Delivering Data Analytics Through Workday SaaS ERP Applications Empowers Business Managers at Actual Decision Points

Transcript of a sponsored BriefingsDirect podcast on benefits of moving to a SaaS model to provide accessible data analytics.

Listen to the podcast. Find it on iTunes/iPod and Podcast.com. Download the transcript. Sponsor: Workday.

See a demo on how Workday BI offers business users a new experience for accessing the key information to make smart decisions.

About Workday
This BriefingsDirect podcast features software-as-a-service (SaaS) upstart Workday, provider of enterprise solutions for human resources management, financial management, payroll, spend management, and benefits management.

Dana Gardner: Hi, this is Dana Gardner, principal analyst at Interarbor Solutions, and you’re listening to BriefingsDirect.

Today we present a sponsored podcast discussion on how software-as-a-service (SaaS) applications can accelerate the use and power of business analytics.

We're going to use the example of a human capital management (HCM) and enterprise resource planning (ERP) SaaS provider to show how easily customizable views on data and analytics can have a big impact on how managers and knowledge workers operate.

Historically, the back office business applications that support companies have been distinct from the category of business intelligence (BI). Certainly, applications have had certain ways of extracting analytics, but the interfaces were often complex, unique, and infrequently used.

Often, the data and/or tools were off-limits to the line-of-business managers and workers, when it comes to BI. And the larger data gathering analytics from across multiple data sources remain sequestered among the business analysts and were not often dispersed among the business application users themselves.

By using SaaS applications and rich Internet technologies that create different interface capabilities -- as well as a wellspring of integration and governance on the back-end of these business applications (built on a common architecture) -- more actionable data gets to those who can use it best. They get to use it on their terms, as our case today will show, for HCM or human resources managers in large enterprises.

The trick to making this work is to balance the needs that govern and control the data and analytics, but also opening up the insights to more users in a flexible, intuitive way. The ability to identify, gather, and manipulate data for business analysis on the terms of the end-user has huge benefits. As we enter what I like to call the data-driven decade, I think nearly all business decisions are going to need more data from now on.

So, to learn more about how the application and interfaces are the analytics, with apologies to Marshall McLuhan, please join me in welcoming our panel today. We have with us Stan Swete, Vice President of Product Strategy and the CTO at Workday, the sponsor of this podcast. Welcome back to the show, Stan.

Stan Swete: Thanks, Dana.

Gardner: We're also here with Jim Kobielus, Senior Analyst for BI and Analytics at Forrester Research. Welcome, Jim.

Jim Kobielus: Hi, Dana. Hello, everybody.

Gardner: And Seth Grimes, Principal Consultant at Alta Plana Corp., and a contributing editor at TechWeb's Intelligent Enterprise. Welcome, Seth.

Seth Grimes: Thank you, Dana.

Gardner: As I said, I have this notion that we're approaching a data-driven decade, that more data is being created, but increasingly more data needs to be brought to more decisions, and the enterprise, of course, is a primary place where this can take place.

So, let me take this first to you, Jim Kobielus. How are business workers and managers inside of companies starting to relate better to data? How is data typically getting into the hands of those who are in a position to take action on it best?

Dominant BI tool

Kobielus: It's been getting into hands of people for quite some time through their spread sheets, and the dominant BI tool in the world is Microsoft Excel, although that’s a well-kept secret that everybody knows. Being able to pull data from wherever into your Excel spreadsheet and model it and visualize it is how most people have done decision, support, and modeling for a long time in the business world.

BI has been around for quite a long time as well, and BI and spreadsheets are not entirely separate disciplines. Clearly, Excel, increasingly your browser increasingly, and the mobile client, are the clients of choice for BI.

There are so many different tools that you can use now to access a BI environment or capability to do reporting and query and dashboarding and the like that in the business world we have a wealth of different access members to analytics.

One of the areas that you highlighted -- and I want to hear what Stan from Workday has to say -- is the continued growth and resurgence of BI integrated with your line-of-business applications. That’s where BI started and that’s really the core of BI -- the reporting that's built-in to your HCM, your financial management systems, and so forth.

Many companies have multiple customer data repositories, and that, by its very nature, creates a quality issue.

Gardner: But, Jim, haven’t we evolved to a point where the quality of the data and the BI and the ability of people to access and use it have, in a sense, split or separated over the years?

Kobielus: It has separated and split simply because there is so much data out there, so many different systems of record. For starters, many companies have multiple customer data repositories, and that, by its very nature, creates a quality issue, consolidating, standardizing, correcting, and so forth. That’s where data warehouses have come in, as a consolidation point, as the data governance focus.

If the data warehouse is the primary database engine behind BI, BI has shared in that pain, in that low quality, relating to the fact that data warehouses aren’t even the solutions by themselves. Many companies have scads of data warehouses and marts, and the information is pulled from myriad back-end databases into myriad analytic databases and then pushed out to myriad BI tools.

Quality of data is a huge issue. One approach is to consolidate all of your data down to a single system of record, transactional, on-line transaction processing (OLTP) environment, a single data warehouse, or to a single, or at least a unified, data virtualization layer available to your BI environment. Or, you can do none of those things, but to try to consolidate or harmonize it all through common data quality tools or master data management.

The quality issue is just the ongoing pain that every single BI user feels, and there’s no easy solution.

Gardner: Stan, we've heard from Jim Kobielus on the standard BI view of the world, but I am going to guess that you have a little different view in how data and analytics should get in the hands of the people who use it.

Tell us what your experience has been at Workday, particularly as you've gone from your Release 9 to Release 10, and some of the experience you have had with working with managers.

Disparate data sources

Swete: A lot of the view that we have at Workday really supports what Jim said. When I think of how BI is done, primarily in enterprises, I think of Excel spreadsheets, and there are some good reasons for that, but there’s also some disadvantages that that brings.

One addition I would have on it is that, when I look at the emergence of separate BI tools, one driver was the fact that data comes from all kinds of disparate data sources, and it needs aggregation and special tooling to help overcome that problem.

Taking an apps focus, there’s another causal effect of separate BI tools. It comes from the fact that traditional enterprise applications, have been written for what I would call the back-office user. While they do a very good job of securing access to data, they don’t do a very good job of painting a relevant picture for the operational side of the business.

A big driver for BI was taking the information that’s in the enterprise systems and putting a view on some dimensionality that managers or the operational side of the business could relate to. I don’t think apps have done that very well, and that’s where a lot of BI originated as well.

From a Workday perspective, we think that you're going to always need to have separate tools to be data aggregators, to get some intelligence out of data from disparate sources. But, when the data can be focused on the data in a single application, we think there is an opportunity for the people who build that application to build in more BI, so that separate tooling is not needed. That’s what we think we are doing at Workday.

Grimes: Dana, I'd love to riff on this a little bit -- on what Jim said and what Stan has just said. We're definitely in a data-driven decade, but there’s just so much data out there that maybe we should extend that metaphor of driving a bit.

The real destination here is business value, and what provides the roadmap to get from data to business value is the competencies, experiences, and the knowledge of business managers and users, picking up on some of the stuff that Stan just said.

It’s the systems, the data warehouses, that Jim was talking about, but also hosted, as-a-service types of systems, which really focus on delivering the BI capabilities that people need. Those are the great vehicle for getting to that business value destination, using all of that data to drive you along in that direction.

Gardner: Traditionally, however, if you look at back office applications -- as on-premises, silo, stack, self-contained, on their own server -- making these integrations and these data connections requires quite a bit of effort from the IT people. So, the IT department crew is between the data, the integrations, the users, and the people.

What’s different now, with a provider like Workday moving to the SaaS model, is that the integration can happen more seamlessly as a result of the architecture and can be built into more frequent updates of the software. The interface, as I said earlier, becomes the analytics, rather than the integration and the IT department becoming the analytics -- or becoming a barrier to the analytics.

I wonder, Jim Kobielus, if you have a sense of what the architecture-as -destiny angle has here, moving to SaaS, moving to cloud models, looking at what BI can bring vis-à-vis these changes in the architecture. What should we expect to see?

Pervasive BI

Kobielus: "Architecture as destiny." That’s a great phrase. You'd better copyright that, Dana, before I steal it from you.

It comes down to one theme that we use to describe where it’s going, as pervasive BI ... Pervading all decisions, pervading everybody’s lives, but being there, being a ready decision support tool, regardless of where you are at and how you are getting into the data, where it’s hosted.

So in terms of architecture, we can look at the whole emerging cloud space in the most nebulous ways as being this new architecture for pervasive, hosted BI. But that is such a vague term that we have to peel the onion just a little bit more here.

I like what you said just before that, Dana, that the interface is the analytics. That’s exactly true. Fundamentally, BI is all about delivering action and more intelligence to decision agents. I use the term agents here to refer to the fact that the agents may be human beings or they may be workflows that you are delivering, analytic metrics, KPIs, and so forth to.

The analytics are the payload, and they are accessed by the decision agents through an interface or interfaces. Really, the interfaces have to fit and really plug into every decision point -- reporting, query, dashboarding, scorecarding, data mining, and so forth.

What we are really talking about is a data virtualization layer for cloud analytics to enable the delivery of analytics pervasively throughout the organization.

If you start to look, then, at the overall architecture we are describing here for really pervasive BI, hosted on demand, SaaS, cloud, they're very important. But, it's also very much the front-end virtualization layer for virtualization of access to this cloud of data, virtualization of access by a whole range of decision agencies and whatever clients and applications and tools they wish, but also very much virtualization of access to all the data that’s in the middle.

In the cloud, it has to be like a cloud data warehouse ecosystem, but it also has to be a interface. The interfaces between this cloud enterprise data warehouse (EDW) and all the back-end transactional systems have to be through cloud and service oriented architecture (SOA) approaches as well.

What we are really talking about is a data virtualization layer for cloud analytics to enable the delivery of analytics pervasively throughout the organization. At the very highest level, that’s the architecture that I can think of that actually fits this topic.

Gardner: All right. That’s the larger goal, the place where we can get to. I think what Workday is showing is an intermediary step, but an important one.

Stan, tell us a little bit about what Workday is doing vis-à-vis your release 10 update and what that means for the managers of HR, the ones that are looking at that system of record around all the employee information and activities and processes.

Swete: I agree with the holistic view of trying to develop pervasive analytics, but the thing that frequently gets left out, and it has gotten left out even in this conversation, is a focus on the transactional apps themselves and the things they can do to support pervasive analytics.

Maintaining security

For disparate data sources, you're going to need data warehouses. Any time you've got aggregation and separate reporting tools, you're going to need to build interfaces. But, if you think back to how you introduced this topic Dana, how you introduced SaaS, is when you look at IT’s involvement, if interfaces need to get built to convey data, IT has to get involved to make sure that some level of security is maintained.

From Workday’s point of view, what you want to do is reduce the times when you have to move data just to do analysis. We think that there is a role that you can play in applications where -- and this gets IT out of it -- if your application, that is the originator of transactional data, can also support a level of BI and business insight, IT does not have to become as involved, because they bought the app with the trust in the security model that’s inherent to the application.

What we're trying to is leverage the fact that we can be trusted to secure access to data. Then, what we try to do is widen the access within the application itself, so that we don’t have to have separate data sources and interfaces.

This doesn’t cover all cases. You still need data aggregation. But, where the majority of the data is sourced in a transaction system, in our case HR, we think that we, the apps vendor, can be relied on to do more BI.

What we've been working on is constantly enhancing managers' abilities to get access to their data. Up through 2009, that took the form of trying to enhance our report writer and deliver more options for reports, either the option to render reports in a small footprint, we call it Worklet, and view it side by side, whether they are snippets of data, or the option to create more advanced reports.

This is an ability to enhance our built-in report writer to allow managers or back-office personnel to directly create what become little analysis cues.

We had introduced a nice option last year to create what we call contextual reporting, the ability to sort of start with your data -- looking at a worker -- and then create a report about workers from there, with guidance as to all the Workday fields, where they applied to the worker. That made it easier for a manager not to have to search or even remember parts of our data dictionary. They could just look at the data they knew.

This year, we're taking, we think, a major step forward in introducing what we are calling custom analytics. This is an ability to enhance our built-in report writer to allow managers or back-office personnel to directly create what become little analysis cues. We call them matrix reports.

That’s a new report type in our report writer. Basically, you very quickly -- and importantly without coding or migrating data to a separate tool, but by pointing and clicking in our report writer -- get one of these matrix reports that allows slicing and dicing of the data and drilling down into the data in multiple dimensions. In fact, the tool automatically starts with every dimension of the data that we know about based on the source you gave us.

If you say, I want the worker, probably we will pop up about 12 different dimensions to analyze. Then, you actually reduce them down to the ones that you want to analyze -- maybe last performance review, business site, management reporting level, for example, and, let’s say, salary level. So, you could quickly create a cue for yourself to do the analysis.

Then, we let you share that out to other managers in a way in which you don’t have to think about the underlying security. I could write the thing and share it with either someone who works for me or a coworker, and the tool would apply the security that they head to the system, based on its understanding of their roles.

We're trying to make it simple to get this analysis into the hands of managers to analyze their data.

Self-service information

Kobielus: What you are saying there is very important. What you just mentioned there, Stan, is one thing I left off in my previous discussion, which is self-service information and exploration through hierarchical and dimensional drill down and also mashup in collaborative sharing of your mashups. It's where the entire BI space is going, both traditional, big specialized BI vendors, but also vendors like yourself, who are embedding this technology into back office apps, and have adopted a similar architecture. The users want all the power and they're being given the power to do all of that.

Swete: We would completely agree with that. Actually, we like to think that we completely thought this up on our own, but it really has been a path we have been pushed along by our customers. We see from the end users that same demand that you're talking about.

Gardner: Seth, to you. You've focused on web analytics and the interfaces involved with text and large datasets. When you hear about a specific application, like a HCM, providing these interfaces through the web browser, rich and intuitive types of menuing and drop-downs and graphics, does something spark an interest in you? When I saw this, I thought, "Wow, why can’t we do this with a lot more datasets across much more of the web?" Any thoughts about how what Workday is doing could be applied elsewhere?

Grimes: Let me pull something from my own consulting experience here. A few years ago I did a consulting stint to look at the analytics and data-warehousing situation at a cabinet level, U.S. federal government agency. It happens to be headed by a former 2008 Presidential candidate, so it’s actually internationally distributed.

They were using some very mainstream BI tools, with conventional data warehousing, and they had chaos. They had all kinds of people creating reports in different departments, very duplicative reports.

The web is going to be a great mechanism for interconnecting all of the distributed systems that you might have and bringing in additional data that might be germane to your business problems.

There was a lot of cost involved in all of this duplication, because stuff had to get re-proven over and over again, except that when you had all those distributed report creation, with no standards, then nothing was ever done quite the same in two different departments, and that only added to the chaos.

There were all kinds of definability problems, all kinds of standardization problems, and so on. When you do move to this kind of architecture that we are discussing here, architecture is destiny again. The architecture maybe isn't the destiny in my mind, but it creates an imprint for the destiny that you are going to have.

Add in the web. The web is going to be a great mechanism for interconnecting all of the distributed systems that you might have and bringing in additional data that might be germane to your business problems, that isn’t held inside your firewall, and all that kind of stuff. The web is definitely a fact nowadays and it’s so reliable finally that you can run operational systems on top of it.

That’s where some of the stuff that Stan was talking about comes into play. Data movement between systems does create vulnerability. So, it's really great, when you can bundle or package multiple functional components on a single platform.

For example, we've been discussing bundling analytics with the operational system. Whether those operational systems are for HCM, ERP, or for other business functions, it makes security sense, but there are a couple of dimensions that we haven’t discussed yet. When you don’t move your data, then you're going to get fresher data available to the analytical systems. When people create data warehouses, they still often do refreshes on a daily or even less-frequent basis.

See a demo on how Workday BI offers business users a new experience for accessing the key information to make smart decisions.

About Workday
This BriefingsDirect podcast features software-as-a-service (SaaS) upstart Workday, provider of enterprise solutions for human resources management, financial management, payroll, spend management, and benefits management.

Data is not moving

You're also going to have better performance, because the data is not moving. All this is also going to add up to lower support costs. We were talking about IT a little bit earlier. In my experience, IT actually wants to encourage this kind of hosted or as-a-service type of use, because it does speed the time for getting the applications in place. That reduces the IT burden and it really leverages the competencies, experience, and knowledge of the line-of-business users and managers. So, there's only good stuff that one can say about this kind of architecture’s destiny that we have been talking about.

Gardner: I'd like to dive in a bit more on this notion of "the interface is the analytics." What I mean by that is, when you open up the opportunity for people to start getting at the data, slicing it and dicing it based on what they think their needs are, to follow their own intuition about where they want to learn more, maybe creating templates along the way so they can reuse their path, maybe even sharing those templates with other people in the organization, it strikes me that you are getting toward a tipping point of some sort.

The more the people use the data, the better they are at extracting value, and the more that happens, the more that they will use the tools and then share that knowledge, and it becomes a bit of a virtuous adoption opportunity. So, analytics takes on a whole new level of value in the organization based on how it’s being used.

Stan, when you have taken what you are doing with Workday -- rolling out update 10 -- what’s been the response? What’s been the behavioral implication of putting this power in the hands of these managers?

We also have stories from customers who have used this in production to create reports for management that would have taken them weeks, and they did it in less than an hour.

Swete: We have been rolling out 10. I think about half of our customer population is on it, but we have worked through design with our customers and have done early testing. We've also gotten some stories from the early customers in production, and it’s playing out along a lot of the lines that you just mentioned.

A customer we worked particularly close with took their first look. We sat back and looked at what they would build for themselves. The very first analysis they did involved an aging analysis by job profile in their company. They were able to get a quick matrix report built that showed them the ages by job code across their organization.

Then, they could not only look at sort of just a high-level average age number, but click down on it and see the concentration of the detail. They found certain job categories where not only was there a high average age, but a tight concentration around that average, which is an exposure. That’s insight that they developed for themselves.

Pre-Workday 10, the thought might have occurred to us to build that and deliver it as a part of our application, but I don’t think it would have been in the top 10 reports that we would have delivered. And this is something that they wrote for themselves in their first hours using the functionality.

We also have stories from customers who have used this in production to create reports for management that would have taken them weeks, and they did it in less than an hour. That’s because we eliminated the need to move data and think about how that data was staged in another tool, secured in another tool, and then put that all back on to Workday.

Aggressive adoption

So, so far so good, I'd say. Our expectation is that these kinds of stories will just increase, as our customers fully get on to this version of Workday. We've seen fairly aggressive adoption of lot of the features that I have mentioned driving into Workday. I think that these requirements will continue to drive us forward to place sort even more power into the insight you can get from our reporting tools.

Grimes: Isn’t that what it's all about, speeding time to insight for the end-users, but, at the same time, providing a platform that allows the organization to grow. That evolves with the organization’s needs, as they do change over time. All of that kind of stuff is really important, both the immediate time to insight and the longer term goal of having in place a platform that will support the evolution of the organization.

Swete: We totally agree with that. When we think about reporting at Workday, we have three things in mind. We're trying to make the development of access to data simple. So that’s why we try to make it always -- never involve coding. We don’t want it to be an IT project. Maybe it's going to be a more sophisticated use of the creation of reports. So, we want it to be simple to share the reports out.

The second word that’s top of my list is relevance. We want the customers to guide themselves to the relevant data that they want to analyze. We try to put that data at hand easily, so they can get access to it. Once they're analyzing the data, since we are a transaction system, we think we can do a better job of being able to take action off of what the insight was.

I call it transalytics. It's a combination of transaction systems and analytics systems. And really it's a closed loop. It must be.

So, we always have what we call related actions as a part of all the reports that you can create, so you can get to either another report or to a task you might want to do based on something a report is showing you.

Then, the final thing, because BI is complex, we also want to be open. Open means that it still has to be easy to get data out of Workday and into the hands of other systems that can do data aggregation.

Kobielus: That’s interesting -- the related action and the capability. I see a lot of movement in that area by a lot of BI vendors to embed action links into analytics. I think the term has been coined before. I call it transalytics. It's a combination of transaction systems and analytics systems. And really it's a closed loop. It must be.

It's actionable intelligence. So, duh, then shouldn't you put an action link in the intelligence to make it really truly actionable? It's inevitable that that’s going to be part of the core uptake for all such solutions everywhere.

Gardner: Jim, have you seen any research or even some anecdotal evidence that making these interfaces available, making the data available without IT, without jumping through hoops of learning SQL or other languages or modeling tools, that it’s a tipping point or some catalyst to adoption? It adds more value to the BI analytics, which therefore encourages the investment to bring more data and analytics to more people. Have you seen any kind of a wildfire like that?

Tipping point

Kobielus: Wildfire tipping point. I can reference some recent Forrester Research. My colleague, Boris Evelson, surveyed IT decision makers -- we have, in fact, in the last few years -- on the priorities for BI and analytics. What they're adopting, what projects they are green lighting, more and more of them involve self-service, pervasive BI, specifically where you have more self-service, development, mashup style environments, where there is more SaaS for quick provisioning.

What we're seeing now is that there is the beginnings of a tipping point here, where IT is more than happy to, as you have all indicated, outsource much of the BI that they have been managing themselves, because, in many ways, the running of a BI system is not a core competency for most companies, especially small and mid-market companies.

The analytics themselves though -- the analysis and the intelligence -- are a core competency they want to give the users: information workers, business analysts, subject matter experts. That's the real game, and they don't want to outsource those people or their intelligence and their insights. They want to give them the tools they need to get their jobs done.

What's happening is that more and more companies, more and more work cultures, are analytic savvy. So, there is a virtuous cycle, where you give users more self-service -- user friendly, and dare I say, fun -- BI capabilities or tools that they can use themselves. They get ever more analytics savvy. They get hungry for more analysis. They want more data. They want more ways to visualize and so forth. That virtuous cycle plays into everything that we are seeing in the BI space right now.

What's happening is that more and more companies, more and more work cultures, are analytic savvy.

Boris Evelson is right now doing a Forrester Wave on BI SaaS, and we see that coming along on a fast track, in terms of what enterprises are asking for. It's the analytics-savvy culture here. There is so much information out there, and analytics are so important.

Ten years ago, it may have seemed dangerous to outsource your payroll or your CRM system. Nowadays, everybody is using something like an ADP or a Salesforce, and it's a no-brainer. SaaS BI is a no-brainer. If you're outsourcing your applications, maybe you should outsource your analytics.

Gardner: Alright, Stan, let's set this up to ask Workday. You've got your beachhead with the HCM application. You're already into payroll. How far do you expect to go, and what sort of BI payoff from your model will you get when your systems of record start increasing to include more and more business data and more applications?

Swete: There are a couple of ways we can go on that. First of all, Workday has already built up more than just HCM. We offer financial management applications and have spend-management applications.

A big part of how we're trying to develop our apps is to have very tight integration. In fact, we prefer not even to talk about integration, but we want these particular applications to be pieces of a whole. From a BI perspective, we wanted to be that. We believe that, as a customer widens their footprint with us, the value of what they can get out of their analysis is only going to increase.

I'll give you an example of that that plays out for us today. In the spend management that we offer, we give the non-compensation cost that relate to your workforce. A lot of the workforce reporting that you do all of a sudden can take on a cost component in addition to compensation. That is very interesting for managers to look at their total cost to house the workforce that they've developed and use that as input to how they want to plan.

Cost analysis

We do a good job of capturing and tracking contingent labor. So, you can start to do cost analysis of what your full-time employees and your contingent workers are costing you.

Our vision is that, as we can widen our footprint from an application standpoint, the payoff for what our end-users can do in terms of analysis just increases dramatically. Right now, it's attaching cost to your HR operations' data. In the future, we see augmenting HR to include more and more talent data. We're at work on that today, and we are very excited about dragging in business results and drawing that into the picture of overall performance.

You look at your workforce. You look at what they have achieved through their project work. You look at how they have graded out on that from the classical HR performance point of view. But, then you can take a hard look at what business results have generated. We think that that's a very interesting and holistic picture that our customers should be able to twist and turn with the tools we have been talking about today.

Grimes: There is a kind of truism in the analytics world that one plus one equals three. When you apply multiple methods, when you join multiple datasets, you often get out much more than the sum of what you can get with any pair of single methods or any pair of single datasets.

Some users are really going to get down and dirty with the data and with the analytical methods, and you want to support them, but you also want to deliver appropriate sophistication of analytics to other users.

If you can enable that kind of cross-business functions, cross-analytical functions, cross-datasets, then your end-users are going to end up farther along in terms of optimizing the overall business picture and overall business performance, as well as the individual functional areas, than they were before. That's just a truism, and I have seen it play out in a variety of organizations and a variety of businesses.

Swete: That’s why we think it’s really important not to introduce any seams in the application. Even today, when we've got a customer looking at their HR data, they're able to do analysis and the dimensions of how their cost centers are structured, not just how their supervisory organization is structured. So, they can get rollups and analysis along those lines. That’s just one example. We have to bridge into wider and wider financial and operational data.

Grimes: You get to a really good place, if your users don’t even know that they are pulling data from multiple sources. They don’t even really know that they are doing analytics. They just think that they are doing their job. That sounds like the direction that you all are going, and I would affirm that’s a very good direction to be going.

Some users are really going to get down and dirty with the data and with the analytical methods, and you want to support them, but you also want to deliver appropriate sophistication of analytics to other users. There are an awful lot of users in the organization who really do need analytics, but they actually don’t need to know that they are doing analytics. They just need to do their job. So, if you can deliver the analytics to them in a very unintrusive way, then you're in really good shape.

Swete: We would agree. Our challenge for doing multidimensional analysis, which you can do on these matrix reports, is to deliver that to a customer without using the word multidimensional.

Grimes: A lot of the jargon words that we have been throwing around in this podcast today, you don’t want to take those words anywhere near your end-users. They don’t need to know, and it might just cause some consternation for them. They don’t really need to know all that kind of stuff. We who provides those services and analyze them need to know that kind of stuff, but the end-users don’t usually.

Using small words

Swete: One vendor, of course, put the word pivot into the name of a product that does this dimensional exploration. Other vendors quite often talk about slice and dice. You definitely want to boil it down to words that maybe have fewer than four syllables.

Gardner: Let me throw this out to our analysts on the call today. Is there something about the SaaS model -- and I'll even expand that to the cloud model -- that will allow BI analytics to move to the end-user faster than it could happen with an on-premise or packaged application? And, is analytics, in effect, an accelerant to the adoption of the SaaS model?

I might be stretching it here, but, Jim Kobielus, what do you think? Is what Workday and Stan have been describing compelling on its own merits, regardless of some of the other SaaS benefit to start adopting more applications in this fashion?

Kobielus: Analytics generally as an accelerant to adopting a SaaS model for platforms and applications?

Grimes: Maybe it's the other way around. Maybe the platform is an accelerant to analytics. As we were talking about before, if you can eliminate some of the data movement and all of the extract, transform, and load, you're going to get faster time to data being analytically ready from the operational systems.

The analytics will migrate to where the data lives. If the data lives in the cloud or in a SaaS environment, the analytics will certainly migrate to that world.

If you adopt it as a service model, then you don’t need to have your IT staff install all the software, buy the machines to host it, all that kind of stuff. That’s a business consideration, not a technical one. You have faster time to analytics, just in the sense of the availability of those analytics.

Then, you also can accelerate the adoption of analytics, because you reduced the entry cost with a hosted solution. You don’t have to lay out a lot of money up front in order to buy the hardware and license the software. The cloud as a service will potentially enable on demand pricing, pay-as-you-go types of pricing. So, it’s a different business model that speeds the availability of analytics, and not even a technical question.

Kobielus: I agree. The analytics will migrate to where the data lives. If the data lives in the cloud or in a SaaS environment, the analytics will certainly migrate to that world. If all your data is in premises-based Oracle databases, then clearly you want a premises-based BI capability as well.

If all your data is in SaaS-based transactional systems, then your BI is going to migrate to that world. That’s why BI SaaS is such a huge and growing arena.

Also, if you look at just the practical issues here, more and more of the BI applications, advanced analytics, that we're seeing out there in the real world involve very large datasets. We're talking about hundreds of terabytes, petabytes, and so forth. Most companies of most sizes, with typical IT budgets, don’t have the money to spend on all of the storage and the servers to process all of that. They'll be glad to rent out a piece of somebody’s external cloud to host their analytical data mart for marketing campaign optimization, and the like.

A lot of that is just going into the SaaS world, because that’s the cheapest storage and the cheapest processing, multitenant. The analytics will follow the data, the huge big datasets to the cloud environment. SaaS is an accelerant for pervasive advanced analytics.

Gardner: Stan, did we miss anything in terms of looking at the SaaS model and your model in terms of where analytics fit in and the role they play?

Change delivery vehicle

Swete: I agree with everything that was just said. The thing that always occurs to me as an advantage of SaaS is that SaaS is a change delivery vehicle. If you look at the trend that we have been talking about, this sort of marrying up transactional systems with BI systems, it’s happening from both ends. The BI vendors are trying to get closer to the transactional systems and then transactional systems are trying to offer more built-in intelligence. That trend has several steps, many, many more steps forward.

The one thing that’s different about SaaS is that, if you have got a community of customers and you have got this vision for delivering built-in BI, you are on a journey. We are not at an endpoint. And, you can be on that journey with SaaS and make the entire trip.

In an on-premise model, you might make that journey, but each stop along the way is going to be three years and not multiple steps during the year. And, you might never get all the way to the end if you are a customer today.

SaaS offers the opportunity to allow vendors to learn from their customers, continue to feed innovation into their customers, and continue to add value, whereas the on-premise model does not offer that.

It’s not just about the time of the journey. It’s about do you bring all your customers along with you, because that’s the real value.

Gardner: So, a logical conclusion from that is that, if an on-premises organization takes three, six, nine years to make a journey, but their competitor is in a SaaS model that takes one, two, three years to make the journey, there is a significant competitive advantage or certainly a disparity between the data and analytics that one corporation is going to have, where it should be, versus the other.

Swete: We think so. It’s not just about the time of the journey. It’s about do you bring all your customers along with you, because that’s the real value, right? If we build the flashiest new analytic tool and there is an expensive upgrade to get there and all of our customers have to go through that at their own pace and with their own on-premise project, that’s sort of one value proposition that’s reduced.

I mentioned we are in the midst of delivering Workday 10. In two or three weeks, all of our customers will be on it, and we'll be looking forward to the next update. That’s the other value of SaaS. Not only are you able to deliver the new functionality, but you are able to keep all your customers up on it.

Gardner: Well, we're just about out of time. We've been discussing how SaaS applications can accelerate the use and power of business analytics.

I want to thank our panel today. We've been joined by Stan Swete. He is the Vice President of Product Strategy and CTO at Workday. Thank you, Stan.

Swete: Thanks.

Gardner: We've also been joined by Jim Kobielus, Senior Analyst at Forrester Research. Thanks, Jim.

Kobielus: It’s been a pleasure.

Gardner: And, Seth Grimes, Principal Consultant at Alta Plana Corp., and a contributing editor at TechWeb's Intelligent Enterprise. Thank you, Seth.

Grimes: You're welcome. Again, I appreciate the opportunity to participate.

Gardner: This is Dana Gardner, Principal Analyst at Interarbor Solutions. You've been listening to a sponsored BriefingsDirect podcast. Thanks for joining us, and come back next time.

See a demo on how Workday BI offers business users a new experience for accessing the key information to make smart decisions.

About Workday
This BriefingsDirect podcast features software-as-a-service (SaaS) upstart Workday, provider of enterprise solutions for human resources management, financial management, payroll, spend management, and benefits management.

Listen to the podcast. Find it on iTunes/iPod and Podcast.com. Download the transcript. Sponsor: Workday.

Transcript of a sponsored BriefingsDirect podcast on moving to a SaaS model to provide accessible data analytics. Copyright Interarbor Solutions, LLC, 2005-2010. All rights reserved.

You may also be interested in:

• HCM SaaS Provider Workday's Advanced Architecture Brings New Business Agility Benefits to Enterprises
  
  
• Executive Interview: Workday's Aneel Bhusri on Advancement of SaaS and Cloud Models for Improved ERP
  
  
• Workday Builds out SaaS Bellwether for Human Capital Management Services and Costs Control

Confluence of Global Trends Ups Ante for Improved IT Governance to Prevent Costly Business 'Glitches'

Transcript of a sponsored BriefingDirect podcast on the growing danger from faulty software and how to overcome it.

Listen to the podcast. Find it on iTunes/iPod and Podcast.com. Download the transcript. Sponsor: WebLayers.

Dana Gardner: Hi, this is Dana Gardner, principal analyst at Interarbor Solutions, and you’re listening to BriefingsDirect.

Today, we present a sponsored podcast discussion on the nature of, and some possible solutions for, a growing parade of enterprise-scale glitches. The headlines these days are full of big, embarrassing corporate and government "gotchas."

These complex snafus cost a ton of money, severely damage a company’s reputation, and most importantly, can hurt or even kill people.

From global auto recalls to bank failures, and the cyber crime that can uproot the private information from millions of users, the scale and damage that technology-accelerated glitches can inflict on businesses and individuals has probably never been higher. So what is at the root?

Is it a technology run amok problem, or a complexity spinning out of control issue -- and why is it seemingly worse now?

A new book is coming out this summer that explores the relationship between glitches and technology, specifically the role of software use and development in the era of cloud computing.

It turns out the role and impact of governance over people, process, and technology comes up again and again in the new book.

We have with us here today the author of the book as well as a software expert from IBM to delve into the causes and effects of glitches and how governance relates to the problem and fixes.

Please join me in welcoming our guests, Jeff Papows, President and CEO of WebLayers, and the author of Glitch: The Hidden Impact of Faulty Software. Welcome to the show, Jeff.

Jeff Papows: Thanks, Dana. Thanks for having us on.

Gardner: We're also here with Kerrie Holley, IBM fellow and Chief Technology Officer for IBM’s SOA Center of Excellence. Welcome to the show, Kerrie.

Kerrie Holley: Thank you, very much.

Gardner: Jeff, let me start with you. Now, the general trends around these complex issues are affecting business and probably affecting just about everyone’s lives. How do these seem to be something that’s different? Is there an inflection point? Is there something different now that 20 years ago in terms of the intersection of business with technology?

Papows: There is. I’ve done a lot of research in the past 10 months and what we're actually seeing is the confluence of three primary factors that are creating an information technology perfect storm of sorts. Some of these are obvious, but it’s the convergence of the three that’s creating problems on the scale that you are describing here.

The first is a loss of intellectual capital. For the first time in our careers -- the three of us have all been at this for a long time now -- we saw, between 2000 and 2007, the first drop in computer science graduates. That's the other side of the dot-com implosion.

Mainframe adoption patterns

While it’s not always popular or glamorous to talk about, 70 percent of the world’s critical infrastructure still runs on IBM mainframes. Yet, the focus of most of our new computer science graduates and early life professionals is on Java, XML, and the open and more modern development languages.

For the first time in our lifetimes and careers, the preponderance of that COBOL-based analytical community is retiring and/or -- God forbid -- aging and dying. That’s created a significant problem, concurrent with a time where the merger and consolidation activity -- the other side of the recession of 2008 -- have created this massive complexity in these giant mash-ups and critical back-office systems. For example, the mergers between Bank of America and Countrywide, and on and on.

The third factor is just the sheer ubiquity of the technological complexity curve. It’s the magnitude of technology that’s now part of our social fabric, whether it’s literally one million transistors that now exist for every human being on the planet or the six billion network devices that exist in the world today, all of which are accessing the same critical, in many cases, back-office structures.

It's reached the point, Dana, from a consumer standpoint, where 60 percent of the value of our automobiles now consists of networked electronic components -- not the drive trains, engines, and the other things. Look at the recent glitches you have seen at places like Toyota.

You take those three meta-level factors and put them together and we're making the morning broadcast news cycles now on a daily basis with, as you said, more and more of these embarrassing things coming to light. They're not just inconvenient, but there are monumental economic consequences -- and we're killing people.

Gardner: Kerrie Holley, we've looked at some of these issues -- society issues, organizational issues, and the technology behind them -- but technology has also been part of the solution or the ability to scale and manage and automate. I think service oriented architecture (SOA) has a major impact on that.

So, are we at a point where the ability of technology to keep up with the rate of growth is out of whack? What do you sense is behind some of this and why hasn't the technology been there to fix it along the way?

Holley: Jeff brought up some excellent points, which are spot-on. The other thing that we see is that we've had this growth of distributed computing. The easy stuff we've actually accomplished already.

If we look at a lot of what businesses are trying to accomplish today, whether it’s a new business model, differentiation, or whatever they're trying to do compete, what we are finding is that the complexity of that solution is pretty significant.

It's something that we obviously can do. If we look at a lot of technologies that are out in the market place, unfortunately, in many cases they are siloed. They repair or they help with a part of the problem, but perhaps they're not holistic in dealing with the whole life-cycle that is necessary to create some of this value.

Secondly -- this is a point-in-time statement -- we're seeing rapid improvements in the technology to solve this. With Jeff's company and other organizations, we are seeing that today. It hasn’t caught up, but I think it will. In summary, Jeff brought up several points in terms of the fact that we have ubiquitous devices and a tremendous amount of computing power. We have programming available to the masses. We have eight-year-olds, grandmothers, and everyone in between, writing software.

Connecting devices

We have a tremendous need to connect mobile devices and front-ends. We have 3D Internet. We just have an explosion of technologies that we have to integrate. Along with that comes some of the challenges in terms of how we make this agile, and how we make it such that it doesn't break. How do we make sure that we actually get the value propositions that we see? Clearly, SOA is a part of the solution, but it's certainly not the end-all in terms of how we repair and how we get better.

Gardner: One of the things that intrigues me about SOA is the emphasis on governance. To get the best out of a distributed services-orientation, you need to think at the very beginning and throughout the process about how to manage, automate, and reuse, as well as the feedback loops into the process -- all on an ongoing basis.

It strikes me that if that works for SOA, it probably also works for management and organizations, and it works for the relationship between workers and customers. Let me take this back to you, Jeff. Is governance also in catch-up mode? Do we have a sense of how to govern the technology, but not necessarily the process? Is that what's behind some of it?

Papows: You're right, Dana. There's a cultural maturation process here. Let's look at a couple of the broad economic planks that have affected how we got here, because I've been in the software industry for 30 years now. Remember that the average computer scientist, at least in North America, on average, makes 32 percent more than the mean average in the U.S. economy. And, software, computer services and infrastructure has accounted for about 37 percent of the growth in the gross domestic product in the United States and Asia in the last decade.

So the economic impact and success of our industry almost can’t be overstated. Because of that, we've grown up for decades now where we just threw more and more bodies at the problem, as
the technological curve grew.

All that means is automating those best practices and turning them inward, so that we’re governing ourselves as an industry the way that we would automate or govern many things.

There was always this never-ending economic rosy horizon, where you would just add more IT professionals and you would acquire and you’d merge systems, but rarely would you render
portions of those workforces redundant.

In 2008, the economic malaise that we’re managing our way through changed all of that. Now, the only way out of this complexity curve that we’ve created, to use Kerrie's terms, is turning the innovation that has been the hallmark of our industry back on ourselves.

That means automating and codifying all of the best practices and human capital that’s been in-place and learning for decades in the form of active policy management and inference engines in what we typically think of as SOA and design-time governance.

Really, all that means is automating those best practices and turning them inward, so that we’re governing ourselves as an industry in the same way that we would automate or govern many things. But now it’s no longer a "nice to have." I would argue that it’s critical, because the complexity curve and the economics have crossed and there is no way to put this genie back in the bottle. There is no way to go backward.

Gardner: Kerrie, any thoughts about what’s perhaps now a critical role for governance, perhaps governance up and down the technology spectrum, design time, runtime, but also governance in terms of how the people and processes come together?

Holley: Absolutely. One of the nice things that the attention to SOA has brought to our marketplace is the recognition that we do need to focus on governance. I don’t know of a single client who’s got an SOA implementation who has not, as a minimum, thought about governance. They may not be doing everything they want to do or should be doing, but governance is clearly on the attention span of everyone in terms of recognizing that it needs to be done.

So, when we look at governance and when we look at it around SOA, IT governance is something that we’ve had for a long time. SOA governance is a subset, you could say. It complements, but at the same time, it focuses our attention on, what some of the deltas have brought to the marketplace that require improved governance.

Services lifecycles

That governance is not only around the technology. It’s not only around the life-cycle of services. It’s not only around the use of addressing processes and addressing application development. Governance also focuses on the convergence that’s required between business and IT.

The synergistic relationship that we seek will be promoted through the use of governance. Change management specifically brings about a pretty significant focus, meaning that there will be a focus on the part of the business and the IT organizations and teams to bring about the results that are sought.

Examples of problems

Gardner: Jeff, in your book you identify some examples. Are there any that really stand out I that we can trace back to some root cause in the software lifecycle?

Papows: There are, and it’s unfortunate. The ones that make the greatest memory points and often the national headlines, characteristically are the ones that affect the consumer broadly as opposed to the corporate ones.

Obviously, Toyota is in the headlines everyday now. Actually, there was another news cycle recently about Toyota’s Lexus vehicles. The new models apparently have a glitch in the software that controls the balance system.

The ones that make the greatest memory points and often the national headlines, characteristically are the ones that affect the consumer broadly as opposed to the corporate ones.

One of the most heartbreaking things in the research for the book was on software that controls the radiation devices in our hospitals for cancer treatment. I ran across a bunch of research where, because of some software glitches and policy problems in terms of the way those updates were distributed, people with fairly nominal cancers received massive overdoses in radiation.

The medical professionals running these machines -- like much of our culture, because something is computerized -- just assume that it’s infallible. Because of the problems in governance or lack of governance policy, people were being over-radiated. Instead of targeting small tumors in a very targeted way, people’s entire upper torsos, and unfortunately, in one case, head and neck were targeted.

There are lots of examples like that in the book that may not be as ubiquitous as Toyota, but there are many cases of widespread health, power, energy, and security risks as a consequence of the lack of policy management or governance that Kerrie was speaking to just a few minutes ago.

Gardner: Well, these examples certainly are very poignant and clearly something to avoid. I wonder if these are also perhaps just the tip of the iceberg. In addition to things that are problematic at a critical level, is there also a productivity hit? Are large aspects of work in process not nearly as optimal as they could be or are plagued by mistakes that drag down the process?

I want to take this over to Kerrie. IBM has its Smarter Planet approach. I think they're talking about the issue that we're just not nearly as efficient as we could be. What makes the headlines are these terrible issues, but what we're really talking about is a tremendous amount of waste. Aren’t we?

Things we could do better

Holley: We are. That’s exactly what inefficiency is. It speaks to a lot of waste and a lot of things we could do better. A lot of what we’ve been talking about from a Smarter Planet standpoint is actually the exact issues that Jeff has talked about, which is that the world is getting more instrumented. There are more sensors. There is a convergence of a lot of different technology, SOA, business process management, mobile computing, and cloud computing.

Clearly, on one end of the spectrum, it’s increasing the complexity. On the other end of the spectrum, it’s adding tremendous value to businesses, but it mandates this attention to governance.

Gardner: Jeff, in your book do you offer up some advice or solutions about what companies ought to be doing in this governance arena to deal with these glitches?

Papows: We do. We talk about what I call the IT Governance Manifesto, for lack of another catchy phrase. I make the argument that it’s almost reached the point now where we need to lobby for legislation that requires more stringent reporting of software glitches in cases where there is human health and life at stake. Or, alternately, that we impose fines upon individuals or organizations responsible for cover-ups that put people at risk. Or, we simply require a level of IT governance at organizations that produce products that directly affect productivity and quality of life issues.

Kerrie said this really well, Dana. Remember that about 70 percent of our computer scientists in a given year are basically contending with maintaining the existing application inventories that run all of our financial transactions in core sub-systems and topologies. So, 70 percent of our human capital is there to basically keep the stuff that’s in place running.

So, 70 percent of our human capital is there to basically keep the stuff that’s in place running.

Concurrently, we have this smarter planet, where we’ve got billions of RFID tags in motion and 64-bit microprocessors have reached a price point where they are making the way into our dishwashers. We’ve got this plethora of hand-held devices and applications that’s exploding.

All of that is against the backdrop of this more difficult economy, where we can’t just hire more people without automation. We haven't a prayer keeping our noses about water here.

So, God forbid that we ask the federal government, which moves at a dinosaur’s pace relative to Internet speed, to intercede and insist on some of the stuff. But, if we don’t police our own industry, if we don’t get more serious about this governance, whether it’s IBM or WebLayers or some other technological help, we run the risk of seeing the headlines we’re seeing today become completely ubiquitous.

Gardner: Kerrie, I understand that you’re also penning a book, and it’s focused on SOA. First, could you tell us about it, but then are there any aspects of it that address this issue of governance, maybe from a self-help perspective and of not waiting for some legislation or external direction on it.

Holley: The book that’s going to be out later this year is 100 SOA Questions: Asked and Answered. What my co-author [Ali Arsanjani] and I are trying to accomplish in the book, which distinguishes us from other SOA books in the marketplace, is based on thousands of questions that we’ve experienced over the decade in hundreds of projects where we’ve had first-hand roles in as consultants, architects, and developers. We provide the audience with a hands-on, prescriptive understanding of some of the more difficult questions, and not just have platitudes as answers, but really give the reader an answer they can act on.

We’ve organized the content in a way that you can go by domain. If you’re a business stakeholder, you can go to particular areas. That gets back to your question, because business clearly has a big role to play here. The convergence or the relationship between business and IT has a big role to play.

You can go directly into those sections. We do talk about governance. The book is not about governance, but a good percentage of the questions are on governance. What we try to do is help organizations, clients, practitioners, and executives understand what works what doesn’t work.

Always a choice

One of the examples, a small example, is that we always have a choice when we do a project. We can do it in multitude of ways, but we have a lot of evidence that when governance is not applied, when it’s not automated, when it’s not thought about upfront, the expense on the back-end side is enormous. That expense could be the cost of not having the agility that you foresaw.

The expense could be not having the cost reduction that you foresaw. The expense could be the defects that Jeff has spoken about -- the glitches. There is a tremendous downside to not focusing on governance on the front-side, not looking at it in the beginning. The book really tries to ask and answer the toughest SOA questions that we’ve seen in the marketplace over the last decade.

Gardner: We’ll certainly look forward to that. Back to you Jeff. When we think about governance, it has a bit of a siloed history itself. There's the old form of management, the red-light, green-light approach to IT management. We’ve seen design-time governance, but it seems to be somewhat divorced from, even on a different plane than, runtime or operational governance.

What needs to happen in order to make governance more holistic, more end-to-end?

Papows: It’s a good question, Dana. It’s like everything else in our industry. We’re sometimes our own worst enemy and we get hung up on language, and God forbid, we create yet another acronym headache.

There's an old expression, "Everybody wants governance, but nobody wants to be governed." We run the risk, and I think we’ve tripped over it several times, where we get to the point where developers don’t want to be slowed down. There is this Big Brother-connotation at times to governance. We’ve got to explore a different cultural approach to it.

Governance, whether it’s design time or run time, is really about automating and codifying best practices.

Governance, whether it’s design-time or run-time, is really about automating and codifying best practices, and it’s not done generically as was once taught. It can be, in my experience, very specific. The things we see Ford Motor Co. doing are very different. They're germane to their IT culture and organization, and very different than what we see the Bank of America do, as an example.

To Kerrie’s point about the cost of a lack of automated best practices, if we can use the new verb, it isn’t always quantitative. Look at the brand damage to a bank when they shut customers out of their ATM network, the other side of turning the switch when they merged back-office systems. Look at the number of people whose automated payment systems and whatnot were knocked out of kilter.

The brand damage affecting major corporations is a consequence of having these inane debates about whether SOA is alive or dead, whether you need design-time governance or run-time governance. What you need is a way to automate what you are doing, so that your best practices are enforced throughout the development lifecycle.

Kerrie answered your question well when he said it really is about waste. It’s not just about wasted human capital or wasted productivity or cycles. It’s about wasted go-to-market opportunity. Remember, we're now living in the era of market-facing systems. For almost every major business enterprise, our digital footprint is directly accessible in the marketplace, whether it’s an ATM network or a hand-held device. The line between our back-office infrastructure and our consumer experience is being obliterated.

I'd argue that rather than making distinctions between design and run-time governance, companies simply, one way or another, need to automate their best practices. The business mandates of the corporations need to be reflected in an automated way that makes it manageable across the information technology life-cycle -- or you exist at your own peril.

Gardner: Kerrie, any thoughts on this concept of governance and how we make it more ubiquitous and more enforced as the pain and the problems grow evident? The solution at a high level seems pretty clear. It seems to be the implementation where we stumble.

Governance mindset

Holley: You hit it on the head, and Jeff made the point as well. A lot of people think governance is onerous, that it’s a structure that forces people to do things a certain way. They look at it as rigid, inflexible, unforgiving. They think it just gets in the way.

That’s a mindset that people find themselves in, and it’s a reason not to do something. But when you think about the goals that you're seeking, most goals have something to do with efficiency, lower cost, customers, and making the company more agile. When you think about this, pretty much everybody in the marketplace knows that you don’t get those goals for free. There is some cultural change that’s often necessary to bring those goals about, some organizational change.

There's automation. You don’t start with automation. You actually start with the problem, the processes, and picking the right tool. But, automation has to be a part of that solution. One end of the spectrum, we’ve got to address this mindset that governance gets in the way, that it’s overhead, and that it’s unnecessary.

We know that organizations that are very successful, that are achieving many of their goals, when we peel the onion back, we see them focused on governance. One advice that we all know is that you shouldn’t boil the ocean, that you should do incremental change. We also need to do this in governance.

We need to have these incremental successes, where we are focused on automation holistically and looking at the life-cycle, not just looking at the part-of-the-problem space.

Looking for automation as a way out of the hole that has been created is a consequence of the industry’s own success.

Gardner: Jeff, it sounds like governance needs a makeover. Is there an opportunity? You are going to be discussing this book at the IBM Impact Conference 2010, their SOA conference? Is this a good opportunity? You have a lot of IT executive and software executives from the variety of enterprises on hand, but what would you tell them in terms of how to make governance a bit more attractive?

Papows: We all need to say, "I am a computer science professional. We have reached a point in the complexity curve where I no longer scale." You have to start with an admission of fact. And the reality is that the demands placed on today's IT organizations, the magnitude of the existing infrastructure that needs to continue to be cared for, the magnitude of application demands for new systems and access points from all of this new technology, simply is not going to correlate without a completely different highly automated approach.

Kerrie is right. You can't boil the ocean and you can’t do it at once, but you have to start with an honest self-assessment that, as an industry, we can't continue to go forward at the rate and pace that we have grown, given everything we know and that we see, without finally eating our own cooking.

Looking for automation as a way out of the hole that has been created is a consequence of the industry’s own success. We didn't get here because we failed to be fair to all of those developers in the audience. They're going to listen to this and say, "Why am I the bad guy?" They're not the bad guys.

The reality is, as I said, that we're responsible for the greatest percentage of growth in the gross domestic product. We're responsible for the greatest percentage workforce productivity. We've changed the way civilization lives and works. We've dealt with a quantum leap -- and the texture of human existence is a consequence of this technology.

It's time that we simply admit that we need to turn back on ourselves in order to continue to manage this or we, literally, I believe, are on the precipice of that digital equivalent of a Pearl Harbor, and the economic and productivity consequences of failing are extreme.

Gardner: Well, we'll have to leave it there. We're about out of time. We've been discussing how glitches in business have highlighted a possible breakdown in the continuity of technology and that governance is an important factor in making technology continue on its productivity curve, without falling at some degree under its own weight.

I want to thank our guests. We have been joined today by Jeff Papows, President and CEO of WebLayers, and the author of the new book, Glitch: The Hidden Impact of Faulty Software. Thank you so much, Jeff.

Papows: Thank you, Dana, and thank you, Kerrie.

Gardner: And, we have been joined also by Kerrie Holley, an IBM Fellow as well as the CTO for IBM’s SOA Center of Excellence. Thanks for your input, and we will look forward to your book as well.

Holley: Thank you, Dana, and thank you, Jeff.

Gardner: This is Dana Gardner, principal analyst at Interarbor Solutions. You've been listening to a sponsored BriefingsDirect podcast. Thanks for listening and come back next time.

Listen to the podcast. Find it on iTunes/iPod and Podcast.com. Download the transcript. Sponsor: WebLayers.

Transcript of a sponsored BriefingDirect podcast on the growing danger from faulty software and how to overcome it. Copyright Interarbor Solutions, LLC, 2005-2010. All rights reserved.

Information Management Takes Aim at Need for Improved Business Insights From Complex Data Sources

Transcript of a sponsored BriefingsDirect podcast on how companies are leveraging information management solutions to drive better business decisions in real time.

Listen to the podcast. Find it on iTunes/iPod and Podcast.com. Download the transcript. Learn more. Sponsor: HP.

Get a free white paper on how Business Intelligence enables enterprises to better manage data and information assets:
Top 10 trends in Business Intelligence for 2010

Dana Gardner: Hi, this is Dana Gardner, principal analyst at Interarbor Solutions, and you’re listening to BriefingsDirect. Today's sponsored podcast discussion delves into how to better harness the power of information to drive and improve business insights.

We’ll examine how the tough economy has accelerated the progression toward more data-driven business decisions. To enable speedy proactive business analysis, information management (IM) has arisen as an essential ingredient for making business intelligence (BI) for these decisions pay off.

Yet IM itself can become unwieldy, as well as difficult to automate and scale. So managing IM has become an area for careful investment. Where then should those investments be made for the highest analytic business return? How do companies better compete through the strategic and effective use of its information?

We’ll look at some use case scenarios with executives from HP to learn how effective IM improves customer outcomes, while also identifying where costs can be cut through efficiency and better business decisions.

To get to the root of IM best practices and value, please join me in welcoming our guests, Brooks Esser, Worldwide Marketing Lead for Information Management Solutions at HP. Welcome, Brooks.

Brooks Esser: Hi, Dana. How are you today?

Gardner: I’m great. We’re also here with John Santaferraro, Director of Marketing and Industry Communications for BI Solutions at HP. Hello, John.

John Santaferraro: Hi Dana. I’m glad to be here, and hello to everyone tuning into the podcast.

Gardner: And also, we’re here with Vickie Farrell, Manager of Market Strategy for BI Solutions at HP. Welcome to the show.

Vickie Farrell: Hi, Dana, thanks.

Gardner: Let me take our first question out to John. IM and BI in a sense come together. It’s sort of this dynamic duo in this era of cost consciousness and cost-cutting. What is it about the two together that you think is the right mix for today’s economy?

Santaferraro: Well, it’s interesting, because the customers that we work with tend to have very complex businesses, and because of that, very complex information requirements. It used to be that they looked primarily at their structured data as a source of insight into the business. More recently, the concern has moved well beyond business intelligence to look at a combination of unstructured data, text data, IM. There’s just a whole lot of different sources of information.

Enterprise IM

The idea that they can have some practices across the enterprise that would help them better manage information and produce real value and real outcomes for the business is extremely relevant. I’d like to think of it as actually enterprise IM.

Very simply, first of all, it’s enterprise, right? It’s looking across the entire business and being able to see across the business. It’s information, all types of information as we identify structured, unstructured documents, scanned documents, video assets, media assets.

Then it’s the management, the effective management of all of those information assets to be able to produce real business outcomes and real value for the business.

Gardner: So the more information you can manage to bring into an analytics process, the higher the return?

Santaferraro: I don’t know that it’s exactly just "more." It’s the fact that, if you look at the information worker or the person who has to make decisions on the front line, if you look at those kinds of people, the truth is that most of them need more than just data and analysis. In a lot of cases, they will need a document, a contract. They need all of those different kinds of data to give them different views to be able to make the right decision.

Gardner: Brooks, tell me a little bit about how you view IM. Is this a life cycle we’re talking about? Is it a category? Where do we draw the boundaries around IM? Is HP taking an umbrella concept here?

Esser: We really are, Dana. We think of IM as having four pillars. The first is the infrastructure, obviously -- the storage, the data warehousing, information integration that kind of ties the infrastructure together. The second piece, which is very important, is governance. That includes things like data protection, master data management, compliance, and e-discovery.

The third, to John’s point earlier, is information processes. We start talking about paper-based information, digitizing documents, and getting them into the mix. Those first three pillars taken together really form the basis of an IM environment. They’re really the pieces that allow you to get the data right.

The fourth pillar, of course, is the analytics, the insight that business leaders can get from the analytics about the information. The two, obviously, go hand in hand. Rugged information infrastructure for your analytics isn’t any better than poor infrastructure with solid analytics. Getting both pieces of that right is very, very important.

Gardner: Vickie, if we take that strong infrastructure and those strong analytics and we do it properly, are we able to take the fruits of that out to a wider audience? Let’s say we are putting these analytics into the hands of more people that can take action.

Very important

Farrell: Yes, it is very important that you do both of those things. A couple of years ago, I remember, a lot of pundits were talking about BI becoming pervasive, because tools have gotten more affordable and easier to use. Therefore anybody with a smartphone or PDA or laptop computer was going to be able to do heavy-duty analysis.

Of course, that hasn’t happened. There is more limiting the wide use of BI than the tools themselves. One of the biggest issues is the integration of the data, the quality of the data, and having a data foundation in an environment where the users can really trust it and use it to do the kind of analysis that they need to do.

What we’ve seen in the last couple of years is serious attention on investing in that data structure -- getting the data right, as we put it. It's establishing a high level of data quality, a level of trust in the data for users, so that they are able to make use of those tools and really glean from that data the insight and information that they need to better manage their business.

Esser: We can’t overemphasize that, Dana. There's a great quote by Mark Twain, of all people, who said it isn’t what you don’t know that gets you into trouble -- it’s what you know for certain that just isn’t so. That really speaks to the point Vickie made about quality of data and the importance of having high-quality data in our analytics.

Gardner: We’re defining IM fairly broadly here, but how do we then exercise what we might consider due diligence in the enterprises -- security, privacy, making the right information available to people and then making sure the wrong people don’t have it? How do you apply that important governance pillar, when we’re talking about such a large and comprehensive amount of information, Brooks?

Esser: I think you have to define governance processes, as you’re building your information infrastructure. That’s the key to everything I talked about earlier -- the pillars of a solid IM environment. One of the key ones is governance, and that talks about protecting data, quality, compliance, and the whole idea of master data management -- limiting access and making sure that right people have access to input data and that data is of high-quality.

Farrell: In fact, we recently surveyed a number of data warehouse and BI users. We found that 81 percent of them either have a formal data governance process in place or they expect to invest in one in the next 12 months. There's a lot of attention on that, as Brooks was talking about.

Gardner: Now, as we also mentioned earlier, the economy is still tough. There is less discretionary spending than we’ve had in quite some time. How do you go to folks and get the rationale for the investment to move in this direction? Is it about cost-cutting? Is it about competitiveness? Is it about getting a better return on their infrastructure investments? John, do you have a sense of how to validate the market for IM?

Santaferraro: It’s really simple. By effectively using the information they have and further leveraging the investments that they’ve already made, there is going to be significant cost savings for the business. A lot of it comes out of just having the right insight to be able to reduce costs overall. There are even efficiencies to be had in the processing of information. It can cost a lot of money to capture data, to store it, and cleanse it.

Cleansing can be up to 70 percent of the cost of the data, trying to figure out your retention strategies. All of that is very expensive. Obviously, the companies that figure out how to streamline the handling and the management of their information are going to have major cost reductions overall.

Gardner: What about the business outcomes? Brooks, do we have a sense of what companies can do with this? If they do it properly, as John pointed out, how does that further vary the profitability, their market penetration, or perhaps even their dominance?

The way to compete

Esser: Dana, it’s really becoming the way that leading edge companies compete. I’ve seen a lot of research that suggests that CEOs are becoming increasingly interested in leveraging data more effectively in their decision-making processes. It used to be fairly simple. You would simply identify your best customers, market like heck to them, and try to maximize the revenue derived from your best customers.

Now, what we’re seeing is emphasis on getting the data right and applying analytics to an entire customer base, trying to maximize revenue from a broader customer base. We’re going to talk about a few cases today where entities got the data right, they now serve their customers better, reduced cost at the same time, and increased their profitability.

Gardner: We’ve talked about this at a fairly high level. I wonder if we could get a bit more specific. I’m curious about what is the problem that IM solves that then puts us in a position to leverage the analytics, put it in the hands of the right people, and then take those actions that cut the costs and increase the business outcome. I’m going to throw this out to anybody in our panel. What are the concrete problems that IM sets out to solve?

Esser: I’ll pick that up, Dana. Organizations all over the world are struggling with an expansion of information. In some companies, you’re seeing data doubling one year over the next. It’s creating problems for the storage environment. Managers are looking at processes like de-duplication to try to reduce the quantity of information.

Lots of information is still on paper. You’ve got to somehow get that into the mix, into your decision-making process. Then you have things like RFID tags and sensors adding to the expansion of information. There are legal requirements. When you think about the fact that most documents, even instant messages, are now considered business records, you’ve got to figure a way to capture that.

The challenge for a CIO is that you’ve got to balance the cost of IT, the cost of governance and risk issues involved in information, while at the same time, providing real insight to your business unit customer.

Then, you’re getting pressure from business leaders for timely and accurate information to make decisions with. So, the challenge for a CIO is that you’ve got to balance the cost of IT, the cost of governance and risk issues involved in information, while at the same time, providing real insight to your business unit customer. It’s a tough job.

Santaferraro: If I could throw another one in there, Dana, I recently talked to a couple of senior IT leaders, and both of them were in the same situation. They’ve been doing BI and IM for 10-plus years in their organization. They had fairly mature processes in place, but they were concerned with trying to take the insight that they had gleaned and turn it into action.

Along with all of the things that were just described by Brooks, there are a lot of companies out there that are trying to figure out how to get the data that last mile to the person on the front line who needs to make a decision. How do I get it to them in a very simple format that tells them exactly what they need to do?

So, it’s turning that insight into action, getting it to the teller in a bank, getting it to the clerk at the point of sale, or the ATM machine, or the web portal, when somebody is logging onto a banking system or a retail site.

Along with all of that, there is this new need to find a way to get the data that last mile to where it impacts a decision. For companies, that’s fairly complex, because that could mean millions of decisions every day, as opposed to just getting a report to an executive.

That whole world of the information worker and the need to use the information has changed as well, driving the need for IM.

Analyze the data

Farrell: Dana, you asked what the challenges are, and one that we see a lot is that people need to analyze the data. They'll traipse from data mart to data mart and pull data together manually. It’s time-consuming and it’s expensive. It’s fraught with error, and the fact that you have data stored in all these different data marts, just indicates that you’re going to have redundant data that’s going to be inconsistent.

Another problem is that you’ll end up with reports from different people and different departments, and they won’t match. They will have used different calculations, different definitions for business terms. They will have used different sources for the data. There is really no consistent reconciliation of all of this data and how it gets integrated.

This causes really serious problems for companies. That’s really what IM is going to help people overcome. In some cases, it doesn’t really cost as much as you’d think, because when you do IM properly, you're actually going to see some savings and correction of some of those things that I just talked about.

Gardner: It also seems to me, if you look at a historic perspective, that many of these information workers we're talking about didn’t even try to go after this sort of analytic information. They knew that it wasn’t going to be available to them. They’d probably have to wait in line.

But, if we open the floodgates and make this information available to them, it strikes me that they are going to want to start using it in new and innovative ways. That’s a good thing, but it could also tax the infrastructure and the processes that have been developed.

Without that close alignment between business and IT, a tie of the IT project to real business outcomes, and that constant monitoring by that group, it could easily get out of hand.

How do we balance an expected increase in the proactive seeking of this information? I guess we are starting to talk about the solution to IM. If we're good at it and people want it, how do we scale it? How do we ramp it up? What about that, John? How do we start in on the scaling and the automation aspect of IM?

Santaferraro: With our customers, some of the strategy and planning that we do up front helps them define IM practices internally and create things like an enterprise information competency center where the business is aligned with IT in a way that they are actually preparing for the growth of information usage. Without that close alignment between business and IT, a tie of the IT project to real business outcomes, and that constant monitoring by that group, it could easily get out of hand. The strategy and planning upfront definitely helps out.

Farrell: I'll add to that. The more effectively you bring together the IT people and the business people and get them aligned, the better the acceptance is going to be. You certainly can mandate use of the system, but that’s really not a best practice. That’s not what you want to do.

By making the information easily accessible and relevant to the business users and showing them that they can trust that data, it’s going to be a more effective system, because they are going to be more likely to use it and not just be forced to use it.

Esser: Absolutely, Vickie. When you think about it, it really is the business units within most enterprises that fund activities via a tax or however they manage to pay for these things. Doing it right means having those stakeholders involved from the very beginning of the planning process to make sure they get what they need out of any kind of an IT project.

Access a free white paper on how Business Intelligence enables enterprises to better manage data and information assets:
Top 10 trends in Business Intelligence for 2010

Gardner: It strikes me that we have a real virtuous cycle at work here, where the more people get access to better information, the more action they can take on, the more value is perceived in the information, the more demand for the information, the more that the IT folks can provide it and then so on and so forth.

Has anybody got an example of how that might show up in the real world? Do we have any use cases that capture that virtuous adoption benefit?

Better customer service

Farrell: Well, one comes to mind. It’s an insurance company that we have worked with for several years. It’s a regional health insurance company faced with competition from national companies. They decided that they needed to make better use of their data to provide better services for their members, the patients as well as the providers, and also to create a more streamlined environment for themselves.

And so, to bring the IT and business users together, they developed an enterprise data warehouse that would be a common resource for all of the data. They ensured that it was accurate and they had a certain level of data quality.

They had outsourced some of the health management systems to other companies. Diabetes was outsourced to one company. Heart disease was outsourced to another company. It was expensive. By bringing it in house, they were able to save the money, but they were also able to do a better job, because they could integrate the data from one patient, and have one view of that patient.

That improved the aggregate wellness score overall for all of their patients. It enabled them to share data with the care providers, because they were confident in the quality of that data. It also saved them some administrative cost, and they recouped the investment in the first year.

Gardner: Any other examples, perhaps examples that demonstrate how IM and HP’s approach to IM come together?

More real-time applications and more mission-critical applications are coming and there is not going to be the time to do the manual integration.

Farrell: Another thing that we're doing is working with several health organizations in states in the US. We did one project several years ago and we are now in the midst of another one. The idea here is to integrate data from many different sources. This is health data from clinics, schools, hospitals, and so on throughout the state.

This enables you to do many things like run programs on childhood obesity, for example, assess the effectiveness of the program, and assess the overall cost and the return on the investment of that program. It helps to identify classes of people who need extra help, who are at risk.

Doing this gives you the opportunity to bring together and integrate in a meaningful way data from all these different sources. Once that’s been done, that can serve not only these systems, but also some of the potential systems more real-time systems that we see coming down the line, like emergency surveillance systems that would detect terrorist threat, bioterrorism threats, pandemics, and things like that.

It's important to understand and be able to get this data integrated in a meaningful way, because more real-time applications and more mission-critical applications are coming and there is not going to be the time to do the manual integration that I talked about before.

Gardner: It certainly sounds like a worthwhile thing. It sounds like the return on investment (ROI) is strong and that virtuous adoption is very powerful. So, John Santaferraro, what is that HP does that could help companies get in the IM mode?

Obviously, this is not just something you buy and drop in. It's more than just methodologies as well. What are the key ingredients, and how does HP pull them together?

Bringing information together

Santaferraro: We find that a lot of our customers have very disconnected sets of intelligence and information. So, we look at how we can bring that whole world of information together for them and provide a connected intelligence approach. We are actually a complete provider of enterprise class industry-specific IM solutions.

There are a lot of areas where we drill down and bring in our expertise. We have expertise around several business domains like customer relationship management, risk, and supply chain. We go to market with specific solutions from 13 different industries. As a complete solution provider, we provide everything from infrastructure to financing.

Obviously, HP has all of the infrastructure that a customer needs. We can package their IM solution in a single finance package that hits either CAPEX or OPEX. We've got software offerings. We've got our consulting business that comes in and helps them figure out how to do everything from the strategy that we talked about upfront and planning to the actual implementation.

We can help them break into new areas where we have practices around things like master data management or content management or e-discovery.

Across the entire IM spectrum, we have offerings that will help our customers solve whatever their problems are. I like to approach our customers and say, "Give us your most difficult and complex information challenge and we would love to put you together with people who have addressed those challenges before and with technology that’s able to help you do it and even create innovation as a business."

Everyone in the IM market partners with other firms to some extent.

When we've come in and laid the IM foundation for our customers and given them a solid technology platform -- Neoview is a great example -- we find that they began to look at what they've got. It really triggers a whole lot of brand-new innovation for companies that are doing IM the right way.

Gardner: Given these vertical industries, I imagine there are some partners involved there, a specialist in specific regions as well as specific industries. Brooks, is there an ecosystem at work here as well, and how does that shape up?

Esser: Absolutely, Dana. Everyone in the IM market partners with other firms to some extent. We've chosen some strategic partners that complement our capabilities as well. For example, we team with Informatica for our data integration platform and SAP BusinessObjects and MicroStrategy for our BI platform.

We work with a company called Clearwell, and we leverage their e-discovery platform to deliver a solution that helps customers leverage the information in their corporate email systems. We work with Microsoft to deliver HP Enterprise Content Management Solution. So we really have an excellent group of go-to-market partners to leverage.

Gardner: We've talked about the context of the market, why the economy is important, and we looked at some of the imperatives from a business point of view, why this is essential to compete, what problems you need to overcome, and the solution.

So, in order to get towards this notion of a payback, it's important to know where to get started. There seem to be so many inception points, so many starting points. Let me take this to you, John. The holistic approach of being comprehensive, but at the same time, breaking this into parts that are manageable, how do you do that?


Best practices

Santaferraro: One of the things that we have done is made our best practices available and accessible to our customers. We actually operationalize them. A lot of consulting companies will come and plop a big fat manual on the desk and say we have a methodology.

We've created an offering called the methodology navigator which actually walks the customers through the entire project in an interactive environment, where depending on whatever step of the project they are in, they can click on a little box that represents that step and quickly access templates, accelerators, and best practices that are directly relevant to that particular step.

We look at this holistic approach, but we also break it down into best practices that apply to every single step along the way.

Gardner: This whole thing sounds like a no-brainer to me. I don’t know whether I am overly optimistic, but I can see applying more information to your personal life, your small business as well as your department and then of course, your comprehensive enterprise.

I think we're entering into a data-driven decade. The more data, the more better decisions, the more productivity. It's how you grow. Brooks, why do you think it’s a no-brainer? Am I overstating the case?

It's how leading edge companies are going to compete, particularly in a tough and the volatile economy.

Esser: I don’t think you are, Dana. It's how leading edge companies are going to compete, particularly in a tough and the volatile economy, as we have seen over the last 5, 7, 8 years. It's really simple. Better information about your customers can help you drive incremental revenue from your existing customer base. The cool part about it is that better information can help you prevent loss of customers that you already have. You know them better and know how to keep them satisfied.

Every marketer knows that it's a lot less expensive to keep a current customer than it is to go out and acquire a new one. So the ROI for IM projects can be phenomenal and, to your point, that makes it kind of a no-brainer.

Gardner: Vickie, we apply this to customers, we apply it to patients, payers, end-users, but are there other directions to point this at? Perhaps supply chain, perhaps thinking about cloud computing and multiple sources of finding social media metadata about processes, customers, suppliers. Are we only scratching the surface in a sense of how we apply IM?

Farrell: I think we probably are. I don’t know that there are any industries that can't make use of better organizing their data and better analyzing their data and making use of that insight that they’ve gained to make better decisions. In fact, across the board, one of the biggest issues that people have is making better decisions.

In some cases, it's providing information to humans through reports or queries, so that they can make the decisions. What we're going to be seeing -- and this gets to what you were talking about -- is that when data is coming in in real time from sensors and things like that, it has location context. It's very rich data, and it provides you with a lot of information and a lot of variables to make the best decisions based on all those variables that are taking place at that time.

Where once we were maybe developing a handful of possible scenarios and picking the closest one, we don’t have to do that anymore. We can really make use of all of that information and make the absolute best decision right then and there. I don’t really think that there are any industries or domains that can't make use of that kind of capability.

Capturing more data

Santaferraro: Dana, I love what we are doing in the oil and gas industry. We have taken the sensors from our printers, and they are some of the most sensitive sensors in the world, and we are doing a project with Shell Oil, where we are actually embedding our sensors at the tip of a drill head.

As it goes down, it's going to capture seismic data that is 100 times more accurate than anything that's been captured in the past. It's going to send it up through a thing called IntelliPipe which is a five-megabyte feed is this correct that goes up through the drill pipe and back up to the well head, where we will be capturing that in real time.

Seismic data tends to be dirty by nature. It needs to be cleansed. So, we're building a real-time cleansing engine to cleanse that data, and then we are capturing it on the back-end in our digital oil field intelligence offering. It's really fun to see as the world changes, there are all these new opportunities for collecting and using information, even in industries that tend to be a little more traditional and mechanical.

Gardner: That's a very interesting point that the more precise we get with instrumentation, the more data, the more opportunity to work with it and then to crunch that in real-time offers us the predictive aspect rather than a reactive aspect.

As I said, it's been compelling and a no-brainier for me. John, you mentioned an on ramp to this, that it's really the methodological approach. Are there any resources, are there places people can go to get more information, to start factoring where in their organization they will get their highest returns, perhaps focus there and then start working outward towards that more holistic benefit?

It's really up to the customers in terms of how they want to start out.

Let me go to you first, Brooks. Where can people go for more information?

Esser: Of course, I'm going to tell folks to talk to their HP reps. In the course of our discussion today, it's pretty obvious that IM projects are huge undertakings, and we understand that. So, we offer a group of assessment and planning services. They can help customers scope out their projects.

We have a couple of ways to get started. We can start with a business value assessment service. This is service that sets people up with a business case and tracks ROI, once they decide on a project. But, the interesting piece of that is they can choose to focus on data integration, master data management, what have you.

You look at the particular element of IM and build a project around that. This assessment service allows people to identify the element in their IM environment, their current environment, that will give them the best ROI. Or, we can offer them a master planning service which generates really comprehensive IM plan, everything from data protection and information quality to advanced analytics.

So, it's really up to the customers in terms of how they want to start out, taking a look at the element of their IM environment, or if they want us to come in and look at the entire environment, we can say, "Here's what you need to do to really transform the entire IM environment."

Obviously, you can get details on those services and our complete portfolio for that matter at www.hp.com/go/bi and www.hp.com/go/im.

Gardner: Vickie, any sense of where you would point people when they ask do I get started, where can I get more information?

Farrell: Well, I think Brooks covered it. All of our information is at www.hp.com/go/bi. We also have another site that's www.hp.com/go/neoview. There is some specific information about the Neoview Advantage enterprise data warehouse platform there.

Gardner: Very well. John Santaferraro, how about from professional services and solutions perceptive; any resources that you have in mind?

Santaferraro: Probably the hottest topic that I have heard from customers in the last year or so has been around the development of the BI competency center. Again if you go to our BI site, you will find some additional information there about the concept of a BICC.

And the other trend that I am seeing is that a lot of companies are wanting to move from just the BI space with that kind of governance. They want to create an enterprise information competency center, so expanding beyond BI to include all of IM.

We have got some great services available to help people set those up. We have customers that have been working in that kind of a governance environment for three or four years. The beautiful thing is that companies that have been doing this for three or four years are doing transformational things for their business.

They are really closely tied to business mission, vision, and objective, versus other companies that are doing a bunch of one-off projects. One customer recently had spent $11 million in a project over the last year, and they were still trying to figure out where they were going to get value out of the project.

Again, heading over to our BI website -- type in BICC, do a search -- there is some great documentation there I think that you will find to help set up some of the governance side.

Gardner: Well great. We've been talking about a natural progression towards data-driven business decisions and using IM to scale that and bring more types of data and content into play. I want to thank our guests for toady's podcast. We've been joined by Brooks Esser, Worldwide Marking Lead for Information Management Solutions at HP. Thank you, Brooks.

Esser: Thanks very much for having me, Dana.

Gardner: John Santaferraro. He is the Director of Marketing and Industry Communications for BI Solutions. Thank you, John.

Santaferraro: Thanks, Dana. Glad to be here.

Gardner: And also, Vickie Farrell, Manager of Market Strategy for BI Solutions. Thanks so much.

Farrell: Thank you, Dana. This is a pleasure.

Gardner: This is Dana Gardner, Principal Analyst at Interarbor Solutions. You’ve been listening to a sponsored BriefingsDirect podcast. Thanks for listening, and come back next time.

Listen to the podcast. Find it on iTunes/iPod and Podcast.com. Download the transcript. Learn more. Sponsor: HP.

Access a free white paper on how Business Intelligence enables enterprises to better manage data and information assets:
Top 10 trends in Business Intelligence for 2010

Transcript of a sponsored BriefingsDirect podcast on how companies are leveraging information management solutions to drive better business decisions in real time. Copyright Interarbor Solutions, LLC, 2005-2010. All rights reserved.

You may also be interested in:

• Information Management Targets E-Discovery, Compliance, Legal Risks While Delivering Long-term BI and Governance Benefits
  
  
• HP Information Management Maven Rod Walker Describes How BI Helps Business Leaders Innovate
  
  
• HP's BTO Chief Ben Horowitz on How Application Lifecycle Optimization Enhances Next Generation Data Centers