How WWT Took an Enterprise Tower of Babel and Delivered Comprehensive Intelligent Search

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.

Gardner

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.

How HPE IDOL

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

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.

Crincoli

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?

How HPE IDOL

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.

How HPE IDOL

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.

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Meet George Jetson – Your New Chief Procurement Officer

Transcript of a discussion on how rapid advances in artificial intelligence and machine learning are poised to reshape procurement.

Listen to the podcast. Find it on iTunes. Get the mobile app. Download the transcript. Sponsor: SAP Ariba.

Dana Gardner: Hi, this is Dana Gardner, Principal Analyst at Interarbor Solutions, and you're listening to BriefingsDirect.

Gardner

Our next technology innovation thought leadership discussion explores how rapid advances in artificial intelligence (AI) and machine learning are poised to reshape procurement -- like a fast-forwarding to a once-fanciful vision of the future.

Whereas George Jetson of the 1960s cartoon portrayed a world of household robots, flying cars, and push-button corporate jobs -- the 2017 procurement landscape has its own impressive retinue of decision bots, automated processes, and data-driven insights.

We won’t need to wait long for this vision of futuristic business to arrive. As we enter 2017, applied intelligence derived from entirely new data analysis benefits has redefined productivity and provided business leaders with unprecedented tools for managing procurement, supply chains, and continuity risks.

To learn more about the future of predictive -- and even proactive procurement technologies -- please join me in welcoming back Chris Haydon, Chief Strategy Officer at SAP Ariba. Good to have you with us, Chris.

Chris Haydon: Great to be here again.

Gardner: It seems like only yesterday that we were content to gain a common view of the customer or develop an end-to-end bead on a single business process. These were our goals in refining business in general, but today we've leapfrogged to a future where we're using words like “predictive” and “proactive” to define what business function should do and be about. Chris, what's altered our reality to account for this rapid advancement from visibility into predictive -- and on to proactive?

Haydon: There are a couple of things. The acceleration of the smarts, the intelligence, or the artificial intelligence, whatever the terminology that you identify with, has really exploded. It’s a lot more real, and you see these use-cases on television all the time. The business world is just looking to go in and adopt that.

And then there’s this notion of the Lego block of being able to string multiple processes together via an API is really exciting -- that coupled with the ability to have insight. The last piece, the ability to make sense of big data, either from a visualization perspective or from a machine-learning perspective, has accelerated things.

These trends are starting to come together in the business-to-business (B2B) world, and today, we're seeing them manifest themselves in procurement.

Gardner: What is it about procurement as a function that’s especially ripe for taking advantage of these technologies?

Transaction intense

Haydon: Procurement is obviously very transaction-intense. Historically, what transaction intensity means is people, processing, exceptions. When we talk about these trends now, the ability to componentize services, the ability to look at big data or machine learning, and the input on top of this contextualizes intelligence. It's cognitive and predictive by its very nature, a bigger data set, and [improves] historically inefficient human-based processes. That’s why procurement is starting to be at the forefront.

Haydon

Gardner: Procurement itself has changed from the days of when we were highly vertically integrated as corporations. We had long lead times on product cycles and fulfillment. Nowadays, it’s all about agility and compressing the time across the board. So, procurement has elevated its position. Anything more to add?

Haydon: Everyone needs to be closer to the customer, and you need live business. So, procurement is live now. This change in dynamic -- speed and responsiveness -- is closer to your point. It’s also these other dimensions of the consumer experience that now has to be the business-to-business experience. All that means same-day shipping, real-time visibility, and changing dynamically. That's what we have to deliver.

Gardner: If we go back to our George Jetson reference, what is it about this coming year, 2017? Do you think it's an important inception point when it comes to factoring things like the rising role of procurement, the rising role of analytics, and the fact that the Internet of Things (IoT) is going to bring more relevant data to bear? Why now?

Haydon: There are a couple of things. The procurement function is becoming more mature. Procurement leaders have extracted those first and second levels of savings from sourcing and the like. And they have control of their processes.

With cloud-based technologies and more of control of their processes, they're looking now to how they're going to serve their internal customers by being value-generators and risk-reducers.

How do you forward the business, how do you de-risk, how do you get supply continuity, how do you protect your brand? You do that by having better insight, real-time insight into your supply base, and that’s what’s driving this investment.

Gardner: We've been talking about Ariba being a 20-year-old company. Congratulations on your anniversary of 20 years.

Haydon: Thank you.

AI and bots

Gardner: You're also, of course, part of SAP. Not only have you been focused on procurement for 20 years, but you've got a large global player with lots of other technologies and platform of benefits to avail yourselves of. So, that brings me to the point of AI and bots.

It seems to me that right at the time when procurement needs help, when procurement is more important than ever, that we're also in a position technically to start doing some innovative things that get us into those words "predictive" and more "intelligent."

Set the stage for how these things come together.

Haydon: You allude to being part of SAP, and that's really a great strength and advantage for a domain-focused procurement expertise company.

The machine-learning capabilities that are part of a native SAP HANA platform, which we naturally adopt and get access to, put us on the forefront of not having to invest in that part of the platform, but to focus on how we take that platform and put it into the context of procurement.

There are a couple of pretty obvious areas. There's no doubt that when you’ve got the largest B2B network, billions in spend, and hundreds and millions of transactions on invoicing, you apply some machine learning on that. We can start doing a lot smarter matching an exception management on that, pretty straightforward. That's at one end of the chain.

It's not about upstream and downstream, it's about end-to-end process, and re-imagining and reinventing that.

On the other end of the chain, we have bots. Some people get a little bit wired about the word “bot,” “robotics,” or whatever, maybe it's a digital assistant or it's a smart app. But, it's this notion of helping with decisions, helping with real-time decisions, whether it's identifying a new source of supply because there's a problem, and the problem is identified because you’ve got a live network. It's saying that you have a risk or you have a continuity problem, and not just that it's happening, but here's an alternative, here are other sources of a qualified supply.

Gardner: So, it strikes me that 2017 is such a pivotal year in business. This is the year where we're going to start to really define what machines do well, and what people do well, and not to confuse them. What is it about an end-to-end process in procurement that the machine can do better that we can then elevate the value in the decision-making process of the people?

Haydon: Machines can do better in just identifying patterns -- clusters, if you want to use a more technical word. They transform category management and enables procurement to be at the front of their internal customer set by looking not just at their traditional total cost of ownership (TCO), but total value and use. That's a part of that real dynamic change.

What we call end-to-end, or even what SAP Ariba defined in a very loose way when we talked about upstream, it was about outsourcing and contracting, and downstream was about procurement, purchasing, and invoicing. That's gone, Dana. It's not about upstream and downstream, it's about end-to-end process, and re-imagining and reinventing that.

The role of people

Gardner: When we give more power to a procurement professional by having highly elevated and intelligent tools, their role within the organization advances and the amount of improvement they can make financially advances. But I wonder where there's risk if we automate too much and whether companies might be thinking that they still want people in charge of these decisions. Where do we begin experimenting with how much automation to bring, now that we know how capable these machines have been, or is this going to be a period of exploration for the next few years?

Haydon: It will be a period of exploration, just because businesses have different risk tolerances and there are actually different parts of their life cycle. If you're in a hyper growth mode and you're pretty profitable, that's a little bit different than if you're under a very big margin pressure.

For example, maybe if you're in high tech in the Silicon Valley, and some big names that we could all talk about are, you're prepared to be able to go at it, and let it all come.

If you're in a natural-resource environment, every dollar is even more precious than it was a year ago.

That’s also the beauty, though, with technology. If you want to do it for this category, this supplier, this business unit, or this division you can do that a lot easier than ever before and so you go on a journey.

If you're in a hyper growth mode and you're pretty profitable, that's a little bit different than if you're under a very big margin pressure.

Gardner: That’s an important point that people might not appreciate, that there's a tolerance for your appetite for automation, intelligence, using machine learning, and AI. They might even change, given the context of the certain procurement activity you're doing within the same company. Maybe you could help people who are a little bit leery of this, thinking that they're losing control. It sounds to me like they're actually gaining more control.

Haydon: They gain more control, because they can do more and see more. To me, it’s layered. Does the first bot automatically requisition something -- yes or no? So, you put tolerances on it. I'm okay to do it if it is less than $50,000, $5,000, or whatever the limit is, and it's very simple. If the event is less than $5,000 and it’s within one percent of the last time I did it, go and do it. But tell me that you are going to do it or let’s have a cooling-off period.

If you don't tell me or if you don’t stop me, I'm going to do it, and that’s the little bit of this predictive as well. So you still control the gate, you just don’t have to be involved in all the sub-processes and all that stuff to get to the gate. That’s interesting.

Gardner: What’s interesting to me as well, Chris, is because the data is such a core element of how successful this is, it means that companies in a procurement intelligence drive will want more data, so they can make better decisions. Suppliers who want to be competitive in that environment will naturally be incentivized to provide more data, more quickly, with more openness. Tell us some of the implications for intelligence brought to procurement on the supplier? What we should expect suppliers to do differently as a result?

Notion of content

Haydon: There's no doubt that, at a couple of levels, suppliers will need to let the buyers know even more about themselves than they have ever known before.

That goes to the notion of content. It’s like there is unique content to be discovered, which is whom am I, what do I do well and demonstrate that I do well. That’s being discovered. Then, there is the notion of being able to transact. What do I need to be able to do to transact with you efficiently whether that's a payment, a bank account, or just the way in which I can consume this?

Then, there is also this last notion of the content. What content do I need to be able to provide to my customer, aka the end user, for them to be able to initiate the business with them?

These three dimensions of being discovered, how to be dynamically transacted with, and then actually providing the content of what you do even as a material of service to the end user via the channel. You have to have all of these dimensions right.

If you don't have the context of the business process between a buyer and a seller and what they are trying to affect through the network, how does it add value?

That’s why we fundamentally believe that a network-based approach, when it's end to end, meaning a supplier can do it once to all of the customers across the [Ariba] Discovery channel, across the transactional channel, across the content channel is really value adding. In a digital economy, that's the only way to do it.

Gardner: So this idea of the business network, which is a virtual repository for all of this information isn't just quantity, but it's really about the quality of the relationship. We hear about different business networks vying for attention. It seems to me that understanding that quality aspect is something you shouldn't lose track of.

Haydon: It’s the quality. It’s also the context of the business process. If you don't have the context of the business process between a buyer and a seller and what they are trying to affect through the network, how does it add value? The leading-practice networks, and we're a leading-practice network, are thinking about Discovery. We're thinking about content; we're thinking about transactions.

Gardner: Again, going back to the George Jetson view of the future, for organizations that want to see the return on their energy and devotion to these concepts around AI, bots, and intelligence. What sort of low-hanging fruit do we look for, for assuring them that they are on the right path? I'm going to answer my own question, but I want you to illustrate it a bit better, and that’s risk and compliance and being able to adjust to unforeseen circumstances seems to me an immediate payoff for doing this.

Severance of pleadings

Haydon: The United Kingdom is enacting a law before the end of the year for severance of pleadings. It’s the law, and you have to comply. The real question is how you comply.

You eye your brand, you eye your supply chain, and having the supply-chain profile information at hand right now is top of mind. If you're a Chief Procurement Officer (CPO) and you walk into the CEO’s office, the CEO could ask, "Can you tell me that I don’t have any forced labor, I don’t have any denied parties, and I'm Office of Foreign Assets Control (OFAC) compliant? Can you tell me that now?"

You might be able to do it for your top 50 suppliers or top 100 suppliers, and that’s great, but unfortunately, a small, $2,000 supplier who uses some forced labor in any part of the world is potentially a problem in this extended supply chain. We've seen brands boycotted very quickly. These things roll.

So yes, I think that’s just right at the forefront. Then, it's applying intelligence to that to give that risk threshold and to think about where those challenges are. It's being smart and saying, "Here is a high risk category. Look at this category first and all the suppliers in the category. We're not saying that the suppliers are bad, but you better have a double or triple look at that, because you're at high risk just because of the nature of the category."

Think larger than yourself in trying to solve that problem differently. Those cloud deployment models really help you.

Gardner: Technically, what should organizations be thinking about in terms of what they have in place in order for their systems and processes to take advantage of these business network intelligence values? If I'm intrigued by this concept, if I see the benefits in reducing risk and additional efficiency, what might I be thinking about in terms of my own architecture, my own technologies in order to be in the best position to take advantage of this?

Haydon: You have to question how much of that you think you can build yourself. If you think you're asking different questions than most of your competitors, you're probably not. I'm sure there are specific categories and specific areas on tight supplier relationships and co-innovation development, but when it comes to the core risk questions, more often, they're about an industry, a geography, or the intersection of both.

Our recommendation to corporations is never try and build it yourself. You might need to have some degree of privacy, but look to have it as more industry-based. Think larger than yourself in trying to solve that problem differently. Those cloud deployment models really help you.

Gardner: So it really is less of a technical preparatory thought process than process being a digital organization, availing yourself of cloud models, being ready to think about acting intelligently and finding that right demarcation between what the machines do best and what the people do best.

More visible

Haydon: By making things digital they are actually more visible. You have to be able to balance the pure nature of visibility to get at the product; that's the first step. That’s why people are on a digital journey.

Gardner: Machines can’t help you with a paper-based process, right?

Haydon: Not as much. You have to scan it and throw it in. Then, you are then digitizing it.

Gardner: We heard about Guided Buying last year from SAP Ariba. It sounds like we're going to be getting a sort of "Guided Buying-Plus" next year and we should keep an eye on that.

Haydon: We're very excited. We announced that earlier this year. We're trying to solve two problems quickly through Guided Buying.

Our Guided Buying has a beautiful consumer-based look and feel, but with embedded compliance. We hide the complexity. We just show the user what they need to know at the time, and the flow is very powerful.

One is the nature of the ad-hoc user. We're all ad-hoc users in the business today. I need to buy things, but I don’t want to read the policy, I don’t want to open the PDF on some corporate portal on some threshold limit that, quite honestly, I really need to know about once or twice a year.

So our Guided Buying has a beautiful consumer-based look and feel, but with embedded compliance. We hide the complexity. We just show the user what they need to know at the time, and the flow is very powerful.

Gardner: Well, it certainly sounds like an area where intelligence would have a very marked improvement, and we'll look for some interesting news there as well.

I'm afraid we'll have to leave it there. You've been listening to a BriefingsDirect thought leadership podcast discussion on how rapid advances in AI and machine learning are poised to reshape procurement.

We've heard how, as we enter 2017, applied intelligence, derived from entirely new data analysis, benefits redefines productivity. Lastly, we've been presented with SAP Ariba’s view on where we can take business intelligence aspects into more types of process and more refinement of the procurement function.

With that, please join me in thanking our guest, Chris Haydon, Chief Strategy Officer at SAP Ariba. Thank you, sir.

Haydon: Thank you.

Gardner: And a big thank you as well to our audience for joining this SAP Ariba-sponsored business innovation thought leadership discussion. I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator. Thanks again for listening, and do come back next time.

Listen to the podcast. Find it on iTunes. Get the mobile app. Download the transcript. Sponsor: SAP Ariba

Transcript of a discussion on how rapid advances in artificial intelligence and machine learning are poised to reshape procurement. Copyright Interarbor Solutions, LLC, 2005-2016. All rights reserved.

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Strategic View Across More Data Delivers Digital Business Boost for AmeriPride

Transcript of a discussion on how improved data allows for more types of work in an improved organization to become even more intelligent, and to find new efficiency benefits.

Listen to the podcast. Find it on iTunes. Get the mobile app. Download the transcript. Sponsor: Hewlett Packard Enterprise.

Dana Gardner: Hello, and 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.

Gardner

Our next case study explores how linen services industry leader AmeriPride Services uses big data to gain a competitive and comprehensive overview of its operations, finances and culture.

We’ll explore how improved data analytics allows for disparate company divisions and organizations to come under a single umbrella, to become more aligned, and to act as a whole greater than the sum of the parts. This is truly the path to a digital business.

Here to describe how digital transformation has been supported by innovations at the big data core, we’re joined by Steven John, CIO at AmeriPride Services in Minnetonka, Minnesota. Welcome, Steven.

Steven John: I’m glad to be here.

Gardner: We’re also joined by Tony Ordner, Information Team Manager at AmeriPride Services. Welcome, Tony.

Tony Ordner: Thank you. I’m happy to be here, too.

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Gardner: Let’s discuss your path to being a more digitally transformed organization. What were the requirements that led you to become more data-driven, more comprehensive, and more inclusive in managing your large, complex organization?

John

John: One of the key business drivers for us was that we're a company in transition -- from a very diverse organization to a very centralized organization. Before, it wasn't necessarily important for us to speak the same data language, but now it's critical. We’re developing the lexicon, the Rosetta Stone, that we can all rely on and use to make sure that we're aligned and heading in the same direction.

Gardner: And Tony, when we say “data,” are we talking about just databases and data within applications? Or are we being even more comprehensive -- across as many information types as we can?

Ordner: It’s across all of the different information types. When we embarked on this journey, we discovered that data itself is great to have, but you also have to have processes that are defined in a similar fashion. You really have to drive business change in order to be able to effectively utilize that data, analyze where you're going, and then use that to drive the business. We're trying to institute into this organization an iterative process of learning.

Gardner: For those who are not familiar with AmeriPride Services, tell us about the company. It’s been around for quite a while. What do you do, and how big of an umbrella organization are we talking about?

Long-term investments

John: The company is over 125 years old. It’s family-owned, which is nice, because we're not driven by the quarter. We can make longer-term investments through the family. We can have more of a future view and have ambition to drive change in different ways than a quarter-by-quarter corporation does.

We're in the laundry business. We're in the textiles and linen business. What that means is that for food and beverage, we handle tablecloths, napkins, chef coats, aprons, and those types of things. In oil and gas, we provide the safety garments that are required. We also provide the mats you cross as you walk in the door of various restaurants or retail stores. We're in healthcare facilities and meet the various needs of providing and cleansing the garments and linens coming out of those institutions. We're very diverse. We're the largest company of our kind in Canada, probably about fourth in the US, and growing.

Gardner: And this is a function that many companies don't view as core and they're very happy to outsource it. However, you need to remain competitive in a dynamic world. There's a lot of innovation going on. We've seen disruption in the taxicab industry and the hospitality industry. Many companies are saying, “We don’t want to be a deer in the headlights; we need to get out in front of this.”

Tony, how do you continue to get in front of this, not just at the data level, but also at the cultural level?

Ordner: Part of what we're doing is defining those standards across the company. And we're coming up with new programs and new ways to get in front and to partner with the customers.

Ordner

As part of our initiative, we're installing a lot of different technology pieces that we can use to be right there with the customers, to make changes with them as partners, and maybe better understand their business and the products that they aren't buying from us today that we can provide. We’re really trying to build that partnership with customers, provide them more ways to access our products, and devise other ways they might not have thought of for using our products and services.

With all of those data points, it allows us to do a much better job.

Gardner: And we have heard from Hewlett Packard Enterprise (HPE) the concept that it's the “analytics that are at the core of the organization,” that then drive innovation and drive better operations. Is that something you subscribe to, and is that part of your thinking?

John: For me, you have to extend it a little bit further. In the past, our company was driven by the experience and judgment of the leadership. But what we discovered is that we really wanted to be more data-driven in our decision-making.

Data creates a context for conversation. In the context of their judgment and experience, our leaders can leverage that data to make better decisions. The data, in and of itself, doesn’t drive the decisions -- it's that experience and judgment of the leadership that's that final filter.

We often forget the human element at the end of that and think that everything is being driven by analytics, when analytics is a tool and will remain a tool that helps leaders lead great companies.

Gardner: Steven, tell us about your background. You were at a startup, a very successful one, on the leading edge of how to do things different when it comes to apps, data, and cloud delivery.

New ways to innovate

John: Yes, you're referring to Workday. I was actually Workday’s 33rd customer, the first to go global with their product. Then, I joined Workday in two roles: as their Strategic CIO, working very closely with the sales force, helping CIOs understand the cloud and how to manage software as a service (SaaS); and also as their VP of Mid-Market Services, where we were developing new ways to innovate, to implement in different ways and much more rapidly.

And it was a great experience. I've done two things in my life, startups and turnarounds, and I thought that I was kind of stepping back and taking a relaxing job with AmeriPride. But in many ways, it's both; AmeriPride’s both a turnaround and a startup, and I'm really enjoying the experience.

Gardner: Let’s hear about how you translate technology advancement into business advancement. And the reason I ask it in that fashion is that it seems as a bit of a chicken and the egg, that they need to be done in parallel -- strategy, ops, culture, as well as technology. How are you balancing that difficult equation?

John: Let me give you an example. Again, it goes back to that idea of, if you just have the human element, they may not know what to ask, but when you add the analytics, then you suddenly create a set of questions that drive to a truth.

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We're a route-based business. We have over a 1,000 trucks out there delivering our products every day. When we started looking at margin we discovered that our greatest margin was from those customers that were within a mile of another customer.

So factoring that in changes how we sell, that changes how we don't sell, or how we might actually let some customers go -- and it helps drive up our margin. You have that piece of data, and suddenly we as leaders knew some different questions to ask and different ways to orchestrate programs to drive higher margin.

Gardner: Another trend we've seen is that putting data and analytics, very powerful tools, in the hands of more people can have unintended, often very positive, consequences. A knowledge worker isn't just in a cube and in front of a computer screen. They're often in the trenches doing the real physical work, and so can have real process insights. Has that kicked in yet at AmeriPride, and are you democratizing analytics?

Ordner: That’s a really great question. We've been trying to build a power-user base and bring some of these capabilities into the business segments to allow them to explore the data.

You always have to keep an eye on knowledge workers, because sometimes they can come to the wrong conclusions, as well as the right ones. So it's trying to make sure that we maintain that business layer, that final check. It's like, the data is telling me this, is that really where it is?

I liken it to having a flashlight in a dark room. That’s what we are really doing with visualizing this data and allowing them to eliminate certain things, and that's how they can raise the questions, what's in this room? Well, let me look over here, let me look over there. That’s how I see that.

Too much information

John: One of the things I worry about is that if you give people too much information or unstructured information, then they really get caught up in the academics of the information -- and it doesn’t necessarily drive a business process or drive a business result. It can cause people to get lost in the weeds of all that data.

You still have to orchestrate it, you still have to manage it, and you have to guide it. But you have to let people go off and play and innovate using the data. We actually have a competition among our power-users where they go out and create something, and there are judges and prizes. So we do try to encourage the innovation, but we also want to hold the reins in just a little bit.

Gardner: And that gets to the point of having a tight association between what goes on in the core and what goes on at the edge. Is that something that you're dabbling in as well?

John: It gets back to that idea of a common lexicon. If you think about evolution, you don't want a Madagascar or a Tasmania, where groups get cut off and then they develop their own truth, or a different truth, or they interpret data in a different way -- where they create their own definition of revenue, or they create their own definition of customer.

If you think about it as orbits, you have to have a balance. Maybe you only need to touch certain people in the outer orbit once a month, but you have to touch them once a month to make sure they're connected. The thing about orbits and keeping people in the proper orbits is that if you don't, then one of two things happens, based on gravity. They either spin out of orbit or they come crashing in. The idea is to figure out what's the right balance for the right groups to keep them aligned with where we are going, what the data means, and how we're using it, and how often.

Gardner: Let’s get back to the ability to pull together the data from disparate environments. I imagine, like many organizations, that you have SaaS apps. Maybe it’s for human capital management or maybe it’s for sales management. How does that data then get brought to bear with internal apps, some of them may even be on a mainframe still, or virtualized apps from older code basis and so forth? What’s the hurdle and what words of wisdom might you impart to others who are earlier in this journey of how to make all that data common and usable?

Ordner: That tends to be a hurdle. As to the data acquisition piece, as you set these things up in the cloud, a lot of the times the business units themselves are doing these things or making the agreements. They don't put into place the data access that we've always needed. That’s been our biggest hurdle. They'll sign the contracts, not getting us involved until they say, "Oh my gosh, now we need the data." We look at it and we say, "Well, it’s not in our contracts and now it’s going to cost more to access the data." That’s been our biggest hurdle for the cloud services that we've done.

Once you get past that, web services have been a great thing. Once you get the licensing and the contract in place, it becomes a very simple process, and it becomes a lot more seamless.

Gardner: So, maybe something to keep in mind is always think about the data before, during, and after your involvement with any acquisition, any contract, and any vendor?

Ordner: Absolutely.

You own three things

John: With SaaS, at the end of the day, you own three things: the process design, the data, and the integration points. When we construct a contract, one of the things I always insist upon is what I refer to as the “prenuptial agreement.”

What that simply means is, before the relationship begins, you understand how it can end. The key thing in how it ends is that you can take your data with you, that it has a migration path, and that they haven't created a stickiness that traps you there and you don't have the ability to migrate your data to somebody else, whether that’s somebody else in the cloud or on-premise.

Gardner: All right, let’s talk about lessons learned in infrastructure. Clearly, you've had an opportunity to look at a variety of different platforms, different requirements that you have had, that you have tested and required for your vendors. What is it about HPE Vertica, for example, that is appealing to you, and how does that factor into some of these digital transformation issues?

Ordner: There are two things that come to mind right away for me. One is there were some performance implications. We were struggling with our old world and certain processes that ran 36 hours. We did a proof of concept with HPE and Vertica and that ran in something like 17 minutes. So, right there, we were sold on performance changes.

As we got into it and negotiated with them, the other big advantage we discovered is that the licensing model with the amount of data, versus the core model that everyone else runs in the CPU core. We're able to scale this and provide that service at a high speed, so we can maintain that performance without having to take penalties against licensing. Those are a couple of things I see. Anything from your end, Steven?

John: No, I think that was just brilliant.

Gardner: How about on that acquisition and integration of data. Is there an issue with that that you have been able to solve?

Ordner: With acquisition and integration, we're still early in that process. We're still learning about how to put data into HPE Vertica in the most effective manner. So, we're really at our first source of data and we're looking forward to those additional pieces. We have a number of different telematics pieces that we want to include; wash aisle telematics as well as in-vehicle telematics. We're looking forward to that.

There's also scan data that I think will soon be on the horizon. All of our garments and our mats have chips in them. We scan them in and out, so we can see the activity and where they flow through the system. Those are some of our next targets to bring that data in and take a look at that and analyze it, but we're still a little bit early in that process as far as multiple sources. We're looking forward to some of the different ways that Vertica will allow us to connect to those data sources.

Gardner: I suppose another important consideration when you are picking and choosing systems and platforms is that extensibility. RFID tags are important now; we're expecting even more sensors, more data coming from the edge, the information from the Internet of Things (IoT). You need to feel that the systems you're putting in place now will scale out and up. Any thoughts about the IoT impact on what you're up to?

Overcoming past sins

John: We have had several conversations just this week with HPE and their teams, and they are coming out to visit with us on that exact topic. Being about a year into our journey, we've been doing two things. We've been forming the foundation with HPE Vertica and we've been getting our own house in order. So, there's a fair amount of cleanup and overcoming the sins of the past as we go through that process.

But Vertica is a platform; it's a platform where we have only tapped a small percentage of its capability. And in my personal opinion, even HPE is only aware of a portion of its capability. There are a whole set of things that it can do, and I don’t believe that we have discovered all of them.

With that said, we're going to do what you and Tony just described; we're going to use the telematics coming out of our trucks. We're going to track safety and seat belts. We're going to track green initiatives, routes, and the analytics around our routes and fuel consumption. We're going to make the place safer, we're going to make it more efficient, and we're going to get proactive about being able to tell when a machine is going to fail and when to bring in our vendor partners to get it fixed before it disrupts production.

Gardner: It really sounds like there is virtually no part of your business in the laundry services industry that won't be in some way beneficially impacted by more data, better analytics delivered to more people. Is that fair?

Ordner: I think that’s a very fair statement. As I prepared for this conference, one of the things I learned, and I have been with the company for 17 years, is that we've done a lot technology changes, and technology has taken an added significance within our company. When you think of laundry, you certainly don't think of technology, but we've been at the leading edge of implementing technology to get closer to our customers, closer to understanding our products.

[Data technology] has become really ingrained within the industry, at least at our company.

John: It is one of those few projects where everyone is united, everybody believes that success is possible, and everybody is willing to pay the price to make it happen.

Gardner: I’m afraid we’ll have to leave it there. We’ve been exploring how linen services industry leader AmeriPride Services uses big data to gain a common and comprehensive overview of its operations, finance, and its culture. And we've learned how improved data allows for more types of work in an improved organization to become even more intelligent, and to find new efficiencies and benefits -- even those that you probably hadn't thought of before.

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So, please join me in thanking our guests, Steven John, CIO at AmeriPride, and Tony Ordner, Information Team Manager at AmeriPride. And a big thank you to our audience as well 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 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 improved data allows for more types of work in an improved organization to become even more intelligent, and to find new efficiency benefits. Copyright Interarbor Solutions, LLC, 2005-2016. All rights reserved.

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