Episode:
55

John Thompson: The art of building analytics teams

Loris Marini - Podcast Host Discovering Data

Analytics requires a real involved effort across the entire organization. So what does it take to build team that not only understands your organization's data but the analytic and business requirements as well?

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Why this episode

Data and analytics is a team effort that involves everyone across the entire organization. It's not just about finding someone smart who is knee-deep in the data and wants to step up. Not just the data engineers, database managers, or data scientists. The business also needs to get involved.

So what does it take to build a data team that not only understands your organization's data but the analytic and business requirements as well?

Today I learn from John Thompson, a best selling author, analytics innovator, data thought leader.  John has written two books, Analytics: How to Win with Intelligence, and Building Analytics Teams: Harnessing analytics and artificial intelligence for business improvement.

John has also worked consistently and diligently to increase access and understanding of data and analytics at the high school and college levels across the US educational system.

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Episode transcripts

Loris Marini: How do we build effective data teams?

Today I speak with John Thompson, bestselling author, analytics innovator, and data thought leader. John is the author of analytics, How To Win With intelligence and Building Analytics Teams, Harnessing Analytics and Artificial Intelligence for Business Improvement. Both links to those books are gonna be in the show notes as usual.

Just a bit of context. John has worked consistently and diligently to increase access and understanding of data analytics at high school and college levels across the US educational system. He regularly is featured on the Top podcast, the Top Data podcast that I personally follow, and I've learned a ton from him over the years, even though this is the first time, actually the second time that we meet live.

really the privilege of having a conversation with you, John. So thank you very much for making the time and welcome to the podcast.

John Thompson: Thanks Loris. I'm so excited to be here. You get rave reviews from people who not only listen to your show, but are on your show. And a number of people had brought it up to me. They said, Oh, if you haven't been on his show, you must and when you reached out, I was really excited. So happy to be here.

Thank you.

Loris Marini: That's good. That's good. The word is getting out there. That's exciting. Cool John, so help me understand I'm trying to really picture the person we are talking to today the primary audience for this conversation as I was going through the, that exercise, I thought of myself when I was leading a team or a stepped up to lead a data team cuz.

Typically happens, you're not born leader. You'd become the person. I go, Okay, let's build a data team. And one of the biggest challenges when you are in that state is that you don't really know best practices. You don't know who to wire first. You follow your instinct and. Bit of gut feeling, a bit of experience 

you work things out along the way, which is great, but also sometimes came backfire. And you you might end up losing a little bit of time or hiring the wrong people in the wrong water. 

So for. People that are in that position that are not, veterans, they're not, they haven't been 20 years leading data teams, but they find themselves building one. The business started to start believing in data. They've got some funding, they have the mandate to do it. They wanna do it well. Where should they start?

John Thompson: It's a great. 

And it's something 

that I've thought about a lot over the last 5, 6, 7, 10 

years. was working 

at Dell and I was traveling all over the world, and I hit about the three, three year mark 

and I thought, I'm talking 

to all these C level executives, 

CEOs, CFOs, COOs, and 

every time I talk to them about starting an analytics team or a 

data team, you could 

see the body language tighten up, you could see them get tense, and I thought, Wow, there's something 

really here.

I see this 

over and over again. So that's why I wrote the first book, Analytics, How To Win With Intelligence. And that book is really a primer for business executives to understand who to hire, when to hire them, how 

much to pay what to expect, 

what not to expect. And how long it's gonna take.

So that was the 

first book and the second 

book was for all the things that you just said. I've done this a number of times. I've built analytics teams 3, 4, 5 times, and I've made mistakes. Every time. We all make mistakes. That's the way it is. Unless someone is there mentoring us and teaching us, and this is really 

still an art.

It's, this 

is not something that's been done for 40, 50 years. This is been done for maybe 6, 7, 10 years in big organizations. We felt our way around for the last 30 years, but nobody's really systematically done it. So that's why I wrote the second. Building analytics teams is because people don't do it well and they make, I'll started some mistakes and 

I thought I've made 

all the mistakes, so let me write it all down so people don't have to do it by trial and error.

Loris Marini: Yeah. 

Fantastic. 

we need, that, we need more of 

that. There's definitely, 

I know why. why can't you just absorb the 

knowledge and insights of someone 

that already figured out 

how to 

do it or, how not to 

do it?

John Thompson: Yeah, more than likely.

Loris Marini: More and more 

likely. Tell me a 

little bit 

more about the context.

If you imagine hopping on a time machine and going back to the day that you resolved to write the first book and 

how you felt 

what prompted you, perhaps 

emotionally or what were you thinking 

when you said, Okay, that's it.

I'm gonna dedicate two hours every day to write this bloody book,

John Thompson: Yeah, I I remember 

the day 

vividly. I was having 

a conversation at 

Dell with my management 

team, my leaders John Swainson and Michael Dell, and they were telling me some very confidential information at the time. They said we're gonna, Sell your division and 

many others to get the 

cash to fund an acquisition of emc.

And I said, Okay what do you 

want me to do? And they said, Keep running the division. Keep running the business unit. Keep doing what 

you're doing 

Until it gets sold and then you'll be very happy. 

And I'm like okay. I guess it's a leap of faith. 

But I 

thought I don't have to work, as many hours 

a day as I had been trying to build the business cuz we're just gonna run it to sell it, which is probably gonna happen in just a few months.

Anyway. So the next day I sat down 

at my desk and my wife 

had been urging me for months at that point to write a book. And I 

thought, what am I 

gonna do with this extra time? And I. 

I'm gonna 

write a book. So I started on it and I didn't really tell anybody, I didn't say anything cuz I thought, I probably won't be able to do this.

It'll probably fall apart. I'll fail, I'll stop, I'll lose 

interest. Something 

like 

that. And my wife asked 

me a couple weeks 

later, she said how, what do you 

think about this idea of writing a book? 

And I said I've written 

two chapters already. And she just threw her hands up and started laughing.

She goes, Only you would just start and not say anything 

about it. 

That's how I did it. So 

I thought I've got 

a lot to say and I got free time, so I might as well.

Loris Marini: Yeah. On behalf 

of the data community, thank you for taking the 

time because I'm going 

through a sort of similar process now where I'm collecting my ideas. I would love to write a book, but the more I think about the structure, the more I realize how big of an effort that is and running out on top of the podcast can be a bit 

challenging.

But, 

Thank you for writing the book and that's what I wanted to 

say because we need this 

sort of stuff more. Honestly had read the 

full book to date. 

It's on my reading list, but 

I did skim it through and 

I wanted to dive more into the structure, the second 

book, the building 

teams.

So let's imagine. 

we work in an organization 

that 

understands the data is 

a strategic imperative that has to be managed well, that there is a ton of business benefits and value that they can extract from it. They don't really have a proof of that, so 

they, but they believe 

in that enough to say, okay.

Let's try, let's give it a good 

go. Let's, dedicate some 

funding and build a team. And the first thing that they need to do is hire somebody or maybe get someone internally to step up. 

Smaller companies 

have seen a lot of the engineers and the software developers being the, typically the ones that step 

up and go Okay, yeah, we've been 

dealing with databases for 20, 30, 40 years.

Surely we can figure out how to do data. 

assuming there's a lot of many more 

scenarios, but what would be your advice to the business looking to set up that function?

John Thompson: Yeah, it's it's a, it 

is a plausible scenario and 

we see it over and 

over again in Fortune 500 companies and smaller 

companies companies 

of 

all sizes. And it is 

something that's very pertinent and relevant to talk about today. And we do see a lot 

of that. Hey, can we 

find someone who 

is smart and.

steeped 

in our data who wants to step up and do more or different. And usually what happens is that doesn't work out too 

well. 

There's a lot to building a data team. There's even more to building an analytics 

function. And it's not 

just understanding 

your data it's about understanding.

Analytic requirements 

about understanding business requirements. It's about understanding the people that are gonna be involved in it. And it's 

not just, the data engineers 

or 

the database managers 

or the data 

scientists. It's really the business 

too. 

The business needs to 

be involved the subject 

matter experts line of business.

It's a team effort 

and analytics 

is not 

something that is a solo kind 

of 

journey. It is a real involved 

effort across the entire 

organization. So my, council 

to anybody who's doing this is go out and find a leader. Go out and find someone that you're gonna 

hire at, a vice president 

level executive director, level senior vice president, whatever makes sense in your organization.

Hire a leader. And have them design it in conjunction with the other, their peers that they're at the VP level design this function with the other vice 

presidents because the success 

of the analytics and the data functions are tied to the commitment of the vice president of supply chain, the vice president of sales, the vice president of 

manufacturing.

If those people 

don't partner with this new leader and this new team. They are not going to have the impact that is expected.

Loris Marini: I love that you're targeting VPs in particular, right? Is it because heads and leads are too, perhaps technical, the not. After the business and executives are too close to the 

business and so VPs sit in 

that sweet

Yeah I use those titles very purposely as you noted, Loris, you need to 

John Thompson: be at a 

level where those people 

have impact and control of the operations of the business. 

Some people would say, Hey, you can hire a chief data scientist or a chief data officer, and they'd be at 

the C level and I would 

still be advocating for the same kind of activities.

But what 

you need is a partnership between. Data analytics 

and the functional areas of the business. Because if this is really gonna make a difference, if you're really going to see value realization, they're gonna have to change how they do business. They're gonna have to change the processes that run the supply chain.

They're gonna have to change manufacturing processes. And if they're not willing to do that, and you as an analytics professional, do not. have The responsibility, authority, or ability to change those processes if you're not doing it in conjunction with those business operators, value realization will not happen.

Loris Marini: Yeah, 

absolutely. 

Let's imagine that we are, flies on the wall 

a room where the conversation 

is happening. 

The business is committed 

and there's a bunch 

of VPs and and a few 

C level folks. They're all together 

trying to run a discovery session. maybe 

there's a candidate to lead a function.

What do you think, in your experience is the number one or maybe the top 

three sort of sources of 

resistance? 

What kind of What kind 

of hurdles can we expect when we say, Hey, you gotta change how you do stuff. And we gonna, do we gonna be data informed? Are we gonna build teams? And you'll have to build relationships.

Is that something that is well received by most people and or do 

we have to think about 

creating an incentive structure before we even start having that

John Thompson: Yeah, usually that's not the setting that I. 

the VP of analytics 

or the VP of data, having a conversation with multiple other people. It's usually a one on one conversation. And usually the way it goes is that you sit down with someone and you 

say, Hey, you've been.

Talking 

about this, supply chain 

efficiency or manufacturing yield or pricing or something like that. You've had a strategic imperative and the CEO's been pressing 

you on it. I think I 

can help you 

with that. Oh really? 

How can you help me 

with that? And we have 

a conversation and start talking about data 

informed analytics 

and process change and those kind of things.

And then people are 

like, Okay I'm getting 

interested. I'm a little bit more intrigued in what you have to. And then there's usually a conversation about a project or a program, 

and I do this 

very 

thoughtfully too, is Okay, we can do 

this project and that's where we'll get 

some data, we'll have 

some of your subject matter experts 

work with us and we'll, bring some 

things together.

We'll do like a proof 

of concept or a pilot, and we'll come back in four to six weeks and we'll have a conversation about what we think can be done with the. And we do that, and we do an exploratory data analysis, and we come back and we show them some insights and ideas and statistics about the reality of the business, how the business is really running, as opposed to what they think the business is doing now.

They usually walk away from that project. Really excited. But it's a project and you haven't done anything to change the business and you haven't asked them to do anything other than let you have access to some systems and maybe a person or two. Very low level, 

low risk. They're not 

putting anything on the table 

really for any for the 

most part.

But I have a conversation all the way through that project about a program. And a program in my mind is where now we're 

really partners. 

You're going to give us access to systems and people, and we're gonna do analytics and we're gonna come back and we're gonna show you how you may have a five step process that really could be a two step process.

Or maybe these two systems need to be integrated or maybe how you're doing personalization on the website needs to be changed. Something that involves a pretty substantial process change or a digital transformation. Usually you leave that project, you have that theme of conversation about program. And then six months later, that person comes back 

and says, those insights 

were really useful.

We really 

found out about some customers or 

operations or, production of waste or 

whatever it was. And we're really interested in possibly changing how we do business. How would we do that program? So then you have a conversation about a true partnership, an investment, and a 

real program to deliver 

value realization.

That's usually how that conversation goes, and it usually happens over from the first conversation until you have the conversation about the program is usually about three months, and then the program delivery is probably six to eight months later, and 

then the fun starts, word 

of mouth starts kicking in, and then people come back and go.

You worked 

with those guys in supply chain and really made a difference for them. Why don't you come over here in pricing and work 

with us,

manufacturing's 

got some real problems over in Germany. Can you give us a hand? So that's usually how those conversations go.

Loris Marini: So proof of value before you dive in with a massive architectural design and the long list of cloud providers that you should partner with. Absolutely. I think that's the critical step. So you said two or three months? What is the best way in your experience to round 

that, proof of concept 

phase in 

terms of do you need 

to have a already a bunch of 

analysts and data engineers, 

or should you 

really treat it as 

viable product?

So it doesn't matter if it's stitched together, it doesn't matter if it's an excel as long as you prove value, because that's not the end goal anyway.

John Thompson: Yeah. I'm a, I'm 

making some assumptions in this conversation. Of 

course, Loris. I'm assuming that, you've already 

hired your team. You've got 'em sitting there, you've got your tools ready to go. Your data 

scientists are onboard 

and 

operating and those that's the 

environment 

you're in.

Generally 

we don't do anything in. Excel We We assume 

that if we do something once, we're going to do it a thousand times. So we don't build things on one off bases. We don't fully automate 'em 

either. But, we do everything 

with databases. We do 

everything with enterprise grade tools and those 

kind of things.

So we don't really ever 

think of a minimum viable product 

in a way that we're gonna throw it. away We're always building things that we assume we're gonna use 

again. We've done 

a bunch of work in the 

last year on donor segmentations. 

We're in a business where we 

have donors, giving us 

plasma donations.

We've also done a bunch of work on customer and center segmentations, and all those things have been built over the last four years, and we never built anything that we threw away. We always built little building blocks that were then used later on. 

There's a related 

thing that people talk about, and it's true for the most part, for many shops, is that data scientists and analysts spend most of their time acquiring and integrating and managing data.

Yeah. Okay. People talk 

about 80% data integration and data acquisition and 20% analytics. 

I think if 

you run your shop in a certain way, that is true. If you run your shop in a different way, that is not. because what we've been doing over the last four years is automating away all our data integration work and building it into a unified repository.

So we have data objects for pricing. We have data objects for donors, we have data objects for customers. We have data objects for factories. So you know, 

when we are, getting into 

different projects now, It's hardly ever that anybody's 

going back and acquiring 

data. 

Of course, we bring in new data all the time, internal and external, but most of the data we use has been acquired.

It's been cleaned, it's been integrated, and it is brought in on a daily basis and 

integrated into our data repository. 

I don't want to use data lake or data warehouse. Those are loaded terms. it's in the back end somewhere. my data scientists are probably on any one project are spending 15, 20% of their time on data acquisition and the rest of the time they're doing analytics.

Loris Marini: Yeah. Yeah. So it's interesting 

you say that. So you're assuming also at certain level 

of maturity in the organizational 

data 

maturity. 

being too scrappy. It's okay to prove 

value and treat things as 

projects to demonstrate that there's business value and then turn into a program.

But you wanna start with the right set of foundations. You don't wanna. Do work for the sake of just showing some numbers to 

somebody and convince them 

that 

there's value. 

You wanna actually build 

the thing a list in its ins basic form. 

of course that conversation would change 

quite a lot in in startups 

or scale up.

So any sort of organization that is trying to figure out the part of the market fit that has, or the 

other way like a big 

enterprise that has been operating for a long time and they. Run on systems that have been in cobble have been written 40 years ago. databases don't talk to one another. So you could go to the VP as a VP to another VP and say, Hey, I know your struggle.

I can 

help you. Let me show 

it to you. But it won't take you three months to get to something that actually works. It'll take you three years if 

you're lucky. So in that situation it's tricky 

to balance, 

scrappy do 

you have to be so that the other person doesn't lose their patience and you get to actually 

demonstrate this has

value and 

then he rate on 

the second time.

John Thompson: those exploratory 

data analysis really change 

people's minds, because you'd notice, 

or at least I've noticed over the last four years in my 

current job, most people 

have been in these jobs for a 

long time and they have. Perceptions 

of how the business is.

And when you show them how it's really 

running, either better 

or 

worse from a data perspective, 

it really gets their 

attention. They will 

call you outside of work hours and say, Hey, I finally got to that email that you sent me, and I'm a little blown 

away by it. Can we.

Loris Marini: Yeah. Interesting. John, let's dive into that process. Slowly. Going backwards from that email is, I think, a critical point. It's a pivot point where you got someone attention. And attention. This days is really hard to get. We're 

bombarded with 

What seems to be an infinite stream of 

priority one attention 

requests.

Only a few of them are actually P ones as we know, and it's very hard to tell '

em apart. So someone that goes Hey, your 

email was really insightful. That is a massive win. Now how do we get to that? 

kind 

of skills do we need to put in place? What kind of people to get at the bottom of the actual business 

need that, that department, 

that function has 

right now?

Does that 

happen in a water cooler? Is that a conversation that we can start there, or do we 

have to, intentionally 

schedule 

John Thompson: You have to be very intentional about 

those things, and it usually 

starts one, one step back. People ask me this all the time. It's like, 

you're working in 12 billion 

public 

company, how do you 

decide what to do first?

It's fairly 

easy to decide what to do first because the CEO and all the C level executives are talking about the challenges that the 

corporation has. Make more money, get more product out of the material 

you have. Serve more 

patients, make the donors, more comfortable 

and have a 

better experience.

Those are 

the things you work on. 

You don't have to create new things to work on. There's plenty of challenges in every corporation, so you go to the people that own those strategic problems and you sit down with them and have that project program conversation and they will tell you in the first 30 seconds that you're sitting with them the first thing outta their mouth is, I'm really struggling with this, and that's what you work 

people are 

always asking me, How do you prioritize?

It's what are the 

problems that the business has? And that's what those business executives are charged with fixing, and that's what they want you to help them fix. So that email comes from, The conversation you had the commitment, you 

got to do the project. 

And that email is the summary of the project, and that's usually when you get 

people's attention.

Loris Marini: of course, 

like I'm imagining there are two scenarios. One, you do get that email and so there's an interest and you demonstrated that you know you can help. The other scenario obviously is that email doesn't come in, whether it's because you know you didn't have the right. 

raw ingredients, 

the right data to do what you wanted to do.

So in terms of risk management, for those that want 

to, Hey I want to 

make this work, I want to help you. So after that conversation, now I know what you're struggling with. 

I wanna make sure that first project is successful. Because if I I don't get 

there, nothing will ever happen. You will lose interest in.

You're like, Why do I have to work 

with you? You clearly 

can't. So I need to 

manage my risks how, the 

way that I would do it, 

first off is once you 

understand the business need, then try to brainstorm what kind of data would you need To solve the problem, of course, there's no way to tell a hundred percent, but you can always make hypothesis and then test it out and rule out, just focus on 

that 20%.

Then you need to get access to the data. And so there is a partnership that you have to have with IT or with 

engineering which might 

not be that easy in large companies. And then you need to put together the 

team starts analyzing 

this data and meshing it all together to try and answer that question.

you would also need a validation step where somebody with a domain expertise looks at what you've done 

and goes Oh, this is 

interesting, or, No, this doesn't make any sense at all. Or perhaps you wanna do that earlier. 

Not sure. I'm just brainstorming here. How would 

you manage risk in that critical

John Thompson: Yeah,

 I've been doing this for nearly four 

decades I have a finely tuned ear to, how people 

describe problems that they have and finding elements of those problems. Can deliver success.

Now that's just me and other people can other people have that skill and you can develop that skill. So you know when someone comes to you and says something along the lines of, Can you predict the mindset of every donor that's gonna come in? And we have a million donors coming in 

every year and give 

me insight into how I can make each of those donors happy.

The answer to that. 

I don't have insight into people's brains. I don't know what thinking. I can't do that project. That's magic. I do data science, so if there's data. Generally there's a good chance we can make it 

work. You lead them 

in a direction 

from this, magical idea 

that they've put forward into something that is data driven that you know, you can get data for, that you can do some work on that you can deliver result that is going to drive their business 

forward.

Often there's 

something related there that they're not saying that is an 

underlying driving force. 

Now in this scenario, in this 

situation the way it 

was 

phrased was, neural implants we don't 

have 

those but we do 

know how people respond to our marketing. We know how they respond to our pricing.

We know how they respond to poor experiences that they have, and all that data is at 

our fingertips. So we can 

collect all that up and do a project that comes back 

and says, the driving 

forces in donor experience in our business are price, experience, 

and, 

customer comfort or donor satisfaction, or something along those lines, or center 

efficiency or employee 

training or something of that nature.

And we actually did that and we came back 

and said, if you want 

to improve x. You need to work on Y, Z and W 

and they that, I got the 

response to the email like, 

holy cow. We've been 

hypothesizing and supposing and theorizing about this forever, but you've actually quantified it, so let's talk.

Loris Marini: Yeah. 

Interesting. That you just 

mentioned is the magical formula to sell anything. If you want x do y. 

It's incredible how well it works. It works with kids too. I don't 

John Thompson: Yeah. 

Loris Marini: know , if anyone 

has 

kids in the audience. But I definitely 

try 

to mine and 

surprise works like a jam.

If you want x you need to do y. Any other thing, any other attempt to try and explain it, Rationalize it doesn't work. You tap into the want and then you tell 'em what they need and the doors, the neural pathways are open

John Thompson: That's right. That's right. Yeah. Our kids are 22 and 25 and it worked 

pretty well and continues 

to work well as this day.

Loris Marini: Cool. 

let me 

recap. So we talked 

about the process of starting a project to demonstrate business value. So creating new 

initiatives, new new use cases 

for data and 

analytics, and get 

to that pivotal 

email that demonstrates 

that there is an interest in that business function to become a partner.

With the data analytics function and explore more and do more, and actually share resources, create a shared budget or whatever needs to happen to 

make sure that collaboration 

keeps on going. Is that it?

John Thompson: No, there, 

there's, there's always more that's how 

you get their attention. 

And then after 

that, and then you get into the programs and you start building things that actually 

show them, how value 

can be 

realized. We're working 

through a project right now.

Where we did forecasting, at a very detailed level and everybody 

came to us 

and said, hey, we only need this, at the day 

level for all the centers for the next eight months. We actually did it at the hour level for every center for the next two years. And 

they said we 

overkill.

We'll never use that. And we said, Yeah, we know you won't, but we are going to. And they're like, What do you mean you're going to, So we did a project then where we 

looked at the arrival 

patterns of all the donors in our centers. And then we went back and we redid all the labor scheduling gonna

Loris Marini: Oh, smart

John Thompson: save tens of millions of 

dollars a year.

And we 

tested it in a small set 

of centers. We tested it in a bigger set of centers and now we're working to roll it out across all those centers and it's gonna save lots and lots of 

money. 

Loris Marini: And And you probably don't need deep learning for that, right?

John Thompson: Statistical 

analysis, correlations. Now that kind of stuff works just as 

well. No one cares 

what it's built on.

They don't care that the forecast is a neural network. They don't care 

that the, that the labor scheduling is K means 

they don't care about 

that stuff. What they 

care about is that it's gonna be tens of millions of dollars in their budget that they get to keep. 

One project 

that we did recently that is nothing to do with analytics was that we had an outside vendor that was gonna 

charge us, a lot of money, 

hundreds of thousands of dollars a year to hand over media performance data.

And we said that's crazy. 

We want that 

data anyway. 

Media 

data on, search engine 

optimization 

display, all this different 

kind of stuff, different 

channels of marketing, execution. 

We said we want that anyway for our analytics. So point us to the source systems, we'll get it, we'll bring it in, we'll clean it up, we'll load it and we'll use it for all sorts of purposes.

And we 

saved them, 

hundreds of thousands of dollars. Our marketing team, we saved them that money, they're happy as clams. And we didn't even write, we didn't do one algorithm. All we did was load 

some data, 

Loris Marini: yeah. And 

you 

prepared the system 

so that you can reuse it 

for whatever other

John Thompson: feeds. 

It feeds every system that reports and analyzes marketing performance.

Loris Marini: So what's a business value of 

that?

John Thompson: Huge.

Loris Marini: It is huge. It's not, it is not a hundred thousand dollars. It's way

John Thompson: No. Yeah it's in that value 

calculation is into the 

millions. And that was 

just, simple. was just listening to a vendor saying what they were 

gonna do with our data. 

We're like, Eh, I think we can do better than that.

Loris Marini: You know what I find? I find that personally I got into 

this data, podcasting 

and data management and the strong passion for trying to understand how to do it well, because I saw at 

some point I had an insight. 

I understood that. If, and this was before I met Doug Laney and read his book before I talked to Bill 

Schmarzo about, the economics 

of data ai.

But I had this really, this gut feeling that there's something special about information and 

knowledge that any 

other asset doesn't 

have. And now I know that is, it's the fact 

that they're intangibles. They can be reused over and over, they don't deplete. 

And you can 

get really principle exponential ROI from them if you manage it properly.

And so that, that really got my 

interest. The idea of 

not doing things once and forget, but. Putting some work, spend some energy, some resources and time to lower the entropy the system. So the chaos data is all over the place now. We bring it 

in and we structure, 

we clean it, we use it, and every time we use it, we improve its quality because people get to see there are inconsistencies or we find other ways, things that are not quite working with the data.

And so it, it becomes this. 

This loop, 

this closed loop that you go through. Not one, not two, not three times, but hundreds of times. And every time you go through that loop, in principle, it should be very easy, should be low 

friction and all it's should 

be required from you as a data team. Is data professional, is that.

I for the 1% improvement because it's, if you combine, if you compound 1% improvements, one on top of the other, you get to incredible results while keeping your resources in the day to day limited, because it's only 1% 

improvement. Talking about 

migrating to a new system, you're just talking about, Hey, did you notice an inconsistency?

Great. 

Flag it, create a ticket 

for it. Spend that extra 10

minutes. 

John Thompson: it. 

Loris Marini: To fix it because tomorrow you'll find it, you'll find 

it fixed. If you continue 

to do that over and over, eventually 

the number, the number 

of inconsistencies gets below a threshold, a critical magic threshold with that, which I don't know how to calculate, but I know how it feels and it feels that people on the team now start thinking.

Heck, I can, I've got data 

for this. The first 

thought is not, I don't know how to do it. And you talk to people. No, I know 

the data. I know it's 

there. We can just use And that is that is a 

massive acceleration compared to, okay, let's sit down and feel, map all the 

databases. It takes forever 

to do it.

John Thompson: It does take forever. 

And Loris, you're 

actually talking about 

something that, we talked 

about 

earlier. We talked 

about the 80 20 on data acquisition 

and and the projects. 

What you're saying in different terms is that if you do this right, You flip that script, just like we talked about earlier.

You have the data. You are refining the data, you are integrating the data, and you are spending less time 

doing that on a ongoing 

basis. You're spending more time on the value added 

analytical work. Now, 

another thing 

that, that I always talk about and I want people 

to be thinking about is. 

it's not 

about how many instances of yellow buses.

You have a 

billion, images to 

train a neural network. It's about the breadth of the sources of data 

you have. We built one 

application in my current role. It's got 12 different data sources in it, in seven different models underneath it, and we're always looking for ways to add more.

So my 

view is that a data ensemble 

of multiple sources of data are much more valuable and much more insightful 

than any, 

one single source of data. Even if that single source of data has a hundred times, a thousand times more 

records. We as if you 

think about how we make decisions 

as humans, we get up 

in the morning and we.

Oh, I'm hungry. Or I want a coffee, or I'm looking outside and it's raining and I need to wear a 

raincoat and I gotta go 

to 

the store and Timmy's 

gotta get 

to school, how many sources 

of data have you just used in that scenario? 10, 15, 20. That's how we make decisions. That's how our analytical systems need to make decisions.

So as we 

integrate more and more 

data into those systems, 

they become better and 

more robust, and more reliable and more useful for us.

Loris Marini: And there lies the 

challenge John? Because 

every data 

integration. Work you do takes time and resources. 

And one of the hard things often is to, 

when we talk 

about selling into the business, the challenge really is you've gotta 

show value. You have to 

build Some sort of solid, ideally business case for whether 

anything you do when you spend 

money, right?

It has to be some sort of clear roi, even though if you can put a, you can put an exact number on it, but it should be clear what problems you're 

solving. Now, I see data 

integration problems become cheaper to solve 

because of, technology getting better 

and better. 

But there are 

other problems with that.

What I 

often see in new teams is yes, 

now you've got all 

your data in one place, 

but there is absolutely no best practice or process to 

transform it. So you get 

things like, and analytics engineers writing sequel to essentially recreate what a financial tool is supposed to have or build within the data warehouse, because now all the data is there, there's obviously a ton of ways you can do it 

wrong.

But I always find 

tricky the process of. It's not just, It's not just connecting to a new data source. It is connecting to a data source and. Making that data compatible with all the other 

data you have so you can use, 

and that, that process, I think it's overlooked. 

Often there's 

no, and it's hard 

to sell, it's hard 

to explain why that matters other than, hey, 80% of our data engineers time is spent fixing bugs or understanding why 

things break, the data pipelines.

And without those, there's no analytics we can do because that stuff comes before. 

And so yeah. , I understand the frustration of a lot of data professionals that I'm trying to do that 

John Thompson: and that's 

where I think the analytics team can help the 

data team, because the 

analytics team should have a vision of where they're going and what they're building and what they're gonna build next. Just like I 

talked about, this forecast 

that we did at a, an exceedingly detailed level that everybody said that is 

overkill.

We knew we 

were going to the next level, so we also know that we're going to the next level after that too. So you can go and talk to the data team and say, Hey, in six months, I'm gonna need this US overlay of crime data and I'm going to need it at the census track level, and I'm gonna need it across the entire United States.

And they come back and they say, Yeah, it's gonna take us a year to integrate that. And I'm like, Great, that's good. I'm gonna need it in a year. So start working on that 

now. You need to 

be 

looking out, you can't 

be 

reactive, you, the analytics 

professionals need to have a vision of where they're going.

Because if you're sitting around waiting for the subject matter experts to come and say, Hey, I had this brilliant idea about supply chain efficiency, they're not gonna do it. They're not incented to do that. They're not compensated to do that. You need to go to them and say, Hey, I got this brilliant idea about supply chain effectiveness.

Let me talk to you about it. Generally, they're gonna look at you and say, That's crazy. And then they're gonna come home, go home, come back a week 

later and go, this doesnt 

seem so crazy anymore. What did, Let's

Loris Marini: Let me get you a coffee. Yeah, 

so is that why you have a specific chapter in your book called Ensuring Engagement with Business Professionals?

John Thompson: true. You can have, 

we can have all sorts of ideas. We don't own the 

business, and I say 

this all the time, Look, I do projects, I do programs, and my job is to make these other people look better. Now, hopefully they'll stand up at some point and say, 

John's team, did all this 

great stuff.

Now if they get promoted and all that other kind of stuff, that's great. 

That's what I'm here to do, is to do things that make the business better, more efficient, more money, make the donors happy, make the patient's lives better, all that kind of stuff. I'm not there to take their glory. I'm there to make their lives easier and better, and they're not compensated to do that, so they're not gonna think about it.

They're just gonna think about, Hey, what are my quarterly objectives? What are my annual objectives? How do I do this? How do I do? 

I try to come 

in and be a support structure that has a vision for the future that will take them to a place that they probably hadn't thought of before.

Loris Marini: Boom. When I extract that as a quote and print it on my I'm going to be a support structure to the team. So it's about enabling. It's not about owning, it's not, it's about coaching for development and performance. It's not about necessarily 

advising and saying this 

is how you're supposed to 

do things.

John Thompson: Because you 

don't own it. You can't tell them how to do it. You can show them a better 

way. It's, the old thing 

about leading a horse to water, but you can't make '

em drink. And it needs to 

be of the scale that it's interesting enough 

to them, this project 

that we're doing, that we're trying to get 

implemented in labor scheduling, 

it's tens of millions of dollars.

Now, when I told people that, they said, Oh, that can't. 

We're better 

than that. 

And I'm like I don't know. 

Let's test it. So we did the first test 

then maybe that's a fluke, Then we did 

a bigger test and 

they're like, I don't think 

it is a fluke. It actually looks like it 

works.

A lot of times 

people, as we, when we first started this 

conversation, people always 

wanna be in 

the know they're the 

experts in these area. They don't want to have someone come in from the outside for the most part and say, Hey, you really 

could be, saving $35 

million here.

That's a big number. 

And if they're 

not secure in who 

they are and feel good 

about where they are in the organization, they might easily reject that. 

But when you 

come around and say, Hey, that 35 million is going right in your 

back pocket, for you to 

use some other. 

Then they're like all right, 

let's work on this.

Loris Marini: Yeah. The incentives the incentive 

is strong enough 

to

John Thompson: Yeah. I don't, my team doesn't 

have a dog in 

that fight. We're just 

there to help.

Loris Marini: Yep. So that's how we select winning 

projects is talk to business lines develop that 

we establish a genuine relationship first, 

and have some sort of, confidence 

that we can solve the problem with the data 

we have. And then so we don't 

make empty 

promises and then demonstrate 

value with the.

define timeframe. 

So a project, not a program. Get to show 

value and then the rest will come. We, when once you convince them, they will partner with and it will share resources 

and you will keep collaborating.

John Thompson: right.

Loris Marini: Awesome. 

Good. So John, help me recap 

this. I've been trying to do this 60-second summary.

It doesn't have to be 

60 seconds, but you tell me when when you're 

ready and we'll try to 

summarize the conversation 

in 60 seconds for someone that just tuned in and I 

don't have 45 minutes to listen

John Thompson: Let's do it. 

The conversation Loris and I have been having today is around the high level concepts of how to be successful in an analytics team, partnering with subject matter experts, partnering with executives, focusing on value realization and picking projects that. Are already aligned with what the business users have and the struggles that they are trying to solve.

So we've just barely scratched 

the surface on building 

and managing analytics teams, and we really tried to talk about how the data and analytics teams can collaborate together, how the subject matter experts and executives can collaborate with the analytics teams and how it can come together in a way that makes a difference for the.

Loris Marini: Boom. Fantastic. Thank you very much, John. That was a fantastic 

summary and I think we 

covered all the main talking points for us. Do you think there's 

anything left to to say or anything that you wanted 

to cover 

before we we wrap it up?

John Thompson: Yeah. I think that 

we didn't get to architecting an analytics 

team. We could probably 

do another entire podcast on 

that. That's a whole 

topic that we didn't 

touch on. We could talk 

about that at a different time. There's just, at a high level, 

there's the, there's the.

Loris Marini: you made

John Thompson: There, there's the factory team where you have individuals that are focused on individual jobs like feature engineering and model 

building. There's the 

other side where you have artisan team members that handle everything about a project. And then there's the hybrid where you have a factory team that supports the 

artisan team.

And that's 

a really interesting topic and I've spent hours talking 

about it. And in my 

current job I started out with an Artisan team, and then I supported it with a factory team. And for the last year 

I've had an artisanal 

or a hybrid 

team. So it's it's intriguing. 

So if 

anybody who really wants 

to delve into the nitty gritty of managing an 

analytics team, we could pick 

that up another time and talk about it.

Loris Marini: That sounds like another podcast, , that's for sure. . Awesome. 

Awesome. John Thompson bestselling author the two books 

we mentioned today. One is Analytics, How to Win with Intelligence. The other one is building analytics teams, harnessing analytics and AI for business improvement, both links and the 

show notes.

As usual, 

John, thank you 

very much for your time. I truly enjoy 

this conversation. I hope you have two and I'll see you on LinkedIn.

John Thompson: Thanks, Loris. I really did enjoy it. I look forward 

to our next conversation.

Loris Marini: Ciao. 

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