WEBVTT

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Information economy as a rod. The
world is teeming with innovation as new business

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models reinvent every industry industry. Inside
Analysis is your source of information and insight

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about how to make the most of
this exciting new era. Learn more at

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inside analysis dot com, Inside analysis
dot com. And now here's your host,

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Eric Kavanaugh and all right, ladies
and gentlemen, headllo and welcome back

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once again, so the only coast
to coast show all about the information economy.

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It's called Inside Analysis, and folks, I'm so excited now to be

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talking to someone Ala Zaken of Athena
Solutions. I'm actually going to be doing

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some work with these folks. Athena
was founded by a guy named Rick Sherman

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who was one of my best friends
of the business. And Rick unfortunately passed

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away last year, which was really
kind of a shocker to me. But

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the light, the cycle of life
goes on, and I decided it's time

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to talk to these people, maybe
do some work with it. So here

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I am. I'm going to talk
to Ala Zaikin. She's a data modeler.

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She's all into the Kimball approach of
guse you studied Kimball back in college,

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right, also tell me what got
you in the data and why do

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you like making order from the chaos
of data. Well, at the time,

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it was just it seemed like a
logical approach to thinking about data,

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and I was very excited that I
found something that resonated with me so much,

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and since then, I literally can't
let it go, both professionally and

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personally. I think I am making
lists and making everything into a table.

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So I am that type of person
for better or worse. That's pretty funny.

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I think it's a wonderful experience when
you find what you enjoy, and

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that's what I do too. Like
for me, I used to watch Mondy

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Python way back in the day,
and Eric Idol used to do all these

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talk show skits where he was being
all good being doing his talk show,

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and I was like, I want
to do that. I want to do

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what he does. Want me just
sit around ask questions to learn things,

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because you get to learn for a
living, but you also get to learn

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from the data. Right, And
you said something I thought that was really

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interesting before the call. You're like, oh, I'm a data modeler,

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so I have two screens. Why
do you need two screens to be a

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data modeler. There's a lot to
absorb, you know, there's when you

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create a data model, there are
many conversations that are happening, the conversations

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with business users, conversations with technical
people, conversations with managers. Everybody has

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their own goals and data model usually
stands kind of in the middle of all

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of it, and you basically combine
all of these aspirations into what you have

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created. That's what I that's what
I found, and I found that a

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lot of meaning can be created in
the process, which is very attractive aspect

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of this whole work for you personally, because you know, by looking by

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combining perspectives, you have a different
view of the data and different you identify

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different relationships and the meaning comes out
in this work. Yeah, well,

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you bring up some really good points
here, and I like your joke about

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ordering things in the form of a
table. Right you have rows and columns

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and the data somewhere between. Then
you have multi dimensional data. Of course

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that is three dimensional or even more
dimensions than three just depending upon the particular

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use case. But when companies are
trying to get value from data, you

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do have to go through this process
of distilling the information. You have understanding

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the relationships between the data points and
then going ahead and building out the model.

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And that has changed somewhat in the
recent years. You know, we

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talk a lot about Snowflake and how
Snowflake really saw an opportunity to allow companies

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to tear down the data warehouse,
redo the schema, and then spin it

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fact up again quickly, because in
the old days that would have been extremely

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difficult to me. Once your data
warehouse is running, if you want to

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start adding tables and things and adding
dimensions is like, oh no, I

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think, really, are you sure
you have to do that? And that's

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changed. But if you would just
kind of walk through a little bit what

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it's like to look at the data
and to understand the relationships, to start

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to map out that data model,
what does that actually look like? What

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are the aha moments? So I
noticed that in the recent years the strictness

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with which we designed solutions kind of
disappeared. And it's not free for all,

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and you still have to understand a
lot. That didn't change. But

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in terms of how to implement and
how you know how to deliver a solution

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piece by piece by piece, I
think that this is what you're talking about

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this is what what changed because the
speed of delivery, Like, nobody wants

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to wait until a big, huge
data warehouse is done finished for you everybody.

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It's an iterative approach, it's it's
an agile at Gail game. And

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that kind of works in with the
technological changes that are happening. Nobody wants

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to wait for a big solution.
You know. You you develop understanding in

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one area and you deliver it.
You develop additional understanding when the when the

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project is delivered. That's that has
been my experience, and then you add

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to it. So a lot of
meaning that I was talking about, I

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like to talk about meaning because this
kind of this kind of I understand.

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I think that this elevates the conversation
a little bit because people people like to

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understand, people like to have meaningful
discussions, people like to speak this same

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language and collaborate. So I feel
like modeling creates this type of conversation.

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But when I was talking about the
hard structure about on delivering a project,

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that seems to be dissipating and people
feel much more empowered and flexible in the

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way they work and in the way
they approach the overall the overall objectives.

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There's a lot of data right now. Companies have a lot of data,

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so they have a lot to work
through. So jumping at everything at once

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is not practical anymore. So you
have to step back and identify your priorities

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and jump where it is most needed. Or you can pick up a low

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hanging fruit and jump there. You
said several really interesting things. One I

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love that line where you said people
like to understand, right, people like

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to have meaningful conversations. And when
you don't understand, that's when you're still

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in discovering mode. You're still asking
questions, You're trying to wrap your head

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around something. You don't get it, and then when you do understand,

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it's like, oh, I get
it now. And that's what you're able

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to do with data. And I
think the really interesting point you made is

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that there's so much data right now. I think, like observability, I

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mean, observeability exploded in the last
few years, which is for good reason.

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I mean, there's reasons why these
things happen, but that you have

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all this other data to make sense
of. So how do you do that?

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I mean, so I'd like to
just throw that back at you and

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say I think it makes a lot
of sense. How do you start with

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with low hanging fruit, especially for
a data model. Do you build out

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one corner of it and then get
that nailed down and then start to incrementally

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move out from there to build out
a bigger model that really represents the business?

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How does that actually work? Usually
you identify an area where it would

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be partical to begin, even though
even though on your roadmap there could be

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much much more. You have to
start to focus, to focus the effort

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in this area and to start developing. It's almost like prototyping. You know,

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you start developing the structures that you
start developing the understanding, you start

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developing a way to deal with the
group with the requirements. So all of

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this goes into the into delivering the
project, into delivering the you know,

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the plate platform as as it is
called now nobody we rarely speak about data

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warehouse as it is now? Did
you notice that because we speak about data

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platforms and this is platform is the
only thing that can resolve you know,

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the problems that people are having with
the data because they're they're so universal and

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they're so broad, and I noticed
that they're pretty they're also pretty standard you

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know, everybody, everybody is dealing
with the amount, like sheer amount of

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data everybody is dealing with, you
know, whether data quality is up to

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the level everybody is dealing with common
creating common understanding around around major concepts in

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the business. So so the more
so Actually the technology reflected this trend,

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I think, because you know,
there are many data catalogs that exist now

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so so and like, honestly one
better than the other. You know,

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you take your pick. They're they're
great and and I understand and every client

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has something needs something different, but
you can you can have your choice at

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this point. But what it gives
is the common creating, the common understanding,

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the creating the common language so that
it is easier to reach the consensus

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and it's easier to build out the
structures that solve the problems for more than

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one group of people. Essentially,
Yeah, that's very interesting. I really

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like this approach that you've taken because
you've come along this journey and you've seen

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the changes, right, I mean, there's so much more data right now.

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It's coming at people so quickly.
And the question with design and the

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data model is to understand the most
efficient way to capture the data to potentially

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refine the data, transform the t
for example, transform the data and deliver

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it to a solution that can be
used for operations or for analytics. I

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resume a lot of what you do
is geared around analysis, is that right?

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Absolutely, We're building the platforms to
facilitate the advanced analytics, to facilitate

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data science, so to you know, in order to along the way,

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along the journy, we pick up
a lot of things like you know,

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glossaries and governance and creating additional processes
around the data and data management in general.

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Yeah, and governance. You know. I was talking just this morning

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about privacy and last week earlier this
week, I should say, we did

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a show two about privacy and I
was like, you know, what is

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privacy really? And when you get
down to it in the data world,

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it has to deal with the appropriate
accessing of information. Right, who should

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have access to this information? Who
should not have access to this information?

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Certain things are private, Certain things
you only want certain people to see.

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But you do want those certain people
to see it. You don't want no

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one to see it, right,
So you have role access role based access

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controls, for example, and you
want to be as as dynamic as possible,

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because if it's very fine grained,
then some person to manually switch things

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on and off, and that can
be a pain. You start to think

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about organizational hierarchies and and how challenging
it is to get the right access for

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the right person at the right time
without disrupting the access right because the people

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who need the data, you want
them to have the data and not to

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have to jump through hoops to get
it. So that's really what it comes

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down to with respected data governance right
is figuring out what are the ideal policies,

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what are the appropriate chow points where
we can require someone to log in,

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where we can require some to validate
who they are without disrupting the workload,

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without disrupting what we want them to
be doing with the data. Right.

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Absolutely, absolutely, and this is
one of the major areas of data

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governance where everybody, you know,
everybody thinks about roll based based access,

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everybody thinks about data classification. So
to implement that roll based access, there

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are a lot of there are a
lot of tools to uh to facilitate security.

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But also, you know, a
big part of an initiative such as

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security is the consensus, you know, and I think that implementation is a

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big piece of it, and there's
technology to choose from again, but you

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know, jumping in and you know, classifying the data elements and developing the

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definitions for the roles is the step
that takes the longest. There is a

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little bit of inertia in people in
general. Everybody, everybody has it,

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but companies do too. So you
know, I think that the compliance requirements

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actually forces people into those areas,
so they have to they have to address

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it. But it's also it's also
logical and the right thing to do,

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given that the data sources that we're
dealing with are incredibly important and incredibly expensive

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and incredibly vast. And I'm just
guessing here because I haven't known all these

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projects myself, but I've been around
them. I'm guessing that the compliance,

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which is a driver. There's no
question in compliance as a driver. But

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what you really want to spread the
business to take more of a proactive approach,

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more of a positive approach about used
in the information. And in that

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sense, the compliance can be a
nice driver. It can be a nice

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mechanism of action to get the business
to pay attention and then build a value

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around that. So it's that the
value isn't just being in compliance. That's

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not the value. That is a
value, But the bigger value is knowing

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how to use the data, knowing
how to leverage the data. And I'm

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guessing that you figured out years ago
and you use it as an opportunity to

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explain to the business the value they
can be getting from this data while being

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in compliance. Right, you don't
want to just be defensive in posture.

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You want to be offensive and you
want to be creative in using this established

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rules to drive change but then bring
value to the business. Right. Yes,

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I think the compliance frameworks such as
GDPR for example, it is a

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framework and there's a benefit, there's
a benefit of to tapping in into a

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framework that's already made. You know, it does it does require an investment

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to develop all of this in an
organization, but I feel like I feel

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very positive about it because you know, there's a lot of thought that has

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been put into those framework and you
know, I feel like we're the benefiticial

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beneficiary of this thought process, which
is you know, this is where the

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time goes, you know, to
develop you know there's time that is spent

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in even adapting it in a company, sure, but I feel like I

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feel like having having a framework to
work with is always always fit. Saves

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a lot of time to mention in
lawsuits. Right, Well, you have

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your guardrails already, you have the
framework so that at least limits the discussion,

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that limits the free play of the
scenario, if you will, And

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it kind of focuses people and keeps
people untasked. Basically, if folks don't

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just that down'll be right back.
You're listening to Inside Analysis with Allah zaken

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from Athena Solutions. Standby, Welcome
back to Inside Analysis. Here's your host,

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Eric Tabanac. All right, folks, back on Inside Analysis talking to

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a Lah zake In of Athena Solutions, and we're talking about data governance and

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data modeling and processes. And you
had a great quote that's where the time

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goes, and it's really in working
through first of all the objectives of the

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business. What do you want to
accomplish with this data? And how can

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you do that while remain in compliance, which of course is very important.

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But what value can you get from
the data? That always has to be

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on the mind, and I'm sure
it's always on your mind. Is thinking,

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how do we align these practices and
programs with some value creating initiative to

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feed people to the data that they
need at the time they needed and what

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sources are available. So I wanted
to talk about data catalogs because to me,

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business glossriries have been around for well, quite frankly, for thousands of

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years. I mean, if you
get all the way back to Mesopotamia,

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they had symbols that they used for
managing things and someone had to learn those

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symbols and know what they meant and
build to do the math in real time

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as they're trading brain and other things
like that. So this is not new.

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It's been around for ages, but
now there's so much more data and

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the business glossary, the data catalog
can be such an enabler by fostering business

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literacy data literacy, and that all
feeds into helping people understand what are we

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trying to accomplish here and use the
data to get things done. Talk about

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data catalogs and how you use them
to help clients figure out what they need

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to get done for their information systems. So there is there is I think

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an image at this point, there's
so many there's so many different products to

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choose from for data catalogs, the
clients always, you know, there is

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always a desire to have it.
Well, that's what I wanted to say.

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There's always a desire to organize,
and there is always a desire to

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understand the data. People are a
little bit shy of purchasing sometimes a data

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catalog because everybody understands the investment and
the you know another it's basically another application

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to maintain. H And I am
starting with this because in my experience there

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is always this hesitation. You know, we have the spreadsheets like in the

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Mesopotamia, right, we have the
spreadsheets. Why do we need to buy

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a tool and then maintain a tool? Okay, but you know, right

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now, the need for creating meaning
out of the data is so so strong,

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and so with meaning, the value
is being drawn out. So the

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data catalog actually does that, and
it works in different ways in different situations.

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Sometimes uh an organization wants to invest
in data governance and they choose a

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data catalog as basically their platform for
data governance, which can be done and

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it's a it's a it's a great
platform for data governance. It allows room

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to you know, to accumulate business
data. It allows the room to to

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accumulate metadata. So the information that
is being stored in one place and used

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by the data stewards, by technical
data stewards, by lay people in a

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company, by business analysts, by
data scientists. So you open up this

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meaning to a variety of audiences that
before that went and used the spreadsheets here,

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and the spreadsheets here and Microsoft Access
databases there. So big huge step

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forward. And another thing that I
think is a benefit is again the framework.

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You know, each of the data
catalogs, you know, you bring

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in a new tool, you bring
in a framework. So you have to

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choose wisely. And the initial stage
of choosing a tool for organization is very

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very important. Proof of concepts are
very very important because every organizations has different

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needs. But once you committed to
a framework, I think it's a benefit.

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Yeah, you bring up a really
you bring up really, I just

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want to dive in this. You
bring up a really good point, which

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is that and I've seen this with
all sorts of different technologies. You really

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need to play around. That's why
I proof of concept is important because each

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tool will have its own first of
all, its own model underneath for how

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it works, it'll have its own
array of functionality. It may have a

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particular bend toward of vertical financial services
insurance for example. Some need very complex

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higher artees, some don't. And
if you don't need these complex hierarchies,

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then you don't need to go down
that road. So you do have to

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kind of understand what are you trying
to do with the tool, what is

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the business problem you're trying to solve. Then be able to talk to an

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expert like yourself and understand what's the
difference between Calibra for example, or data

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dot World and any number of other
solutions. And once you wrap your head

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around what's available and the functionality and
kind of how companies, how individuals will

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use the technology, that's when you
can make the hard decision. And like

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you said, you have to choose
wisely because you know, I mean,

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you know, we talk about this
in the cloud all the time. I've

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talked about companies that moved off of
part Dot onto HubSpot and then a year

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later they go back to part Ot. You're like, oh, the engineer

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is like all the people who had
to do all that work are just like

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are you kidding me? Like we
have to go through the gainations. Oh,

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there's nothing more painful than doing a
job the second time because something messed

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up at the end. I have
I mean, I have a fear of

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that, Like I can't stand Like
if I in the old days, you'd

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write an article and if you lost
it because the floppy disk broke or something,

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you're just like, oh, no, I have to rewrite this thing.

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We've now baked in auto save to
all these tools, right, so

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you don't have that problem as much
anymore. But nonetheless, the point is

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you have to be careful in that
decision, and that's where a trusted advisor

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comes in handy, Right. Absolutely, there is so much power that modern

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technology gives us so that we can
move with like much greater speed, but

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we might move in the wrong direction. So now this this decision becomes even

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more important. Yeah, And when
you work with clients on that particular subject

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area, I mean, I'm guessing
it's a series of meetings where you sit

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down or you try to understand what
is the nature of this business. You

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know, of course, where does
your revenue come from? That's always a

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major topic to understand where is the
revenue coming from? And then how do

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you leverage data for marketing purposes,
how do you leverage data for reporting purposes?

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How do you leverage data for product
design and service design? These are

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all the kinds of decisions that can
be really aided by having the right sets

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of data rated in a data catalog. Perhaps, so just kind of walk

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us through what it looks like in
the early stage. Is what are some

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of the questions you ask and how
do you sort of shape that journey to

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choosing the right data catalog? I
think the journey to implement a data catalog,

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I guess, and to implement any
project actually starts with understanding what doesn't

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work now? All right, yes, so I would I would say that

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this has to be that that has
to be the first step. But then

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you need to balance it out.
What what are the aspirations in the business?

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You know, where where they see
the improvement? You know, sometimes

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we ask a question in the perfect
world, you know, what would you

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like to see? There are no
limitations, you know, what would you

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like to see? And sometimes this
is this conversation is very talent. Then

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you get into the requirements, Like
I said, the requirements for the data

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catalog. Everybody has slightly different situations
the you know, the need is there

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to In some cases business glossary needs
to be so you know, developed and

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so precise. In some cases,
the technical side of it, the said

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technical metadata side of it, and
they have availability of that metadata has to

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be you know, carries much more
weight, so you have to you have

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to take this into consideration when you're
choosing a tool. Also, is it

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is it an older company that is
mostly on prem Is it a company that

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is cloud with the high security requirements
that you know need to be taken into

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consideration, and that because because you
are, you will be exposing some some

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mean so you have to take into
account the technical architecture that exists in the

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organization as well as the compliance requirements. So all of this becomes a long

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list of requirements and I think the
you know it worth it, is worth

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it investing a little bit more in
developing these requirements before jumping into the pool

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of different data catalogs. Right.
Well, no, and I'm guessing that

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here's how my mind works, Right, I'm always trying to kill three birds

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with every stone. I'm guessing that
you go through the process of getting to

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know the company, you are understanding
bit by bit what makes it special because

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every company has their own DNA,
and this is why templates only go so

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far. You know, insurance companies
may have a particular focus or a particular

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area of specialization where they're very good
at things, but what they're good at

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requires additional data sets. And and
what you said is you know what's not

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working now? Or I think maybe
the better way to put it is where

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is their friction right now? Where
is something difficult that should be easy,

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And especially in terms of data being
fed to someone, right, sometimes you

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can feed one more data point to
someone and you just solve the whole array

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of problems. And I'll give you
an interesting example. I remember there was

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a company when I was just when
I was learning about process mining, which

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is fascinating stuff, and they were
looking at the processes as they actually occur

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in the business because they've instrumented all
the different information systems. And what they

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saw is that there's this big bottleneck
on a credit check and like, huh,

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so every time someone requested something,
they had to go through a credit

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check, and that was the biggest
part of the whole process in terms of

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the time it took, in terms
of the difficulties and all these things and

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they realized, hey, for most
people, we don't need to do this

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credit check. For our established accounts, we don't have to do the credit

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check, So let's just cut that
part out and own use it when we

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need to. And they would not
have ever known that had they not looked

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at the process and looked at the
data and understood what was happening. And

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I'm like, wow, let's just
do this. We'll check and see is

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an established account with credit already with
us? And then skip that part and

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they're like, Holy Christmas, it's
sped up. Like a whole department got

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like three times as much work done
because they weren't wasting time on an unnecessary

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process. So I throw that out
to you because I guarantee as you're on

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site working with clients, there are
little bits and pieces that you pick up

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in conversations where you're like, hey, wait a minute, there's a different

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way to do this. And that's
what a consultant is supposed to do.

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Right, is recognized where there's a
process that is not efficient that can be

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greatly improved, and a lot of
times it just takes someone from the outside

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to come and say, hey,
have you thought about doing it this way,

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and usually there's someone who goes,
yeah, I suggested that last year,

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right, and it's like, but
that's the exactly like it is.

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It is so true, very very
often you get in to an organization and

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revive the efforts that were you know, done and forgotten, and you you

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know, you're able to kind of
bring it back to life and get somewhere

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with it. But you know,
another interesting like I think another common example

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of it's more more over not a
process mining, but I guess it's related

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to the data and to the definitions. And this is another area where I

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feel like there is a lot of
benefits benefits and of course the data catalogs,

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they are storages of the definitions.
You know, metadata is nothing more

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than a technical definition, and then
you bring business definition definition definitions. So

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differences and definitions, which is very
benign as it seems, create big differences

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in reporting. And this is when
the hours go when people try to figure

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out the differences and they investigate and
there could be you know, literally weeks

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of time of the most you know, valuable people and the teams that go

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into that. So so this is
usually one of the problems that people cite

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and this is one of the problems
that you know you you try to address

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sooner rather than later. Either you
approach it with the catalog or you get

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into the you know, the architectures, the database designs. But but somehow

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you need you know there, you
know at the at the end when the

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data is being analyzed, it needs
to show consistent results. Right. No,

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you a really good point about,
for example, what is an active

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customer versus a passive customer. So
if someone wants to report on all of

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our active customers and how much revenue
they have, if you haven't defined that

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uniformly across an environment, especially where
there are mergers and acquisitions and you have

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different systems that are being pulled in. Now, that can be very misleading

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because it can look like the number
is low and then you realize, oh

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wait, we've miscategorized twenty percent of
the companies in this report and they shouldn't

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even be in here. Bring the
numbers down. This guy only wants to

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know what the active clients are doing. Those are not active clients. So

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these are the kinds of definitions you're
talking about, right where you get into

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what goes into the reporting that gets
delivered to your sales teams or your senior

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management or whatever it is. What
are those definitions and are they applied evenly

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in all situations. That's a system's
issue, right And if you don't get

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that right, then numbers are going
to be wrong and you're going to get

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unhappy clients. So folks, don't
touch up the dow and be right back.

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We're talking to Allah zake In from
Athena Solutions. You're listening to Inside

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00:31:52.599 --> 00:32:05.960
analysispect you welcome back to Inside Analysis. Here's your host, Eric Tabanaugh.

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All right, folks, back on
Inside Analysis, talking to Allah Zaekin of

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00:32:12.119 --> 00:32:15.519
Athena Solutions, and we're talking all
about data governance and data catalogs. Made

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00:32:15.559 --> 00:32:20.279
a couple of really good points.
Allah wants that a data catalog is a

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good mechanism for data governance. And
you know, I've been around long enough

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to remember that back in the old
days, data governance either occurred at the

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database level or at the application level, like either you have access to the

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00:32:30.960 --> 00:32:34.799
data or you don't, or you
have access to the application or you don't.

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And that's a very archaic and very
difficult and challenging and manual way of

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00:32:39.720 --> 00:32:43.680
dealing with things in which you always
wanted was something in the middle. And

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that's what a data catalog can provide
because it can be your source, your

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marshaling area for definitions what do these
things mean? But those definitions can come

401
00:32:52.480 --> 00:32:55.720
into play in reporting an analysis that
we're talking about a minute ago. How

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00:32:57.200 --> 00:33:00.440
how you define an active customer,
for example, or a prospect In the

403
00:33:00.480 --> 00:33:06.720
marketing world, what's a marketing qualified
lead versus a sales qualified lead? How

404
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do you push someone over the edge
from MQL into SQL and actually not struct

405
00:33:10.720 --> 00:33:15.920
your career language sales qualified lead and
actually get them into the hands of a

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salesperson. That's a big thing,
And that's a definition that probably changes a

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00:33:21.119 --> 00:33:24.240
lot these days because it's all based
upon the data that's coming in, and

408
00:33:24.279 --> 00:33:27.880
it used to be based on just
gut instinct. You would say, oh,

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I don't know the salesperson, go
oh, I think he's a marketing

410
00:33:30.319 --> 00:33:32.519
qualified lead. He's a sales qualified
lead. Really, like, what is

411
00:33:32.559 --> 00:33:37.039
the data say about that? And
when you can get into using the data,

412
00:33:37.440 --> 00:33:40.279
that's much more accurate, that's much
more reliable than someone's gut instinct.

413
00:33:40.400 --> 00:33:50.440
Right, This is the difference between
the old ways and the data driven culture

414
00:33:50.480 --> 00:33:55.000
that a lot of organizations trying to
establish in their in their companies. And

415
00:33:55.559 --> 00:34:00.680
the way to get into this data
driven culture is to start the data governance

416
00:34:00.680 --> 00:34:07.720
program because it goes into the areas
which improve this maturity, improve the data

417
00:34:07.759 --> 00:34:15.360
literacy, improve the compliance, create
overall across the board improvement and not to

418
00:34:15.800 --> 00:34:22.480
jump in at everything at once.
But at this point we're very lucky again

419
00:34:22.440 --> 00:34:27.079
we have you know, we have
a lot of We have thought about data

420
00:34:27.119 --> 00:34:31.800
governance a lot. You know,
I think as a humanity, we thought

421
00:34:31.800 --> 00:34:37.840
about it a lot. And right
now, official and unofficial data governance frameworks

422
00:34:37.880 --> 00:34:44.159
they look similarly. You know,
everybody is talking about metadata management, everybody

423
00:34:44.239 --> 00:34:49.519
is talking about data quotes, everybody
is talking about compliance, everybody is talking

424
00:34:49.519 --> 00:34:53.519
about data architecture, give and take
some other categories. But this is what

425
00:34:53.840 --> 00:34:59.199
this is what the focus is,
and this is what we can talk about

426
00:34:59.280 --> 00:35:06.559
in organization and in essence, the
conversation is not completely new. So we

427
00:35:06.880 --> 00:35:09.960
are very like in terms in terms
of like in comparison to what it was

428
00:35:10.000 --> 00:35:16.320
ten years ago when people started talking
about data governance. But people understood it

429
00:35:16.400 --> 00:35:22.159
so differently that the conversation like went
much slower. There was a lot of

430
00:35:22.199 --> 00:35:27.000
people. You know, you would
have to get a buy in and sell

431
00:35:27.039 --> 00:35:31.559
it much much more and the results
of your selling would be much more modist

432
00:35:32.719 --> 00:35:40.159
right now, And also the amounts
of data organizations are dealing with is increased

433
00:35:40.199 --> 00:35:47.280
so significantly that and everybody feels the
pain so acutely if something doesn't work that

434
00:35:47.920 --> 00:35:54.679
it is almost it is almost easier
to get into it and start conversations,

435
00:35:54.840 --> 00:36:00.559
and start conversations on multiple levels,
on the executive level, the department head

436
00:36:00.639 --> 00:36:06.920
level. You know, you pull
people into data governance organization that would be

437
00:36:07.360 --> 00:36:12.480
focusing on this type of work,
and people are you know, willing and

438
00:36:12.519 --> 00:36:17.079
excited and you know, hopeful,
I want to say, because nobody wants

439
00:36:17.119 --> 00:36:22.599
to spend you know, fifteen days
on debugging or reconciling two different reports that

440
00:36:22.599 --> 00:36:25.920
are equally important. That's I mean, you hit the nail on the head

441
00:36:25.920 --> 00:36:29.880
there, and I think that's one
of the selling points, right, is

442
00:36:29.920 --> 00:36:34.760
that people realize that there are things
that can be automated. And once you've

443
00:36:34.800 --> 00:36:38.320
realized something that can be automated and
something that can be instrumented, so you

444
00:36:38.360 --> 00:36:42.239
can see what's happening, you know, I mean, I'm old enough to

445
00:36:42.239 --> 00:36:45.800
remember the earliest days of email marketing, and it was nineteen hundred and ninety

446
00:36:45.880 --> 00:36:50.920
nine when I first used an email
marketing platform and I could see who opened

447
00:36:50.960 --> 00:36:53.880
the emails and who clicked on the
links and how many times they collected,

448
00:36:53.920 --> 00:36:59.400
and I was like, wow,
that just changed the game, because before

449
00:36:59.400 --> 00:37:01.679
you just have to call everybody and
hope that someone cared, and that's very

450
00:37:01.719 --> 00:37:06.760
bad for morale. But when you
know who cares because it's in the data,

451
00:37:07.119 --> 00:37:10.559
then you call the right person and
that person is interested. And consulting

452
00:37:12.199 --> 00:37:14.760
is a lot like sales. I
mean, yes, you have to sell

453
00:37:14.880 --> 00:37:17.719
the service, but then you have
to sell yourself every engagement, every time

454
00:37:17.760 --> 00:37:22.320
you're talking to the client, in
a way, you're selling your awareness and

455
00:37:22.360 --> 00:37:25.599
your knowledge and your ability to ask
good questions. And one of my soapbox

456
00:37:25.679 --> 00:37:30.280
topics is morale. And when morale
is high in an organization, really good

457
00:37:30.280 --> 00:37:35.280
things happen. When morale is low, good things do not happen, like

458
00:37:35.480 --> 00:37:38.039
bad things happen. And when you
can get the right data to the right

459
00:37:38.119 --> 00:37:44.199
people and give them that observability and
give them the meaning that you're talking about,

460
00:37:44.320 --> 00:37:46.360
then it gets back to what you
said earlier, which is people like

461
00:37:46.440 --> 00:37:51.880
to be understood, They like to
understand things, they like to have meaningful

462
00:37:51.920 --> 00:37:55.760
conversations, because that's when stuff gets
done. That's when you can convince someone

463
00:37:55.800 --> 00:38:00.480
in a different department, hey,
we really would love to get that turns

464
00:38:00.599 --> 00:38:05.079
data that you have earlier in the
process, because you know, if a

465
00:38:05.119 --> 00:38:08.119
pock, if a package isn't delivered
on time, if we can tap into

466
00:38:08.320 --> 00:38:14.719
UPS's feed for that for their tracking
service, that helps us know which products

467
00:38:14.719 --> 00:38:17.920
to bring in the other side for
manufacturing for example. That's just one example

468
00:38:17.960 --> 00:38:22.320
of how the right data to the
right person can make a huge difference in

469
00:38:22.360 --> 00:38:25.559
the business to keep morale high,
to keep things humting, to keep profit

470
00:38:25.679 --> 00:38:30.119
going. So it's usually these days
boiled down to getting access to one particular

471
00:38:30.199 --> 00:38:34.960
data feed, getting it in concert
with other data so you can see it

472
00:38:34.960 --> 00:38:37.000
and understand it. Because that's why
you need the two screens, right,

473
00:38:37.079 --> 00:38:39.840
because you can't see it all on
one screen, right. You have to

474
00:38:39.840 --> 00:38:45.039
see the model sworn and the wrong
over here, and there's a lot to

475
00:38:45.079 --> 00:38:49.599
see and absorb, and so that's
I'm kind of rambling here, But the

476
00:38:49.679 --> 00:38:53.719
point is when when you're in this
business and you realize how much difference one

477
00:38:53.840 --> 00:39:00.920
more source of data can make that's
a magical moment, right I would say,

478
00:39:00.440 --> 00:39:09.400
I would say, there's I think
that what you talked about is trust.

479
00:39:09.760 --> 00:39:14.880
You know, people are afraid to
trust the data. And I started

480
00:39:15.079 --> 00:39:21.920
talking about data driven culture, and
this is exactly the definition of it.

481
00:39:21.920 --> 00:39:25.760
It's the culture where people are not
afraid to trust their data. They when

482
00:39:25.760 --> 00:39:31.480
they faced with the choice of relying
on the intuition and relying on the results,

483
00:39:31.559 --> 00:39:35.840
they can trust the results more.
You know. Nobody said that you

484
00:39:35.880 --> 00:39:39.159
don't meet your business knowledge like and
you know, the years of your business

485
00:39:39.199 --> 00:39:46.719
experience. It's not true. But
often the results that they that that organizations

486
00:39:46.760 --> 00:39:52.519
get in the data are so weak. So the process is to get them

487
00:39:52.559 --> 00:39:59.239
stronger, you know. And so
this is where the data literacy helps and

488
00:39:59.440 --> 00:40:05.360
the data go nance helps all of
these things. It's it's almost you know,

489
00:40:05.480 --> 00:40:09.719
you you it's a it's a complicated
and big problem, but you and

490
00:40:09.760 --> 00:40:14.800
you don't address it all at once, but you address it from many places.

491
00:40:15.000 --> 00:40:21.320
And as long as the direction is
as long as the movement is in

492
00:40:21.360 --> 00:40:24.400
the right direction. This is what
this is what's important, and this is

493
00:40:24.440 --> 00:40:31.559
why people talk about levels of data
maturity. Ah I love that. This

494
00:40:31.679 --> 00:40:36.480
is the truth point. You know, it's not one level of maturity.

495
00:40:36.519 --> 00:40:39.679
There are many levels of maturity,
and there's a there's a way. This

496
00:40:39.760 --> 00:40:45.360
is a nice way to estimate where
you are and where the progress is made

497
00:40:45.400 --> 00:40:49.400
and where more progresses needed. Yeah, that's a really good point too,

498
00:40:49.519 --> 00:40:55.159
because understanding your maturity will help you
know what is possible right now. And

499
00:40:55.280 --> 00:40:59.679
you know, for example, we
talk a lot about artificial intelligence and AI

500
00:40:59.800 --> 00:41:02.400
love data. It consumes data,
It needs data to be able to train

501
00:41:02.480 --> 00:41:06.320
models, and then it has to
be curated over time. You don't just

502
00:41:06.360 --> 00:41:08.440
turn it on and walk away.
But the point is, if you don't

503
00:41:08.440 --> 00:41:13.000
have your data house in order,
you better hold off on trying to leverage

504
00:41:13.039 --> 00:41:15.119
too much AI. Right, It's
like, don't you have to crawl?

505
00:41:15.320 --> 00:41:20.119
Walk run? Don't go from crawl
to run because you're going to fall over

506
00:41:20.159 --> 00:41:22.480
and cause some problems. And that's
where the data maturity comes into place.

507
00:41:22.519 --> 00:41:27.800
And that's a combination of things.
Right. It's understanding what information do you

508
00:41:27.840 --> 00:41:32.000
have, It's understanding how how skilled
are you are your people, how data

509
00:41:32.079 --> 00:41:37.679
literate are they. All that goes
into understanding your maturity right now, right,

510
00:41:37.760 --> 00:41:44.719
just one more minute, go ahead. Yes. People's side of data

511
00:41:44.800 --> 00:41:50.880
maturity is how well people understand their
role and organization and how well they're able

512
00:41:50.960 --> 00:41:57.559
to follow processes associated with the data. So that that is another aspect that

513
00:41:58.159 --> 00:42:00.079
you know that goes into data maturity, which is not data at all.

514
00:42:00.400 --> 00:42:06.480
This is this is people. This
is organizing, uh work essentially right,

515
00:42:06.599 --> 00:42:10.960
day to day day to day work, whether you you know your actions actually

516
00:42:12.039 --> 00:42:15.679
improve the situations or you keep patching
the old problem and it will never go

517
00:42:15.719 --> 00:42:21.360
away. Right right, That's that's
it, well, folks. Podcast bonus

518
00:42:21.360 --> 00:42:23.239
segments up next, stand by,
and we're talking to Allah Zaekin of Athena

519
00:42:23.280 --> 00:42:30.159
Solutions. Standby, right, folks, back here on Inside Analysis with Allah

520
00:42:30.320 --> 00:42:36.039
Zain and Athena Solutions, having a
great conversation about data governance, data maturity

521
00:42:36.639 --> 00:42:40.079
and data catalogs and how to get
the most value from your data and allow

522
00:42:40.119 --> 00:42:44.639
you reminded me of one of my
favorite cliches. It's a Russian proverb that

523
00:42:44.800 --> 00:42:49.960
just gives me chills. It says
there is nothing more permanent than a temporary

524
00:42:50.039 --> 00:42:54.599
solution. Oh that isn't that a
good one? It's like, oh,

525
00:42:54.760 --> 00:43:00.800
no's this for now? It's like
no, no care butle to be like

526
00:43:00.840 --> 00:43:05.360
that forever and you have. But
that is a good clue to understand a

527
00:43:05.519 --> 00:43:08.000
the maturity and be understand all right, what are the next steps? What

528
00:43:08.000 --> 00:43:13.480
can we take in terms of steps
now in light of your situation when you

529
00:43:13.559 --> 00:43:16.079
see what's broken and you have to
talk to the people, you got to

530
00:43:16.119 --> 00:43:22.239
talk to sales, maybe marketing,
administration, where do you find the frustration?

531
00:43:22.400 --> 00:43:27.239
I think that's really the heat map
for where to focus. I mentioned

532
00:43:27.280 --> 00:43:30.119
friction as a term earlier. Where's
the friction? Where are the rough spots?

533
00:43:30.159 --> 00:43:34.880
What are people complaining about? And
then you look to see what data

534
00:43:34.880 --> 00:43:38.159
do we have available that can fulfill
this gap or fill this gap and get

535
00:43:38.159 --> 00:43:42.039
people happy again, right, because
you want your marketing, you want ever

536
00:43:42.079 --> 00:43:44.880
want to be happy pretty much in
your company. You want them to be

537
00:43:44.920 --> 00:43:51.239
working hard and focused, but you
don't want them frustrated and anxious and untrusting,

538
00:43:51.639 --> 00:43:53.199
right, And the problem with data
is that if they don't trust the

539
00:43:53.280 --> 00:43:57.880
data, it's hard to get that
trust back. But I'll just turn that

540
00:43:57.960 --> 00:44:00.679
over to you. The temporary solutions
you got to watch out for that,

541
00:44:00.079 --> 00:44:05.719
but they are good signals to know
where to focus attention in the near term.

542
00:44:05.760 --> 00:44:07.719
Because you have to do these things
incrementally, right, You have to

543
00:44:07.760 --> 00:44:12.599
sort of tick off the boxes and
climb the mountain step by step before you

544
00:44:12.599 --> 00:44:14.960
can get up to there and use
this great AI. You got to do

545
00:44:15.000 --> 00:44:19.920
the blocking and tackling necessary first,
and that's going to revolve around the frustrations.

546
00:44:20.039 --> 00:44:24.079
Is that about right? I agree
with you. One thing I want

547
00:44:24.119 --> 00:44:29.800
to say, don't trip off the
bend aid until you're ready, until the

548
00:44:29.840 --> 00:44:37.079
wound isn't healed, right, So
we can't really rush things too much.

549
00:44:38.679 --> 00:44:45.760
I want I want to say that
it is important to be respectful to the

550
00:44:45.920 --> 00:44:52.960
existing culture in the company and an
existing platform and existing data architecture. You

551
00:44:53.000 --> 00:45:00.000
know, the project has to start
within this framework because everything can be changed

552
00:45:00.159 --> 00:45:05.119
at once. However, you know, it's important to keep track. It's

553
00:45:05.159 --> 00:45:09.639
important to keep track of the you
know, the old patched wounds that exist

554
00:45:09.800 --> 00:45:14.519
and they as fester. You know, you you have to you have to

555
00:45:15.239 --> 00:45:21.719
give it a thought. You have
to see where you know where it could

556
00:45:21.760 --> 00:45:27.519
work too, you know where the
improvement could work, what could help it.

557
00:45:29.920 --> 00:45:37.239
Ah. However, I think that
the detailed understanding of the current environment

558
00:45:38.480 --> 00:45:45.760
goes hand in hand with the creating
a vision you know, this is this

559
00:45:45.840 --> 00:45:51.360
is where, this is where the
morale like you are talking about the morale.

560
00:45:51.400 --> 00:45:54.960
And I really like that idea because
personally, for me, it is

561
00:45:55.039 --> 00:46:00.679
very important to have the vision and
have have like kind of the next steps

562
00:46:00.719 --> 00:46:06.599
outlined for myself and you know,
see that what I'm doing is beneficial.

563
00:46:07.519 --> 00:46:15.639
So creating the vision together with the
top management understanding the business goals, understanding

564
00:46:15.039 --> 00:46:19.960
goals for each department, maybe on
a little bit more of a more detailed

565
00:46:20.039 --> 00:46:25.360
level, allow to marry the two. You know, when you know the

566
00:46:25.760 --> 00:46:32.880
current situation is understood and the vision
is developed, it's easier to bring them

567
00:46:32.880 --> 00:46:37.559
together. Yeah, that's great.
That's a great way to end our conversation

568
00:46:37.679 --> 00:46:42.440
too. I love your energy about
this stuff, and I love your little

569
00:46:42.480 --> 00:46:45.599
pithy quotes, and I think you're
exactly right that you have to have the

570
00:46:45.679 --> 00:46:49.719
vision. You got to know where
you're going and then really understand the different

571
00:46:49.800 --> 00:46:52.360
stepping stones to get there, because
it is kind of like stepping stones across

572
00:46:52.360 --> 00:46:55.400
the river, right, you have
to be careful and hit those stones properly.

573
00:46:55.679 --> 00:46:58.760
Don't want to slip off of them. You don't want to wind up

574
00:46:58.840 --> 00:47:01.039
swimming in the river. Want to
get across the river, and it's the

575
00:47:01.079 --> 00:47:05.159
stepping stones that get you there,
and sometimes you have to build a little

576
00:47:05.159 --> 00:47:07.159
bridge for example. There are different
things you'll have to do. But when

577
00:47:07.159 --> 00:47:10.400
you can see the vision, and
this is why you talk to the clients,

578
00:47:10.440 --> 00:47:14.920
when you can see where things need
to go and then understand the steady

579
00:47:14.920 --> 00:47:17.599
state. And probably one of the
most important things you said is respect the

580
00:47:17.639 --> 00:47:23.400
culture of the company, respect how
they do things. Now, having some

581
00:47:23.480 --> 00:47:27.639
big bang approach is probably never going
to work. So you really have to

582
00:47:27.679 --> 00:47:31.519
figure out what is your access point, what is your who's flexible and who's

583
00:47:31.559 --> 00:47:36.119
not, and be very careful about
how you move forward because at the end

584
00:47:36.159 --> 00:47:38.480
of the day, when you get
the catalog delivered and they start using it,

585
00:47:38.920 --> 00:47:43.760
that's when the business changes. That's
when really cool things start happening with

586
00:47:43.800 --> 00:47:46.840
your prospects, with your customers,
with your partners. They're all on board,

587
00:47:46.840 --> 00:47:52.199
and that's when morale stays high.
Right, yes, yes, no,

588
00:47:52.280 --> 00:47:55.320
you're right, Eric, this is
this is something that is always important

589
00:47:55.320 --> 00:47:59.760
to consider. Yeah, well,
Allah, thank you so much for your

590
00:47:59.760 --> 00:48:01.559
time today. It's so fun talking
to you, and folks, send an

591
00:48:01.559 --> 00:48:05.039
email if you want to learn more
about what's going on. Here info at

592
00:48:05.079 --> 00:48:07.400
insidanalysis dot com. We'll talk to
you next time. You've been listening to

593
00:48:07.480 --> 00:48:15.559
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into the Faran Dozier Show Music Marks
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ray of music, talk, sports, community outreach, and veteran resources.

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The hits for your sixties, seventies, eighties, nineties, and today's hits.

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at four pm right here on KSEAA
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one, five, nine, nine, ninety eight hundred with sixty years

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go to this time in nineteen fifty
six. Terrytoons cartoons, which are going

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strong on Saturday mornings with cbstv's Mighty
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more TV stations. Terrytoons cartoons were
originally created for the theaters. I Come

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Save the Day. That means that
Mighty Mouse is on the way and from

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this time in nineteen sixty six,
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the game show Password with host Alan
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password is aquarium. Ten points Carol, see if you can get Laine Merlin

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to say that word. The password
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on fast Words, Alan Lundon and
from around It. This time in nineteen

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seventy six, comedian David Brenner makes
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Johnny Carson. David Brenner's become so
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in a new sitcom called Snips about
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here. Good news for once.
My neighbors is jealous of me. You

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want to know why? Because my
grass is growing and looking green, and

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I can sell my sofa out in
front yard and I don't even have to

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overwater it anymore. You know how
I did it? I listened to damn

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Waterboys Underwaters on every Thursday night on
KCIA. Well, I got me a

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smart controller and now at Water's at
night, miard looks darn tooting no more

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sneaking around and hooking up my horse
to my neighbors Spigott in the middle of

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the night, and his dog won't
bite me anymore. And you can do

698
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it too. Listening is easier than
ever. KCIA is now screaming online.

699
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It's streaming what it's streaming? You
dumm Well, I don't know much about

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streaming, but they doing it apparently
at KCA radio dot com. So AnyWho

701
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listen to the water Zone and fix
your yat up rant right here at k

702
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CIA, the station that leaves no
listener behind, have something you want to

703
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say, Thanks for calling the k
c AA rant line at nine oh nine

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three five three ten fifty for comments, concerns and complaints. Please hold call

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the k CIA ramp line today and
let us hear your two cents worth might

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even make it on the air.
Call nine O nine three five three ten

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fifty and let us know what's really
on your mind. The truth you can't

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handle the true a CIA radio,
the station that leaves no ranter behind nine

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O nine three five three one oh
five. Oh, far a line,

710
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and you are tuna. I would
swim out in the middle of the ocean

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and freaking eh you, let's hear
your voice. Make it loud and proud

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on our k c A A rant
line. Nine O nine three five three

713
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one oh five. Oh that number
again, nine O nine three five three

714
00:57:58.360 --> 00:58:02.679
one oh five. Oh thanks for
calling the KCAA rent line for comments,

715
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concerns, and complaints. Please hold. When Donald Trump declared he would fix

716
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the US Postal Service, he was
using the word fix the same way veterinarians

717
00:58:16.239 --> 00:58:21.400
do when you bring in your dog. Trump wasted an inordinate amount of his

718
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presidential power and prestige in a failed
attempt to neoter an agency that literally delivers

719
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for the people. Extraordinary Postal workers
move our letters and packages by truck,

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car, airplane, boat, motorbike, mule, and of course by foot

721
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to any address across town or across
the country, both essential and effective.

722
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It's the most popular federal agency,
with ninety one percent of the public approving

723
00:58:45.559 --> 00:58:51.239
its work. Thus, an uproar
of protests killed Trump's attempt to gut it.

724
00:58:51.960 --> 00:58:54.440
When it comes to bad public policy, however, failure is just a

725
00:58:54.480 --> 00:58:59.559
way of saying, let's try the
back door. Trump was defeated, but

726
00:58:59.840 --> 00:59:05.480
he he left behind and undistinguished Postmaster
general named Lewis de Joi, who had

727
00:59:05.519 --> 00:59:09.280
only two qualifications for the job.
He was a Trump megadonner, and he

728
00:59:09.400 --> 00:59:15.039
was a peer of corporate powers that
have long wanted to privatize the postal service.

729
00:59:15.679 --> 00:59:19.760
In March, before the new Biden
presidency had taken charge of the postal

730
00:59:19.800 --> 00:59:23.119
system, de Joy popped through the
back door with his own ten year plan

731
00:59:23.360 --> 00:59:30.000
to fix the agency. Rhetorically,
his plan promised to quote achieve service excellence

732
00:59:30.199 --> 00:59:36.880
by making mail delivery more consistent and
reliable, how by consistently cutting service and

733
00:59:36.960 --> 00:59:44.000
reliably gouging customers. Specifically, to
Joy proposed to close numerous mail processing facilities,

734
00:59:44.280 --> 00:59:47.719
eliminate jobs, reduce post office hours
of service, and cut the standard

735
00:59:47.760 --> 00:59:52.639
of delivering our first class mail from
three days to five. Oh, also

736
00:59:53.159 --> 00:59:59.760
raise stamp prices. This is Jim
Highter saying delivering lousy service at higher prices

737
01:00:00.119 --> 01:00:04.519
is intended to destroy public support,
opening up the service to take over by

738
01:00:04.559 --> 01:00:07.440
private profiteers, and who gets joy
from that