The next BriefingsDirect Voice of the Customer digital transformation 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 empower users, the powerful internal index 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 are James Nippert, Enterprise Search Project Manager, and Susan Crincoli, Manager of Enterprise Content, both at World Wide Technology in St. Louis. The discussion is moderated by BriefingsDirect's Dana Gardner, Principal Analyst at Interarbor Solutions.
Here are some excerpts:
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.
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.
Humanizes Machine Learning
For Big Data Success
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?
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.
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 theGoogle 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?
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.
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.
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.
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.
Humanizes Machine Learning
For Big Data Success
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.
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.
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