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Insideanalysis dot com. And now here's your host, Eric Kavanaugh. All right, ladies and gentlemen, Hello and welcome back once again, so the only coast to coast show all about the information economy. It's called Inside Analysis and folks, I'm so excited now to be talking to someone Ala Zaken of Athena Solutions. I'm actually going to be doing some work with these folks.
Athena was founded by a guy named rich Sherman was one of the best friends of the business and Rick unfortunately passed away last year, which was real kind of a shocker to me. But the light, the cycle of life goes on, and I decided it's time to talk to these people, maybe do some work with them. So here I am. I'm going to talk to
Ala zaikin she's a data modeler. She's all into the Kimball approach of guse you studied Kimball back in college, right, Alas, so tell me what got you in the data and why do you like making order from the chaos of data. Well, at the time, it was just it seems like a logical approach to thinking about data. And I was very excited that I found something that resonated with me so much, and since then, I literally can't let it go, both professionally and personally. I think I am making
lists and making everything into a table. So I am that type of person. For better or worse, that's pretty fine. I think that it's a wonderful experience when you find what you've in joy and that's what I do too. Like for me, I used to watch Money Python way back from the day, and Eric Idol used to do all these talk show skits where he was being all being doing his talk show, and I was like, I
want to do that. I want to do what he does. I want me just sit around and ask questions to learn things, because you get to learn for a living, but you also get to learn from the data. Right, And you said something I thought that was really interesting. Before the call, you're like, Oh, I'm a data modeler, so I have two screens. Why do you need two screens to be a data modeler. There's a lot to absorb, you know. There's when you create a data
model, there are many conversations that are happening. The conversations with business users, conversations with technical people, conversations with managers. Everybody has their own goals and data model usually stands kind of in the middle of all of it, and you basically combine all of these aspirations into what you you have created.
That's what I that's what I found, and I found that a lot of meaning can be created in the process, which is very attractive aspect of this whole work for me personally, because you know, by looking by combining perspectives, you have a different view of the data and different you identify different relationships and the meaning comes out in this work. Yeah, well, you bring up some really good points here, and I like your joke about ordering things
in the form of a table. Right you have rows and columns and the data somewhere between, and you have multi dimensional data of course that is three dimensional or even more dimensions than three just depending upon the particular use case. But when companies are trying to get value from data, you do have to go through this process of distilling the information you have, understanding their relationship between the data points, and then going ahead and building out the model. And
that has changed somewhat in the recent years. You know, we talk a lot about Snowflake and how Snowflake really saw an opportunity to allow companies to tear down the data warehouse, redo the schema, and then spin it back up again quickly, because in the old days that would have been extremely difficult. I mean, once your data warehouse is running, if you want to start adding tables and things and adding dimensions is like, oh no, I think,
really, are you sure you have to do that? And that's changed. But if you would just kind of walk through a little bit what it's like to look at the data and to understand the relationships, to start to map out that data model, what does that actually look like? What are the aha moments? So I noticed that in the recent years the strictness with which we designed solutions kind of disappeared. And it's not free for all,
and you still have to understand lot. That didn't change, but in terms of how to implement and how you know how to how to deliver a solution piece by piece by piece. I think that this is what you're talking about. This is what what changed because the speed of delivery, Like, nobody wants to wait until a big, huge data warehouse is done finished for you everybody. It's an iterative approach. It's it's an agile at Gail game,
and that kind of works in with the technological changes that are happening. Nobody wants to wait for a big solution. You know. You you develop understanding in one area and you deliver it. You develop additional understanding when the when the project is delivered. That's that has been my experience, and then you add to it. So a lot of meaning that I was talking about, I like to talk about meaning because this kind of this kind of I understand.
I think that this elevates the conversation a little bit because people like to understand, people like to have meaningful discussions, people like to speak the same language and collaborate. So I feel like modeling creates this type of conversation. But when I was talking about the hard structure about on delivering a project, that seems to be dissipating, and people feel much more empowered and flexible in the way they work and in a way they approach the overall the overall objectives.
There's a lot of data right now. Companies have a lot of data, so they have a lot to work through. So jumping at everything at once is not practical anymore. So you have to step back and identify your priorities in jump where it is most needed, or you can pick up a low hanging fruit and jump there. Yeah, you said several really interesting things. One I love that line where you said people like to understand, right,
people like to have meaningful conversations. And when you don't understand, that's when you're still in discovery mode. You're still asking questions, You're trying to wrap your head around something. You don't get it, and then when you do understand, it's like, oh, I get it now. And that's what you're able to do with data. And I think the really interesting point
you made is that there's so much data right now. I think like observability, I mean, observe ability exploded in the last few years, which is for good reason. I mean, there's reasons why these things happen, but you have all this other data to make sense left, So how do you do that? I mean, so I'd like to just throw that back at you and say, I think it makes a lot of sense. How do
you start with with low hanging fruit, especially for a data model. Do you build that one corner of it and then get that nailed down and then start to incrementally move out from there to build out a bigger model that really represents the business. How does that actually work? Usually you identify an area where it would be practical to begin, even though even though on your roadmap there could be much much more. You have to start to focus, to
focus the effort in this area and to start developing. It's almost like prototyping, you know, you start developing the structures that you start developing the understanding, You start developing a way to deal with the group with the requirements. So all of this goes into the into delivering the project, into delivering the you know, the plate platform as as it is called now. Nobody we
rarely speak about data warehouse as it is now? Did you notice that because we speak about data platforms and this is platform is the only thing that can resolve you know, the problems that people are having with the data because they're they're so universal and they are so broad, and I noticed that they're pretty
they're also pretty standard. You know, everybody, everybody is dealing with the amount, like sheer amount of data everybody is dealing with, you know, whether data quality is up to the level everybody is dealing with common creating common understanding around around major concepts in the business. So the more so. Actually the technology reflected this trend, I think, because you know, there are many data catalogs that exist now so so, and like, honestly one better
than the other. You know, you take your pick. They're great, and I understand and every client has something needs something different, but you can have your choice at this point. But what it gives is the common creating, the common understanding, the creating the common language so that it is easier to reach the consensus and it's easier to build out the structures that solve the problems for like more than one group of people. Essentially, Yeah, that's
very interesting. I really like this approach that you've pagan because you've come along this journey and you've seen the changes, right, I mean, there's so much more data, right, now it's coming at people so quickly. And the question with design and the data model is to understand the most efficient way to capture the data, to potentially refine the data, transform the t for example, transform the data and deliver it to a solution that can be used
for operations or brand analytics. I presume a lot of what you do is geared around analysis, is that right? Absolutely? We're building the platforms to facilitate the advanced analytics, to facilitate data science, so to know. In order to along the way, along the joy, we pick up a lot of things like you know, glossaries and governance and creating additional processes around the data and data management in general. Yeah, and governance. You know.
I was talking just this morning about privacy and last week earlier this week, I should say, we did a show two about privacy and I was like, you know, what is privacy really And when you get down to it in the data world, it has to deal with the appropriate accessing of information, right who should have access to this information? Who should not have access
to this information? Certain things are private, Certain things you only want certain people to see, but you do want those certain people to see it. You don't want no one to see it, right, So you have role access role based access controls, for example, and you want to be as as dynamic as possible because if it's very fine grained, then some person has
to manually switch things on and off and that can be a pain. You start to think about organizational hierarchies and how challenging it is to get the right access for the right person at the right time without disrupting the access right because the people who need the data, you want them to have the data and
not to have to jump through hoops to get it. So that's really what it comes down to with respected data governance right is figuring out what are the ideal policies, what are the appropriate choke points where we can require someone to log in, where we can require some to validate who they are without disrupting the workload, without disrupting what we want them to be doing with the data.
Right. Absolutely, absolutely, And this is one of the major areas of data governance where everybody, you know, everybody thinks about roll based based access, everybody thinks about data classification. So to implement that role based access, uh, there are a lot of there are a lot of tools to to facilitate security. But also, you know, a big part of an initiative such as security is the consensus, you know, and I think that
implementation is a big piece of it. And there's technology to choose from. Again, but you know, jumping in and uh, you know, classifying the data elements and developing the definitions for the roles is the step that takes the longest. There is a little bit of inertia in people in general. Everybody everybody has it, but companies do too, So you know, I think that the compliance requirements actually forces people to those areas, so they have
to address it. But it's also it's also logical and the right thing to do, given that the data sources that we're dealing with are incredibly important and incredibly expensive and incredibly vast. And I'm just guessing here because I haven't done all these projects myself, but I've been around them. I'm guessing that the
compliance which is a driver. There's no question in compliance as a driver, but what you really want is for the business to take more of a proactive approach, more of a positive approach about used in the information, and in that sense, the compliance can be a nice driver it can be a nice mechanism of action to get the business to pay attention and then build a value around that. So it's that the value isn't just being in compliance. That's
not the value. That is a value, But the bigger value is knowing how to use the data, knowing how to leverage the data, and I'm guessing that you figured out years ago and you use it as an opportunity to explain to the business the value they can be getting from this data while being in compliance. Right, you don't want to just be defensive in pasture. You want to be offensive and you want to be creative in using the established
rules to drive change but then bring value to the business. Right. Yes, I think the compliance frameworks such as GDPR for example, it is a framework and there's a benefit, there's a benefit of to tapping in into a
framework that's already made. You know, it does it does require an investment to develop all of this in an organization, But I feel like I feel very positive about it because you know, there's a lot of thought that has been put into those framework and you know, I feel like we're the beneficial beneficiary of this thought process, which is you know, this is where the time goes, you know, to develop, you know there's time that is
spent in even adopting it in the company. Sure, but I feel like I feel like having having a framework to work with is always always fit, saves a lot of time. Not to mention in lawsuits. Right, Well, you have your guardrails already, you have the framework, so that at least limits the discussion, that limits the free play of the scenario, if you will, and it kind of focuses people and keeps people untasked basically, But folks, don't just that down. Will be right back. You're listening
to Inside Analysis with Allah zeke In from Athena Solutions. Stand by, Welcome back to Inside Analysis. Here's your host, Eric Tabanac. All right, folks, back on Inside Analysis talking to a lah zake In of Athena Solution and we're talking about data governance and data modeling and processes. And you had a great quote that's where the time goes, and it's really in working through
first of all the objectives of the business. What do you want to accomplish with this data and how can you do that while remaining in compliance, which of course is very important. But what value can you get from the data that always has to be on the mind, and I'm sure it's always on your mind, is thinking how do we align these practices and programs with some value creating initiative to feed people to the data that they need at the time
they needed and what sources are available. So I wanted to talk about data catalogs because to me, business colossies have been around for well, quite frankly, for thousands of years. I mean, if you get all the way back to Mesopotamia, they had symbols that they used for managing things and someone had to learn those symbols and know what they meant and build to do the math in real time as they're trading brain and other things like that. So
this is not new. It's been around for ages, but now there's so much more data and the business glossary, the data catalog can be such an enabler by fostering business literacy data literacy, and that all feeds into helping people understand what are we trying to accomplish here and then use the data to get things done. Talk about data catalogs and how you use them to help clients figure out what they need to get done for their information systems. So there
is there is I think an image at this point. There's so many there's so many different products to choose from for dat catalogs. The clients always, you know, there is always a desire to have it. Well, that's what I wanted to say. There's always a desire to organize and there is always a desire to understand the data. People are a little bit shy of purchasing sometimes a data catalog because everybody understands the investment and the you know another
it's basically another application to maintain. And I am starting with this because in my experience there is always this hesitation. You know, we have the spreadsheets, like in the Mesopotamia, right, we have the spreadsheets. Why do we need to buy a tool and then maintain a tool? Okay, but you know right now, the need for creating meaning out of the data is so so strong, and so with meaning, the value is being drawn out. So the data catalog actually does that, and it works in different ways
in different situations. Sometimes an organization wants to invest in data governance and they choose a data catalog as basically their platform for data governance, which can be done and it's a it's a it's a great platform for data governance. It allows room to, you know, to accumulate business data. It allows the room to to accumulate metadata. So the information that is being stored in one place and used by the data stewards, by technical data stewards, by laid
people in a company, by business analysts, by data scientists. So you open up this meaning to a variety of audiences that before that went and used the spreadsheets here, and the spreadsheets here and Microsoft Access databases there. So big, big, huge step forward. And another thing that I think is a benefit is again the framework. You know, each of the data catalogs, you know, you bring in a new tool, you bring in a framework. So you have to choose wisely. And the initial stage of choosing
a tool for your organization is very very important. Proof of concepts are very very important because every organizations has different needs. But once you committed to a framework, I think it's a benefit. Yeah, you bring up really, you bring up really, I just want to dive in this. You bring up a really good point, which is that and I've seen this with all
sorts of different technologies, you really need to play around. That's why I proove a concept is important because each tool will have its own first of all, its own model underneath for how it works. It'll have its own array of functionality. It may have a particular bend toward a vertical financial services insurance for example. Some need very complex higher r keees, some don't, And if you don't need these complex higherarchies, then you don't need to go down
that road. So you do have to kind of understand what are you trying to do with the tool, what is the business problem you're trying to solve. Then be able to talk to an expert like yourself and understand what's the difference between Calibra and Elation for example, or data dot World and any number of other solutions. And once you wrap your head around what's available and the functionality and kind of how companies, how individuals will use the technology, that's
when you can make the hard decision. And like you said, you have to choose wisely because you know, I mean, you know, we talk about this in the cloud all the time. I've talked about companies that moved off a part Ot onto HubSpot and then a year later they go back to Partot. You're like Oh, the engineer is like, all the people who had to do all that work are just like, are you kidding me?
Like, we have to go through the gaination side. Oh, there's nothing more painful than doing a job the second time because something messed up at the end. I have I mean I have a fear of that, Like I can't stand Like if I in the old days, you'd write an article and if you lost it because the floppy disk broke or something, you're just like, oh, no, I have to rewrite this thing. We've now baked in auto save to all these tools, right, so you don't have that
problem as much anymore. But nonetheless, the point is you have to be careful in that decision, and that's where a trusted advisor comes in handy. Right. Absolutely, there is so much power that modern technology gives us so that we can move with like much greater speed, but we might move in
the wrong direction. So now this this decision becomes even more important. Yeah, And when you work with clients on that particular subject area, I mean, I'm guessing it's a series of meetings where you sit down and you try to understand what is the nature of this business you know of course, where does your revenue come from? That's always a major topic to understand where is the revenue coming from? And then how do you leverage data for marketing purposes?
How do you leverage data for reporting purposes? How do you leverage data for product design and service design? These are all the kinds of decisions that can be really aided by having the right sets of data curated in a data catalog. Perhaps, so just kind of walk us through what it looks like in the early stages. What are some of the questions you ask, and how do you sort of shape that journey to choosing the right data catalog? I think the journey to implement a data catalog, I guess, and to
implement any project actually starts with understanding what doesn't work now? All right, yes, so I would I would say that this has to be that that has to be the first step. But then you need to balance it out. What what are the aspirations in the business, you know, where where they see the improvement? You know, sometimes we ask a question, in the perfect world, you know, what would you like to see if there are no limitations? You know, what would you like to see? And
sometimes this is this conversation is very telling. Then you get into the requirements. Like I said, the requirements for the data catalog everybody has slightly different situations. The you know, the need is there to In some cases business
glossary needs to be so you know, developed and so precise. In some cases, the technical side of it, the said technical metadata side of it, and they have availability of that metadata has to be you know, carries much more weight, so you have to you have to take this into consideration
when you're choosing a tool. Also, is it is it an older company that has mostly on prem Is it a company that is cloud with the high security requirements that you know need to be taken into consideration, and that because because you are, you will be exposing some meaning, so you have to take into account the technical architecture that exists in the organization as well as the
compliance requirements. So all of this becomes a long list of requirements, and I think the you know it worth it is worth it investing a little bit more in developing these requirements before jumping into the pool of different data catalogs. Right, Well, no, and I'm guessing that here's how my mind works, right, I'm always trying to kill three birds with every stone. I'm guessing that if you go through the process of getting to know the company,
you are understanding bit by bit what makes it special. Because every company has their own own DNA, and this is why templates only go so far. You know, insurance companies may have a particular focus or a particular area of specialization where they're very good at things, but what they're good at requires additional data sets. And what you said is you know what's what's not working now? Or I think maybe the better way to put it is where is their
friction right now? Where is something difficult that should be easy, And especially in terms of data being fed to someone right sometimes you can feed one more data point to someone and you just solve the whole array of problems. And I'll give you an interesting example. I remember there was a company when I was just when I was learning about process mining, which is fascinating stuff, and they were looking at the processes as they actually occur in the business because
they've instrumented all the different information systems. And what they saw is that there's this big bottleneck on a credit check and like, huh, so every time someone requested something they had to go through a credit check, and that was the biggest part of the whole process in terms of the time it took, in terms of the difficulties and all these things. And they realized, Hey,
for most people, we don't need to do this credit check. For our established accounts, we don't have to do the credit check, So let's just cut that part out and only use it when we need to. And they would not have ever known that had they not looked at the process and looked at the data and understood what was happening. And I'm like, wow, let's just do this. We'll check and see is an established account with credit already with us? And then skip that part and they're like, Holy
Christmas, it's sped up. Like a whole department got like three times as
much work done because they weren't wasting time on an unnecessary process. So I throw that out to you because I guarantee as you're on site working with clients, there are little bits and pieces that you pick up in conversations where you're like, hey, wait a minute, there's a different way to do this, And that's what a consultant is supposed to do, right, is recognized where there's a process that is not efficient that can be greatly improved, and
a lot of times it just takes some of the outside come and say, hey, you thought about doing it this way, and there's someone who goes, yeah, I suggested that last year, right, and it's like, but that's that's exactly you know, someone like it is. It is so true, very very often you get in to an organization and revive the efforts that were you know, done and forgotten, and you you know, you're able to kind of bring it back to life and get somewhere with it.
But you know, another interesting like I think another common example of it's more more over not a process mining, but I guess it's related to the data and to the definitions. And this is another area where I feel like there is a lot of benefits benefits and of course the data catalogs they are storages of the definitions. You know, metadata is nothing more than a technical definition, and then you bring business definition definition definitions. So differences and definitions,
which is very benign as it seems, create big differences in reporting. And this is when the hours go when people try to figure out the differences and they investigate and there could be you know, literally weeks of time of the most you know, valuable people and the teams that go into that. So so this is usually one of the problems that people cite, and this is one of the problems that you know, you you try to address sooner rather
than later. Either you approach it with the catalog or you get into the you know, the architectures the database designs. But but somehow you need you know there, you know, at the at the end when the day that is being analyzed, it needs to show consistency results. Right. No, you bring up a really good point about, for example, what is an
active customer versus the passive customer. So if someone wants to report on all of our active customers and how much revenue they have, if you haven't defined that uniformly across an environment, especially where there are mergers and acquisitions and you have different systems that are being pulled in. Now, that can be very misleading because it can look like the number is low and then you realize, oh, wait, we've miscategorized twenty percent of the companies in this report and
they shouldn't even be in here. Bring the numbers down. This guy only wants to know what the active clients are doing. Those are not active clients. So these are the kinds of definitions you're talking about right where you get into what goes into the reporting that gets delivered to your sales teams or your senior management or whatever it is. What are those definitions and are they applied
evenly in all situations. That's a system's issue, right, And if you don't get that right, then numbers are going to be wrong and you're gonna get unhappy clients. So folks, don't touch up that. I'll be right back. We're talking to Allah zake In from Athena Solutions. You're listening to Inside Analysis. Welcome back to Inside Analysis. Here's your host, Eric Tabanac.
All right, folks, back on Inside Analysis, talking to a Lah zake In of Athena Solutions, and we're talking all about data governance and data catalogs. Made a couple of really good points. Allah wonders that a data catalog is a good mechanism for data governance. And you know, I've been around long enough to remember that back in the old days, data governance either occurred at the database level or at the application level. Like either you have
access to the data or you don't. Or you have access to the application or you don't. And there's a very archaic and very difficult and challenging and manual way of dealing with things in which you always wanted was something in the middle. And that's what a data catalog can provide because it can be your source, your marketing area for definitions what do these things mean? But those definitions can come into play in reporting and analysis that we're talking about a minute
ago. How how you define an active customer, for example, or a prospect. In the marketing world, what's a marketing qualified lead versus a sales qualified lead? How do you push someone over the edge from MQL into SQL and actually not structure career language sales qualified lead and actually get them into the hands of a salesperson. That's a big thing, and that's a definition that probably changes a lot these days because it's all based upon the data that's coming
in and it used to be based on just gut instinct. You would say, oh, I don't know the salesperson, go oh, I think he's a marketing qualified lead. He's a sales qualified lead. Really, like, what is the data say about that? And when you can get into using the data that's much more accurate, that's much more reliable than someone's gut instinct. Right. This is the different between the old ways and the data driven culture that a lot of organizations trying to establish in their in their companies.
And the way to get into this data driven culture is to start the data governance program because it goes into the areas which improve this maturity, improve the data literacy, improve the compliance, create overall across the board improvement and not to jump in at everything at once. But at this point we're very lucky. Again we have you know, we have a lot of we have thought about data governance a lot. You know, I think as as a humanity,
we we thought about it a lot. And right now, official and unofficial data governance frameworks they look similarly. You know, everybody is talking about metadata management, everybody is talking about data quotes, everybody is talking about compliance, everybody is talking about data architecture, give and take some other categories. But this is what this is what the focus is, and this is what we can talk about in organizations, and in essence, the conversation is not
completely new. So we are very like in terms in terms of like in comparison to what it was ten years ago when people started talking about data governance, But people understood it so differently that the conversation like went much slower.
There was a lot of people. You know, you would have to get a buy in and sell it much much more and the results of your selling would be much more modist right now, and also the amounts of data organizations are dealing with is increased so significantly that and everybody feels the pain so acutely if something doesn't work that it is almost it is almost easier to get into it and start conversations and start converzations on multiple levels, on the executive level,
on the department head level. You know, you pull people into data governance organization that would be focusing on this type of work, and people are you know, willing and excited and you know, hopeful, I want to say, because nobody wants to spend you know, fifteen days on debugging or
reconciling two different reports that are equally important. That's I mean, you hit the nail on the head there, and I think that's one of the selling points, right, is that people realize that there are things that can be automated and once you've realized something that can be automated and something that can be
instrumented so you can see what's happening, you know. I mean, I'm old enough to remember the earliest days of email marketing, and it was nineteen hundred and nine nine when I first use an email marketing platform and I could see who opened the emails and who clicked on the links, who and how many times they clicked And I was like, wow, that just changed the game, because before you just have to call everybody and hope that someone cared,
and that's very bad for morale. But when you know who cares because it's in the data, then you call the right person and that person is interested. And you know, consulting is a lot like sales. I mean, yes, you have to sell the service, but then you have to sell yourself every engagement, every time you're talking to the client, in a way, you're selling your awareness and your knowledge and your ability to ask good questions. And one of my soapbox topics is morale. And when morale is
high in an organization, really good things happen. When morale is low, good things do not happen like bad things happen. And when you can get the right data to the right people and give them that observability and give them the meaning that you were talking about, then it gets back to what you said earlier, which is people like to be understood, They like to understand things, they like to have meaningful conversations, because that's when stuff gets done.
That's when you can convince someone in a different department, hey, we really would love to get that returns data that you have earlier in the process, because you know, if a pocket, if a package isn't delivered on time, if we can tap into UPS's feed for that for their tracking service, that helps us know which products to bring in the other side for manufacturing
for example. That's just one example of how the right data to the right person can make a huge difference in the business to keep morale high, to keep things humting, to keep profit going. So it's usually these days boiled down to getting access to one particular data feed, getting it in concert with other data so you can see it and understand it. Because that's why you need the two screens right, because you can't see it all on one screen,
right. You have to see the modeler see and the wrong over here. And there's a lot to see and absorb, and so that's I'm kind of rambling here, But the point is when when you're in this business and you realize how much difference one more source of data can make, that's a magical moment, right I would say, I would say, there's I think that what you talked about is trust. You know, people are afraid to
trust the data. And I started talking about data driven culture, and this is exactly the definition of it. It's the culture where people are not afraid to trust their data. They when they faced with the choice of relying on the intuition and relying on the results, they can trust the results more. You know. Nobody said that you don't meet your business knowledge like and you
know the years of your business experience, it's not true. But often the results that they that that the organizations get in the data are so weak. So the process is to get them stronger, you know. And so this is where the data literacy helps and the data governance helps all of these things.
It's it's almost you know, you you it's a it's a complicated and big problem, but you and you don't address it all at once, but you address it from many places, and as long as the direction is as long as the movement is in the right direction, this is what This is what's important, and this is why people talk about levels of data maturity. Ah. I love that this is the problem. You know, it's not one level of maturity. There are many levels of maturity, and there's a
there's a way. This is a nice way to estimate where you are and where the progress is made and where more progresses. Yeah, that's a really good point too, because understanding your maturity will help you know what is possible right now. And you know, for example, we talk a lot about artificial intelligence, and AI loves data. It consumes data. It needs data to be able to train models, and then it has to be curated over
time. You don't just turn it on and walk away. But the point is, if you don't have your data house in order, you better hold off on trying to leverage too much AI. Right, It's like, don't you have to crawl walk run? Don't go from crawl to run because you're going to fall over and cause some problems. And that's where the data maturity comes into place. And that's a combination of things, right, it's understanding what information do you have. It's understanding how how skilled are your people,
how data literate are they? All that goes into understanding your maturity right now, right, just one more minute, go ahead. Yes, people's side of data maturity is how well people understand their role in organization and how well they're able to follow processes associated with the data. So that that is another aspect that you know that goes into data maturity, which is not data at
all. This is this is people. This is organizing, uh work essentially right, day to day day to day work, whether you you know your actions actually improve the situations or you keep patching the old problem and it will never go away. Right right, That's that's it. Well, folks, Podcast Ponus segments up next, stand by, and we're talking to Allah Zak
and of Athena Solutions. Standby, right, folks, back here on Inside Analysis with Allah Zakin Athena Solutions, having a great conversation about data governance, data maturity and data catalogs and how to get the most value from your data and allow you reminded me of one of my favorite cliches. It's a Russian proverb that just gives me chills. It's says there is nothing more permanent than a temporary solution. Oh, isn't that a good one? It's like,
oh no, let's just do like this for now. It's like, no, no, be careful to be like that forever. And you have. But that is a good clue to understand a the maturity and be understand all right, what are the next steps? What can we take in terms of steps now in light of your situation. When you see what's broken and you have to talk to the people you got to talk to sales maybe marketing, administration, where do you find the frustration? I think that's really the heat
map for where to focus. I mentioned friction as a term earlier. Where's the friction? Where are the rough spots? What are people complaining about? And then you look to see what data do we have available that can fulfill this gap or fill this gap and get people happy again, right, because you want your marketing, you want ever want to be happy pretty much in
your company. You want them to be working hard and focus, but you don't want them frustrated and anxious and untrusting, right, And the problem with data is that if they don't trust the data, it's hard to get that
trust back. But I'll just throw that over to you. The temporary solutions you got to watch out for that, but they are good signals to know where to focus attention in the near term because you have to do these things incrementally, right, You have to sort of tick off the boxes and climb the mountain step by step before you can get up to there and use this great AI. You got to do the blocking and tackling necessary first, and
that's going to revolve around the frustrations. Is that about right? I agree with you. One thing I want to say, don't trip off the band aid until you're ready, until the wound isn't healed, right, So we can't really rush things too much. I wouldn't. I want to say that it is important to be respectful to the existing cult in the company, and existing platform and existing data architecture. You know, the project has to start
within this framework because everything can be changed at once. However, you know, it's important to keep track. It's important to keep track of the you know, the old patched wounds that exist and they a fester. You know, you you have to you have to give it a thought. You have to see where you know where it could work too, you know, where the improvement could work, what could help it. Ah. However, I think that the detailed understanding of the current environment goes hand in hand with the
creating a vision. You know, this is this is where, this is where the morale, like you are talking about the morale. And I really like that idea because personally, for me, it is very important to have the vision and have have like kind of the next steps outlined for myself and
you know, see that what I'm doing is beneficial. So creating the vision together with the top management understanding the business goals, understanding goals for each department, maybe on a little bit more of a more detailed level, allow to marry the two, you know, when we when you know the current situation is understood and the vision is developed, it's easier to bring them together. Yeah, that's great. That's a great way to end our conversation too.
All. I love your energy about this stuff, and I love your little pithy quotes, and I think you're exactly right that you have to have the vision. You got to know where going and then really understand the different stepping stones to get there, because it is kind of like stepping stones across the river, right, you have to be careful and hit those stones properly. Don't want to slip off of them. You don't want to wind up swimming in the river. You want to get across the river, and it's the
stepping stones that get you there. And sometimes you have to build a little bridge for example. There are different things you'll have to do. But when you can see the vision, and this is why you talk to the clients, when you can see where things need to go, and then understand the steady state. And probably one of the most important things you said is respect the culture of the company, respect how they do things. Now, having
some big bang approach is probably never going to work. So you really have to figure out what is your access point, who's flexible and who's not, and be very careful about how you move forward. Because at the end of the day, when you get the catalog delivered and they start using it, that's when the business changes. That's when really cool things start happening with your prospects, with your customers, with your partners. They're all on board,
and that's when morale stays high. Right, yes, yes, no, you're right, Eric, this is something that is always important to consider. Yeah, well, Alla, thank you so much for your time today. It's so fun talking to you and folks sending an email. If you want to learn more about what's going on here, info at insideanalysis dot com. We'll talk to you next time. You've been listening to Inside Analysis KCAA where every day is a great day, KCAA Loma, Linda and now the voices
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