LIVE: 7 data management lessons for 2023 with Winfried Adalbert Etzel

**Loris Marini:** [00:00:00] This morning was three degrees at 5:00 AM in the morning. So we are not that far. Incredible. Sydney, like Norway, . So this is definitely a first I'm popping a couple links to our show on LinkedIn. If you guys are following from LinkedIn, you'll find them in the chart hopefully.

Okay, so I think links have been posted. we have 13 people joining us, 11 on YouTube, and a few more on LinkedIn. So Thank you for joining. And perhaps not sure whether you can type at the moment or you are, maybe traveling, going somewhere. But if you can type, let us know where you are connecting from. Cuz the show here is is really an opportunity to connect with all of you. And op hopefully connect all of you with all of you, create a big opportunity to connect and learn from one another.

This this has been a hell of a year. Winfrey. What What, are your thoughts? Come on. Can someone say something about our hats please. Cuz it's put a lot of effort

**Winfried:** I even got some lighting on that man. I think you do too. Alright.

**Loris Marini:** Oh, look at that. Okay. I don't go that advance on mine. I can only turn it on and turn it off. . So if I had an extra minute and a couple lines of Python, I would probably, I could automate this, but

I'm gonna leave them on,

I'm gonna leave them on

**Winfried:** took mine off there. A bit of

**Loris Marini:** Okay.

**Winfried:** too much reflection there. But yeah, it's been a crazy year for us on, the podcast business, but also on . the data business. So much happening. And you and I together, we talked to what 50 data leaders which is an impressive number. And I've, I tried to compare notes over the weekend, tried to look at everything that we've been through, all the thoughts we had all the great feedback we got from our data leaders.

And it's just so much . It's impossible.

**Loris Marini:** Yeah. And yeah, so this is what happens when you write a copy for an event. We were winfred and I have been talking to one another, what are we gonna say? What are we gonna talk

about? And obviously there's only so much you can brainstorm my email, which is good because you think, I have not, I don't have a lot of space to type.

Surely I will keep it nice and concise. But in those seven points, so we got, what do we have, let's go in order, we

have, this is the promise, right? Then let's see how much we can deliver in the time we have. But the seven points are in no particular order. Lead data is a shared asset. We share benefits and data quality is a shared responsibility. Which by itself could be like , a conference or a summit.

Network, tips to build stronger relationships with your stakeholders. Again, probably a pile of books in my read to read the list just on that sell map, the compelling business need behind your data management initiative. Yeah, we're not overselling at all.

Over promising. Over promising at all. be memorable. A simple framework to increase your storytelling. 10 x This is me by the way. So simple framework. I don't know if I can cover that simple framework and 40 minutes, but we'll see. And then the other three Winfrey, we got support, enable the business to take lead, provide guidance.

And this was one of your points we have observed as well. Don't jump to conclusions, but

observe the demographics, ways of

**Winfried:** If you have time to do that,

fantastic.

Oh,

**Loris Marini:** Yeah. And have fun. Provide a frame

framework to ensure collaborative readiness and psychological safety and encourage failure as a

means to learn.

**Winfried:** this one is really

good

because.

**Loris Marini:** so.

yeah,

**Winfried:** Says have fun. and then it comes like a long one liner and show collaborative readiness and

psychological safety. It doesn't really sound like fun when you type it like that that's our job to make it fun.

**Loris Marini:** Yeah. And Christmas hats are part of that . No. But in a in a way it is because we are not taking, we're not taking ourselves too seriously. And I think that's an important point. was reflecting on, saw a Ted video. Ted's got thousand, thousands of great videos, but one of them recently was about this person that for a lifetime spent spent like studied how to make. Marketing more effective. So it's all about the copywriting, and the words, and the action, and the verbs, all of that, right? And then one day out of fun really started to draw cartoons for fun. And those cartoons started, ci circulating at work. And before he realized it had gathered a hundred thousand plus people reading his newsletter, those cartoons turned into a newsletter.

A newsletter grew. And and eventually he ended up on a stage of Ted talking about the power of humor And

not and really how powerful it is when you have an important message to share, but you don't take yourself too seriously so that people go ah, this is not threatening, this is cool, but still they get a chance to laugh

And hear your message.

So that was cool.

**Winfried:** Yeah. Interesting.

For that today.

**Loris Marini:** hey This is not, we don't really have apart from these talking points, we don't really have a structure. So because there is no writing, there is no wrong unless someone in the chat stops us or asks a question or decides that we should talk about something else.

Which by the way, you should totally do that if you want maybe you can start random. Let's do a random number shuffling here, [00:05:00] which is not random at all because I'm doing it by hand, but like super random. Let's start with Le . I'm playing with notion, moving things around, so that tells you the level of how prepared we are for this live people network.

Let's how we

network teams to build stronger relationships with your stakeholders.

Boom. Winfrey, do you wanna take that

**Winfried:** can start with one perspective here. I had

I think two or three

episodes with public services.

and there's something about

the more you automate public services, the more you work with with data and public

services

the more connection you need to get to your citizens.

And this is an important point.

We talked about data lineage for example, and how important data lineage is to create good services

For your citizens, but at the same time

also give your citizens the possibility

to to trace those decisions that you're doing. And another aspect in that is also how do

we get explainable about the way, we. we work with

data. So we talked about

explainable ai. For example, AI shouldn't be a black box where it's like an algorithm that you don't really know how it works and what criteria and attributes it uses.

But how do you make that transparent, right? I think that's an important

part. Any thoughts

from your

**Loris Marini:** it is. And yeah. Yeah, it is. And there's there's one thing that we do really like poorly, I think in data in general, and especially in data management, which is communication. I know we think that people have the same understanding of why we need to manage data and why data is an asset or why we need certain, like architectural systems.

The infrastructure to run. To run, to keep running the show and keep producing insights. But it's not, it's, it doesn't match reality. There's a huge disconnect between what the professionals,

the leaders know in the heads, and what more the normal folks, those that don't have data in their title what they, what's in the heads, how they understand it how they think about it.

So it's almost weird, but like building stronger relationships starts obviously with listening, and you can't listen if you're not present. So I think my, in my personal experience, presence is key. And presence means, or sometimes feeling. Out of place and that you're not, that you don't belong.

And it's happening to me right now. Like I I'm, I spend many hours a week in a reality that is far away from everything that I've done so far. Which is great because it's an opportunity to learn and that's what drives me. But it's also strange because I feel like I don't have any weapons with me.

I don't I don't know the context, I have to rebuild everything from scratch. And sometimes presence also means to be to accept the fact that there are so many experts in the organization. And the answers most of the time are already in their heads. It's just that nobody has taken the time to listen and combine them together into a single puzzle.

What do you think

about that? Did you have a similar experience?

Cuz I guess it depends also

on how big the organization

**Winfried:** Oh, definitely it does. And I think we haven't done another part on observe,

and if you have the

chance to be with every business unit and observe hands on how they're working with data,

then you're much better prepared. What this points to is that domain-driven design.

How do we get data closer to the business? How do we get data knowledge closer to where the actual value is created? So

data management is, it's not something that

just happens on the sidelines anymore. We are part of

the business and then we have to be in the business. And I think that

I think Mical bendixen, he framed it wide quite well.

And that still sticks with me as drop that data management lingo.

Nobody cares. Nobody cares that you're using the fancy words. It's about being understood and understandable.

And yeah. Why are we talking about metadata management? Why are we talking about

master data management? If the business doesn't understand what it means and what it entails, so just try to use the business lingo

instead.

**Loris Marini:** Yeah, it's a big one. Be understandable. Use the language. I've heard this tip a billion times in the last two years. You gotta speak the language of the business, speak a language of people, understand. And I think it took me a while to really internalize this lesson.

And I still fail, often it happened to me just last week, I was giving a presentation on a project at work, and I just, being a podcast host also helps. I love how my red Christmas uh, decoration keeps bouncing up and down. This is so weird, , but Yeah, I was in the room.

I was looking at these people and because I'm asking a podcast, I'm

used to try and listen to my voice

as I speak. So that's what saved the day, a bit of that. And

also reading the room, everybody was, they were engaging

two minutes before and now they were just looking at me as if I was from Mars and

I guess there's a bit of a tanning cheek here because I'm actually

working for Mars. But Mars, the planet and.

I and that's, that was the signal that I had to change a little bit the language. But it's not just the vocabulary, the words and [00:10:00] the key wordss we use. It's also like the famous is so what?

It's starting from the need, the compelling need, the frustration, the problem the the objective

I guess that the audience is trying to to go towards and why they're not getting that. And there's no way to know that unless we ask questions, right? There's no textbook, there is no magic formula.

It's just asking the question and getting people to talk to us. Being present is definitely is definitely the start and to become understandable

without learning anything, without taking extra courses necessarily. Just knowing

what people are talking about, I

**Winfried:** I think this one you can tie

directly back to what you said earlier about being involved in the business. I think for us to understand the business need is it sounds easy but it really isn't right. And there's a difference between asking someone, so what do you need? And asking someone, what do you want?

You get two different set of answers, .

It even though you are preparing, even though you are trying to understand the business part from a data management perspective, you're still doing it from your perspective. And then you have to go that extra mile to to look at the other, on the other side, to see it from their perspective. Try to get become a part of,

**Loris Marini:** Yeah. I wonder if there's anybody that have tips here, maybe, feel free to, to add in the chat because there's so much that can vary from culture to culture as well. I've worked in different organizations in different places, I guess we'll put on the planet, and people speak, think in a different way.

If you go to the US it's all,

there's a type of culture, people tend to celebrate wins a lot. Everything is a big deal. Everything's awesome. If you go to

Australia, we are very much the opposite. People don't show that they're excited about things. So everything is like a no-brainer, like a, not a biggie, not a big deal, and

So it's hard sometimes to even understand or read the temperature in the room. So you have to live the country to to absorb that norm. But most people, I'm new to my job, but I think it's safe to assume that a lot of people are not in their first three months of onboarding.

So you've seen people, you have a feel for what the culture in the place is. once you've reached that state, I suppose a question for everybody, for you Winfrey for me, but also for the people that are listening, like what, how do you make people interested in talking to you as a di management professional?

What do you do? Do you have, do you offer free beer? Is it about non-alcoholic drinks? Is it about playing soccer or cricket or what's your strategy? The one thing that never fails, curious to hear, let us know in the chat.

**Winfried:** Yeah. That's a good, that's a good one. I've , I did a couple episodes on data culture not just in 2020 two, but also 2021.

And we talked a bit about what you what you mentioned, that there different

cultures in different places. And that also reflects back on the data

culture, right? And if you do a data transformation program

trying to foster a data culture in your

business, you have to be aware of where you are. And , there was someone on the podcast saying that if you do it in the US you get 18

month of people being just excited about it. They just want to get, just want to get started.

And then they're starting during those 18 month to, to talk on the back room about,

oh, this is not gonna work out . And in, in Europe it's the opposite. It's like

you start your 18 month and there are

18 months of resistance. And once you get through those , it's established, everyone's on board. So it's the opposite,

but what we also talked about, and one sorry I give you the chance to be

blind, that one. But there was one, one point I thought was really interesting because we had talking a lot about P t people, process technology and how important it's to get the people on board.

But, and yeah what do we say 5% of it is about technology. The rest is about people and processes. And there's an important point here that good technology services can help foster that data culture. And if you actually deliver good services, data technology, services it's much.

**Loris Marini:** Yeah. And there's also another aspect here as well, which is perhaps more in this strategic direction, of the conversation. Because if a technology is a matter of having money to buy it or rent it or, subscribe to it, a process and people is, the unclonable stuff is what ultimately makes you hard to be To be copied.

The way they you do things is specific to that organization. So it's probably another type of intangible asset that we don't, I don't think we, we have a way to capture processes as an intangible asset in a p and l, but, A profit and loss statement, but it should definitely be there. We don't have data in the profit and loss statement.

And that's one of the big lessons I learned from Doug Laney from his book Phonemics. So if we don't have data there what hope do we have to find processes as an intangible asset? It's not gonna work anytime soon, but it is a way to think about him. Yet back on the relationships, and I'm reading this with a bit of delay, so apologies to those that are engaging in the chat.

Keep [00:15:00] doing that and hopefully I'm not gonna butcher your name.

Elhaj. You correct me and tell me one to 10 as an , someone from Italy that's lived 10 years in Australia. How close did I get to your actual name pronunciation? Hopefully that's not too far. But Mota says, knowing my customer and telling my story is one of the hardest things to do. But once the communication channel is made and we become on the same page of understanding each other, that the rest is easier, at least it becomes easier.

And I think there's an interesting point here. The

communication channel, we think about being present to being understandable,

but perhaps of the very first step is

to open up, like to create the

space. It could be teams, it could be a room, a physical meeting. and that's one piece. you gotta be present in the right space. Cuz if you're present in the wrong space, people don't

have time to listen to you but then there's also the element of actually being will, generally willing

to listen. Yeah. Eh, I'm sure you have stories,

As I'm telling, completing the sentence, there are stories of memories that bubble up.

When was the last time that you had an important message

to share to someone, a stakeholder. You knew

enough about that problem to go Hey, we need to collaborate

here. We can't, we're not enemies. We're not

on just the fact that we are in two different departments. It doesn't mean we can't partner up.

It doesn't mean I can't add value to your work.

But when was the

last time that you remember not

seeing that engagement and wondering why do people don't get it?

I'm here to help them out. I'm here to improve data quality or data availability or uptime

or whatever it is that you're trying to do to make your life better.

How on earth you don't understand that It happened to me a lot. . So I'm curious to hear from you

Winfrey, if That happens to you as well. how do you deal

how

do you deal

**Winfried:** No, that never

happened

to

me

**Loris Marini:** that goes to the cell

point.

**Winfried:** No I'm kidding. That happens

every

day.

**Loris Marini:** Yeah,

**Winfried:** That's .

**Loris Marini:** There you go. Winfrey is offering free courses from tomorrow live on

YouTube

if you wanna.

**Winfried:** I think it's, I think it's a lot about TR traditions, right? And you

kind. you establish a way of working a routine in how you do your practices. And if you come in with that data knowledge and that new way of working, it's a crash, it always is.

And there's no real solution to it. That will always happen. And you need to find a way to to introduce it. Different approaches to it, right? You can do a big bang or you can do like the non-invasive shout out to Bob Signer, , the non-invasive approach to it.

But in the end, you're still gonna crash at one point. because it's about culture, it's about ways of work, and it's about identity. It's about because a lot of people take their identity from work, right? This is my job, this is what, this is my title, this is who I am. And now you come and tell me I have to do my work differently.

It's gonna be it's gonna be hard. I did an earlier project a conscious decision to sit physically with each business unit. So I moved around a lot,

So every month I moved around to a different place. But the idea was to free time in the calendar and just be there and observe respect the ways people have been working respect the processes, and then find a way to, to adapt the new by respecting the old.

That's a tough one, but that's, I think that's the only way to do it.

**Loris Marini:** It is. Yeah, it's tough. And it goes into the other promise we have there in the description for the event to sell, right? Mapping the compelling business need behind your data management initiative, which sounds like an easy thing, but compelling. I think that's the verb here that that we need to focus on, but also mapping, so has to be a mapping, but it has to be compelling. Compelling to me. In large organizations there's business updates regularly that you, every, anyone can attend. Public forums where, business leaders set the strategy. They say, they talk about what they want to achieve, whether the key parameters the targets and the timeframe, right?

What are they really aiming to to achieve? And it's not that, that, that's not the easy part, that's not the hard part for us because someone else has worked hard to create those compelling, that compelling business need. The hard part for us is to map it to the data management work that we are doing and to communicate it in a way that people see what's in need for them at all levels.

And that's, I think, really the hard part because we talk, we mentioned that data is a shared asset and a shared benefit, right? But I think it's intrinsic to this aspect of data. And I'm keen here to hear as well, you and the audience that. If you buy a cup cup of coffee, you drink it and you benefit from it, you can buy two cup of coffees, one from me, one from Winfrey, and we both enjoy coffee.

But it ends there, right? There's no sharing my coffee because I had it, it's in my system. I can't even if I want it, I I can't share with someone else. With data. Data is different because I touch a piece of data, I do something with it, and next I know someone else will consume and maybe merge

and [00:20:00] join that piece of

data with 20, 30 more pieces in a larger data set to solve another

business problem. It's hard to,

it's hard to communicate what's in it for you individually.

Often that value is not in a

transaction. The most of,

that, some percentage of

that value is transactional.

But there's a larger component that is nont transactional, that is has to do with, Hey, if we have

better systems and better data, we

can answer better ques, more complicated questions.

We, as a business and

organization, not just me individually, so

how do, I don't know as a, does anyone have an idea? How do you balance?

the long term, thinking and tr how do you convey this message that did as a shared benefit while

simultaneously making ultra clear to whoever is listening, what's need for them as a person, as an individual?

Because in the end, we are not heroes. We wanna go home. We have our family, we have our hobbies, we wanna sleep at night, so nobody's a data management hero. We can be leaders, but no heroes. So I don't know. How do you feel about

This trade off, and what do you do to,

To make it happen?

**Winfried:** Yeah I love

this question. I would love to hear from the people in the audience about that. I've, we did an episode on return of investment for data quality work which I thought was really interesting. And it's hard to explain the return of investment for data quality work.

It's much easier to explain the return of investment for machine learning projects or artificial intelligence or data science projects. But when you talk about data quality, it's there's a step in between, right? . You need data quality to do something that creates value. And I think that's that's the tough part to explain.

I encourage everyone to listen to that episode because there's so much valuable information in there. And there were two, two students throughout their master thesis on the topic which it's really interesting.

**Loris Marini:** Wow.

Which episode is this?

**Winfried:** Oh, I think it's called Return of Investment on Data Quality

**Loris Marini:** All right? Yeah. I'm checking it out, eh, on apple Podcast, diamond Norway. If you are wondering and you have time to browse, I'm gonna add a link to

LinkedIn as well.

**Winfried:** Perfect.

**Loris Marini:** And to YouTube. If you guys are on YouTube and you're following us, you should be able to find it over there. Yeah, definitely. I'll check it out, that's for sure.

Data is a shared asset with shared benefits. Data quality is a shared responsibility. I think there is a lot, there is a lot here. And You know what I think we do a lot, winfred is stop, like we get frustrated very quickly as professionals because we have gone through the cycles of learning and rethinking what data is. And that's why most of us, I think, became passionate about the pro the topic because we, we see the impact that data can have on organizations, the transformational power of this thing, whatever it is, right?

An asset benefit, shared benefit. I personally enjoy the challenge, the human challenge which is why I created this covering data as a platform, as a space to explore what it is. What it, what does it even mean? Isn't data just a collection of databases isn't just a bunch of bots, bunch of code with with all the automation and the AI on top to keep it running?

Yes, that's part of it. But then that's all the interface between the database and the knowledge that is captured with the knowledge that it is not captured. And that in most organization is the larger the largest majority of knowledge, of information that flows through the bigger system made of, hardware, computers, and people like the big organization of the organization

as a whole.

And that's what it, at the same time, I feel attracted

by the challenge, but I overwhelmed. I.

feel, especially when I observe what happens in the

real world, in, when I see big meetings,

operational meetings, people talking about what's next, what is the problem? Even basic diagnostic, a lot of it has to do

with with the subject matter experts knowing from experience, they've seen this thing happen in different context.

And so there is a sense almost that experience and memory is more valuable than the data that is in the database. It's always a logging. thing, yeah. Maybe we'll look in some database, but 90% of the time people already know that data's gonna be not not useful. .So it's a huge barrier, right?

Because you're like, come on. We need to make that data better. We need to, we can benefit enormously if we do that, but they go yeah, but now it's crap. And yeah, you've got this visionary journey to make it better, but who's gonna pay for it? . And that's

where, yeah, that's where I go oh, I need to double down on my storytelling and get

better

at that

**Winfried:** But you are touching in on thumb that is really. Existential for us, right? And for the work we are doing. And I think that

data management by itself, that's no reason to do data management by itself. It has a goal, right? It has a goal to, to create value and to gain value for the business. And if people rather use their intense nor the thought for the the [00:25:00] tradition to argue instead of data, then what's the value of data management?

**Loris Marini:** Yeah. Yeah, if anyone has any idea on this, by the way, feel free to pop it in the chat for everyone else to to learn. It could be a conversation starter as well for right now or for our next live as well. We do have some feedback, which I just got to read right now, so apologies if I am not really reactive on this one.

When someone says that there are some issues with our videos, hopefully this is not too too bad. Let us know in the chat again, we're, I'm following LinkedIn and YouTube chat, so if you write anything in one of those two, I'll be able to to see it and engage with you hopefully.

And so what's next?

But let me ask you a question, Winfrey, why did you start

your

podcast?

**Winfried:** Yeah, this is a nice interlude. Yeah, I started my podcast to to promote Nordic data experts. So I've had what, there were a lot of podcasts out there at that point already. I just wanted to promote the people that are not normally on the podcast. That was the starting point, right?

Those practitioners who work with data every day and through my work, I saw a lot of really good work happening in the Nordics, not just in Norway. So I thought why not just talk to those people and record it and then And go from there? And that's how it started. And I really enjoyed a journey and

Gotten some bigger

names on the podcast afterwards.

So it's not exclusively

those those experts that are that are just

working on the data. But that's still

my goal, right? To promote those people who normally are

not on, on podcasts. And I'm doing this as my, my, my hobby,

right? This is my, my free time activity. And I

couldn't enjoy it

more. It's just great conversations, great learnings every time. Great

people.

Same for you

**Loris Marini:** In a way, yeah, I feel like I got really passionate about the topic after working as a data scientist and realizing that we just didn't have the systems and the data to do what we wanted to do, to be honest as much as, I could have done a lot of storytelling, and surely my storytelling improved in the last two, three years.

But even then, like if you don't, if you're missing the ingredients, you can't cook a meal. So you can't just talk about it, then you have to do it, right? Like storytelling is talking and motivating and being and persuading, which is good. But it's, you do that. Af you gotta follow up with an actual meal something or substance something even small, right?

But it has to be, you have to be able to prove some sort of value or at least give a hint to that, Hey, we just scratched the surface. There is a mine here. There's literally a gold mine. We can do so much more if only we could trust our data. And I think that's a mistake that I did at the beginning and that led me to appreciate data engineering.

And I entered data management from the engineering angle really trying to

be, try to build systems that would give me reliable. data that would match as close as possible, what was already in the heads of people, so that my sort of influencing w was easier. And I got lost in there. So my idea was quickly fix that and go back to the data science and I lost, I was , it was the beginning of a journey that then I understood, okay, data manage management, it's a thing, it's a journey.

It's a a way of living. It's a philosophical choice. It's a career choice, . And so that's what and also to be honest, I l I lost a little bit of interest in data science because I was like 90% of the time we can deliver value with a linear linear fit, a linear model, or some simple, caines classification algorithm

or trees, a random forest.

This stuff is. In a pretty standard, it's been in the

literature for decades. So it's not state

of the art. We don't really need to use the

latest generative AI to improve systems in a business, depends on, the level of maturity. But as we know, and that's another lesson from Doug Glenne I think Wass the the episode what I learned from Doug is that, after 17 years at Gartner the c o office, they did a lot of surveys and they found that, there're five, levels of data maturity and there's a distribution.

Only a small percentage of companies are in level five like this, less than 5% most. Most of the organizations are getting there. They're discovering, and the stuff matters. They're started investing, but the culture change takes a lot longer than just two, three years. And that's the problem because the lifespan, the, not the lifespan the average tenure , the lifespan now outfits much But the average tenure of CDOs is two years, two and a half years.

It's complicated. How do you make change happen

when you have only two and a half years?

It's a tough one.

**Winfried:** That is a tough one. Yeah. I see that as a question from the chat about I feel like that most of us have the same problems in most parts of the world, so why are we still calling them cultural challenges? Any comment from you, Laura?

**Loris Marini:** a good point. Yeah. I think culture here, the [00:30:00] way that I understand it, at least I don't think of the culture as in, Italy, Norway, France, Argentina. I think about culture as the unspoken rules and the norms and behaviors that people follow without even thinking. So it's part of habit.

Yeah. Part of it is a habit. Like for example, Mac Current company, there's a culture of there's a problem in the factory. We go and see. , right? We don't just stay in the office, talk about, and potentially come up with theories for what the problem might be. There is a culture, there is a, there is an unspoken, automatic, instinctive behavior.

Something goes wrong. We just drop our laptop, we put our safety boots, and we go straight into the factory to try and see what the problem is. That's culture to me. But I don't know,

Maybe people interpret culture in a different way. It's a loaded word, so I wouldn't be surprised if

we have different views.

**Winfried:** This is an interesting one because

that put on your

boots and go down and see,

That's not, every company, right? This is specific to your your company. And that's one part of the culture, right? That specific company culture. And the more you work with especially data governance and trying to establish rules and

regulations and and principles the more you see that culture unraveling, unfolding.

And the more challenges you get to tackle inside the company. And then there's the culture outside of the company. And I think we had an episode on EU

legislations

about data sharing, about ai artificial intelligence act this entirely

different thing than what you would see.

in the

us.

My, my wife is also working in information management. She's

working for public services

for ID

control.

How do we check IDs in a way that we can ensure that they're trustworthy? When people come to Norway

and she's been at a conference

just a couple weeks back and she saw someone from the US speaking up about , what rules they have in place and what mechanisms they have in place.

And everyone in Europe got shocked about you can't do that. There's something about privacy rights. And this is a typically example for that, our cultural differences in countries. Privacy is one of the easiest to explain and easiest to see across across the world. And I don't know how how it is in a.

**Loris Marini:** Yeah, it would be, yeah definitely. It's a topic that he is in. Top, top three. And when you talk about data, it's just because the, there's an expectation now that people are following the the latest regulations when it comes to privacy. And, there's a huge actually market growing in at the moment around becoming in control as a user of a platform taking control of your data and monetizing, deciding actually whether you wanna monetize it or you wanna stay completely anonymous.

Companies that embed that way of thinking in that product are going to be a step ahead. And in that segment of companies, I put myself as well because the difference between MEMA and discovering data is that discovering data is a business, is a business of one. I'm running the show with my core. Business two, a business of two. now a business of two. It's been a business of one for a long time. Sorry. Ran . This is just old habits. It's a business of two. But it started out just with myself for 18 months and now we expanded. We're a team of two, but a small business, right? And what we're trying to do is create opportunities for people to learn from one another.

And that's the learning that I didn't have when I was Consuming content on YouTube. I found that you, when you are actively learning, you learn a lot more. It's more fun. And so meeting people and guests for the last two years, over 50 now episodes, that's been a thrill.

It pushes you. When I'm especially living in Australia, most of them are in the US so we record at weird times. Normally it's 6:00 AM in Australia. No matter I, I feel like no matter what I do, it's always 6:00 AM here and so 6:00 AM awake in front of a camera, in front of a microphone.

Try to formulate questions that make sense, pushing the boundary, of what I think I know and often prove, be proven wrong. And that's the whole thrill, right? That's why I do it fundamentally. Then it's also a business. Some of you in the audience are looking for podcasts to tell your story.

You should definitely reach out to me because we have, we're planning now the strategy for next year, but that's a bit of a plug. But what I wanted to say, back to that comment on culture, is there one example of in that habit to, that instinctive behavior is really hard to form and to sustain and it's even harder to spread.

Think about how hard it is for us individuals to get new habits. I decided I want to lose weight . I said, okay, I'm gonna buy a bicycle. I'm gonna ride to work.

It sounds like a no brainer, but it's actually super hard to

get to, get that habit going for one person. Now imagine become building a habit for an organization.

It becomes incredibly complicated. And that's why we need to stay patient, keep the eye on the

ball, and think okay. We are here for the long term, right? Because we are data management professionals, so we

can't just focus on short-term stuff. But if we don't focus on [00:35:00] short-term stuff, people are not gonna listen to us.

They're not gonna invest,

we're not gonna secure the funding that we need to do the things we need to do. And that, I think this personally,

I think this is the challenge, right?

Of, of the category as professionals. How do we

deliver sugar

in the short term to get those those hormone spikes to fire.

And people go get addicted to it. Hey, this stuff is good. I want more. A positive, healthy addiction to quality data to. systems that are integrated without sacrificing the long term view. Otherwise, we lose trust long term. And nobody wants to be, on the way home with a big paper box with all your stuff in it because you've been made redundant because some, someone in the business thinks that your work is absolutely useless.

That's

**Winfried:** Yeah, I think we had,

Yeah, I feel like everyone has been there

Cause it's, back to, it's really hard to explain the value. We are doing the long term value. And then you lose people on the, on during the journey. Marty on the podcast, he said something about long-term commitments to data quality data management are important, but whilst you do that, you have to do speedy day-to-day operations with little time to insight.

And that has been key to their success. And this is what you're saying, like small sugar cubes. So. yeah, I think the, what, how do you find those low hanging fruits? How do you find those? Okay. Let's do this to keep everyone on track while we are doing the long term.

Anyone in the audience maybe

that will

be

**Loris Marini:** Yeah, definitely fill, jump in the chat. Let us know. I suppose filling the void, what a quick thing that I can share is the fact that I think we do a lot of that challenge has to do with how we think. And let me unpack it. Imagine it hap it happens to me constantly, right?

I. examples of bad data hygiene all over pretty much, I don't even have to open my eyes to see bad data hygiene. I know that there are people that just wing it. They put things together, just to get the task, get it done, tick the box, move on. And for them it's absolutely fine. Like they don't they're happy people, they don't go like home, think. God, that was really bad. What did I do? I did a a disfavor to myself and to everybody that will come in touch with that data set tomorrow and weeks and months in the future. I envy them. I envy those that can go home thinking everything is fine, because I would love sometimes to be them.

But other than that, other than sharing this, how I feel about them, the, it teaches me something, right? That I have because the way of working. You mentioned before Winfrey that we need to adapt to the way of working. Yes, we are catalysts. Yes. We wanna make change happen long term. We want business transformation to enable digital transformation.

All of those things. Culture and people like yes, but. Fundamentally, we are lazy machines. We want to do the least amount of effort, spend the least amount of energy to ensure that we can procreate, have a happy life, and go about our journey in life, right? Without too many words, that's the natural state.

Now, I don't know why data management professionals, including me, because I can't identify with data management professionals as well as data science. Bit

of a hybrid. But why do we instead feel that, we have this big mission that we need to make sure that

data is the right quality for the organization.

Can we just take it, lower the volume a little bit, just focus on something smaller and it's easy to hear to say you don't boil the ocean, but that's what a

meal it means to me. Excel being used, misused.

Take a note, right? That join the community. Come on the podcast, talk to your favorite podcast host.

Let's create a data therapy session for everybody, for every professional, right?

But you are not gonna change, you are not going to

change the, habit

of that person using Excel or Power bi, whatever it is. In that way, they

built a habit out of the need

of surviving in the business. So that's the last thing you're gonna

change.

And that's another mistake

I've added in the, past. Coming in and going like, oh,

you guys are using Excel

losers. I show you a better way,

Sorry people. I didn't say losers, but that's what I was thinking, and

that, that thinking, I think transpires people see through, see their actions and go

Are you a partner or are you a

threat?

And we don't wanna be seen

As threats.

**Winfried:** Yeah, really good. And that gets me to number six, observe. Don't jump to conclusions. And I had a great episode about power BI governance where we talked about companies who have that magic goal, like 80% of our users going to use Power bi. What did you do your homework before you got to that conclusion?

Have you checked how people are actually working with data, how to actually get their insights? [00:40:00] And if most of the people are happy with using Excel, then where does that number come from? Then you haven't checked your demographics, right? . And other thing that you talked about is number five, right?

Enable the business to take lead. Our role is to provide guidance. Our role is not to jump to conclusions not to be the driving force, but rather helping the business to, to do that change. And and the last point you talked about people want to do the least amount of effort.

And I think that from a data management perspective, we see that the amount of effort will be

much less if we have good data quality, if we have good routines, if we have good standards and practices in place. So the big picture for us is using less energy on the stuff that is the basic of the foundation, and then use more energy on the fun and exciting and sexy stuff.

That's our long-term goal, and that's why we're driving towards it,

**Loris Marini:** and I, you know what? It's easy to it's easy to go. I don't know. I feel like a mix of emotions when I see. the shortcuts, like happening in front of my eyes and thinking, come on, like you're just generating tech debt or informational knowledge debt without even realizing. But the thing is I'm not entirely sure people realize when they do it, it's part of data literacy, I or data management literacy.

I, I raised this a long time ago with with my guest data literacy.com. Come on, Laurie, you can do it. It was a long time ago, which tells you that my memory is bad, but we talked about data literacy. Ben Jones, of course. Ben Jones talked about dental literacy episode 16. So that was more than a year ago. and his his point, like my point in that episode was, Hey, we do a lot of data literacy, like

being able to read and write and, influence change

with data, but we don't really explain the importance of the stuff That nobody sees. The behind the stage, everybody just looks at

the stage, whether it's power bi, a dashboard or a bottle.

In Slack

teams, whatever it is the interface. But behind that interface, there's a factory, There's a digital factory. There's whole beautiful world made

of stuff that you don't see, that you can't

smell, that you can't touch that is necessary to deliver the things you need in the real

world to do the things you wanna do.

It's complicated. It's just complicated to explain. It, we know we, we are enlightened just because we've seen it. We build

it, we smell it, even if it doesn't have any smell.

But people that don't know,

that have never been in touch for them, it's what are you, I don't even know why you paid, why are we paying you?

Why do I keep you on on the books? What is your value again? Ah yeah. Data quality. Isn't just data, good data by default? Do we really need to worry about data quality? Yes, you do. .

And so

yeah, communicating that is,

Hard, emotionally.

**Winfried:** should we last five minutes? Talk about number seven a bit. To have a round

up. And we have talked about all the seven lessons. That will be

good. I've number seven is fun. But there, there are a couple parts here, right? You had an episode about psychological safety. I did some work on that collaborative readiness which is basically you can say that while this business unit is mature enough to go to the next step in their data management majority we do, we love to do that, right?

There's majority assessments. But there's something about how ready are you actually? And there's something about organizational readiness. There's something about technological readiness, but there's also something about collaborative readiness. And that means how ready are you to share, how ready are you to share your failures?

How ready are you to share that? This just didn't work out. And to create that openness around it. I think that is really important.

**Loris Marini:** I love it. I love it. That's vulnerability. And vulnerability is this thing where, automatically creates that safe space where people talk about their failures and embrace them, that they don't just talk about it, then go like they're proud of telling the story of why they failed because it's not, they don't see failure in itself.

They don't feel it, they don't feel failure as a negative thing. They generally believe that there's no other way to learn. And so that's the environment where you I do my best work and I think we all can share that. It can be hard to create it in in, again, cultures

that that are different.

But there is, there, there's one empowering reality. I think if I.

I think about it then is the we always underestimate the power of

influence. We can in, we, even if we're nobody's and we never, gave

a talk to a thousand

employees before in our little actions in the things we do every day, the way we behave.

Even the side projects, for example, that we take on,

No, nothing stops

us from example. For example, right? Tomorrow we go to work and

we create an

event maybe not tomorrow, but in January we create an event for the week after lunch and learn special talk on

Shortcuts them in Excel and [00:45:00] how

they impact your workflow a week, four weeks, six months down the track.

That could be a piece of data literacy, data management literacy that, explains people, Hey, maybe you never thought about it, but here's the data run. He's how data moves and here's the impact of everything you do for yourself and for your colleagues that are gonna come tomorrow.

And they wanna misunderstand it, they will recreate it. Now you have two copies and good luck deciding which one is which one is the truth. so there's a lot that we can do. I think it's easy to underestimate the impact that one single person can have with enough passion, enough drive that takes the courage to go out there and start speaking, and say,

Hey, this is what I think, do you think differently? Fine.

**Winfried:** I don.

**Loris Marini:** let's kick off

A,

**Winfried:** something like

damer. That's what we are about, right? To providing that space outside of

your company where you can exchange with others that are on the same, have the same problems facing the same challenges. That's what DAMA is about. And I think that all the principles about data management are about collaboration, right?

This is what keeps us alive, . And there is and Peter Reichen, our president from Damon International, he framed it like there is a responsibility for each and every one of us to share our knowledge.

**Loris Marini:** Yeah. Yeah. Which, this is a topic that we haven't touched, but one of the, and we have, we don't have a time cuz the stream is about to end. We, what it take, what does it take to build a podcast? I suppose maybe we should do another Live Winfrey to tell the story of this, cuz it, there's a lot of pe our listeners found our shows extremely interesting and valuable.

I think at least that's a story that we like to tell ourselves anyways. . Most people that reach out, they go, they're very excited about our shows that the others don't probably reach out but , if that's true then the next step is, okay, what, how do we make this sustainable? And I know that dma makes a, it's a huge part of the support and the help that keeps making possible your

show.

With my show, I decided to be to start

it from scratch and, and some learnings that I'm be happy to share. But at the moment the, that's The point where every creator really has to face the reality. Do you want your listeners to be the product or do you want them to be

The customer. And in a word that

it's filled with free content. That's a hard question to ask. And so yeah, different shows take different directions, and I, I see from my favorite podcast, people, podcasts that started out independent, and now they are, they've been conditioned to take one direction and tell one story, one version of the story.

I don't like that. And so I want to, I hope that I'll manage to keep discovering data independent as for as long as possible. That doesn't mean we're not open to sponsorships, but it's a different way of sponsoring. So again, if anyone is interested or anyone that might be interested let them know.

Feel free to reach out PM on LinkedIn or on the website as well.

**Winfried:** Fantastic.

**Loris Marini:** Yeah. Cool. That's yeah, we're at the end of it. I don't know. Last thoughts, last wishes for Christmas. If

I was Santa, look at me with this hat. You gotta don't you feel like

writing a letter?

**Winfried:** one one of my

**Loris Marini:** What would you like Winrey? What are

you

**Winfried:** yesterday. And you already mentioned Argentina and France, so I'm really happy about that

one. That was a fantastic game. , I

don't know if you've seen that, but Wow.

**Loris Marini:** I, wish I could say that I've seen it. No, it was at 1:00 AM

I was fast, fast asleep. So I'm gonna tune in and watch a relay

now after

**Winfried:** Alright. Merry Christmas to

you.

**Loris Marini:** Alright. Merry Christmas to you as well. Merry Christmas to everybody in the audience. Thank you again for tuning in and sharing some thoughts. Would be really nice to have more sessions with you guys together to earn stead on LinkedIn. Thank you to, seems like this conversation could go on much longer.

Absolutely. And I don't blame you if you disconnected and went to get a coffee because we tend to talk quite a lot here. But it's definitely appreciated your help. And the presence of everybody else. We got 14 at the moment, connected, 14 people that stuck with us for an hour. This is an result.

So this is not a short TikTok. 62nd. This is a deep conversation on a Monday before Christmas. So I'm gonna say, you well done to all of you big clubs from here and I'll definitely tune in to Winfield podcast meta

Dema. You'll find it

on

Apple, everywhere. Really.

DEMA Norway,

**Winfried:** And the same goes for

discovering data,

right?

**Loris Marini:** discovering data.

Of course.

**Winfried:** Exactly.

And

**Loris Marini:** Discovering data on discovering

**Winfried:** Lauras and I

talked about providing that live session we just

did on our post as well. So if you haven't had the chance to tune

in,

**Loris Marini:** Oh yeah.

**Winfried:** there's definitely possibility

to,

**Loris Marini:** Good point. Yes. This is gonna go live in the next 24 hours. Yeah. On our Respe respective podcast. Thanks to the magic work of winf Fried Ins spare time, and ran my podcast co-producer. Thank you very much to everybody. And all the best. All the best. My wish is gonna be better data for [00:50:00] everybody and a more sane state of mind so we don't lose it.

We can keep, we can keep going and be happy and smile and be, have that energy that everybody can absorb and, get infected with, and make transformation

long-term happen by aligning small pieces of sugar, one after the other, ,the su the sugar

**Winfried:** sugar train. Yeah,

**Loris Marini:** Cool. Cool. It's a big chow for me. Everyone. Winfrey will gonna catch up very soon. And all the best.

**Winfried:** thanks. Merry Christmas. Bye.