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What six point five FM man to KCAA ten to fifty am was one minute
at the multi format network. The information economy as a rod. The world
is teeming with innovation as new business models. We invent every industry industry.
Inside Analysis is your source of information and insight about how to make the most
of this exciting new era. Learn more and Inside Analysis dot Comside Analysis dot
Com. And now here's your host, Eric Kavanaugh. All right, folks,
welcome to the future. Indeed, George Julie Eric Kavanaugh here on Inside
Analysis, the only coast to coast radio show all about the information economy and
folks, so I get to go on a trip down memory lane today with
our guests. Who knows a lot about a technology that East throw years ago.
Master data management, Master data manager? What is that stuff? Master
data is very important because it allows you to virtually reconcile information system so you
have your customer database, your product database, your ARP, your data warehouse,
et cetera. All these different systems while many of them are using the
same data or should use the same data, but a lot of times it's
not aligned well. And so for years we went through this whole rigamarole of
trying to do top down master data management initiatives to be able to reconcile all
these different systems so you can see the big picture. That's really what it
really boils down to is being able to see the big picture to understand how
do all these component parts come together to make the business that we're trying to
run here. And obviously you can imagine that's very useful stuff where it's done
correctly, but the top down approach always had a very very difficult time because
you need people to adhere to very strict behavioral standards. How many people know
people who would like to adhere to strict behavioral standards? Not too many.
It's just not a very human thing to do. But people will align if
they see the vision of something and understand the value. Where our guest today,
Brett Hansen, is CMO for a company called so Mark, and they
do some pretty clever stuff with bottom up MDM, and so we're going to
kind of tie that into the whole go to market theme today about how things
are changing and how you have to change your go to market strategy to get
to the right people. Certainly, if you're in the software world, that's
a much different world now than it was five years ago, ten years ago.
You know, product led growth stuff like that. We didn't have product
led growth ten years ago. Now it's a huge thing. A lot of
companies do so without further ado. Brett Hanson from Samark, Welcome to Inside
Analysis. Tell us a bit about your company and the cool things you're doing
to help companies get value from their data. Thanks Eric, I think you
said it really well that there has been this ongoing effort and it applies that
just to MBM. It's really the data space in general. We're surely didn't
very tops down it is. We're going to have centralized organizations. We're going
to control data, we're going to govern it, we're going to first out
how we get into different groups. And the fact the matter is, despite
billion and billions and billions of investment, most companies would say, we're still
not a data driven organization, right, We're still not making decisions at a
practitioner level that are data driven. Maybe at the top level they're getting the
data they need, but in terms of line level workers, are they able
to access data utilize that data to make decisions to improve customer outcomes, improve
patient outcomes, improve efficiencies and all those things that we want to achieve with
all the data collection. And a big reason why is because we're still taking
this top down approach. Our philosophy is the other direction, as you called
out, how do we deliver a data product that is specifically designed and delivered
for a business requirement and how does that data product then evolve and grow as
business requirements change? And that's the samark the approach. Let's get into the
hands of business practitioners the tools they need to be successful, versus try to
force them down a symplatize highly regiment approach that has been proven not to work
now for decades. Yeah, I mean, that's such an excellent point,
and I think you guys are really onto something here, especially the data product
concept, So that is really taking off now. And it's interesting because you've
been around as long as I have, I think, or maybe almost or
just about so you've seen some of these trends kind and go big, big,
data, the no sequel movement, and we're going to forget about sequel,
like really are you? And then snowfully turns along with the biggest IPO
in history, saying, yeah, actually, sequel still matters. Actually a
matter of fact, maybe you want to focus on that a little bit,
but getting because here's the thing. The data warehouse, of course, is
this large construct that was designed to build a pull data out of all these
different operational systems, put it into a manageable space that we can run analyzes
on and kind of understand and trends and things. Because we figured out you
can't do analytics on an RP. It just wasn't designed to do that.
It's designed to do the business, to run the business. So that's where
this whole thing came from. And then trying to sort of build this next
generation of apps, if you're trying to do it again from this sort of
top down perspective, you're nowhere near close enough the ground level where this thing
is going to be used and so and even thinking of the traditional way of
building apps and just connecting it to a database like you know IBM or Microsoft
Access or whatever. A sequel server like that has so many limitations in terms
of the flow of data and how performance it can be, how malleable it
is to be able to do interesting analyzes. Whereas if you take the approach
that you folks are taking that some market is taking, and you ask the
business, look, what do you need, then we're going to help you
build out an application that will pull whatever data it needs. And by the
way, as we're doing that, we're going to be increment to leave fashioning
your reference architecture, which is basically what MDM is designed to do. Right.
It's designed to give you a hard and fast reference architecture. But reference
architecture is never hard and fast, right. That's the problem is that they're
kind of lucy, you see, but you get them just enough to get
their data products. So you've solved the business need first of all. That's
the important thing. Now the business people are happy and they're actually using data,
and it's data that is governed and trusted. Right, So you've kind
of, I'm a football fan, you kind of pulled an end around on
this whole thing. Right, You're just going straight to the users and saying,
hey, what do you guys need, Let's build that for you.
And in that process. On that journey, you're going to be building out
what is essentially your MDM architecture. Is that about right? That's very record
and I love the football reference. Not too long before we get to get
back into the season, and it's still done with the data analysts and experts,
right, So I don't want to I don't want to suggest that we're
they're not an important part of this, right, But like you said,
it's all about understand what the business objectives are, right, Do I need
a customer three sixty view? Do I need an inventory view? To I
need a location perspective? And of course that's the only the top of You're
going to get much more detailed. And no two companies are exactly alike,
and no two data projects are exactly alike. And so being able to start
with a set of reasonable business objectives defining what those business outcomes are, and
then we have a tool for lack of a better term, that generates the
code on your behalf. So we're not writing code, this is cogeneration.
We can provide typically a first usable solution in less than twelve weeks, so
in less than a quarter we can have something that the business user can get
their hands on, start to play with They're going to have positive reactions,
they're going to have adverse reactions. But when they have those adverse reactions,
they have additional requirements. Perfect we put that in back into the tool,
build that out, and start to resolve those as we're building out. Interesting
and so rather than trying to go a very tops down, year long approaches,
Hey, let's get something in the hands of business, you're in their
quarter, and then grow evolved with them because we know the business isn't standing
still during the time that we're doing this implementation. Yeah. The other interesting
thing too is that by starting at this ground level and of course working with
the data people, I appreciate that you are number one, you're on the
street, right, so you're out there where something needs to happen. Right.
One of my favorite analysts years ago said something I thought that was kind
of profound, almost tautological. He said, Look, if something needs to
happen in a large organization, it is happening. You just may not know
how, but I promise you it's happening. It could be a flappy drive
carried across the hall or something. You never know what the workarounds are but
so first of all, to focus on the business value, that tangible business
value on the front line, wherever that is. Maybe it's in marketing,
maybe it's in finance, whatever, but you will learn things as you pull
data together from different systems. You'll learn about this system, we learn about
that system where it is. With the top down approach, you're kind of
presuming that it's all going to work, and of course you find in the
process that it doesn't always work. But those hiccups wind up being two three
four weeks or two three four months that they cause disruption, whereas in your
world it's a faster path to finishing something. And by the way, learning
a lot about these different component systems along the way, right absolutely, And
you know, think of this almost like an agile development approach, right,
so we deploy, we engage, we learn, we adapt, we deploy
in and again, because it's a cogeneration tool, because we're not I'm not
tearing up templates and starting, you know, hiring developers on hand, we
can innovate very quickly. That's interesting and I think you know the challenge that
somebody companies have is they've they've run through an MBM project it's not like,
you know, we have customers companies like I've never ever heard of MBM.
Now that's very few. Most of them have used MBM, like yourself.
It's been around for more than a decade or so. But it was highly
templatized, as in highly structured, as in tops down, and it might
have worked for their initial engagement. It might have been what they needed at
that time. But think of what's happened over the last decade, right,
we have much more data and more locations and more silos. The expectations are
higher, all those storylines that we hear and over again, and so an
approach that might have been effective a decade or more ago is no longer affective
in today's world. And I would argue that it probably never really achieved the
outcomes at that company's desired because it was based around, again, a top
side approach. You know what I find interesting is, you know, we
all in the technology industry come back to the technology, right, I got
something better, faster? And if you look at data, you know,
why have companies struggled with really achieving the data desired outcomes that they have?
It's cultural, right, you know, there's lots of studies out there that
you know, can point to the fact that culture and adoption at the individual
level or the biggest ahead of the inhibitors. Right, and so again that's
what we're trying to get overcome, which is, hey, business leader,
with your data architect, your data team, let's put together your dream application,
and let's put it together and deliver in the way that you can innovate
and be a part of this versus you know, you sent over a couple
of requirements, you close your eyes. A year later, something big and
unwieldy and completely unflexible comes back at you. That's right, right. No,
I love that you've got this data products folks and focus. And frankly,
I think you're a bit ahead of the of the curve on that one
because just now companies are starting to figure out what do you mean a data
product? Right? It's it's interesting because we have this old mindset of application
and database. Application and database. Okay, well, which database we're going
to choose for this application? It's like exactly okay, Like you know,
databases have their own schema, they have their own performance components, they have
their own way of working with them. They're you know, they're different in
many different ways, depending about what you want to do with them. But
now, of course I'll lets talk about data fabric and some other interesting things
going on. Data mash came along and captured the imagination, even though no
one was able to really identify exactly what it is, Like, what exactly
are you talking about? So like, all these things are happening, but
what people really want is a data fueled product. They want an application they
can use to do their job, whatever it is. So it's like really
stripping away some of the component parts in our own mental map to understand this
is a new general ration of functionality. We don't need to think in this
linear point to point app database mindset anymore. You can think about data products,
and as people work with them more and more, they figure out,
oh, I understand now I don't need to have eighty seven columns and forty
eight million rows in the thing that drives this particular product. It only needs
ten columns and two hundred fifty thousand rows or whatever. Where I'm kind of
going with this is we have to change the mindset about how applications are built
and designed and how the sort of totality of data has to be appreciated but
not consumed at once. What do you think? Right, Yeah, and
that's that's an evolution again. It goes back to people, right, And
you're only going to achieve that if you're getting them the tools that they need
versus attempting to subjugate them to some larger approach. Well, you know,
I think back, let's do a fun trot down memory lane back even in
two thousand and oh gosh, I guess this is like two thousand and eight
for a big media company in New York. They were actually my first partner
for DM Radio way back then when we launched that, and they were using
a new cr at relatively new CRM solution, and it was an absolute nightmare,
right, So basically almost no one used it. A few people would
use it, and the people who used it put tremendous amounts of information in
there. So every call they would put in like thirty paragraphs of stuff,
whereas most people would just put a couple things in. And what did you
have? You had all of these hard and fast rules like no, you
must enter the notes field, you must enter this field, you cannot skip
that field, and all this stuff. So what happens is people just stop
using it. And then it was slow as molasses. I mean it was
brutal, like you'd log in and just sit there and wait for it to
load for like, you know, thirty seconds, forty seconds, Like,
who can do that anymore? You can't do that. You have to be
able to move at the speed of your thought, at the speed of emails
coming in and people ring in your phone to tell yourself that has to get
done. That's the speed you have to move at. And so that's where
these data products come into play, as opposed to the old monolithic application design,
which is what we all live through until the last you know, ten
years. Arguably, now that we have these much more federated worlds and we
understand that we can leverage that kind of stuff to build these applications, that's
a whole new ball game in terms of how you go about designing a data
product, how you go about conceiving one of these things, and then of
course you're using it, and that's squarely where you're focused right now, right
absolutely, And that's what how we measure ourselves is are we finding our customers
are successful in their data initiatives? So you know, one is certainly we
strive to have functional MBM solutions up and running in twelve weeks. Sometimes it
goes a little longer, sometimes it goes a little shorter, but that's our
goal, right. And you know, we have a contractor that we actually
built what we call our our blueprints, which provide, you know, step
by step this is this is our recommendation based off of literally hundreds of successful
MBM projects, how we would approach this. Once we have that and you
have your first implementation. I'm not saying it's going to be multi demain or
I'm not saying it's going to solve every business challenge. It's going to address
a critical need where you want to get started. Then we can start to
grow and expand, and then we can say, okay, we've solved location
based services, what's next? So you know, I find the greatest satisfaction
when I'm working with customers that you know, not only started with us and
or successful, but we've grown over time. So I think, you know,
one of my favorite stories to talk about is Chapulte, something you know
we all can recognize. Now they started with us with location based services.
Basically they are struggling with Okay, where are all of our franchises. Something
pretty simple, right, not a big deal. We head them up and
running in six weeks, address that project from there. You know, most
chapults have unique menus. We addressed that. Then we said okay for media
and used ingredients for ingredients to compliance, from compliance to supply chain, and
we're able to work with him through this journey and at every stage help them
find success. Not because we had this massive vision at the start of the
project to go and say, hey, we're going to realign all of your
data. It is, let us solve a problem, let us learn from
that solution, and then let's go solve the next role. Again. I
almost go back to agile development. It's like that's the way to go versus
if we had started off and said, okay, we're going to try to
reimagine your entire inventory, menu, management, compliance all at the same time,
we would fail. Never happened. Now, that's that's right, never
happened. That's really fascinating stuff. Well, folks were talking to Brian Hanson
from some Marquis don't touch dot dot. We're talking all about ground up master
data management and data products and how they can help you go to market We'll
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all of our whereas we'll qualify terms and conditions apply equal housing opportunity. Welcome
back to Inside Analysis. Here's your host, Eric Tavanaugh. All right,
Sols, take us to the future. Indeed, your host Eric having on
here with Grett Hanson from Samarki's we're talking about going to market and the way
you do that is you understand your business better. Quite frankly, that's what
this whole data world is about. It's about understanding your business. And we've
been talking about master data management and ground up versus top down. And the
ground up is great because you learn a lot of things along the way and
then what you learn can help you to design the next data product and the
next data product. But I did want to give the audience some background on
you know, what are the kinds of solutions that you can solve using Master
Data Management. You mentioned a couple in the break three hundred and sixty degree
view of the customer. How long have we've been talking about that? For
like twenty five years, they've been talking about three hundred and sixty three view
of the customer, and really the whole time has been about a thirty six
degree view of the customer and together, you know, the other three hundred
what twenty four are just waiting, but maybe just explain to the audience how
you can enable customer three sixty using this kind of MDM data product approach.
Well, it's obviously a topic that's near and near to my heart as someone
that has marketing as part of their responsibilities. You know, how do I
understand my customers in a holistic way? And like you said, it's you
know right now, it's twenty five or thirty degrees. So, first off,
we have our serum system Salesforce or HubSpot or any of the forty six
other serum systems you use. That's great, that's a great starting point,
right, But that that in itself is a data silo, right, You're
typically going to want to be able to pull in information from whatever system you're
using for finance, you know, so I can understand what are my most
profitable customers. I want to be able to pull in information from my stomer
support, which customers are having good experiences, which customers are struggling right now.
The last he will do is be sending out some sort of promotional content
talking about some exciting new product to a customer who is right now failing to
be successful in the implementation. Right that's going to have a complete opposite experience
that we're trying to have. So what indim allows us to do is starting
to get that what we call golden record. I know it's a cliche,
but I can look and say, Eric Havanaugh, who is he not just
what his address and email is, but his address and email, how much
he's spent with us, what is this customer service experience? What products has
he had? What produces of services is he using actively? And that allows
me to actually drive some really powerful insights into how I approach you versus generic
span email, which of course we know never works or whatever to highly targeted,
thoughtful type communications. We work with a lot of ancial institutions. That's
one of our primary vertical sectors. And what they're looking at is, quite
honestly, how do I optimize each and every customer Because financial institutions, like
mean institution, the cost of acquisition is exceptionally high, So they're trying to
figure out, you know, for every one of my customers, now that
I have a portfolio of different financial solutions for them, everything from banking to
credit cards to loans or whatever else, how do I bring this to in
a thoughtful way. And it all begins by understanding who they are, what
is their current status, what do they have? And without that MBM you're
looking at different pieces. You're now able to have that total three sixty perspective
that allows you to achieve your goals of being able to deliver targeted solutions,
targeted offerings to that customer. Right, that's right. You know, you're
reminding me of how complex the landscape can get. But if you think about
the amount of signal that you could acquire from a HubSpot or a Marcato or
any one of these other tools, there's a lot of valuable information in there
about each individual person who is in the system. Well, just you can't
just create a pipe from one to the other and expect that data to come
through. So what often happens is you'll use that to segment people and say,
Okay, these are buyers of green clothes, these are buyers of red
cars or whatever. That's one path, but that's not really a good path
to true rich personalization. For rich personalization, you need to be able to
understand the nature of this person and then connect that to some operational system that's
doing the outreach. And that's where these data products come into play. Right,
So some markey comes in says, okay, we're going to tap into
Marcato, We're going to tap into this other operational system that you have and
a couple other things, and we're going to give you a view of the
world where you can start to understand, Hey, I think these people over
here are cohort. Let's market to them now. And I'm an email marketer.
So I've been doing email marketing, i joke, for twenty three years
now, which is about three times as long as the average veteran in the
field. So I've seen a lot of changes over the years, and it's
getting a lot harder to do it. You have to get much better about
how on target you are with a message, with when you send things like
all these features that you used to be nice to have are now absolutely must
haves or you're going to burn through your list and kill that list. And
when a list is killed, it's dead. So this is really important stuff,
right, Well, and I'll take it a step further. It sounds
like it should be easy to bring all these different pieces together. Right,
there's what our capital, right, Well, is there one or is there
three? Or four? And how do I make sure I'm associating these different
records appropriately? Maybe you have a different spelling or maybe you have a middle
initial in some of these records are the ones you don't. So it goes
a step further in terms of its I was like, okay, I can
just bring all this stuff together. Well wait a second, data is not
that simple, right, You've got to be able to go and look at
this and be able to understand, all right, how do I bring these
sources together so that I do have an accurate gold record? And of course
our systems can help with that. But you know, that's a really important
thought that often gets lost is Oh, it's just you know, we're just
mashing all the data together. No, no, no, there's a lot
of data quality that gets built in here to make sure that what is golden
is truly golden. M. Well that's the other very interesting things. So
to get back to MDM top down versus bottom up, Well, what happened
in the old days is that you would do your requirements academy. You look
at all the systems you want to and style you big, big project to
do all that at some sort of foundational level. Well, what you're actually
doing by taking this start small. And everyone says that starts small, right,
don't buw the ocean, and yet you get people put on the ocean.
So you got to start small. But you're going to learn things about
each and every one of these data sources that it is going to be very
valuable for the second and third and fourth data products that you generate, and
also will be valuable for the teams who own these different systems. Say hey,
did you guys know that you have this combination problem in this field and
we're missing that data when we come over. Right, that's what all this
observability stuff is about, to see what's going where. And because you've taken
this I think very clever approach of starts small, build incrementally you're learning a
lot all along the way such that each subsequent project becomes a little bit easier
and a little bit easier. And that's how you build momentum in the organization.
That's how you help your clients go to market with their technologies and their
solutions. Right, absolutely, Yeah, And that concept of learning as you
go is so important, right because we all start every project by assuming that
we have all the answers, right, And what we find very often is
after the first phase again ideally ten weeks or so, you know, we
get it into hands and we learned from that, and then we can adapt
to grow the other thing, which I just you know I've mentioned once when
I stressed it again. Business is continuing to change, right, Right.
We actually were with the customer working on their implantation and halfway the invitation they
acquired in their company that obviously has profound implications to what we're trying to achieve,
right, So right, we had to take a step back and say,
okay, what do we want to do. That project was not twelve
weeks, I'll be honest, because we kind of pushed it out further because
we had to think about what are the implications, how is this going to
work? What new data sectors are we going to go? And again,
you know, trying to boil the ocean. But you needed to be cognizant
of the broader business requirements that had changed posts making an acquisition. Right,
Yeah, that's a that's a big deal. And you know, I'm connecting
some dots in my head. Here is here talking and I'm realizing the importance
of change and the importance of appreciating change. I was talking to some vcs
probably six months ago, and this one guy made a really good point.
He said, we know that whatever we design today in six months is going
to be disrupted, So how do you plan for that? I mean,
how do you plan for the whole market changing? And then, of course
since that time, the large language models took center stage. So they had
been around chat, GPT two and three, etc. It's not new,
but when open Ai rolled it out, that was a huge eye opener,
one of the biggest I've ever seen in terms of just shaking the foundation of
what is accepted as normal, what is accepted as the way to do business.
You know, we're going to go through I think probably a two to
five year period now of just remarkable bottom line day to day workflow transformation because
of these engines and because what they're capable of doing. But you know that
change is really important, and it's very important for marketers and for salespeople to
kind of understand this like you think about So let's get back to three to
sixty view of the customer. Let's do three sixty view of the prospect.
Right. If you're trying to figure out who to market to, who to
spend time and money building content, sending information to, etc. Well you
really want those teams, So it's go to market teams to be able to
fail fast forward if they say, you want to be able to try lots
of different things and see what works and what doesn't work. Now, there
is something to be said for consistency and newsletters that go out once, so
we can think of this nature. But these days, man, you better
be trying new stuff to get through to people or you're going to be in
trouble. What do you think? Yeah, I mean the continuous innovation is
certainly part of everything we're doing today. You know from marketer perspective, you
know, Chat, GPT and these other AI tools aren't truly changing the landscape.
You know, we use them in a number of different ways as Samarchi,
and we're a relatively small company, and I can tell you that they've
already had profound implications in terms of how I spend dollars and how I execute.
But I also have learned that, like with any new technology, you
have to be careful about embracing adoption. Right So there's lots of great opportunities
for utilizing it and helping streamline content production, helping you become more targeted in
your outbound But at the same time, you know, like I've always said,
you know, new technology has to be vetted and thoughtfully applied or it
can be more detrimental than beneficial. Sure, so we've seen some of that
with the largest language models already with the hallucinations, right yeah, because they
are generative. So it's funny. I was covering the Data Bricks conference,
their Generation AI conference, and the girl was talking about how she was doing
a demo about how they train the models, etc. And you know,
my wheels are always spinning like, Okay, how much time is it going
to take the train these models? And you know, where does the value
really accrue and go beyond just you know, solving some small problems like where
do you actually get the inflect point where this thing takes off? Well,
that's going to be a while, I think with many of these, for
the reasons that you've outlined. But while I was tweeting about this, someone
from chat GPT on Twitter came back and say, hey, wait a minute,
you know we don't use that data for our models. Data bricks knows
this, etc. So here we are in this real time conversation with the
major players in the space, and I chose, because I'm a journalist,
to ask a few more questions and say, well, what do you do
about hallucinations? And he said, remember, these are really more of a
feature than a bug, right, This is what these things are designed to
do, to fuse together different vectors of information to create prose that will ideally
satisfy the request and the prompt. Right. Well, there are lots of
ways you can do that, and we talked about it on other shows.
But you can either embed your corporate data into the model, which is pretty
complex thing to do, or you can use a vector database and kind of
get this anchor of truth, which I think is probably the clever way to
go. But in that sense you can start leveraging these things, and you
know, to your point, you do have to be careful, walk through
step by step, make sure you know what you're doing. But the power
of these things to just look at the world in a different way. I
mean, I was a philosophy major many years ago when our teacher used to
always say different perspectives, like that's what you want? And I think you're
kind of singing that tune on the people front, right if you want to
talk to the people, who are these people that we're working with and understand
they're all different. Every data project is different, every person is different.
So that seems to be core to your approach for working with them, right,
absolutely, And I think going back to the you know, the AI
pieces, it's something which is almost every conversation we have a customers Againstrado people
are excited about it. Right, this is game changing, this is this
is you know, the next you know evolution of our economy from industrial the
digital to now you know, AI based. We've taken a very pragmatic approach
and you know, we actually have an open architecture so you can plug into
chet GPT and utilize chat. GPT is part of your MDIM project. We've
long been able to tie into Google Translator and any of this recognition, so
we've long sort of said like, hey, you know, our belief is
there are great tools out there to help enrich your data, utilize them,
versus jumping down the path like you were talking about of saying, Okay,
we're going to bed a bunch of AI technology within our MBM and then it
becomes a black box. And where that scares me is okay, if I
don't properly train that is that going to have implications to compliance or security or
accuracy. And especially given the fact that I mentioned we have lots of customers
and finance, we have lots of customers in healthcare like making mistakes there pretty
catastrophic type situation. So our perspective is, yeah, there are some really
exciting things that are going to be coming with AI and mL, the ability
to do match and merge to you know, have streamline and novelty, infection
and correction. Huge implications outstanding, But just like with everything else, it
is going to need to be unique to that individual company, certainly that imagual
you know, sector at the very least, but ideally that individual customer because
if not, okay, well now we have compliance implications, Now we have
security implications, Now we have accuracy account and that you know. So it's
exciting and we're like embracing that, you know, the potential here, but
we're doing it as you said, and that vector database sort of approach,
which is, don't make it a black box. Don't build this so deeply
into your environment that I don't actually know what's happening. You're going to need
to have still controls over that and build it in just like everything else,
which is, you know, have an incremental plan to go and be able
to embrace. Yeah, that's exactly right. Incremental plan, that's what you
want. Folks, don't boil the ocean. Never boil the ocean. Step
by step iterative, iterative development, that's what they call it. Well,
folks, don't touch it. That will be right back with Bret Hanson of
Simarchi or left things that inside and out. Do you own an annuity either
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hundred two five four thirty two eighteen paid for. Welcome back to Inside Analysis.
Here's your host, Eric Kavanaugh. All right, folks, back here
on Inside Analysis, talking all things data products and master data management. Top
up or bottom I'm sorry, top down or bottom up? Bottom up is
the way to go, folks. Top down, We tried it for years.
We tried it. Their whole companies that disappeared, that did end,
and they're not even around anymore because they were doing the top down. Not
all the people like that. Well, we're talking to Brett Hanson of Samarky
and he's getting me all excited about data products and to kind of put this
back into the go to market framework. You know, going to market is
a very interesting and I would say sort of dicey enterprise these days, because
you have so many options about where you can spend money, of so many
ways that you can do things now, all these different social engines. Of
course, you've got first party data, email marketing, traditional stuff, conferences,
going to talk to people, getting your messaging right. Well, data
products can be extremely valuable in terms of understanding that in terms of developing your
go to market strategy. Even if it's just understanding where are people coming from
when people interact with us, what is the preponderance of people who are calling
us on a call center versus coming into stores like Chipotle versus on the app
or social or whatever. Just understanding how people get to you is a data
product, right absolutely, And I think that you know, marketing go to
market continues to evolve very rapidly, and it is easy to be overwhelmed by
the information that's available. Right, So any channel you can look at fifteen
twenty different data points set and tell you how to optimize PPC or your webinar
spend, or your emails or your face. I mean, it's just goes
on and on and on. And what I try to bring my team back
consistently too, is what does your prospect or your customer need provide value to
them? You know, I know we think our job is to market to
them and sell to them and try to force them down our imaginary buyer's journey.
Our real responsibility is to help them with whatever tasks they have in front
of them. It might be the fact that we're not the right company for
them, and what our help is to point them in some other direction.
But you know, ideally we're providing valuable information that helps them along their buyer's
journey, not ours theirs. Right now, what I like for that buyer's
journey to ultimately be a product purchase from my company? Absolutely. Do I
feel confident that I have a great product, Yes, I do, But
that doesn't mean that I should be forcing my views down their throats, right,
So the whole constpt of the Golden rule and really trying to understand what
they need and then being able to speak to them and their and their language
that their point in their buyer's journey is absolutely essential. Yeah, that's right.
I just throw out this down our imaginary buyers journey. That's a book
title. By the way, that's a very book title. I like that
you're honest about recognizing that, yes, every company will have their imaginary buyer's
journey what they think people are going to do to get to them. And
sometimes you're right and sometimes you're not right, but you do have to keep
an open mind that people come to you various ways, and then people use
the technology in different ways too. I mean, one of the coolest things
about software that I've learned, it was actually the MDM company in particular,
is that you can have your thoughts about how people are going to use your
tech, and then you'll do a user conference and you get all these people
coming to you showing you things that you never imagined which are possible because of
your tech. So I would tend to think that's especially true with you guys,
because again, you're focused on building data products, right, and the
thing is you don't have to do some sort of one to one map between
the product and an Excel spreadsheet, which is kind of how people still think
in terms of accessing data for the app, right. Don't think of it
that way. You know, we've had data marks, for example, we've
had old app cubes. There are all these sort of constructs we've come up
with over the years, but they're all fairly linear and staid, I guess,
Whereas what's interesting about your approach is that you're working with the business to
figure out how to solve some particular challenge. And let's say it's this understanding
your prospects, so we could do go to market, Well, who are
our prospects? You sit down, you look at what are the prospect databases?
Our email database, our social media feeds, things of these nature.
These are all the sort of entrance points. But who is coming here?
How can we analyze this? Can we get web trends data to see who
is clicking, who's not? What are the RL's pointing to us? All
this stuff, which lives in lots of different silos doesn't have a lot of
value until you've pulled it together in some meaningful way. And that's what you're
doing with these data products, right absolutely. And I mean we actually have
an Indian project going on in our way in our own company. So we're
a relatively small company. I mean we're not you know, global fifty type
company. We're talking about hundreds of employees. As I said, we have
a few hundred customers globally. But we have found actually that even we have
the need to really very able to bring all these different data sources together so
that I can have more intelligent conversations with my customers and prospects. And I
think that's that's the key is it's I'm marking to them. It's not I'm
selling to them. I'm trying to have an intelligent conversation so that I can
help provide them what they need right now. In that process, I am
learning more about them. I am gathering more information about who they are and
what business challenges they might have, what technologies are utilizing, so I can
become a better communicator back into them, while at the same time, you
know, progressing them ideally through a buyers journey that ultimately results in a sale
with us. But as I mentioned, you know, and you called this
out, I thought, really well, because we have a data product,
and yes, there are certainly use cases that are consistent, like customer three
sixty or product three sixty, but even then that spans a tremendous amount of
range. Right what I consider be a customer three sixty might be very different
than you know, a financial institution that's global versus of mid tier bank.
I mean, they all have different requirements and so I can't force my perspective
onto them. I can provide them with information and education and tools to help
them learn, but I can't be you know, our way is the only
way, and I think that's how we have found success is. It's a
massive market out there. There's a lot of noise. As you mentioned,
the market has been a top down approach for a decade and if you look
at the largest customers or companies excuse me, that are servicing MBM, they're
all cops down and they have a lot more money to spend it. They're
going to tell everyone that this is the best way to do it. And
I can't compete with that. But why can do is provide an educational package
back into the marketplace saying okay, that's an option, here's how we approach
things, and here are some of the best practices that we have. Hopefully
this will help you learn as you're going through your buy journey. That's great.
I think that's really cool and I love this quote you just said.
I can't force my perspective on them, and that's very smart. Right.
They're coming to you because they want to solve some problem. Maybe they had
a different problem to solve before they started talking to you, But it's in
the conversation, the meaningful, intelligent conversation that you can come across certain touchstones
and then leap forward and they'll mention something about, oh, what this friction
point we have, Oh what is that about. That's when you can kind
of dig in and you do have to share information. You have to share
your insights. You know. It's not like the old days of just showing
someone a brochure and say, oh, here we buy our product online or
I mean, you're talking about complex enterprise software that is designed to give a
better view of a particular business aspect, is really what you're talking about.
But that's been the vision all along. Like that's the funny thing is that
was the vision even in the top down monolithic age. It's just that you
had to hope that someone on the client on the vendor side would listen to
you enough for your feature to make the next release right. And how often
was that? Not very often? And so you're kind of again pulling an
end around on that old monolithic waterfall approach to building things, and you're saying,
no, we can build you a product. It'll only take six to
twelve weeks or so and you'll have something functional. And by the way along
that path, you're going to get better and better at really understanding what your
de facto reference architecture is. It's going to be unique, it's going to
be different than anyone else is in the world. And thus the more intel
you gat about your environment, the better off you're going to be for the
next data product and the next data product. Right. Yea, yeah,
every company, every solution is a saflake. It is unique, right and
as if you if you think about it that way, you think about and
we say this all time. Data is the blood of your of your organization,
right, It's what drives your decisions, what helps shoot different to your
srself from your competitors. Right. If that's really true, then you're going
to need a solution that's unique, bespoke and you're not going to achieve that
if you're starting off with a template tops down approach. You know, being
able to generate a bespoke application that services your specific business requirements, whether it
again that's a customer three sixty or you know, you know location inventory product,
that's what's going to allow you to be successful. And it's it's not
a single exercise, it's journey. We're going to be on a journey.
It's you to help you achieve your business outcomes. Yeah. And the difference
in when you use this term, because I've heard that term before talking about
things changing over time. The difference is that ten years ago, that journey
was going to be like six months to two years, and it was going
to be to stand up one big honk in the thing that was going to
allow you to do all sorts of amazing magic with your data. And that
is not the correct path. Like, what you want to do is have
a journey where you get small winds than bigger winds and bigger winds but targeted
focused on particular business use cases, solving problems for the business while you're incrementally
building out what is essentially your de facto data fabric. But folks, what
a fantastic conversation with Brett Hanson. Send me an email. You're listening to
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