Transcript
of a discussion on how WWT reached deep into its applications data
and content to rapidly and efficiently create a powerful Google-like,
pan-enterprise search capability.
Listen to the podcast. Find it on iTunes. Get the mobile app. Download the transcript. Sponsor: Hewlett Packard Enterprise.
Dana Gardner: Welcome to the next edition to the
Hewlett Packard Enterprise (HPE) Voice of the Customer podcast series. I’m
Dana Gardner, Principal Analyst at
Interarbor Solutions,
your host and moderator for this ongoing discussion on digital
transformation. Stay with us now to learn how agile companies are
fending off disruption in favor of innovation.
Our next enterprise case study highlights how
World Wide Technology,
known as WWT, in St. Louis, found itself with a very serious yet
somehow very common problem -- users simply couldn’t find relevant
company content.
We'll explore how WWT reached deep into its applications, data, and content to rapidly and efficiently create a powerful
Google-like,
pan-enterprise search capability. Not only does it search better and
power users, it sets the stage for expanded capabilities using advanced
analytics to engender a more
productive and proactive digital business culture.
Here
to describe how WWT took an enterprise
Tower of Babel and delivered
cross-applications intelligent search, we’re joined by
James Nippert, Enterprise Search Project Manager at World Wide Technology. Welcome, James.
James Nippert: Hello, thank you for having me.
Humanizes Machine Learning
For Big Data Success
Gardner: We're also here with
Susan Crincoli, Manager of Enterprise Content at World Wide Technology. Welcome, Susan.
Susan Crincoli: Good afternoon.
Gardner: It seems pretty evident that the
better search you have in an organization, the better people are going
to find what they need as they need it. What holds companies back from delivering results like people are used to getting on the web?
Nippert: It’s the way things have always
been. You just had to drill down from the top level. You go to your
Exchange, your email, and start there. Did you save a file here? "No, I think I saved it on my
SharePoint site," and so you try to find it there, or maybe it was in a file directory.
Those
are the steps that people have been used to because it’s how they've
been doing it their entire lives, and it's the nature of beast as we
bring more and more enterprise applications into the fold. You have
enterprises with 100 or 200 applications, and each of
those has its own unique data silos. So, users have to try to juggle all
of these different content sources where stuff could be saved. They're
just used to having to dig through each one of those to try to find
whatever they’re looking for.
Gardner: And we’ve
all become accustomed to instant gratification. If we want something,
we want it right away. So, if you have to tag something, or you have to
jump through some hoops, it doesn’t seem to be part of what people want.
Susan, are there any other behavioral parts of this?
Find the world
Crincoli: We, as consumers, are getting used to the Google-like searching. We
want to go to one place and find the world. In the information age, we
want to go to one place and be able to find whatever it is we’re looking
for. That easily transfers into business problems. As we store data in
myriad different places, the business user also wants the same kind of
an interface.
Gardner: Certain tools that can only
look at a certain format or can only deal with certain tags or taxonomy
are strong, but we want to be comprehensive. We don’t want to leave any
potentially powerful crumbs out there not brought to bear on a problem.
What’s been the challenge when it comes to getting at all the data,
structured, unstructured, in various formats?
Nippert:
Traditional search tools are built off of document metadata. It’s those
tags that go along with records, whether it’s the user who uploaded it,
the title, or the date it was uploaded. Companies have tried for a long
time to get users to tag with additional
metadata that will make documents easier to search for. Maybe it’s by department, so you can look for everything in the HR Department.
At
the same time, users don’t want to spend half an hour tagging a
document; they just want to load it and move on with their day. Take
pictures, for example. Most enterprises have hundreds of thousands of
pictures that are stored, but they’re all named whatever number the
camera gave, and they will name it DC0001. If you have 1,000 pictures named that you can't have a successful search, because no
search engine will be able to tell just by that title -- and nothing else
-- what they want to find.
Gardner: So, we have a
situation where the need is large and the paybacks could be large, but
the task and the challenge are daunting. Tell us about your journey.
What did you do in order to find a solution?
Nippert:
We originally recognized a problem with our on-premises Microsoft SharePoint
environment. We were using an older version of SharePoint that was
running mostly on metadata, and our users weren’t uploading any metadata
along with their internet content.
Your
average employee can spend over an entire work week per year searching
for information or documentation that they need to get their job done.
We
originally set out to solve that issue, but then, as we began
interviewing business users, we understood very quickly that this is an
enterprise-scale problem. Scaling out even further, we found out it’s
been reported that as much as 10 percent of staffing costs can be lost
directly to employees not being able to find what they're looking for.
Your average employee can spend over an entire work week per year
searching for information or documentation that they need to get their
job done.
So it’s a very real problem. WWT noticed it
over the last couple of years, but as there is the velocity in volume of
data increase, it’s only going to become more apparent. With that in
mind, we set out to start an
RFI process for all the enterprise search leaders. We used the
Gartner Magic Quadrants and started talks with all of the Magic Quadrant leaders. Then, through a down-selection process, we eventually landed on HPE.
We
have a wonderful strategic partnership with them. It wound up being that we went with the
HPE IDOL
tool, which has been one of the leaders in enterprise search, as well
as big data analytics, for well over a decade now, because it has very
extensible platform, something that you can really scale out and
customize and build on top of. It doesn’t just do one thing.
Gardner:
And it’s one solution to let people find what they're looking for, but
when you're comprehensive and you can get all kinds of data in all sorts
of apps, silos and nooks and crannies, you can deliver results that the searching party didn’t even know was there. The results can be perhaps more
powerful than they were originally expecting.
Susan,
any thoughts about a culture, a digital transformation benefit, when
you can provide that democratization of search capability, but maybe
extended into almost analytics or some larger big-data type of benefit?
Multiple departments
Crincoli:
We're working across multiple departments and we have a lot of
different internal customers that we need to serve. We have a sales
team, business development practices, and professional services. We have
all these different departments that are searching for different things
to help them satisfy our customers’ needs.
With HPE
being a partner, where their customers are our customers, we have this
great relationship with them. It helps us to see the value across all
the different things that we can bring to bear to get all this data, and
then, as we move forward, what we help
people build more relevant results.
If something is
searched for one time, versus 100 times, then that’s going to
bubble up to the top. That means that we're getting the best information
to the right people in the right amount of time. I'm looking forward to
extending this platform and to looking at analytics and into other
platforms.
That means that we're getting the best information to the right people in the right amount of time.
Gardner: That’s why they call it "intelligent search." It learns as you go.
Nippert:
The concept behind intelligent search is really two-fold. It first
focuses on business empowerment, which is letting your users find
whatever it is specifically that they're looking for, but then, when you
talk about business enablement, it’s also giving users the intelligent
conceptual search experience to find information that they didn’t even
know they should be looking for.
If I'm a sales representative and I'm searching for company "X," I need to find any of the
Salesforce
data on that, but maybe I also need to find the account manager, maybe I
need to find professional services’ engineers who have worked on that,
or maybe I'm looking for documentation on a past project. As Susan said,
that Google-like experience is bringing that all under one roof for
someone, so they don’t have to go around to all these different places;
it's presented right to them.
Gardner: Tell us about World Wide
Technology, so we understand why having this capability is going to be
beneficial to your large, complex organization?
Humanizes Machine Learning
For Big Data Success
Crincoli: We're a $7-billion organization and we have strategic partnerships with
Cisco, HPE,
EMC, and
NetApp,
etc. We have a lot of solutions that we bring to market. We're a
solution integrator and we're also a reseller. So, when you're an
account manager and you're looking across all of the various solutions
that we can provide to solve the customer’s problems, you need to be
able to find all of the relevant information.
You
probably need to find people as well. Not only do I need to find how we
can solve this customer’s problem, but also who has helped us to solve
this customer’s problem before. So, let me find the right person, the
right pre-sales engineer or the right post-sales engineer. Or maybe
there's somebody in professional services. Maybe I want the person who
implemented it the last time. All these different people, as well as
solutions that we can bring in help give that sales team the information
they need right at their fingertips.
It’s very
powerful for us to think about the struggles that a sales manager might
have, because we have so many different ways that we can help our
customer solve those problems. We're giving them that data at their
fingertips, whether that’s from Salesforce, all the way through to
SharePoint or something in an email that they can’t find from last year.
They know they have talked to somebody about this before, or they want
to know who helped me. Pulling all of that information together is so
powerful.
We don’t want them to waste their time when
they're sitting in front of a customer trying to remember what it was
that they wanted to talk about.
Gardner: It
really amounts to customer service benefits in a big way, but I'm also
thinking this is a great example of how, when you architect and deploy
and integrate properly on the core, on the back end, that you can get
great benefits delivered to the edge. What is the interface that people
tend to use? Is there anything we can discuss about ease of use in terms
of that front-end query?
Simple and intelligent
Nippert:
As far as ease of use goes, it’s simplicity. If you're a sales rep or
an engineer in the field, you need to be able to pull something up
quickly. You don’t want to have to go through layers and layers of
filtering and drilling down to find what you're looking for. It needs to
be intelligent enough that, even if you can’t remember the name of a
document or the title of a document, you ought to be able to search for a
string of text inside the document and it still comes back to the top.
That’s part of the intelligent search; that’s one of the features of
HPE IDOL.
Whenever
you're talking about front-end, it should be something light and
something fast. Again, it’s synonymous with what users are used to on
the consumer edge, which is Google. There are very few search platforms
out there that can do it better. Look at the Google home page. It’s a
search bar and two buttons; that’s all it is. When users are used to
that at home and they come to work, they don’t want a cluttered, clumsy,
heavy interface. They just need to be able to find what they're looking
for as quickly and simply as possible.
Gardner:
Do you have any examples where you can qualify or quantify the
benefit of this technology and this approach that will illustrate why
it’s important?
It’s
gotten better at finding everything from documents to records to web
pages across the board; it’s improving on all of those.
Nippert:
We actually did a couple surveys, pre- and post-implementation. As I
had mentioned earlier, it was very well known that our search demands
weren't being met. The feedback that we heard over and over again was
"search sucks." People would say that all the time. So, we tried to get a
little more quantification around that with some surveys before and
after the implementation of
IDOL
search for the enterprise. We got a couple of really great numbers out
of it. We saw that people’s satisfaction with search went up by about 30
percent with overall satisfaction. Before, it was right in the middle,
half of them were happy, half of them weren’t.
Now,
we're well over 80 percent that have overall satisfaction with search.
It’s gotten better at finding everything from documents to records to
web pages across the board; it’s improving on all of those. As far as
the specifics go, the thing we really cared about going into this was,
"Can I find it on the first page?" How often do you ever go to the
second page of search results.
With our pre-surveys,
we found that under five percent of people were finding it on the first
page. They had to go to second or third page or four through 10. Most of
the users just gave up if it wasn’t on the first page. Now, over 50
percent of users are able to find what they're looking for on the very
first page, and if not, then definitely the second or third page.
We've
gone from a completely unsuccessful search experience to a valid
successful search experience that we can continue to enhance on.
Crincoli:
I agree with James. When I came to the company, I felt that way, too -- search sucks. I couldn’t find what I was looking for. What’s really
cool with what we've been able to do is also review what people are
searching for. Then, as we go back and look at those analytics, we can
make those the best bets.
If we see hundreds of people
are searching for the same thing or through different contexts, then we
can make those the best bets. They're at the top and you can separate
those things out. These are things like the handbook or
PTO request forms that people are always searching for.
Gardner:
I'm going to just imagine that if I were in the healthcare, pharma, or
financial sectors, I'd want to give my employees this capability, but
I'd also be concerned about proprietary information and protection of
data assets. Maybe you're not doing this, but wonder what you know about
allowing for the best of search, but also with protection, warnings,
and some sort of governance and oversight.
Governance suite
Nippert:
There is a full governance suite built in and it comes through a couple
of different features. One of the main ones is induction, where as
IDOL
scans through every single line of a document or a PowerPoint slide of a
spreadsheet whatever it is, it can recognize credit card numbers,
Social Security numbers anything that’s
personally identifiable information (PII) and either pull that out, delete it, send alerts, whatever.
You
have that full governance suite built in to anything that you've
indexed. It also has a mapped security engine built in called
Omni Group,
so it can map the security of any content source. For example, in
SharePoint, if you have access to a file and I don’t and if we each ran a
search, you would see a comeback in the results and I wouldn’t. So, it
can honor any content’s security.
Gardner: Your policies and your rules are what’s implemented, and that’s how it goes?
Nippert: Exactly. It is up to as the search team or working with your compliance or governance team to make sure that that does happen.
Gardner:
As we think about the future and the availability for other datasets to
be perhaps brought in, that search is a great tool for access to more
than just corporate data, enterprise data and content, but maybe also
the front-end for some advanced querying analytics,
business intelligence (BI), has there been any talk about how to take what you are doing in enterprise search and
munge that, for lack of a better word, with analytics BI and some of the other big data capabilities.
It
is going to be something that we can continue to build on top of, as
well and come up with our own unique analytic solutions.
Nippert: Absolutely. So HPE has just recently released
BI for Human Intelligence (BIFHI), which is their new front end for
IDOL
and that has a ton of analytics capabilities built into it that really
excited to start looking at a lot of rich text, rich media analytics
that can pull the words right off the transcript of an MP4 raw video and
transcribe it at the same time. But more than that, it is going to be
something that we can continue to build on top of, as well and come up
with our own unique analytic solutions.
Gardner: So talk about empowering your employees. Everybody can become a
data scientist eventually, right, Susan?
Crincoli:
That’s right. If you think about all of the various contexts, we
started out with just a few sources, but we also have some excitement
because we built custom applications, both for our customers and for our
internal work. We're taking that to the next level with building an
API and pulling that data into the enterprise search that just makes it even more extensible to our enterprise.
Gardner:
I suppose the next step might be the natural language audio request
where you would talk to your PC, your handheld device, and say, "World
Wide Technology feed me this," and it will come back, right?
Nippert: Absolutely. You won’t even have to lift a finger anymore.
Cool things
Crincoli: It would be interesting to loop in what they are doing with
Cortana
at Microsoft and some of the machine learning and some of the different
analytics behind Cortana. I'd love to see how we could loop that
together. But those are all really cool things that we would love to
explore.
Gardner: But you can’t get there until
you solve the initial blocking and tackling around content and
unstructured data synthesized into a usable format and capability.
Nippert:
Absolutely. The flip side of controlling your data sources, as we're
learning, is that there are a lot of important data sources out there
that aren’t good candidates for enterprise search whatsoever. When you
look at a couple of terabytes or petabytes of
MongoDB data that’s completely unstructured and it’s just binaries, that’s
enterprise data, but it’s not something that anyone is looking for.
The
flip side of controlling your data sources, as we're learing, is that
there are a lot of important data sources out there that aren’t good
candidates for enterprise search.
So even though
our original knee-jerk is to index everything, get everything to search,
you want to able to search across everything. But you also have to take
it with a grain of salt. A new content source could be hundreds or
thousands of results that could potentially clutter the accuracy of
results. Sometimes, it’s actually knowing when not to search something.
Gardner: That would be the "not-too-intelligent" search, right?
Nippert: Exactly.
Gardner:
It sounds like this is an essential part of any organization to become a
digital company and data-driven, an intelligent and fit-for-purpose
approach to gathering that assets wherever they are.
I
want to thank our guests. We've been exploring with World Wide
Technology how a very serious and somehow difficult problem of users
simply finding relevant content can be solved. We've seen how WWT has
reached deep into its applications data and content to rapidly and
efficiently create a powerful Google-like, pan-enterprise search
capability.
So, please join me in thanking our guests, James Nippert, the Enterprise Search Project
Manager at World Wide Technology. Thanks, James.
Nippert: Thank you very much for having me.
Humanizes Machine Learning
For Big Data Success
Gardner:
And we've also been joined by Susan Crincoli, Manager of
Enterprise Content at World Wide Technology. Thank you, Susan.
Crincoli: Thanks, Dana, I appreciate it.
Gardner:
And a big thank you as well to our audience for joining us for this
Hewlett-Packard Enterprise Voice of the Customer digital transformation discussion.
I'm Dana Gardner, Principal Analyst at
Interarbor Solutions, your host for this ongoing series of HPE-sponsored
interviews. Thanks again for listening, and please do come back next
time.
Listen to the podcast. Find it on iTunes. Get the mobile app. Download the transcript. Sponsor: Hewlett Packard Enterprise.
Transcript
of a discussion on how WWT reached deep into its applications data
and content to rapidly and efficiently create a powerful Google-like,
pan-enterprise search capability. Copyright Interarbor Solutions, LLC,
2005-2016. All rights reserved.
You may also be interested in:
Räsänen:
Our data warehouse
solution was a traditional data warehouse, and had been around
for 10 years. Different things had gone wrong. One of the key issues we
faced was that our data wasn’t real-time. It was far from real
time -- it was data that was one or two days old.
