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inside analysis dot com, Inside analysis dot com. And now here's your host,
Eric Kavanaugh and all right, ladies and gentlemen, headllo 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 Rick Sherman
who was one of my best friends of the business. And Rick unfortunately passed
away last year, which was really 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 it. 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, also 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 seemed 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 funny.
I think it's a wonderful experience when you find what you enjoy, and
that's what I do too. Like for me, I used to watch Mondy
Python way back in the day, and Eric Idol used to do all these
talk show skits where he was being all good being doing his talk show,
and I was like, I want to do that. I want to do
what he does. Want me just sit around 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 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 you 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. Then 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
the relationships 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
fact up again quickly, because in the old days that would have been extremely
difficult to me. 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 a lot. That didn't change. But
in terms of how to implement and how you know 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 people like to
understand, people like to have meaningful discussions, people like to speak this 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 the 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
and jump where it is most needed. Or you can pick up a low
hanging fruit and jump there. 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 discovering 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, observeability exploded in the last few years, which is for good reason.
I mean, there's reasons why these things happen, but that you have
all this other data to make sense of. 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 out
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 partical 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're 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 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 they're great and and I understand and every client
has something needs something different, but you can 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 more than
one group of people. Essentially, Yeah, that's very interesting. I really
like this approach that you've taken 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 for analytics. I
resume 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 you know, in order to along the way,
along the journy, 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 to manually switch things
on and off, and that can be a pain. You start to think
about organizational hierarchies and 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 chow 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 roll based access, there
are a lot of there are a lot of tools to uh 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 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 into those areas, so they have to 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 known 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 to spread 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 posture.
You want to be offensive and you want to be creative in using this 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 benefiticial
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 adapting it in a 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 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, if folks don't
just that down'll be right back. You're listening to Inside Analysis with Allah zaken
from Athena Solutions. Standby, 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 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 remain 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 glossriries 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 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 data 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. H 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 uh 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 lay 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 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 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 a 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 proof of 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 of vertical financial services insurance for example. Some need very complex
higher artees, some don't. And if you don't need these complex hierarchies,
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 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 of part Dot onto HubSpot and then a year
later they go back to part Ot. 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 gainations. 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 or 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 rated in a data catalog. Perhaps, so just kind of walk
us through what it looks like in the early stage. Is 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? There are no limitations, you know, what would you
like to see? And sometimes this is this conversation is very talent. 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 is 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 some
mean 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 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 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 and what you said is you know 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 own 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 someone from the outside
to come and say, hey, have you thought about doing it this way,
and usually there's someone who goes, yeah, I suggested that last year,
right, and it's like, but that's the exactly 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
data is being analyzed, it needs to show consistent results. Right. No,
you a really good point about, for example, what is an active
customer versus a 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 going to get
unhappy clients. So folks, don't touch up the dow and be right back.
We're talking to Allah zake In from Athena Solutions. You're listening to Inside
analysispect you welcome back to Inside Analysis. Here's your host, Eric Tabanaugh.
All right, folks, back on Inside Analysis, talking to Allah Zaekin of
Athena Solutions, and we're talking all about data governance and data catalogs. Made
a couple of really good points. Allah wants 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 that'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
marshaling area for definitions what do these things mean? But those definitions can come
into play in reporting an 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 struct
your 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 difference 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 a humanity, 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 organization 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 conversations on multiple levels, on the executive level, 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 ninety
nine when I first used an email marketing platform and I could see who opened
the emails and who clicked on the links and how many times they collected,
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 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're 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 turns
data that you have earlier in the process, because you know, if a
pock, 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 model sworn 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 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 go nance 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 truth point. 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 needed. 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
love 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 you 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 and 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 bonus
segments up next, stand by, and we're talking to Allah Zaekin of Athena
Solutions. Standby, right, folks, back here on Inside Analysis with Allah
Zain and 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 says there is nothing more permanent than a temporary
solution. Oh that isn't that a good one? It's like, oh,
no's this for now? It's like no, no care butle 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 focused, 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 turn 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 bend aid until you're ready, until the
wound isn't healed, right, So we can't really rush things too much.
I want I want to say that it is important to be respectful to the
existing culture in the company and an 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 as 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 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. 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 you're 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. 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, what is your 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 this is something that is always important
to consider. Yeah, well, Allah, thank you so much for your
time today. It's so fun talking to you, and folks, send an
email if you want to learn more about what's going on. Here info at
insidanalysis dot com. We'll talk to you next time. You've been listening to
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