Big Data Pushes Enterprises into Data-Driven Mode, Makes Demands for More Apps-Based Data10312013 scribe1

Big Data Pushes Enterprises into Data-Driven Mode, Makes
Demands for More Apps-Based Data
Transcript of a BriefingsDirect podcast on how creating big-data capabilities are new top
business imperatives in dealing with a flood of data from disparate sources.
Listen to the podcast. Find it on iTunes. Sponsor: Scribe Software

Dana Gardner: Hi, this is Dana Gardner, Principal Analyst at Interarbor Solutions, and you’re
listening to BriefingsDirect.
Today, we present a sponsored podcast discussion on how creating big-data
capabilities and becoming a data-driven organization are new top business
imperatives.
We’ll examine how such business-intelligence (BI) trends that are requiring
access to and automation from data flows require those from a variety of
sources, formats, and across many business applications.
Our discussion will focus on ways that enterprises are effectively harvesting data in all its forms
and creating integration that fosters better use of data throughout the entire data lifecycle.
Here now to share their insights into using data strategically by exploiting all of the data in all of
the applications across entire business ecosystems, we’re joined by Jon Petrucelli, the Senior
Director of Hitachi Solution Dynamics, CRM, and Marketing Practice based in Austin, Texas.
Welcome, Jon.
Jon Petrucelli: Thanks, Dana.
Gardner: We’re also here with Rick Percuoco the Senior Vice President of Research &
Development at Trillium Software at Bedford, Massachusetts. Welcome, Rick.
Rick Percuoco: Hi, Dana. Thank you.
Gardner: And we're also joined by Betsy Bilhorn, Vice President of Product Management at
Scribe Software in Manchester New Hampshire. Welcome, Betsy.
Betsy Bilhorn: Thank you, Dana.
Gardner: Betsy, let me start with you. We know that more businesses are trying to leverage and
exploit their data, helping them to become more agile, predictive, and efficient. I’m wondering -what's been holding them back from gaining access to the most relevant data? What's the
roadblock here?

Bilhorn: There are a couple of things. One is the explosion in the different types and kinds of
data. Then, you start mixing that with legacy systems that have always been
somewhat difficult to get to. Bringing those all together and making sense of
that are the two biggest ones. Those have been around for a long, long time.
That problem is getting exponentially harder, given the variety of those data
sources, and then all the different ways to get into those. It’s just trying to put all
that together. It just gets worse and worse. When most people look at it today, it
almost seem somewhat insurmountable. Where do you even start?
Gardner: Jon, how about your customers, at Hitachi? What are you seeing in terms of the
struggle that they're facing in getting better data for better intelligence and analytics?

Legacy systems

Petrucelli: We work a lot of large enterprise, global-type customers. To build on what Betsy
said, they do have a lot of legacy systems. There's a lot of data that’s captured
inside these legacy systems, and those systems weren’t designed to be open
architected and sharing their data with other systems.
When you’re dealing with modern systems, it's definitely getting easier. When
you deal with middleware software like Scribe, especially with Scribe Online, it
gets much easier. But the biggest thing that we encounter in the field with these
larger companies is just a lack of understanding of the modern middleware and
integration and lack of understanding of what the business needs? Does it really need a real-time
integration?
Some customers that we come across definitely have a good understanding of what the business
wants and what the customers want, but usually the evaluator, decision-maker, or architect
sometimes doesn’t have a strong background in data integration.
It's really a people issue. It's an educational issue of helping them understand that this isn't as
hard as they think it is. Let's scope it down. Let's understand what the business really needs.
Usually, that becomes something a lot more realistic, pragmatic, and easier to do than they
originally anticipated going into the project.
In the last 5 to 10 years, we've seen data integration get much easier to do, and a lot of people
just don’t understand that yet. That’s the lack of understanding and lack of education around data
integration and how to exploit this big-data proliferation that’s happening. A lot of users don't
quite understand how to do that, and that’s the biggest challenge. It’s the people side of it. That’s
the biggest challenge for us.

Gardner: Rick Percuoco at Trillium, tell us what you are seeing when it comes to the impetus
for doing this. Perhaps in the past, folks saw this as too daunting and complex or involved skill
sets involved that they didn't have, but it seems now that we've got a rationale for wanting to
have a much better handle on as much data as possible. What's driving the need for this?
Percuoco: I would definitely agree with what Betsy and Jon said. In dealing with that kind of
client base, I can see that a lot of the principles and lot of the projects are in their
infancy, even with some of the senior architects in the business. Certain
companies, by their nature, deal with volume data. Telecom providers or credit
card companies are being forced into building these large data repositories
because the current business needs would support that anyway.
So they’re really at the forefront of most of these. What we have are large datamigration projects. There are disparate sources within the companies, siloed bits
of information that they want to put into one big-data repository.
Mostly, it's used from an analytics or BI standpoint, because now you have the capability using
big-data SQL engines to link and join across disparate sources. You can ask questions and get
information, mines of information, that you never could before.
We’re seeing these large data-consolidation projects. The aspect of extract, transform, load
(ETL) will definitely be affected with the data volumes, as you can't move the data like you used
to in the past. Also, governance is becoming a stronger force within companies, because as you
load many sources of data into one repository, it’s easier to have some kind of governance
capabilities around that.

Higher scales

Gardner: Betsy, back to you. It sounds that as if the technology has moved in such a way that
the big-data analysis, the platform for doing analysis, has become much more capable dealing at
higher scales, faster speeds at lower costs. But we still come back to
that same problem of getting to the data, putting it in a format that
can be used, directing it, managing that flow, automating it, and
then, of course, dealing with the compliance, governance, risk, and
security issues.
Is that the correct read on this that we've been able to move quite well in terms of the analytics
engine capability, but we're still struggling with getting the fuel to that engine?
Bilhorn: I would absolutely agree with that. When you look at the trends out there, when we talk
about big data, big analytics and all of that, that's moved much faster than capturing those data
sources and getting them there. Again, it goes back to all of these sources Jon was referring to.
Some of these systems that we want to get the data from were never built to be open. So there is
a lot of work just to get them out of there.

The other thing is that a lot of people like to talk about an application programming interface
(API) economy. We will have an API and we can get through web services and all this great
stuff, but what we’ve seen in building a platform ourselves and having that connectivity, is that
not all those APIs are created equal.
The vendors who are supplying this data, or these data services themselves, are kind of shooting
themselves in the foot and making it difficult for the customer to consume them, because the
APIs are poorly written and very hard to understand or they simply don’t have the performance
to even get the data out of the system.
On top of that, you have other vendors who have certain types of terms of service, where they cut
off the service or they may charge you for it. So when they talk about how it's great that they can
do all these analytics, in getting the data in there, there are just so many show stoppers on a
number of fronts. It's very, very challenging.
Gardner: Let's think about what we are doing in terms of expanding the requirements for
business activities and values here. Customer relationship management (CRM), I imagine, paved
the way where we’re trying to get a single view of the customer across many different data type
of activities. But now, we’re pushing the envelope to a single view of the patient across multiple
healthcare organizations or a single view of a process that has a cloud part, an on-premises part,
and an ecosystem supply-chain part.
It seems as if we’ve moved in complexity here. Jon Petrucelli, how are the systems keeping up
with these larger complex demands, expanding concentric circles of inclusion, if you will, when
it comes to a single view of an object, individual, or process.
Petrucelli: That’s a huge challenge. Some people might call it data taxonomy, data structuring,
or data hygiene, but you've got to be able to define a unique identifier for your primary object in
the data. That’s what we see. Sometimes, businesses have a hard time deciding on that, but
usually it jumps out at you pretty good.
The only things that will transact business with you in the world are people or organizations,
generally speaking. A dog, a tree, or an asset is not going to actually transact business with you.

Master key

We have specialists on our team that do this taxonomy, architects that help our organizations,
figure out what a master key is, a master global unique identifier for an object. Then, you come
up with a schema that allows you to either use one that’s existing or you concatenate a bunch of
the data together to create one. That becomes the way that you relate all of the objects to each
other that sets the foreign key that they hook up to.

Gardner: I think that helps illustrate how far you can go with this. It seems, though, as if you
have to get your own house in order, your own legacy applications, your own capabilities, before
you can start to expand and gain some of these competitive advantages by doing that. It seems
that the more data you can bring it to bear on your analytics, the more predictive, the more
precise, and the more advantageous your business decisions will be.
I think we understand the complexity, but let's take it back inside the organization. Rick, tell us
ﬁrst about what Trillium Software does and how you're seeing organizations take the steps to
begin to get the skills, expertise, and culture to make data integration and data lifecycle
management happen better, so that they can then expand that into all of these other areas that are
now becoming available.
Percuoco: Trillium Software has always been a data-quality company. We have a fairly mature
and diverse platform for data that you push through. Because for analytics, for risk and
compliance, or for anything where you need to use your data to calculate some kind of risk
quotient ratios or modeling whereby you run your business, the quality of your data is very, very
important.
If you’re using that data that comes in from multiple channels to make decisions in your
business, then obviously data quality and making that data the most accurate that it can be by
matching it against structured sources is a huge difference in terms of whether you'll be making
the right decisions or not.
With the advent of big data and the volume of more and varied unstructured data, the problem of
data quality is like on steroids now. You have a quality issue with your data. If anybody who
works in any company is really honest with themselves and with the company, they see that the
integrity of the data is a huge issue.
As the sources of data become more varied and they come from unstructured data sources like
social media, the quality of the data is even more at risk and in question. There needs to be some
kind of platform that can ﬁlter out the chatter in social media and the things that aren't important
from a business aspect.
Gardner: Betsy Bilhorn, tell us about Scribe Software and how what Trillium and Hitachi
Solutions are doing that bring together a higher-level solution for organizations when it comes to
data management and exploiting data in the big-data era?
Bilhorn: We look at ourselves as the proverbial PVC pipe, so to speak, to bring data around to
various applications and the business processes and analytics. Where folks like Hitachi leverage
our platform, as Jon noted earlier in the podcast, is in being able to make that process as easy and
as painless as possible.
We want people to get value out of their data, increase the pace of their business, and increase
the value that they’re getting out of their business. That shouldn’t be a multi-year project. It
shouldn’t be something that you’re tearing your hair out over and running screaming off the
bridge.

As easy as possible

Our goal here at Scribe is to make that data integration and get that data where it needs to go,
to the right person, at the right time, as easily and simply as possible for companies like Hitachi
and their clients.
Working with Trillium, one of the great things with that partnership is obviously that there is the
problem of garbage in/garbage out. Trillium provides that platform by which not only can you
get your data where you need it to go, but you can also have it clean and you can have it
deduped. You can have a better quality of data as it's moving around in your business. When you
look at those three aspects together, that’s where Scribe sits in the middle.
Petrucelli: We used to do custom software integration. With a lot of our customers we see lot of
custom .NET code or other types of codesets, Java for example, that do the integration. They
used to do that, and we still see some bigger organizations that are stuck on that stuff. That’s a
way to paint yourself into a corner and make yourself captive to some developer.
We highly recommend that people move away from that and go to a platform-based middleware
application like Scribe. Scribe is our preferred platform middleware, because that makes it much
more sustainable and changeable as you move forward. Inevitably, in integration, someone is
going to want to change something later on.
When you have a custom code integration someone has to actually crack open the code, take it
ofﬂine, or make a change and then re-update the code and things like and its all just pure
spaghetti code.
With a platform like Scribe, its very easy to pick up industry-standard training available online.
You’re not held hostage anymore. It’s a graphical user interface (GUI). It's literally drag-anddrop mappings and interlock points. That’s really amazing, being this nice capability in their
Scribe Online service. Even children can do an integration. It’s a teaching technique that was
developed at Harvard or MIT about how to put puzzle pieces together through integration. If it
doesn’t work, the puzzle pieces don’t ﬁt.
They’ve done a really amazing job of making integration for rest of us, not just developers. We
highly recommend people to take a look at that, because it just brings the power back to the
business and takes it away from just one developer, a small development shop, or an outsourced
developer.
That’s one thing. The other thing I want to add is that we see integration as critical to all of the
successor projects at the high levels of adoption and return on investment (ROI). Adoption by the
users and then ultimately ROI by the businesses is important, because integration is like gas in
the sports car. Without the gas, it's not going to go.

We want to give them one user experience or one user interface to productive users, especially
sales reps in the CRM world and customer service reps. You don’t want them all tabbing between
a bunch of different systems. So we bring them into one interface, and with a platform like
Microsoft CRM, they can use their interface of choice.
They can move from a desktop, to a laptop, to a tablet, to a mobile device and they’re seeing one
version of the truth, because they’re all looking into windows looking into the same realm. And
in that realm what is tunneled in comes through pipes that are Scribe.

Built-in integration

What we do for a lot of customers is intentionally build integration into it using Scribe,
because we know that if we can take them down from ﬁve different interfaces, you're looking at
getting a 360-degree view of the customer that’s calling them or that they’re about to call on. We
can take that down to one interface from ﬁve.
They’re really going to like that. Their adoption is going to be higher and their productivity is
going to be higher. If you can raise the productivity of the users, you can raise the top line of the
company when you’re talking about a sales organization. So, integration is the key to drive high
level of adoption and high level of ROI and high levels of productivity.
Gardner: Let's talk about some examples of how organizations are using these approaches,
tools, methods, and technologies to improve their business and their data value. I know that you
can’t always name these organizations, but let's hear a few examples of either named or nonnamed organizations that are doing this well, doing this correctly, and what it gets for them.
How about we start with you, Jon? Any thoughts about exemplary use cases that illustrate what
we are talking about?
Petrucelli: One that pops to mind, because I just was recently dealing with them, is the
Oklahoma City Thunder NBA basketball team. I know that they’re not a humongous enterprise
account, but sometimes it's hard for people to understand what's going on inside an enterprise
account.
Most people follow, understand, and are aware of sports. They have an understanding of buying
a ticket, being a season ticket holder, and what those concepts are. So it's a very universal
language.
They had a problem where they were using a ticketing system that would sell the tickets, but they
had very little CRM capabilities. All this ticketing was done at the industry standard for ticketing
and that was great, but there was no way to track, for example, somebody's preferences. You’d
have this record of Jon Petrucelli who buys season tickets and comes to certain games. But that’s
it; that’s all you’d have.

They couldn’t track who my favorite player was, how many kids I have, if I was married, where I
live, what my blog is, what my Facebook is. People are very passionate about their sports team.
They want to really be associated with them and they want to be connected with those people.
And the sports teams really want to do that too.
So we had a great project, an award winning project. It's won a Gartner award and Microsoft
awards. We helped the Oklahoma City Thunder to leverage this great amount of rich interaction
data, this transactional data, the ticketing data about every seat they sat in, and every time they
bought.

Rich information

That’s a cool record and that might be one line in the database. Around that record, we’re able
to wrap all the rich information that’s on the internet, and that customer, that season ticket holder,
wants to share, so they can have a much more personalized experience.
Without Scribe and without integration we couldn’t do that. We could easily deploy Microsoft
CRM and integrate it into the ticketing system, so all this data was in one spot for the users. It
was a real true win-win-win, because not only did the Oklahoma City Thunder have a much
more productive experience, but their season ticket account managers could now call on
someone and could see their preferences. They could see everything they needed to track about
them and see all of their ticketing history in one place.
And they could see if they’re attending, if they are not attending, everything about what's going
on with that very high-value customer. So that’s a win for them. They can deliver personalized
service. On the other end of it, you have the customer, the season ticket holder and they’re
paying a lot of money. For some of them, it’s a lifelong dream to have these tickets or their
family has passed them down. So this is a strong relationship.
Especially in this day and age, people expect a personalized touch and a personalized experience,
and with integration, we were able to deliver that. With Scribe, with the integration with the
ticketing system, putting that all in Microsoft CRM where it's real time, it's accessible and it's
insightful.
It’s not just data anymore. It's real time insights coming out of the system. They could deliver a
much better user experience or customer experience and they have been benchmarked against the
best customer organizations in the world by Gartner. The Oklahoma City Thunder are now rated
as the top professional sports fan experience. Of all professional sports, They have the top fan
experience and it's directly relatable to the CRM platform and the data being driven into it
through integration.
Gardner: Great. You can actually see where there is transformational beneﬁt. They’re not just
iterative or nice to have. It really changes their business in a major way. Rick Percuoco, any

thoughts there at Trillium Software of some examples that exemplify why these approaches are
so powerful?
Percuoco: I’ve seen a couple of pretty interesting use cases. One of them is with one of our
technical partnerships. They have a data platform also where they use a behavior account-sharing
model. It's very interesting in that they take multiple feeds of different data, like social media
data, call-center data, data that was entered into a blog from a website. As Jon said, they create a
one-customer view of all of those disparate sources of data including social media and then they
map for different vertical industries behavioral churn models.
In other words, before someone churns their account or gets rid of their account within a
particular industry, like insurance for example, what steps do they go through before they churn
their account? Do they send an e-mail to someone? Do they call the call center? Do they send
social media messages? Then, through statistical analysis, they build these behavioral churn
models.
They put data through these models of transactional data, and when certain accounts or
transactional data fall out at certain parts, they match that against the strategic client list and then
decide what to do at the different phases of the account churn model.
I've heard of companies, large companies, saving as much as $100 million in account churn by
basically understanding what the clients are doing through these behavioral churn models.

Sentiment analysis

Probably the other most prevalent model that I've seen with our clients is sentiment analysis.
Most people are looking at social media data, seeing what people are saying about them on social
media channels, and then using all different creative techniques to try and match those social
media personas to client lists within the company to see who is saying what about them.
Sentiment analysis is probably the biggest use case that I've seen, but the account churn with the
behavioral models was very, very interesting, and the platform was very complex. On top, it had
a productive analytics engine that had about 80 different modeling graphs and it also had some
data visualization tools. So it was very, very easy to create shots and graphs and it was actually
pretty impressive.
Gardner: Betsy, do you have any examples that also illustrate what we're talking about when it
comes to innovation and value around data gathering analytics and business innovation.
Bilhorn: I’m going to do a little bit of a twist here on that problem. We have had a recent
customer, who is one of the top LED lighting franchisors in United States, and they had a
different bit of a problem. They have about 150 franchises out there and they are all
disconnected.

So, in the central office, I can't see what my individual franchises are doing and I can't do any
kind of forecasting or business reporting to be able to look at the health of all my franchises all
over the country. That was the problem.
The second problem was that they had decided on a standardized NetSuite platform and they
wanted all of their franchises to use these. Obviously, for the individual franchise owner,
NetSuite was a little too heavy for them and they said overwhelmingly they wanted to have
QuickBooks.
This customer came to us and said, “We have a problem here. We can't find anybody to integrate
QuickBooks to our central CRM system and we can't report. We’re just completely flying blind
here. What can you do for us?”
Via integration, we were able to satisfy that customer requirement. Their franchises can use
QuickBooks, which was easy for them, and then through all of that synchronized information
back from all of these franchises into central CRM, they were able to do all kinds of analytics
and reporting and dashboarding on the health of the whole business.
The other side benefit, which also makes them very competitive, is that they’re able to add
franchises very, very quickly. They can have their entire IT systems up and running in 30 minutes
and it's all integrated. So the franchisee is ready to go. They have everything there. They can use
a system that’s easy for them to use and this company is able to have them up and are getting
their data right away.

Consistency and quality

So that’s a little bit different. Big data is not social, but it’s a problem that a lot of businesses
face. How do I even get these systems connected so I can even run my business? This rapid
repeatable model for this particular business is pretty new. In the past, we’ve seen a lot of people
try to wire things up with custom codes, or every thing is ad hoc. They’re able to stand up full IT
systems in 30 minutes, every single time over and over again with a high level consistency and
quality.
Gardner: Well we have to begin to wrap it up, but I wanted to take a gauge of where we are on
this. It seems to me that we’re just scratching the surface. It’s the opening innings, if you will,
given that this was a big week in baseball.
Will we start getting these data visualizations down to mobile devices or people are inputting
more information about themselves, their devices, internet of things? Let's start with you, Jon.
Where are we on the trajectory of where this can go, and how excited are you about some of the
newer things that we expect to be happening very soon vis-à-vis mobile, location, commerce,
and even more and more data?

Petrucelli: We’re working on some projects right now with geolocation, geocaching, and
geosensing, where when a user on a mobile device comes within a range of a certain store, it will
serve that user up, if they have downloaded the app. It will be an app on their smartphone and
they have opted into those. It will serve them up special offers to try to pull them into the store
the same way in which, if you’re walking by a store, somebody might say, “Hey, Jon.” They
know who I am and know my personalization, when I come in a range, it now knows my
location.
This is somebody who has an affinity card with a certain retailer, or it could be a sports team in
the venue that the organization knows during the venue, it knows what their preferences are and
it puts exactly the right offer in front of the right person, at the right time, in the right context,
and with the right personalization.
We see some organizations moving to that level of integration. With all of the available
technology, with the electronic wallets, now with Google Glass, and with smart watches, there is
a lot of space to go. I don’t know if it's really relevant to this, but there is a lot of space now.
We’re more in the business app side of it, and I don’t see that going away. Integration is really
the key to drive high levels of adoption which drives high levels of productivity which can drive
top line gain and ultimately a better ROI for the company that’s how we really look it integration.
Gardner: Okay, Rick Percuoco, very quickly. Where are we on the trajectory here for using
these technologies to advance business? I just wanted to cap things off with a little vision of what
we might expect in the future.
Percuoco: You mentioned specifically location information and, as Jon mentioned, it is germane
to this discussion. There’s the concept of digital marketing, marketing coupons to people in real
time over their smartphones as they’re walking by businesses and so forth. That’s definitely one
of the very prevalent use cases for location objects.

Shopping patterns

There’s also an interesting one that kind of goes on top of that, where you evaluate web traffic
shopping patterns of people, using Google location objects. For large ticket items, you can
actually email them, in real time, competitor coupons. For example, a mile down the street, this
one company has something for $100 or $200 less.
It's another interesting use case kind of intelligent marketing through digital media in the mobile
market. I also see the mobile delivery of information being critical as we move forward.
Pretty much all data integration or BI professionals are basically working parents. It’s very, very
important to be able to deliver that information, at least in a dashboard format or a summary
format on all the mobile devices. You could be at your kid’s Little League game or you could be
out to dinner with your wife, but you may have to check things.

The delivery of information through the mobile market is critical, although the user experience
has to be different. There needs to be a bunch of work in terms of data visualization, the user
experience, and what to deliver. But the modern family aspects of life and people working are
forcing the mobile market to come up to speed.
The other thing that I would say is in terms of integration methods and what Jon was talking
about. You do have to watch for custom APIs. Trillium has a connectivity business as does
Scribe.
As long as you stick with industry-standard handshaking methods, like XML or JSON or web
services and RESTful APIs, then usually you can integrate packages fairly smoothly. You really
need to make sure that you're using industry-standard handoffs actually for a lot of the
integration methods. You have four or five different ways to do that, but it’s pretty much the
same four or five.
Those would be my thoughts on the future. I also see cloud computing, platform as a service
(PaaS), and software as a service (SaaS) really taking hold of the market. Even Microsoft and
some of the other platform tools like Office 365 and the email systems in CRM, are all cloudbased applications now, and to be honest, they’re better. The service is better, and there’s no onpremise footprint. I really see the market moving towards PaaS and SaaS to the cloud computing
market.
Gardner: Betsy, last word to you. Briefly what is Scribe Software's vision and what are the next
big challenges that you will be taking your technology to?
Bilhorn: Rick has brought up a really good point as to where our vision is and where we’re
seeing things going, especially when we talk about PaaS and that standardization of the API.
Ideally, what I would like to see and what I’m hoping is is that with mobile and when we talk
about consumerization of IT and where you’re begin to see business apps act more like consumer
apps, having more standard APIs and forcing that kind of plug and play, would be great for
business. That’s what we’re trying to do, in absence of that, is be able to create that plug-andplay environment is making that, as Jon said, so easy a child can do it.

Seamless integration

Our vision in the future is really flattening that out, but also being able to provide seamless
integration experience between this break systems, where at some point you wouldn’t even have
to buy middleware as an individual business or a consumer of that where these vendors.
These cloud vendors, legacy vendors could embed integration and truly embed it and then be
able to have really a plug and play that the individual user could be doing integration on there

own, and that’s where we really like to get to. That’s the vision and where the platform is going
for Scribe.
Gardner: Well, great. I’m afraid we’ll have to leave it there. We've been listening to a sponsored
BriefingsDirect podcast discussion on how business intelligence and big-data trends are requiring
improved access in automation to data flows from a variety of sources. We've learned of ways
that enterprises are effectively harvesting data in all it's forms and creating integrations that
foster better use of data throughout the entire lifecycle. The result has been the ability to exploit
data strategically among more aspects of enterprise businesses and across more types of
applications and processes.
So a huge thanks to our guests Jon Petrucelli Senior Director of Hitachi Solutions Dynamic
CRM and Marketing Practice. Thanks so much Jon.
Petrucelli: Thank you, glad to be here.
Percuoco: Also Rick Percuoco, Senior Vice President of Research & Development at
Trillium Software. Thank you so much, Rick.
Percuoco: You’re welcome, Dana.
Gardner: And Betsy Bilhorn, Vice President of Product Management at Scribe Software. Thank
you, Betsy.
Bilhorn: Thank you again, Dana.
Gardner: And then also a huge thank you to our audience for joining this insightful discussion.
This is Dana Gardner, Principal Analyst at Interarbor Solutions. Don’t forget to come back and
listen next time.
Listen to the podcast. Find it on iTunes. Sponsor: Scribe Software
Transcript of a BriefingsDirect podcast on how creating big-data capabilities are new top
business imperatives in dealing with a flood of data from disparate sources. Copyright
Interarbor Solutions, LLC, 2005-2013. All rights reserved.

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