Delivering Data Analytics Through SaaS ERP Applications Empowers Business Managers at Actual Decision Points
Delivering Data Analytics Through SaaS ERP Applications
Empowers Business Managers at Actual Decision Points
Transcript of a sponsored BrieﬁngsDirect podcast on 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:
See a demo on how Workday BI offers business users a new experience for accessing the key
information to make smart decisions.
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
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.
But, by using SaaS applications and rich Internet technologies that create different interface
capability, 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 Corporation, 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 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 spreadsheets,
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 application.
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.
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 records. 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
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
Gardner: Okay. Stan, we've heard from Jim Kobielus 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
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
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
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?
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.
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 has 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 number 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.
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 anyway, 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
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
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
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
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
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.
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 towards 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
Swete: We are right in the state of 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
We've also got 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.
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
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.
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
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?
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
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.
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
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 records 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
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
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.
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.
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
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.
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
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.
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.
Gardner: We've also been joined by Jim Kobielus. He is the Senior Analyst at Forrester
Research. Thanks, Jim.
Kobielus: It’s been a pleasure.
Gardner: And, Seth Grimes, Principal Consultant at Alta Plana Corporation, 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.
Listen to the podcast. Find it on iTunes/iPod and Podcast.com. Download the transcript. Sponsor:
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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