next 12 - 18 months?'' And they're like ''That will not only would make me happy, it would be heavenly. Is that even possible?'' Now you've started a dialogue. You've whetted their appetite. You've taken them from a ''Hey, I want to look at a review of the last five years'' to ''We can actually predict what's going to happen next year''. That dialogue alone will get you in the room for many, many, many more meetings. So it's not being someone who answers the first question that's asked. It's someone that then gently probes ''What are you trying to get to''. Just like you said, Jonas. You said it very well. What is the question you're trying to answer? Really tell me what you're trying to figure out. What I say to executives all the time is like ''Don't overthink it. If you have a problem and you think you need an answer, send me an email right away. Don't overthink it. Don't sand off the rough edges. The rough edges are where the fun part is for the analytics team''. So I get questions all the time from people ''Hey, can we do this? Can we do that?'' Or ''I need to understand this''. That's where the fun comes in.
Jonas Christensen 22:23
Yeah, very interesting. Because there is this dynamic of - and we go back here to how do you select the right projects- this constant dynamic attention of selecting the most valuable use cases, therefore we may need to almost base this case up. What is it worth and why is that? And that means holding those questions and really sharpening them, which takes time. And typically, we start with a very basic question. But we still want the stakeholder to ask that question, so we can see it, right. It's this smoke that comes before the volcanic eruption, if you can use that analogy. I just made that up on the fly. It's not the best analogy I could come up with, but that's actually really tricky. And I think that's what separates good from average analytics teams. It's if you're an average analytics team, that question comes in, you answer it and ''Here you go. We told you''. Versus a great analytics team that really starts digging into the essence of what's happening and what their stakeholder really wants to know. So John, when you go out into business and source information, where do you do that in the organisation? There is grassroots and there is executive management. How do you engage with the business? What are the processes you have for capturing these ideas, so that they don't just happen sort of randomly or whoever is the most analytically adept, the one that always gets in front of the queue?
John Thompson 23:42
Yeah, that's a great question Jonas. And generally, what I try to think of is: I try to think in themes. We in our team use the concept of ''Personal project portfolio''. So, our data scientists have questions that come in from the executives that have to be answered immediately. We call those ''Service Requests''. We have short projects that, you know, may take a week or a month or two months or something like that. And we have major projects that may take two years to get done. So I try to put our team on themes. So, we're working on things that have long term value. We're associating different projects with different programmes, like one that we did. This so far sounds abstract, but let me make it a little bit more concrete. So, we built a forecasting system that forecasts what's going to happen in all our US centres. All 300+ of them. How much volume is going to come through every one of those centres. We did that and it works really well. It forecasts every day for the next 18 months and it works great. So you know, then we took it and we forecasted it at the hour level. Now, why would you do that? You wouldn't need to know that. It's overkill. But we have the computing power. We had the people. We had the models. We had the data. So we could do it. And we did it because the reason was that the next step in the theme was optimised scheduling, driven by demand. So we took the scheduling system and we looked at all our employees. And we did all these analytics around optimal skills mix on the floor and then we built a labour scheduling system, that is being tested right now and it looks like it willl save us somewhere between 20 - 30 million dollars a year. So we took what was just a simple forecasting system and we extended it to be a labour scheduling system. And we have a couple more steps to extend it further. So if you look at these projects, in a thematic view, you can actually build upon them. And what you then end up with is the people in the business will fund your organisation because they know what you're doing and they want you to keep doing it. And then you can actually go do some other work too. So, it's one of those things that you end up getting people to work on a problem for a much longer time and it benefits the organisation in a much more rich way.
Jonas Christensen 26:07
And I think what you're highlighting there is also a great example of this research and development nature of analytics, right. Where you actually - You've built the product. An internal product. And you've building solutions on top of that. Again, all of a sudden, real value comes out when you lay it, all these things. So, great example. John, big question before we get to analytics leadership, specifically. So it's easy to ask how to answer. If you were to design the perfect data driven organisation, what would it look like and why?
John Thompson 26:41
Well, we have some really intriguing examples of that. Procter & Gamble, the global CPG company, consumer packaged goods company was probably the first data driven organisation that ever was. They looked at it from a consumer research perspective, a pricing, promotion, place, all the four P's in marketing. They were the and probably still are to some extent a featurely data-driven organisation. Amazon looks a lot like what data driven organisation should be. So if someone said ''You had to pick an organisation'' and they were going to be the model for a data-driven organisation, I probably pick Netflix.
Jonas Christensen 27:18
And what are of the hallmarks of Netflix as structured behaviour that you see that you would want to pull out specifically?
John Thompson 27:25
When the whole organisation is imbued with a view of data and analytics, everything they do is data driven and analytically driven. They're constantly testing, A/B testing, all sorts of different mixes and problems and ideas. The executive team listens to ideas from people not only below them, but multiple levels below them. It's one of those organisations that is saturated with data. And it's populated with people who understand data and analytics. And the people at the top get it. That's part of the big problem that we see in these large organisations that have been around for hundreds of years is that you probably get up to one or two or three levels at the top. And the people below those levels get data and analytics, but the people above those levels don't even understand it anyway, at all. So you know, you're blocked from any true strategic change, because the people at the top don't understand or want to understand data and analytics. That's not the case in any of the organisations that I just cited: Procter & Gamble, Amazon and Netflix. And I think that's part of the biggest problem for organisations. It's that executives don't understand how to leverage and use and gain value from data and analytics.
Jonas Christensen 28:35
Couldn't agree more. The analogy that I've used many times in this show is 40 years ago, 30 years ago, we had lots of executives that had never used the computer, maybe even the first CIOs have never used the computer and you're just not going to get the same understanding and interaction with that, if you haven't done that. Now, everyone's computer native and there's no problem there at all. But even within what we call, maybe computers and internet and so on, there have been these evolutions where the executive necessarily had to be first computer native, then internet native, then social media native, etc, for those things to really start sticking in the organisation and from the top down, sort of, cultural aspect to really stick. And I think we're in the same situation here. We are typically in analytics in that very early period where the first CIOs who are coming up and and so on, early 90s. And speaking of that, John, we are now going to talk about, not CIOs, but Chief Analytics Officers, Chief Data Officers, etc. Because we did promise that we're going to talk about the future of analytics leadership and I put a number on it but it's not really needed. But sort of in the last five years or so, we've seen an increasing number of these Chief Data Analytics Officers ascend into C-suite positions. But in my opinion, it's still a challenge for most executives committees to understand what they do with these senior analytics leaders that are a little bit different, and also their functions. Let's start with a definitional question. What should the main purpose and remit be of Chief Data and Analytics officers?
John Thompson 30:16
It's a great question. And I'm going to take it back a few steps before I go forward on the answer. It's that if we look at organisations, you think about accounting: It's been with us for thousands of thousands of years. Goes back to the Sumerians and 5000 BC. You look at distribution and supply chain and price management. Thousands and thousands of years. IT and technology and the use of computers has been around for 50. So when you look at organisations, there's a well understood culture around supply chain and accounting and manufacturing, because they've been around for a long time. The thing around IT and technology: It's just a few decades. So, it's not very well understood and there's not a whole lot of culture around it as there is with the other functions. Now, data and analytics, even less. We're talking a handful of years. So, it's not surprising that these things are not well understood in the C- suite and at the board level as well. So with that as a contextual backdrop, let's go forward. So data and analytics from a Chief Data Officer, the way I look at it: That's a defensive position. That's someone that's setting up the data architecture and the governance and how the data is going to flow and where it goes from SAP transactional systems into analytic environments or into reporting environments, and ultimately into analytic environments. So Chief Data Officers, in my opinion, is the first evolution in this role. And it's a defensive position. It's a foundational position. It really doesn't drive much change at all. Now, Chief Data Officers are screaming around the world saying ''Hey, we make a difference and it's all good and you're you're diminishing our value''. I'm not diminishing your value, but it is a defensive kind of position. Chief Analytics Officers, now on the other hand, that's an offensive position. Those are the people that are going to take the data, they're going to integrate it together, they're going to do all the things that we were talking about and have been talking about in this conversation and the previous conversation. I see that as an offensive position. I see that as someone that comes in and makes a difference in the price model of the organisation, the distribution model, the partners that they choose and work with. I see the Chief Analytics Officer as someone who is an active forward-looking, proactive change agent.
Jonas Christensen 32:37
Great. And that concept of defensive and offensive is actually really important. Listeners, if you haven't heard of it before, I encourage you to Google that because it is a really helpful framework for you to use in your organisation. I think Randy Bean and Tom Davenport originally came up with that, from memory, John, So there's some good articles on that particular topic out there. So I encourage you to go and read up on that. Thank you for introducing that, John. So we now have an idea of what the role of the CDAO should be. And as I said before, it's a new role on the Executive Committee. People are trying to figure out what does it do. Its peers on the executive committee, but also the boss there, the CEO is trying to figure out ''How do I use this person and their team in the right way? We've promoted them now''. Well, hopefully, they know that since they move them into that part of the organisation. However, there is always a challenge of creating a bit of staying power. So how do CDAOs create staying power and continue to earn their rights in the C-suite, John?
John Thompson 33:38
It's a difficult concept, a CDAO. And I kind of see it in a historical context, like a Vice President of sales and marketing. If you look at most of the people that have those roles, they're salespeople. You know, marketing is an afterthought for them. And I hear people screaming about that too. But that's generally the way I've seen it. It's that if you're a VP of sales and marketing, you're selling and marketing as an afterthought. Same with the CDAO and this is an evolutionary period we're in and this is a function of the time we're in. If you're a CDAO right now, you're generally a Chief Data Officer. You know, you're focused on the infrastructure and the governance and the servers and making sure no one's stealing your data. You're really not doing any AO work. You're not doing any proactive analytics work. And that's just the nature of where we are in the evolution of the function. So I'm not a fan of the VP of Sales and Marketing title. I'm not a fan of the CDAO. You know, at this point, if you're a Chief Data Officer, then your title should be CDO. A CDAO, you know the A is usually not very well addressed at this point. And you probably should take that off the table until you're either ready to focus on that, if you've got the data in place and the architecture working enough, you can do the analytics, then sure you have the CDAO title and you've earned it. Or are you going to have a CDO and a CAO. Either way works but at this point in time, I think the CDAO is a poor title to give someone.
Jonas Christensen 35:01
I think you've partly answered my next question, which was going to be: Should we have a Chief Analytics Officer or a Chief Data Analytics Officer? Should we have the two roles split basically or in one? And I'm hearing you say split. I will tell you I'm very glad you're saying that, because I think it is easy to understand why it's seen as a continuous skill set. But in my opinion, it's very different skill set.
John Thompson 35:26
Yes, the people that are Chief Data Officers are not very good Chief Analytics Officers and vice versa. There are two different skill sets. Now we can see for all the reasons that we've had in this conversation, why people could say ''Hey, it's data and analytics. They go together like peanut butter and jelly''. Not so.
Jonas Christensen 35:42
No, and I am one of those people who would not make a very good Chief Data Officer, I can tell you. But I would very happily take on the Chief Analytics Officer role, because I see that as my wheelhouse, of course. But it's also - you used terminology of ''Offensive'' and ''Defensive''. Another lens to it is a business function, which is the analytics function versus a - I have to be careful here what I say - but it's more akin to a technology function, which is the data function. Which is they're getting the data in the right form and so on. And I'm lucky in my organisation, that's exactly what we've done. We have taken the data function and put it in the IT department and we sit much closer to the business. That's the way it actually should be, in my opinion. And I'm very happy to continue to have that split.
Hi, there, dear listener. I just want to quickly let you know that I have recently published a book with six other authors, called ''Demystifying AI For The Enterprise: A Playbook For Digital Transformation''. If you'd like to learn more about the book, then head over to www.leadersofanalytics.com/ai. Now back to the show.
Okay, so we've established now what kind of roles we should have. We should have two of them. Now here is the contentious question, John. Should both of these roles ascend to the C-suite or only one of them?
John Thompson 37:04
You answered the question earlier. And I'll just clarify and extend what you already said. The Chief Data Officer is not a Chief Data Officer. They can be the VP of data architecture and Director of data architecture, whatever. No, they should not be a C-level function. And it can go on the IT department and be very effective there. The Chief Analytics Officer is a C-level function and it should be in the C-suite. So, you need the data and you have to be able to access it and use it. But that's not a strategic function.
Jonas Christensen 37:34
Great. So we all agree here. That's good. Now, John, we talked a little bit about it, but it seems like there's, sort of, still a little bit of a glass ceiling for us data analytics leaders to break through. What will it take for us to break through the ceiling in any sort of typical organisation? I know, it's very broad brush and it can depend on the organisation and so on. But what does it really require of us? That's really the essence. What are the habits, behaviours, skillsets that we must present to the business for that glass ceiling to be broken? And what does it require of those organisations that we work in for that to happen?
John Thompson 38:13
You know, typically, it comes down to a few different things. Desire: You have to want to do it, because there's a lot of people that want to be in the C-suite. So, number one: You have to really want to do it. Number two: You have to focus on business value. And we've talked about this before, all our projects, all the projects I do are denominated in the dollars of the people that are making the decision. Where in the United States, it's denominated in dollars. In Australia: Australian dollars. In Japan: yen. All across the European Union: euros. And in the UK: Pounds. So you have to really think as a business person. Why would we do this? If you don't have that lens and you don't have the desire, then you don't deserve to be at the C-suite. Another thing that has to happen and this is really a message that most people don't want to hear is that time. Time has to evolve. There's a variety of people in the CEO level, the COO level, that are in their late 60s and 70s. Those people have to go away. There was an article written recently. I can't remember who wrote it but it was a very, very popular person that said ''Look, you know, boomers, you gotta go'' and I agree. It's time for those people to retire. And until that group of people leaves the C-suite, you're not going to see people from a data and analytics lineage ascending to those positions of power.
Jonas Christensen 39:33
Yep. So we got to have some patience as well. That's good. I think you covered lots of good stuff in there, John. So it's in summary, a lot up to ourselves, but only partly. There is a lot of luck involved. And we can say that organisations are structured logically and it's all been done objectively, but it's been done by humans. So there is the desire for people to stick around and there's the desire for people to look at their own patch and take care of that, and so on. So all of this matters a lot. And I think there's also a very simple thing that people overlook. It's that for new roles to report to a CEO, oftentimes somebody else has to go, leave because they don't want 50 functions reporting to them. They might want seven or eight, typically. It's probably sort of where the middle of the bell curve is. It has a very steep decline. The bell curve, but it's probably somewhere between 5 - 10 really, typically, and anything else than that is unrealistic. So if there's already 10 on there, good luck, unfortunately. Now, you've already mentioned that we see other executive functions also going through these sort of teething issues with the ''Do we belong to the C- suite? And what how should we be structured?'' and so on. And one of the fixes that we see is to combine more than one function under one executive. So we see these functions coming up like Chief Customer Officers, Chief Experienced Officers, Chief Digital Officers, Chief Marketing and Sales Officers, etc, etc. Are there any appropriate combinations of Chief Analytics Officer and something else? So, analytics and strategy, or analytics and digital experience or anything like that? Or should it be just its own function?
John Thompson 39:41
It's a good question and there are certainly proliferation of very fancy titles out there. Many of them just come down to sales, marketing, operations, manufacturing, but with the new label laid on it. You know, my view of analytics, and we've talked about this a little bit is that it is a data-driven, innovation function. So you know, you can put user experience in there. You can put customer experience in there. You can put your digital transformation in there. So the analytics function really should be a digital innovation function focused on driving transformation. So you know, all these people that go out and do digital transformation from a process perspective, no disrespect meant, but I think that's why we see 2/3 of digital transformation projects fail. You really need to understand what's driving the business and you need to do it on a routine and regular basis. So, if your organisation can understand and ingest innovation and understand the output from data and analytics, you will transform and you will do it organically. And you don't need these $500 million Albatross projects that fail at monstrous rates. The analytics function really should be an innovation-driven transformation function.
Jonas Christensen 41:26
I think a lot of people listening to this will have seen some of these million dollar transformation projects and 2/3 failing is probably also about the right statistic. So, John, looking at that, if you pick your generic transformation programme that's about to fail, how could analytics, sort of, help save that or how could we have done it differently?
John Thompson 43:02
I'm not sure analytics can save those projects. If they're well down the road, then they're going to have to crash on the rocks on their own. But the different approach is going at it and understanding the business. What's happening in the business today? I've had many conversations over the last four years with people that are like - they talk about tribal knowledge and they talk about how the business operates. When you really look at the data that shows how the business operates and how people interact with it, not only donors and patients and partners, they operate dramatically different. We just went through a huge pandemic that has changed the way the business operates. And businesses have been around for five decades or ten decades, you know, they evolve. So this tribal knowledge gets out of date fairly quickly, especially in the cycles we've seen now. So, I wouldn't use data and analytics to fix these programmes. I would shut them down and try to save as much money as possible, and I would turn to data and analytics to understand what is true, what is happening in the business and what are the insights that we can gain so we can change the business, so it operates more efficiently and effectively.
Jonas Christensen 44:08
Very clear. So it's really about analytics, measuring upfront and helping to understand where should we embed these transformational initiatives rather than ''Big Bang: We think we know it all upfront'' kind of programmes. Now, John, before we finish up, I have one important topic that I want to cover with you. And because the other day you were talking about having a job versus having a career, and I thought it was a really astute and interesting observation that you made around this. Could you share your views on having a job versus having a career and what that means for all of us?
John Thompson 44:46
Sure. Thanks, Jonas. Appreciate the opportunity to talk about this. I have always been just absolutely focused on data and analytics for the last 37 years. It's my passion. It's what I love to do. It's what I want to do. And I've observed that other people come into the working world and they just want a job. They'll do anything you ask them to do and they're happy about it. And a lot of these people are in the IT organisation: ''Stand up the server'', ''Administer this database'', ''Go over and work on network security''. That's a job, in my opinion. Going and doing different things and those can be very fulfilling. I'm not against people having a job. That's not what I'm saying. But what I'm saying is that if you have a career, you have a focus. You have a passion. You have a desire to do things. And when you have people who have jobs, trying to manage people who have careers, you're going to have a natural mismatch, because the people who have jobs see no issue with asking you to do something that has no relevance to what you were hired to do. You know, like, sometimes they'll say ''Oh, we want you to go build this website'' Why would you have me building a website? It makes no sense. You know, it's a difference between just doing what you need to do to get a paycheck and doing what you love to do. That's the way I look at it.
Jonas Christensen 46:02
Yeah, so talk to us about this scenario of the manager who is in a job and the employee that's in a career. So the employee there, what should they do when they're in that situation? And how might it affect their so-called career when the manager is in that situation? Other than what you've just described, of course.
John Thompson 46:20
Yeah, I will maintain that, you know, if you have a manager who has a job and you have a career, your career is about ready to be disrupted. So it's just dangerous, because these people will not take your best interest in mind. You know, their best interest trumps all. So whenever they see the wind blowing in a different direction, that's where they're going. So those people are dangerous from my perspective and management realm. So if you're in that situation, what I would suggest you do is look around the organisation for someone who has a passion that aligns with yours. Find someone else in the organisation that has a view for data and analytics and start building a relationship with them and hopefully move over to work with them. Or you could just sit it out, you know, because people who have a job will move, and there's a lot of fluidity in the job market, and they'll go wherever they think they can make the most money. So either find a place to go in the organisation or find a place to go outside the organisation or just wait them out. Those are the paths that I see.
Jonas Christensen 46:20
Very sage advice, John. And I think this is a great place to round off the podcasts, because that's something we can all go in and ponder. I will hazardly guess that if someone is listening to these sort of podcasts, they're probably more in a career than a job. They need to think about what that means for them, but also what it means for them when they hire people into managerial roles and what they want to create underneath that manager. Thank you for that. John K. Thompson, thank you, again, for being on Leaders of Analytics, not once, but twice. It was an absolute pleasure to do this again. And I know you're writing a new book, so maybe there'll be a third time when you have that ready and you want to present that to the world. We're very happy to have you back on here. It's always a pleasure. For now, enjoy your day and thank you for your contribution.
John Thompson 48:08
Thanks, Jonas. We'll talk again soon
