We're going to help answer that by examining a human capital management (HCM) and enterprise resource planning (ERP) SaaS provider, Workday, and 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.
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.
To learn more about how the application and interfaces are the analytics, with apologies to Marshall McLuhan, please join me in welcoming Stan Swete, Vice President of Product Strategy and the CTO at Workday; Jim Kobielus, Senior Analyst for BI and Analytics at Forrester Research, and Seth Grimes, Principal Consultant at Alta Plana Corp., and a contributing editor at TechWeb's Intelligent Enterprise. The discussion is moderated by me, BriefingsDirect's Dana Gardner, principal analyst at Interarbor Solutions.
Swete: 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.
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.
Also, 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.
Kobielus: 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.
... I like what you said, that the interface is the analytics. That’s exactly true. Fundamentally, BI is all about delivering action and more intelligence to decision agents. 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.
... 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.
Grimes: 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.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.
Swete: The thing that frequently gets left out is a focus on the transactional apps themselves and the things they can do to support pervasive analytics.
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.
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.
We're trying to make it simple to get this analysis into the hands of managers to analyze their data.
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.
... 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.
Grimes: 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.
Swete: 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.
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 transanalytics. 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.
... 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.
Swete: Or 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.
And Workday has already built up more than just HCM. We offer financial management applications and have spend-management applications.
So 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.
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.
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: 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.
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