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.

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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.

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BriefingsDirect Analysts Unpack the Psychology of Project Management Via 'Pragmatic Enterprise 2.0' and SOA

Edited transcript of BriefingDirect Analyst Insights Edition podcast, Vol. 47 on new tools for measuring and building trust in technology adoption.

Listen to the podcast. Find it on iTunes/iPod and Podcast.com. Download the transcript. Charter Sponsor: Active Endpoints. Also sponsored by TIBCO Software.

Special offer: Download a free, supported 30-day trial of Active Endpoint's ActiveVOS at www.activevos.com/insight.

Dana Gardner: Hello, and welcome to the latest BriefingsDirect Analyst Insights Edition, Volume 47. I'm your host and moderator Dana Gardner, principal analyst at Interarbor Solutions.

This periodic discussion and dissection of IT infrastructure related news and events -- with a panel of industry analysts and guests -- comes to you with the help of our charter sponsor, Active Endpoints, maker of the ActiveVOS visual orchestration system, and through the support of TIBCO Software.

Our topic this week on BriefingsDirect Analyst Insights Edition, and it is the week of Nov. 9, 2009, centers on how to define, track and influence how people adapt to and adopt technology.

Any new information technology might be the best thing since sliced bread, but if people don’t understand the value or how to access it properly -- or if adoption is spotty, or held up by sub-groups, agendas, or politics -- then the value proposition is left in the dust.

A crucial element for avoiding and overcoming social and user dissonance with technology adoption is to know what you are up against, in detail. Yet, data and inferences on how people really feel about technology is often missing, incomplete, or inaccurate.

Today, we're going to hear from two partners who are working to solve this issue pragmatically. First, with regard to Enterprise 2.0 technologies and approaches. And, if my hunch is right, it could very well apply to service-oriented architecture (SOA) adoption as well.

I suppose you can think of this as a pragmatic approach to developing business intelligence (BI) values for people’s perceptions and their ongoing habits as they adopt technology in a business context.

So join fellow ZDNet bloggers, Michael Krigsman, president and CEO of Asuret, as well as Dion Hinchcliffe, founder and chief technology officer at Hinchcliffe & Co. as they explain how Pragmatic Enterprise 2.0 works. Together with our panel, we can plumb whether this could help with SOA adoption -- and maybe even other types of technology- or creative pursuit-adoptions as well.

Before we delve into this and hear more about Pragmatic Enterprise 2.0, please allow me to introduce our panel this week. We're joined by Joe McKendrick, prolific blogger and analyst. Welcome to the show, Joe.

Joe McKendrick: Thanks, Dana. Pleased to be here, and hello to everybody.

Gardner: We’re also joined by Miko Matsumura, vice president and chief strategist at Software AG. Welcome, Miko.

Miko Matsumura: Great. Good to be here.

Gardner: Ron Schmelzer, managing partner at ZapThink. Welcome back, Ron.

Ron Schmelzer: Hola. Bienvenido.

Gardner: Tony Baer, senior analyst at Ovum. Hi, Tony.

Tony Baer: Hey, Dana. Hi, everybody. Good to be here.

Gardner: We're also joined by Sandy Rogers, independent industry analyst. Welcome, Sandy.

Sandy Rogers: Great, Dana. Glad to be here.

Gardner: And, last but not the least, Jim Kobielus, senior analyst at Forrester Research. Welcome, Jim.

Kobielus: Selamat petang. There, I matched Ron Schmelzer in gratuitously using a foreign language to welcome everybody.

Gardner: Okay, let's hand this off to Dion Hinchcliffe. Tell me, Dion, about how you thought that you needed more pragmatism when it came to bringing Enterprise 2.0 technologies into practice, how you found Michael Krigsman, and how you two hooked up on this?

Social software

Dion Hinchcliffe: Absolutely, Dana, thanks for having us on the show. It's a real honor to be in front of such an august audience. As many of you know, we've been spending a lot of time over the last few years talking about how things like Web 2.0 and social software are moving beyond just what’s happening in the consumer space, and are beginning to really impact the way that we run our businesses.

More and more organizations are using social software, whether this is consumer tools or specific enterprise-class tools, to change the way they work. At my organization, we've been working with large companies for a number of years trying to help them get there.

This is the classic technology problem. Technology improves and gets better exponentially, but we, as organizations and as people, improve incrementally. So, there is a growing gap between what’s possible and what the technology can do, and what we are ready to do as organizations.

I've been helping organizations improve their businesses with things like Enterprise 2.0, which is social collaboration, using these tools, but with an enterprise twist. There are things like security, and other important business issues that are being addressed.

Businesses are about collaboration, team work, and people working together . . .

But, I never had a way of dealing with the whole picture. We find that that folks need a deep introduction to what the implications are when you have globally visible persistent collaboration using these very social models and the implications of the business.

We see organizations like the public sector getting more into this. Government 2.0 is one of the hottest topics. Increasingly, as these things become reality in large organizations, people are worried about what kind of control they're giving up and what kind of risk they're incurring?

Michael, of course, is famous for his work in IT project risk -- what it takes for projects to succeed and what causes them not to succeed. I saw this as the last leg of the stool for a complete way of delivering these very new, very foreign models, yet highly relevant models, to the way that organizations run their business.

Businesses are about collaboration, team work, and people working together, but we have used things like email, and models that people trust a lot more than these new tools.

Understanding projects

I've been working on big IT projects most of my life. I've been a solo architect and also focused on people in the enterprise. What Michael brings to the table is all his experience in terms of understanding. But, it requires understanding what’s really taking place inside projects where, one, the technology is not necessarily well-understood by the organization and, two, the implications on the business side are not well-understood.

There is usually a lot of confusion and uncertainty about what’s really taking place and what the expectations are. And Michael, with Asuret, brings something to the table. When we package it as a service that essentially brings these new capabilities, these new technologies and approaches, it manages the uncertainty about what the expectations are and what people are doing.

What we have designed is not specific to Enterprise 2.0 at all. It's really for when you are bringing in any new transformative technology with the same set of issues. I want Michael to speak more about what he is doing and how his side works.

Gardner: Sure. Michael, tell us a little bit about Asuret, and how your process works, maybe specifically with Enterprise 2.0, but with technology generally?

Michael Krigsman: Think about business transformation projects -- any type. This can be any major IT project, or any other type of business project as well. What goes wrong? If we are talking about IT, it's very tempting to say that the technology somehow screws up. If you have a major IT failure, a project is late, the project is over budget, or the project doesn’t meet expectations or plan, it's extremely easy to point the finger at the software vendor and say, "Well, the software screwed up."

If we look a little bit deeper, we often find the underlying drivers of the project that is not achieving its results. The underlying drivers tend to be things like mismatched expectations between different groups or organizations.

For example, the IT organization has a particular set of goals, objectives, restrictions, and so forth, through which they view the project. The line of business, on the other hand, has its own set of business objectives. Very often, even the language between these two groups is simply not the same.

As another example, we might say that the customer has a particular set of objectives and the systems integrator has its own objectives for the particular project. The customer wants to get it done as fast and as inexpensively as possible. The systems integrator is often -- and I shouldn’t make generalizations, but -- interested in maximizing their own revenue.

If we look inside each of these groups, we find that inside the groups you have divisions as well, and these are the expectation mismatches that Dion was referring to.

If we look at IT projects or any type of business transformation project, what’s the common denominator? It's the human element. The difficulty is how you measure, examine, and pull out of a project these expectations around the table. Different groups have different key performance indicators (KPIs), different measures of success, and so forth, which create these various expectations.

Amplifying weak signals

How do you pull that out, detect it inside the project, and then amplify what we might call these weak signals? The information is there. The information exists among the participants in the project. How do you then amplify that information, package it, and present it in a way that it can be shared among the various decision-makers, so that they have a more systematic set of inputs for making decisions consistently based on data, rather than anecdote? That’s the common thread.

Gardner: Michael, what you're doing here is providing this as a service, right? People don’t have to install this. They don’t have to do it themselves. You're offering a web interface-based approach to gathering inference from different players within this project, portfolio, what have you, implementation adoption pattern, sussing out what those folks are feeling, and then bringing that in a visual way to the attention of the project leaders. Is that right?

Krigsman: Yes. We offer a service. We're not selling software. We offer a service, and the service provides certain results. However, we've developed software, tools, methods, techniques, and processes that enable us to go through this process behind the scenes very efficiently and very rapidly.

Gardner: I had a chance to look at a demo of this. I haven’t tried it myself. I can’t vouch for it and I am not endorsing it, but what struck me as interesting is the fact that we're actually approaching the right part of the problem, when it comes to adoption.

Let's go to Sandy Rogers. Now, you headed up the SOA practice at IDC not too long ago. We kept encountering, in some of the discussions I had with you, this issue about why the projects stumble from time-to-time. What’s the hold up? It almost always came back to these people issues, and yet we had very little at our fingertips to apply to it. Does this sound like something that is a start for going in the right direction. Do you have any takeaways?

Rogers: What we discovered in our studies is that one of the fundamental needs in running any type of business project -- an SOA project or an Enterprise 2.0 IT project -- is the ability to share information and expose that visibility to all parties at levels that will resonate with what matters to them the most, but also bring them outside of their own domain to understand where dependencies exist and how one individual or one system can impact another.

One of the keys, however, is understanding that the measurements and the information need to get past system-level elements. If you design your services around what business elements are there and what matters to the business, then you can get past that IT-oriented view in bringing business stakeholders in aligned management and business goals to what transpires in the project.

Any way that you can get out -- web-based, easy-access dashboard with information -- and measure that regularly, you can allow that to proliferate through the organization. Having that awareness can help build trust, and that’s critical for these projects.

Gardner: Ron Schmelzer, we had a roundtable discussion in Boston several months ago, looking at the SOA topic and whether it was dead or not. I think some of the feelings from the panelists there were that it's not dead, but that it needs to be done better and differently. Is this what we're describing here with Michael and Dion? It sounds like it's moving in the right direction toward that balance of people, process, and technology.

More than technology

Schmelzer: Certainly, a lot of what people are doing with SOA is really just trying to do the things that people have established with enterprise architecture (EA). As we all know EA isn't about technology and buying things. It's about applying things and it's about people and process, much more than it’s about technology. That’s the hope.

The last thing you want to be doing is constantly scrambling and redoing your architecture because somebody somewhere in the organization has introduced some new technology. The wonderful paradise that we're trying to achieve is the stability of architecture, even though everything else is changing, the process is changing, and the technology is changing.

Given that, one of the things that we realized pretty early in this coverage of SOA, maybe about a decade ago, is that companies really need to manage their people, their governance, and their organization much more than they need to worry about buying the right tools.

As a matter of fact, you can buy the wrong tools, have great processes, and still have great outcomes. But if you buy the best tools, whatever that means, and you've got poor processes, you are guaranteed to have poor outcomes.

. . . It's mostly up to people and process to make sure the whole thing functions in a way that's returning value to the business.

It's like buying a pair of pants and putting them on. We have a complex system with a lot of moving parts, with a lot of interactions that are visible and hidden, and it's mostly up to people and process to make sure the whole thing functions in a way that's returning value to the business.

Gardner: Joe McKendrick, you’ve been covering SOA for some time, as a blogger on ebizQ and ZDNet. Often times, these topics around politics, fiefdoms, misunderstandings, and allowing people to communicate well come up again and again. Like the weather, we keep talking about it, but no one does anything about it.

It sounds as if Michael and Dion are trying to do something about it, at least for Enterprise 2.0. How does this strike you as to getting inside in data, into perceptions, and then being able to work with that? Is it a significant part of the problem and solution?

All about organization

McKendrick: Michael and Dion, I think you're on the right track with that. In fact, it's all about organization. It's all about the way IT is organized within the company and, vice-versa, the way the company organizes the IT department. I’ll quote Mike Hammer, the consultant, not the detective, "Automate a mess and you get an automated mess." That's what's been happening with SOA.

Upper management either doesn't understand SOA or, if they do, it's bits and pieces -- do this, do that. They read Enterprise Magazine. The governance is haphazard, islands across the organization, tribal. Miko talks about this a lot in his talks about the tribal aspect. They have these silos and different interest groups conflicting.

There's a real issue with the way the whole process is managed. One thing I always say is that the organizations that seem to be getting SOA right, as Michael and Dion probably see with the Enterprise 2.0 world, are usually the companies that are pretty progressive. They have a pretty good management structure and they're able to push a lot of innovations through the organization.

The companies that really could use these processes, the companies that really could use a good dose of service orientation, are the companies that just don't get it. It's a paradox.

Gardner: Miko Matsumura, as a supplier of software and services at Software AG, are you all looking for people like Dion and Michael to come up with these ways in which those tribal elements can be addressed? Is this something that intrigues you?

Matsumura: Absolutely. I had a wonderful conversation with Michael earlier and I appreciate his invite to come join this conversation. This type of an approach really reflects the evolution of the best practice of adoption. Some of the themes that we've been talking about today around this sharing of information, communication, and collaboration, are really are essential for success.

I do want to caution just a little bit. People talk about complexity and they create a linkage between complexity and failure. It's more important to try to look at, first of all, the source of the problem. Complexity itself is not necessarily indicative of a problem. Sure, it's correlated, but ice-cream consumption is correlated with the murder rate, just as a function of when temperatures get hot, both things happen to increase. So complexity is also a measure of success and scale.

I’d like to point to a different culprit, which I call "entropy" or "waste," and look at waste as being either over-complexity -- or over-simplicity in some cases. Over-simplicity can be as much of a villain as over-complexity. To me one of the biggest sources of complexity is tribalism and people fighting each other.

Providing a really transparent flow of measurements and metrics is obviously a tremendously important step. We have a methodology that we call the performance-driven organization that uses KPIs to increase organizational alignment. But, really, what you're doing is just shifting the fight. You're basically saying, "Let's not fight about one set of things. Let's fight about a set of so-called objective KPIs."

All about trust

The issue it comes down to for me is what Sandy said, which is that the word "trust," which is thrown in at the very end, turns out as extremely expensive. That alignment of organization and trust is actually a really important notion.

What happens with trust is that you can put things behind a service interface. Everything that's behind a service interface has suddenly gotten a lot less complex, because you're not looking at all that stuff. So, the reduction of complexity into manageability is completely dependent on this concept of trust and building it.

Gardner: The interesting thing you mentioned here is the metrics and the data. Having some kind of objective or constant way of evaluating what's going on and how that's changing over time, whether it's positive or negative, and then how to adapt, creates some sort of a positive feedback process loop.

Jim Kobielus, you deal with data analysis all the time. Tell me your impressions about bringing a data-analysis capability to how people react to something like implementing and adopting and adapting to Enterprise 2.0 or SOA.

Kobielus: A dashboard is so important when you are driving a vehicle, and that's what a consolidated view of KPIs and metrics provides. They are a dashboard in the BI sense, and that's what this is, project intelligence dashboard for very complex project or mega programs that are linked projects. In other words, SOA in all of its manifestations.

In organization, you have to steer your enterprise in a different direction. You need obviously to bring together many projects and many teams across many business domains. They all need to have a common view of the company as a whole -- its operations, various stakeholders, their needs, and the responsibilities internally on various projects of various people. That's highly complex. So, it’s critical to have a dashboard that's not just a one-way conduit of metrics, from the various projects and systems.

In the BI world, which I cover, most of the vendors now are going like crazy to implement more collaboration and work-flow and more social community-style computing capabilities into their environments. It's not just critical to have everybody on the same page in terms of KPIs, but to have a sideband of communication and coordination to make sure that the organization is continuing to manage collectively according to KPIs and objectives that they all ostensibly agree upon.

This is important. Social computing must come to the very heart of dashboarding to enable collaborative SOA project governance.

Gardner: But perhaps not just social from the gut, but social with some science, metrics, and real data.

Kobielus: Exactly. It has to be real data that's grounded in project objectives and in current status and delivering on those objectives.

Gardner: Tony Baer, what are we missing here? Is there some part of this equation that we're glossing over? Is there any cold water we should be pouring on here, just to be safe?

Recipe for tribalism


Baer: Oh, you read my mind on this one. I can quote from a project that my wife is currently involved with, which is basically a whole recipe for what you're talking about.

What Dion and Michael are talking about is an excellent idea in terms of that, in any type of environment where there is a lack of communication and trust, data is essential to really steer things. Data, and also assurances with risk management and protection of IT and all that. But, the fact is that there are some real clear hurdles, especially when you have what Miko talks about with tribes.

An example is this project that my wife is working on at the moment. She was brought in as a consultant to a consulting firm that's working for the client, and each of them have very different interests. This is actually in a healthcare-related situation. They're trying to do some sort of compliance effort, and whoever was the fount of wisdom there postponed the most complex part of this problem to the very end. At the very end, they basically did a Hail Mary pass bringing a few more bodies.

They didn't look for domain expertise or anything. Essentially it's like having eight women be pregnant and having them give birth to a baby in a month. That's essentially the push they are doing.

On top of that, there is also a fear among each tribe of the other coming up with a solution that makes the other tribes look bad. So, I can't tell exactly the feedback from this, but I do know that my wife came in as a process expert. She had a pretty clear view on how to untie the bottlenecks.

As soon as the project leader learned that she had this expertise, she was excluded from this, because this consulting firm was very afraid that her knowledge would make their firm look bad to the customer. In this case, they would rather risk complete failure of the project than have the firm be upstaged by someone who had been brought late in the process.

That pattern that you described there is essentially a factor about distribution of individual risk versus enterprise risk. The enterprise becomes a dumping ground for individual risk and it creates this kind of very large aggregate risk.

This is just an example of social and tribal challenges that you face. I very much agree that having a data-oriented approach and a risk management approach won't necessarily solve the problem. But, in case like this, that might be the only way out, provide cold, hard data from some neutral third-party.

Matsumura: I just want to jump in quickly and, first of all, applaud Tony Baer, the carrier of the cold water. That pattern that you described there is essentially a factor about distribution of individual risk versus enterprise risk. The enterprise becomes a dumping ground for individual risk and it creates this kind of very large aggregate risk.

Gardner: Let's take that point to Michael Krigsman. Michael, in what you're doing, are you allowing risk to be assigned? Are you be able to identify risk factors across different groups of people involved in a fairly large project? Is that part of what's going on here?

Essential elements

Krigsman: We gather a lot of data. The essential elements have been identified during this conversation. As Miko said, it's absolutely accurate to look at this tribally. Tony spoke about tribal divisions and the social tribal challenges.

The fundamental trick is how to convert this kind of trust information. Jim was talking about collaborative project governance. All of this relates to the fact that you've got various groups of people. They have their own issues, their own KPIs, and so forth. How do you service issues that could impact trust and then convert that to a form that can then be examined dispassionately. I'd love to use the word "objectively," but we all know that being objective is a goal and it's never outcome that you can ultimately reach.

At least you have a way to systematically and consistently have metrics that you can compare. And then, as Miko said, when you want to have a fight, at least you are fighting about KPIs, and you don't have people sitting in a conference room saying, "Well, my group thinks this. We believe the project 'blank.' If somebody says the same, my group thinks that." Well, let's have some common data that's collected across the various information silos and groups that we can then share and look at dispassionately.

Gardner: So, we want to get some objectivity about perception. It almost sounds like an oxymoron, but actually I think it's quite essential. Let's go back to Dion Hinchcliffe. Dion, you announced your Pragmatic Enterprise 2.0 initiative just a week or two ago. There is quite a bit of information about it on your website at Hinchcliffe & Co. Tell me a little bit about what the results are. When you bring this to bear, are they tangible results? Is there data about how well your data-driven process works?

Hinchcliffe: The way the process works is that we come into a client with an end-to-end service. Most organizations -- and this is going to be true of Enterprise 2.0 or SOA -- are looking at solving a problem. There's some reason why they think that this is going to help, but they're often not sure.

There are often a lot of unstated assumptions about how to apply technology to a business problem and what the outcome is going to be.

We start with this strategy piece that looks at the opportunity and tries to identify that for them and helps them correct the business case to understand what the return on investment (ROI) is going to be. To do that, you really have to understand what the needs of the organization are. So, one of the first things we do is bring Michael's process in, and we try and get ground truths.

There are often a lot of unstated assumptions about how to apply technology to a business problem and what the outcome is going to be. Particularly with SOA, you have so many borders that are typically involved. It's the whole concept around Conway's Law that the architecture tends to look back at the structure of the organization, because those are the boundaries in which everything runs.

One of the ways that we can assure that we have ground truth is by applying this dispassionate measurement process upfront to understand what people's expectations are, what their needs are, and what their concerns are. It's much more than just a risk-management approach. It's a way to get strategic project intelligence in a way that hasn't been possible before. We're really excited about it.

A lot of uncertainty

My specialty has always been focusing on emerging technology. There is always a lot of uncertainty, because people don't know necessarily what it is. They don't know what to expect. They have to have a way of understanding what that is, and you have an array of issues including the fact there are people who aren't willing to normally admit that they don't know things.

But, here is a way to safely and succinctly, on a regular basis, surface those issues and deal with them before they begin to have issues in the project. We then continue on through implementation and then regular assessments on the KPIs that can cause potential issues down the road. I think it's a valuable service. It's low impact, compared to another traditional interview process. This is something most organizations can afford to do on a regular basis.

Gardner: I'd like to go around our panel and get some more reaction to this.
Ron Schmelzer, the idea of this strategic project management caught my attention when Dion mentioned it. We've had lots of software products thrown at project management and portfolio management. Those don't seem to work. What's the difference between the project and portfolio management approach to some of these issues -- and what Michael Krigsman and Dion Hinchcliffe are doing with this more social inference gathering and measurement approach.

Schmelzer: I'm glad that you brought up the difference between project and portfolio management. This may be something unique in our perspective, or maybe it's becoming common. It's hard to tell when you talk to yourself a lot. We think that the whole idea of project management is just an increasing fallacy in IT anyway. There is no such thing now. It's really a discrete project.

Can you really say that some enterprise software that you maybe buying or building yourself or maybe even sourcing as a service is really completely disconnected from all the other projects that you have going on or the other technology? The answer is, they are not.

The enterprise is a collection of many different IT projects, some of which are ongoing, some of which may have been perceived to be dead or no longer in development, or maybe some are in the future.

So, it's very hard to do something like discrete project management, where you have defined set of requirements and a defined timeline and defined budget, and make the assumption or the premise, which is false, that you're not going to be impacting any of the other concurrently running projects.

We think of this like a game of pick-up sticks. The enterprise is a collection of many different IT projects, some of which are ongoing, some of which may have been perceived to be dead or no longer in development, or maybe some are in the future. The idea that you could take any one of those little projects, and manipulate them without impacting the rest of the pile is clearly becoming false.

In portfolio management you're basically managing a variety of ongoing concurrent tasks that either have budget or don't have budget and you're trying to achieve some sort of change with the least form of destruction within the business requirements and the money and the resources you have.

That's very different from this whole idea of, "Let's put together a Gantt chart. Let's throw a bunch of resources at it. Let's have some defined requirements. Let's build to it. Let's hope and pray that we're right." The industry, as a whole, is moving away from this idea of discrete IT project management.

Gardner: Joe McKendrick, thinking about discrete as something in the rear-view mirror, that means that we need to factor in cloud computing and software as a service (SaaS). They were not just going to have internal constituencies that need to be monitored and brought to some sort of a level set for understanding. We're going to have external influences, be they hosting organizations or applications that are being delivered and pulled across the wire.

How do you view the complexity of a project or portfolio management or enablement, when we're starting to bring in more and more parties to the process?

IT no longer internally focused

McKendrick: Dion, I'm an avid fan of your writings and, in particular, your ideas around web-oriented architecture (WOA), the next evolution of SOA, Enterprise 2.0, and those forces converging. I love the way you express it.

Dana is exactly right. IT is no longer an internally-focused effort. There are a lot of external factors at play. In the first stage, you have a lot of external business partners you need to expose interfaces to and you need to share information with. Right there, that dramatically increases the complexity of what you need to do.

Down the road, as you talk about cloud, you're talking about the sharing of services across enterprise borders. Everyone is going to be a producer, a publisher, or a creator of services, as well as consumers of services. It's going to be a two-way street.

There is a lot of discussion about cloud computing and the way these services will be consumed from the cloud. I don't think enough people are thinking about the services they will be producing and offering up to the cloud for others to consume. I'd be curious. Dion and Michael, do you address that in your model, in your web-based offering?

Hinchcliffe: Right now, we're going to validate some Enterprise 2.0 markets, looking at potential things as how they process. Then, of course, we'll be expanding particularly on next-generation SOA maturity. Enterprise 2.0 is getting very big right now, so that's our focus at the moment.

It needs to be much more federated, and a lot of companies, when they first took on SOA, tried to control things from a central unit.

Gardner: Sandy Rogers, another thought that I've had about this is how important governance, policy, and automation are in making SOA successful. If we have more inference information, a dashboard if you will, about the social landscape, about the buy-in or lack of buy-in from different participants in a adoption and/or execution phase of this sort of thing, can we take some of that information and then use it in the context of governance, policies, and management that are more traditional software-based SOA functions and features?

Rogers: One of the keys here is that it's a constant feedback loop of what you can automatically provide in what you are measuring and assessing, and then be able to look at that and change whether something should be standardized and should be collected.

It becomes this incremental cycle of building out that information. One of the keys that everyone is talking about here is this needs to be much more distributed. It needs to be much more federated, and a lot of companies, when they first took on SOA, tried to control things from a central unit.

When you start expanding SOA into the enterprise, especially with Enterprise 2.0, the idea of changing behaviors is something that has to start. This information that's distributed could help individuals gain knowledge and then be able to change their own behaviors.

Everyone realizes that people need to understand current state, before they can actually get to that next state, and then eventually to that ideal state. They also need to feel comfortable that in this federated approach. They may not want to share everything right-away, but incrementally contribute to the whole, and make it much more of a community.

Analysis benefit

Gardner: Michael Krigsman, we were using words like feelings and behavior. Is it fair to say that you're bringing some sort of an analysis benefit to an IT project or adoption pattern? Are we getting closer to a psychological participation project?

Krigsman: I am so hesitant to use the term psychological, because it has so many connotations associated with it. But, the fact is that we spoke about perception earlier, and there has been a lot of discussion of trust and community and collaboration. All of these issues fundamentally relate to how people work together. These are the drivers of success, and especially the drivers of lack of success on projects of every kind.

It therefore follows that, if we want our projects to be governed well and to succeed, one way or another we have to touch and look at these issues. That’s precisely what we're doing with Asuret and it’s precisely the application that we have taken with Dion into Pragmatic Enterprise 2.0. You have to deal with these issues.

Gardner: Jim Kobielus, this kind of reminds me of Star Trek: The Next Generation where there was this counselor. Deanna Troi was on the bridge with all the technicians, the leaders, drivers at warp speed, and the executive decision-makers. Is that what we need in IT, a virtual counselor along the way with us?

Kobielus: Virtual counselor? Hmm. I’ll answer that by tossing another metaphor. It really seems like the enterprise is becoming a cloud of stakeholders and interested parties that coalesces based on various needs and then scatters in the way that clouds tend to do.

The way open-source projects coalesce, certain people are first among equals, and they are the committers who defend, the general scope of common hopes and dreams.

The common denominator for getting things done in this new world is that responsibility needs to somehow precipitate out of the cloud and that certain individuals or teams take it upon themselves to get certain things done at certain times, because they recognize that things, results, need to happen.

So, virtual counselor ... I like that concept. The virtual counselor in this federated, distributed, or social-SOA governance environment. A virtual counselor is simply that person who takes command of or masters a set of channels or media -- Twitter, Facebook, blogging, and whatever else you have out there -- to be able to share all the KPIs and metrics to get others to wheedle them or cajole them to taking their responsibilities and their domains to get things done.

That person or persons will make sure there's one particular work stream within this broader project or program. This one person makes sure that certain things happen at certain times, and then gracefully, when necessary, hands off that virtual counselor post to others who pick up the baton. I'm extending metaphors here.

That’s absolutely what has to happen in a world of shifting alliances, shifting responsibilities, and shifting budgets across domains. It's like the open-source world. The way open-source projects coalesce, certain people are first among equals, and they are the committers who defend, the general scope of common hopes and dreams.

Gardner: Miko Matsumura, do you agree with my perception that this is a big step forward? In the context of a IT project or roll out, they're thinking about people’s feelings and behaviors and perceptions. It strikes me as a big step forward. Isn’t this long overdue?

Objectivity and rationality

Matsumura: About two months ago, I tweeted, "Enterprise does not need architecture. Enterprise needs psychiatrists." It does sort of preface some of this discussion. The reason I made such a point is that, the word "architecture" unfortunately implies this kind of objectivity and rationality that I, to some extent, resist when I hear words like data, rationality, objectivity, whatever.

The reason I rail against it is that the system aggregate in enterprise has in it a vicious cycle. It's not passively complex. It's not static complexity.

Ron was talking about the generation of the project management paradigm, the huge Gantt chart. Those huge Gantt charts are indicative of static complexity, and static complexity is actually not the paradigm. What Sandy was saying that I really appreciated is this notion of iteration, which is really critical.

When you get these “objective KPIs” to align organization, the next thing that happens is that organizations gain the hell out of KPIs, especially if you tie them to job review, performance evaluation, and, God forbid, bonuses.

You're going to ask people who are going to spend 40 hours a week, drilling away at ways to gain the KPIs to advantage themselves and maximize their own personal game, and it's not to say actively perverse, but essentially "to hell with everyone else."

. . . KPIs are well and good, but as soon as you institutionalize them, be ready to change them, because you will have unexpected outcomes.

The nature of the beast is such that when you encounter this kind of scenario, it's not merely this notion of lack of information or confusion. This is active perversity on an organizational and individual level. The point I'd make is that KPIs are well and good, but as soon as you institutionalize them, be ready to change them, because you will have unexpected outcomes.

Gardner: Tony Baer, last word to you. An important aspect of what Michael Krigsman and Dion are doing is that this can be anonymitized. The ability to draw inference, feelings, and perceptions from people can be done in a way that they feel empowered, that they can share their feelings without it becoming a political football or a hot potato perhaps by being anonymous. But, what you get is the insight into what the thinking is, the feelings are, the perceptions across the portfolio of participants in a project.

Does that strike you as an important factor? I want to ask you also about this counselor or analyst’s features. Do we need to bring a purple dinosaur into each SOA activity -- "I love you, you love me, let's talk about our feelings?" How do we stop being silly, but still get the benefit of this sharing going on?

Baer: I agree with you that basically that trust is really important. And, when I say trust here, it's trust in feeling that I can give information without it being used against me. No project can function in an atmosphere where everybody is just presenting basically what management wants. That eventually becomes an emperor’s new clothes situation. So obviously, I think that’s really essential.

All become counselors

I am a little cynical about the idea of a counselor, per se. I'm very much a fan of internalizing, so we all become counselors. I really like Sandy’s ideas of distributed governance, where Jim was talking about making this data-driven. I see this becoming a self-learning governance, because you can govern from the top based on assumptions that you make at the outset of a project that are totally oblivious to the conditions on the ground.

Therefore, you have to set this up so that you need to have an atmosphere of trust, where we can contribute information without fingers being pointed, and therefore names being given.

At the same time, we can then use this information to adapt. As Miko was saying, be prepared to change those KPIs, if those KPIs are not relevant. We should not be measuring to last week’s objectives, if, all of a sudden, the world has changed. So the short answer is, I agree that the anonymization is essential. I am leery about the idea of a counselor, but I am very much a very believer in everybody taking responsibility in this, and we all become counselors.

Gardner: Very good. I am afraid we’ll have to leave it there. I encourage folks to check this out. It really opened my thinking about how to make these projects more successful. It's a new dimension that I think needs to be brought in increasingly across a variety of different activities, and that would be at a business level, technology level, or a combination.

There is a lot more information available at the Hinchcliffe & Co., as well as Asuret, and of course. You can also find a lot more at the ZDNet blog that Michael Krigsman has been doing for several years now, the Project Failures blog.

I want to thank everyone for joining. We’ve been here with Dion Hinchcliffe, founder and chief technology officer at Hinchcliffe & Co. We’ve also been joined by Michael Krigsman, president and CEO of Asuret.

Please also join me in thanking our panel, Joe McKendrick, a prolific blogger and IT analyst. Thank you, Joe.

McKendrick: Thanks, Dana. Glad to be here.

Gardner: Miko Matsumura, vice president and chief strategist at Software AG. Thanks, Miko.

Matsumura: Thank you very much.

Gardner: Ron Schmelzer, managing partner at ZapThink.

Schmelzer: Muchas gracias.

Gardner: Tony Baer, senior analyst at Ovum.

Baer: Great, as always, Dana.

Gardner: Sandy Rogers, independent industry analyst. Thanks, Sandy.

Rogers: Thank you.

Gardner: Jim Kobielus, senior analyst at Forrester Research.

Kobielus: Great. I will sign off in a deep dose of alliterative English. I think it was a deep dose of domain expertise from SOA specialists.

Gardner: And I also want to thank the sponsors for the BriefingsDirect Analyst Insights Edition podcast series, Active Endpoints and TIBCO Software.

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

Listen to the podcast. Find it on iTunes/iPod and Podcast.com. Download the transcript. Charter Sponsor: Active Endpoints. Also sponsored by TIBCO Software.

Special offer: Download a free, supported 30-day trial of Active Endpoint's ActiveVOS at www.activevos.com/insight.

Edited transcript of BriefingDirect Analyst Insights Edition podcast, Vol. 47 on new tools for measuring and building trust in technology adoption. Copyright Interarbor Solutions, LLC, 2005-2009. All rights reserved.

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