The next BriefingsDirect big data innovation case study interview highlights how
Rolta AdvizeX in Independence, Ohio creates analytics-driven solutions in the healthcare and retail industries.
We'll also delve into how the right balance between
open-source
and commercial IT products helps in creating a big-data capability, and
we'll further explore how
converged infrastructure solutions are hastening
the path to big-data business value and cloud deployment options.
To learn more
about how big data can be harnessed for analysis benefits in healthcare
and retail, please join me in welcoming our guests,
Dennis Faucher, Enterprise Architect at Rolta AdvizeX, and
Raajan Narayanan, Data Scientist at Rolta AdvizeX. The discussion is moderated by me,
Dana Gardner, Principal Analyst at
Interarbor Solutions.
Here are some excerpts:
Gardner: Dennis, what makes big data so beneficial and so
impactful for the healthcare and retail sectors?
Faucher: What we're finding at Rolta AdvizeX
is that our customers in healthcare and retail have always had a lot of
data to make business decisions, but what they're finding now is that
they want to make real-time decisions -- but they've never
been able to do that. There was too much data, it took too long to
process, and maybe the best they could do was get weekly or maybe
monthly information to improve their businesses.
We're
finding that the most successful healthcare and retail organizations are
now making real-time decisions based upon the data that's coming in every
second to their organization.
Gardner: So it's
more, faster, and deeper, but is
there anything specific about healthcare, for example? What are some top
trends that are driving that?
Two sides of healthcare
Faucher:
You have two sides of healthcare, even if it's a not-for-profit
organization. Of course, they're looking for better care for their
patients. In the research arms of hospitals, the research arms of
pharmaceutical companies, and even on the payer side, the insurance
companies, there is a lot of research being done into better healthcare
for the patient, both to increase people's health, as well as to reduce
long-term costs. So you have that side, which is better health for
patients.
On
the flip side, which is somewhat related to that, is how to provide
customers with new services and new healthcare, which can be very, very
expensive. How can they do that in a cost-effective manner?
So
it's either accessing research more cost-effectively or looking at
their entire pipeline with big data to reduce cost, whether it's
providing care or creating new drugs for their patients.
Gardner:
And, of course, retail is such a dynamic industry right now. Things are
changing very rapidly. They're probably interested in knowing what's
going on as soon as possible, maybe even starting to get proactive in
terms of what they can anticipate in order to solve their issues.
Faucher:
There are also two sides to retail as well. One is the traditional
question of, How can I replenish my outlets in real time? How can I get
product to the shelf before it runs out? Then, there's also the
traditional side of the cross-sell, up-sell, and what am I selling in a
shopping cart, to try to get the best mix of products within a shopping
cart that will maximize my profitability for each customer.
Those
are the types of decisions our customers in retail have been making for
the last 30-50 years, but now they have even more data to help them
with that. It's not just the typical sales data that they're getting
from the registers or from online, but now we can go into social media
as well and get sentiment analysis for customers to see what products
they're really interested in to help with stocking those shelves, either
the virtual shelves or the physical shelves.
So it's either accessing research more cost-effectively or looking at their entire pipeline with big data to reduce cost.
The
second side, besides just merchandising and that market-basket
analysis, is new channels for consumers. What are the new channels? If
I'm a traditional brick-and-mortar retailer, what are the new channels
that I want to get into to expand my customer base, rather than just the
person who can physically walk in, but across many, many channels?
There
are so many channels now that retailers can sell to. There is, of
course, their online store, but there may be some unique channels, like
Twitter and
Facebook
adding a "buy" button. Maybe they can place products within a virtual
environment, within a game, for customers to buy. There are many
different areas to add channels for purchase and to be able to find out
real-time what are people buying, where they're buying, and also what
they're likely to buy. Big data really helps with those areas in retail.
Gardner: Raajan, there are
clearly some compelling reasons for looking at just these two specific
vertical industries to get better data and be more data-driven. The
desire must be there, even the cost efficiencies are more compelling
than just a few years ago. What’s the hurdle? What prevents
them from getting to this goal of proactive, and to the insights that
Dennis just described?
Main challenge
Narayanan:
One of the main challenges that organizations have is to use the
current infrastructure for analytics. The
three Vs: velocity, variety and the volume of data serve up a few challenges for organizations in
terms of how much data I can store, where do I store it, and do I have
the current infrastructure to do that?
In a traditional business, versus the new
flash areas, how do you best access the data? How fast you need to access the
data is one of the challenges that organizations have.
In
addition, there are lots of analytics tools out there. The ecosystem is
growing by the day. There are a few hundred offerings out there and
they are all excellent platforms to use. So the choice of what kind of
analytics I need for the set purpose is the bigger challenge. To
identify the right tool and the right platform that would serve my
organization needs would be one of the challenges.
The
third challenge would be to have the workforce or the expertise to
build these analytics or have organizations to address these challenges
from an analytical standpoint. This is one of the key challenges that
organizations have.
Gardner: Dennis, as
an
enterprise architect at Rolta AdvizeX, you must work with clients who
come at this data issue compartmentalized. Perhaps marketing did it one
way;
R and D did it another; supply chain and internal
business operations may have done it a different way. But it seems to me
that we need to find more of a general, comprehensive approach to big
data analytics that would apply to all of those organizations.
We work with a company, look at everything they're doing, and set a
roadmap for the next three years to meet their short-term and long-term
goals.
Is there some of that going on, where
people are looking not just for a one-off solution different for each
facet of their company, but perhaps something more comprehensive,
particularly as we think about more volume coming with the
Internet of Things (IoT)
and more data coming in through more mobile use? How do we get people
to think about big-data infrastructure, rather than big-data
applications?
Faucher: There are so many solutions around data analytics,
business intelligence (BI), big data, and
data warehouse.
Many of them work, and our customers unfortunately have many of them
and they have created these silos of information, where they really
aren’t getting the benefits that they had hoped for.
What
we're doing with customers from an enterprise architecture standpoint
is looking at the organization holistically. We have a process called
Advizer, where we work with a company, look at everything they're doing,
and set a roadmap for the next three years to meet their short-term and
long-term goals.
And what we find when we do our
interviews with the business people and the IT people at companies is
that their goals as an organization are pretty clear, because they've
been set by the head of the organization, either the CEO or the chief
scientist, or the chief medical director in healthcare. They have very
clear goals, but IT is not aligned to those goals and it’s not aligned
holistically.
Not organized
There
could be skunk works that are bringing up some big-data initiatives.
There could be some corporate-sponsored big data, but they're just not
organized. All it takes is for us to get the business owners and the IT
owners in a room for a few hours to a few days, where we can all agree
on that single path to meet all needs, to simplify their big data
initiatives, but also get the time to value much faster.
That’s
been very helpful to our customers, to have an organization like Rolta
AdvizeX come in as an impartial third-party and facilitate the coming
together of business and IT. Many times, as short as a month, we have
the three-year strategy that they need to realize the benefits of big
data for their organization.
Gardner: Dennis,
please take a moment to tell us a little bit more about AdvizeX and
Rolta.
We don’t lead with products. We develop solutions and strategy for our customers.
Faucher: Rolta AdviseX, is an international systems integrator.
Our US headquarters is in Independence, Ohio, just outside of Cleveland.
Our international headquarters are in Mumbai, India.
As
a systems integrator, we lead with our consultants and our
technologists to build solutions for our customers. We don’t lead with
products. We develop solutions and strategy for our customers.
There
are four areas where we find our customers get the greatest value from
Rolta AdvizeX. At the highest level are our advisory services, which I
mentioned, which set a three-year roadmap for areas like big data,
mobility, or
cloud.
The second area is the application side. We have very strong application people at any level for
Microsoft,
SAP, and
Oracle. We've been helping customers for years in those areas.
The
third of the four areas is infrastructure. As our customers are looking
to simplify and automate their private cloud, as well as to go to
public cloud and
software as a service (SaaS), how do they integrate all of that, automate it, and make sure they're meeting compliance.
The
fourth area, which has provided a lot of value for our customers, is
managed services. How do I expand my IT organization to a 7x24
organization when I'm really not allowed to hire more staff? What if I
could have some external resources taking my organization from a single
shift to three shifts, managing my IT 7x24, making sure it’s secure,
making sure it’s patched, and making sure it’s reliable?
Those are the four major areas that we deliver as a systems integrator for our customers.
Data scientists
Gardner: Raajan, we've heard from Dennis about how to look at this from an
enterprise architecture perspective, taking the bigger picture into account, but what about
data scientists?
I hear frequently in big data discussions that companies, in this case
in healthcare and retail, need to bring that data scientist function
into their organizations more fully. This isn't to put down the
data analysts or
business analysts.
What is it about being a data scientist that is now so important? Why,
at this point, would you want to have data scientists in your
organization?
Narayanan: One of the key
functions of a data scientist is to be able to look at data proactively.
In a traditional sense, a data analyst's job is reflective. They look
at transactional data in a traditional manner, which is quite
reflective. Bringing in a data scientist or a data-scientist function
can help you build predictive models on existing data. You need a lot of
statistical modeling and a lot of the other statistical tools that will
help you get there.
This function has been in
organizations for a while, but it’s more formalized these days. You need
a data scientist in an organization to perform more of the predictive
functions than the traditional reporting functions.
We're seeing that in the open-source, big-data tools as well. Customers have embraced open-source big-data tools rapidly.
Gardner:
So, we've established that big data is important. It’s huge for certain
verticals, healthcare and retail among them. Organizations want to get
to it fast. They should be thinking generally, for the long term. They
should be thinking about larger volumes and more velocity, and they need
to start thinking as data scientists in order to get out in front of
trends rather than be reactive to them.
So with that,
Dennis, what’s the role of open source when one is thinking about that
architecture and that platform? As a systems integrator and as
enterprise architect, what do you see as the relationship between going
to open source and taking advantage of that, which many organizations I
know are doing, but also looking at how to get the best results quickly
for the best overall value? Where does the rubber hit the road best with
open source versus commercial?
Faucher: That’s
an excellent question and one that many of our customers have been
grappling with as there are so many fantastic open-source, big-data
platforms out there that were written by Yahoo, Facebook, and Google for
their own use, yet written open source for anyone to use.
I see a little bit of an analogy to
Linux back in 1993, when it really started to hit the market. Linux was a free alternative to
Unix.
Customers were embracing it rapidly trying to figure out how it could
fit in, because Linux had a much different cost model than proprietary
Unix.
We're seeing that in the open-source, big-data
tools as well. Customers have embraced open-source big-data tools
rapidly. These tools are free, but just like Linux back then, the tools
are coming out without established support organizations.
Red Hat
emerged to support the Linux open-source world and say that they would
help support you, answer your phone calls, and hold your hand if you
needed help.
Now we're seeing who are going to be the
corporate sponsors of some of these open-source big data tools for
customers who may not have thousands of engineers on staff to support
open source. Open-source tools definitely have their place. They're very
good for storing the reams and reams,
terabytes,
petabytes, and more of data out there, and to search in a batch manner, not real time, as I was speaking about before.
Real-time analytics
Some
of our customers are looking for real-time analytics, not just batch.
In batch, you ask a question and will get the answer back eventually,
which many of the open-source, big-data tools are really meant for. How
can I store a lot of data inexpensively that I may need access to at
some point?
We're seeing that our customers have this mix of open-source, big-data tools, as well as commercial big-data tools.
I
recently participated in a customer panel where some of the largest
dot-coms talked about what they're doing with open source versus
commercial tools. They were saying that the open-source tools was where
they may have stored their data lake, but they were using commercial
tools to access that data in real time.
They were
saying that if you need real-time access, you need a big-data tool that
takes in data in parallel and also retrieves it in a parallel manner,
and the best tools to do that are still in the commercial realm. So they
have both open source for storage and closed source for retrieval to
get the real-time answers that they need to run their business.
Gardner:
And are there any particular platforms on the commercial side that
you're working with, particularly on that streaming, real-time, at
volume, at scale equation?
Faucher: What we see on our side with the partners that we work with is that
HP Vertica
is the king of that parallel query. It’s extremely fast to get data in
and get data out, as well as it was built on
columnar, which is a
different way to store data than
relational is. It was really meant to
get those unexpected queries. Who knows what the query is going to be?
Whatever it is, we'll be able to respond to it.
Another very popular platform has been
SAP HANA, mostly for our
SAP
customers who need an
in-memory columnar database to get real-time data
access information. Raajan works with these tools on a daily basis and
can probably provide more detail on that, as well as some of the
customer examples that we've had.
Gardner:
Raajan, please, if you have some insight into what’s working in these
verticals and any examples of how organizations are getting their big
data payoff, I'd be very curious to hear that.
Narayanan:
One of the biggest challenges is to be able to discover the data in the
shortest amount of time, and I mean discovery in the sense that I get
data into the systems, and how fast I can get some meaningful insights.
Works two ways
It
works two ways. One is to get the data into the system, aggregate it
into your current environment, transform it so that data is harmonious
across all the data sources that provide it, and then also to provide
analytics over that.
In a traditional sense, I'll
collect tons and tons of data. It goes through reams and reams of
storage. Do I need all that data? That's the question that has to be
answered.
Data discovery is becoming a science as we speak. When I get the data, I need to see if this data is useful, and if so, how do I process it.
These
systems, as Dennis alluded to, Vertica and SAP HANA, enable that data
discovery right from the get-go. When I get data in, I can just write
simple queries. I don't need a new form of analytic expertise. I can use
traditional
SQL
to query on this data. Once I've done that, then if I find the data
useful, I can send it into storage and do a little bit more robust
analytics over that, which can be predictive or reporting in nature.
A few customers see a lot of value in data discovery. The whole equation of getting in
Hadoop as a
data lake is fantastic, and these platforms play very well with the Hadoop technologies out there.
Once you get data into these platforms, they provide analytic
capabilities that go above and beyond what a lot of the open-source
platforms provide.
Once you get data into these
platforms, they provide analytic capabilities that go above and beyond
what a lot of the open-source platforms provide. I'm not saying that
open source platforms don’t perform these functions, but there are lots
of tools out there that you need to line up in sequence for them to
perform what Vertica or SAP HANA will do. The use cases are pretty
different, but nevertheless, these platforms actually enable lot of
these functions.
Gardner: Raajan, earlier in our
discussion you mentioned the importance of skills and being able to
hire enough people to do the job. Is that also an issue in making a
decision between an open-source and a commercial approach?
Narayanan:
Absolutely. With open source, there are a lot of code bases out there
that needs to be learned. So there is a learning curve within
organizations.
Traditionally, organizations rely more
on the reporting function. So they have a lot of the SQL functions
within the organization. To retrain them is something that an
organization would have to think about. Then, even to staff for new
technologies is something that an organization would have to cater for
in the future. So it’s something that an organization would have to plan
in their roadmap for big-data growth.
Gardner:
Dennis, we can back at the speed and value and getting your big data
apparatus up and running, perhaps think about it holistically across
multiple departments in your organization, and anticipate even larger
scale over time, necessitating a path to growth. Tell us a little bit
about what's going on in the market with converged infrastructure, where
we're looking at very tight integration between hardware and software,
between servers that are supporting workloads, usually virtualized, as
well as storage also usually virtualized.
For big
data, the storage equation is not trivial. It’s an integral part of
being able to deliver those performance requirements and
key performance indicators (KPIs). Tell us a bit about why converged infrastructure makes sense and where you're seeing it deployed?
Three options
Faucher:
What we're seeing with our customers in 2015 is that they have three
options for where to run their applications. They have what we call
best-of-breed, which is what they've done forever. They buy some servers
from someone, some storage from someone else, some networking from
someone else, and some software from someone else. They put it together,
and it’s very time-consuming to implement it and support it.
They
also have the option of going converged, which is buying the entire
stack -- the server, the storage, and the networking -- from a single
organization, which will both factory integrate it, load their software
for them, show up, plug it in, and you are in production in less than 30
days.
The third option, of course, is going to
cloud, whether that’s
infrastructure as a service (IaaS) or SaaS, which can also provide quick time to value.
For
most of our customers now, there are certain workloads that they are
just not ready to run in IaaS or SaaS, either because of cost, security,
or compliance reasons. For those workloads that they have decided are
not ready for Saas, IaaS, or
platform as a service (PaaS) yet, they need to put something in their own
data center. About 90 percent of the time, they're going with converged.
Our customers’ data centers are getting so much bigger and more complex that they just cannot maintain all of the moving parts.
Beside
the fact that it’s faster to implement, and easier to support, our
customers’ data centers are getting so much bigger and more complex that
they just cannot maintain all of the moving parts. Thousands of virtual
machines and hundreds of servers and all the patching needs to happen,
and keeping track of interoperability between server A, network B, and
storage C. The converged takes that all away from them and just pushes
it to the organizations they bought it from.
Now, they
can just focus on their application and their users which is what they
always wanted to focus on and not have to focus on the infrastructure
and keeping the infrastructure running.
So
converged infrastructure has
really taken off very, very quickly with our customers. I would say
even faster than I would have expected. So it's either converged --
they're buying servers and storage and networking from one company,
which both pre-installs it at a factory and maintains it long-term -- or
hyper-converged, where all of the server and storage and networking is
actually done in software on industry-standard hardware.
For private cloud, a large majority of our customers are going with converged for the pieces that are not going to public cloud.
Gardner:
So 90 percent; that’s pretty impressive. I'm curious if that’s the rate
of adoption for converged, what sort of rate of adoption are you seeing
on the
hyper-converged side where it’s as you say software-defined
throughout?
Looking at hyper-converged
Faucher:
It’s interesting. All of our customers are looking at hyper-converged
right now to figure out where it is it fits for them. The thing about
hyper-converged, where it’s just industry standard servers that I'm
virtualizing for my servers and storage and networking, is where does
hyper-converged fit? Sometimes, it definitely has a much lower entry
point. So they'll look at it and say, "Is that right for my tier-1 data
center? Maybe I need something that starts bigger and scales bigger in
my tier-1 data center."
Hyper-converged may be a
better fit for tier-2 data centers, or possibly in remote locations.
Maybe in doctor's offices or my remote retail branches, they go with
hyper-converged, which is a smaller unit, but also very easy to support,
which is great for those remote locations.
You also
have to think that hyper-converged, although very easy to procure and
deploy, when you grow it, you only grow it in one size block. It’s like
this block that can run 200 virtual machines, but when I add, I have to
add 200 at a time, versus a smaller granularity.
So
it’s important to make the correct decision. We spend a lot of time with
our customers helping them figure out the right strategy. If we've
decided that converged is right, is it converged or is it
hyper-converged for the application? Now, as I said, it typically breaks
down to for those tier 1 data centers it’s converged, but for those
tier 2 data centers or those remote locations, it’s more likely
hyper-converged.
But some of the vendors that provide cloud,
hyper-converged and converged, have come up with some great solutions
for rapid scalability.
Gardner: Again,
putting on your enterprise architect hat, given that we have many times
unpredictable loads on that volume and even velocity for big data, is
there an added value, a benefit, of going converged and perhaps
ultimately hyper-converged in terms of adapting to demand or being fit
for purpose, trying to anticipate growth, but not have to put too much
capital upfront and perhaps miss where the hockey puck is going to be
type of thinking?
What is it about converged and
hyper-converged that allow us to adapt to the IoT trend in healthcare,
in retail, where traditional architecture, traditional siloed approaches
would maybe handicap us?
Faucher: For some of
these workloads, we just don’t know how they're going to scale or how
quickly. We see that specifically with new applications. Maybe we're
trying a new channel, possibly a new retail channel, and we don’t know
how it’s going to scale. Of course, we don’t want to fail by not scaling
high enough and turning our customers away.
But some
of the vendors that provide cloud, hyper-converged and converged, have
come up with some great solutions for rapid scalability. A successful
solution for our customers has been something called flexible capacity.
That’s where you've decided to go private cloud instead of public for
some good reasons, but you wish that your private cloud could scale as
rapidly as the public cloud, and also that your payments for your
private cloud could scale just like a public cloud could.
Typically,
when customers purchase for a private cloud, they're doing a
traditional capital expense. So they just spend the money when they have
it, and maybe in three or five years they spend more. Or they do a
lease payment and they have a certain lease payment every month.
With
flexible capacity, I can have more installed in my private cloud than
I'm paying for. Let’s say, there is 100 percent there, but I'm only
paying for 80 percent. That way, if there's an unexpected demand for
whatever reason, I can turn on another 5, 10, 15, or 20 percent
immediately without having to issue a
PO
first, which might takes 60 days in my organization, then place the
order, wait 30 days for more to show up, and then meet the demand.
Flexible capacity
Now
I can have more on site than I'm paying for, and when I need it I just
turn it on and I pay a bill, just like I would if I were running in the
public cloud. That’s what is called flexible capacity.
Another
options is the ability to do cloud bursting. Let’s say I'm okay with
public cloud for certain application workloads -- IaaS, for example --
but what I found is that I have a very efficient private cloud and I can
actually run much more cost-effectively in my private cloud than I can
in public, but I'm okay with public cloud in certain situations.
Well,
if a burst comes, I can actually extend my application beyond private
to public to take on this new workload. Then, I can place an order to
expand my private cloud andwait for the new backing equipment to show
up. That takes maybe 30 days. When it shows up, I set it up, I expand my
on-site capability and then I just turn off the public cloud.
The
most expensive use of public cloud many times is just turning it on and
never turning it off. It’s really most cost-effective for short-term
utilization, whether it’s new applications or development or
disaster recovery (DR). Those are the most cost-effective fuses of public cloud.
Gardner: As a data scientist, you're probably more
concerned with what the systems are doing and how they are doing it, but
is there a benefit from your perspective of going with converged
infrastructure or hyper-converged infrastructure solutions? Whether it’s
bursting or reacting to a market demand within your organization, what
is it about converged infrastructure that’s attractive for you as a data
scientist?
One of the biggest challenges would be to have a system that will allow an organization to go to market soonest.
Narayanan:
One of the biggest challenges would be to have a system that will allow
an organization to go to market soonest. With the big-data platform,
there are lots of moving parts in terms of network. In a traditional
Hadoop technology, there are like three copies of data, and you need to
scale that across various systems so that you have high availability.
Big-data organizations that are engaging big data are looking at high
availability as one of the key requirements, which means that anytime a
node goes down, you need to have the data available for analysis and
query.
From a data scientist standpoint, stability or
the availability of data is a key requirement. The data scientists, when
they build your models and analytic views, are churning through tons
and tons of data, and it requires tremendous system horsepower and also
network capabilities that pulls data from various sources.
With
the converged infrastructure, you get that advantage. Everything is in a
single box. You have it just out there, and it is very scalable. For a
data scientist, it’s like a dream come true for the analytic needs.
Gardner:
I'm afraid we are coming up towards the end of our time. Let’s look at
metrics of success. How do you know you are doing this well? Do you have
any examples, Dennis or Raajan, of organizations that have thought
about the platform, the right relationship between commercial and open
source, that have examined their options on deployment models, including
converged and hyper-converged, and what is it that they get back? How
would you know that you are doing this right? Any thoughts about these
business or technology metrics of success?
New application
Faucher:
I have a quick one that I see all the time. Our customers today measure
how long it takes to get a new business application out the door.
Almost every one of our customers has a measurement around that. How
quickly can we get a business application out the door and functional,
so that we can act upon it?
Most of the time it can be
three months or six months, yet they really want to get these new
applications out the door in a week, just constant improvement to their
applications to help either their patients or to help their customers
out or get into new channels.
What we're finding is
they already have a metric that says, today it takes us three months to
get a new application out the door. Let’s change that. Let’s really look
at the way we are doing things -- people, process and IT end-to-end --
typically where they are helped through something like an Advizer, and
let’s look at all the pieces of the process, look at it all from an
ITIL standpoint or an
ITSM standpoint and ask how can we improve the process.
There are tons of data sources out there. The biggest challenge would be
to integrate all that in the fastest amount of time and make sure that
value is realized at the soonest.
And then let’s
implement the solution and measure it. Let’s have constant improvement
to take that three months down to one month, and down to possibly one
week, if it’s a standardized enough application.
So
for me, from a business standpoint, it’s the fastest time to value for
new applications, new research, how quickly can I get those out the door
better than I am doing today.
Narayanan: From a
technical standpoint Dana, it’s how much data I can aggregate at the
fastest. There are tons of data sources out there. The biggest challenge
would be to integrate all that in the fastest amount of time and make
sure that value is realized at the soonest. With the given platform, any
platform that allows for that would definitely serve the purpose for
the analytic needs.
Gardner: Listening to you
both, it almost sounds as if you're taking what you can do with big data
analytics and applying it to how you do big data analytics, is there
some of that going on?
Faucher: Absolutely.
It’s interesting, when we go out and meet with customers, when we do
workshops and gather data from our customers, even when we do Advizers
and we capture data from our customers, we use that. We take all
identifying customer information out of it, but we use that to help our
customers by saying that of the 2,000 customers that we do business with
every year, this is what we are seeing. With these other customers,
this is where we have seen them be successful, and we use that data to
be able to help our customers be more successful faster.
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