Livre Blanc Cloud et BI - english

Cloud &
Business Intelligence :
A Winning Combination?
White paper

2013 WHITE PAPER
#2
Cloud & Business Intelligence : A Winning Combination?
Contents
Cloud BI and Performance Management 5
A Renewed Interest?
Departmental BI in the Cloud 9
Why did BI remain hermetic against the Cloud success?
Enterprise BI in the Cloud 15
Cloud On Demand leads to new BI use-cases
Conclusion 21

#3
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Introduction
According to Gartner and Forrester, the percentage of
business intelligence (BI) applications deployed in the cloud
is lower than 3%. Why is adoption so low and will the trend
last?
In 2006, Business Objects, then an independent vendor
and the BI market leader, enthusiastically introduced
CrystalReports.com. This was its «on-demand» version —
the word “cloud” had yet to become a mainstream term —
of its reporting environment. A few weeks later, Cognos, the
number two player in the market and also an independent at
the time, followed suit with the acquisition of Celequest.
Introduction

2013 WHITE PAPER
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That was seven years ago, when the cloud BI market seemed at the dawn of a
bright future, especially since the market’s main leaders had anticipated its launch.
But its expected heyday never came, despite the fact that during this time the cloud
had managed to take other business application market segments by storm. These
segments included CRM with 36% of its applications being delivered on software-
as-a-service (SaaS) infrastructure (according to Gartner) and 70% of organizations
expressing interest in such a delivery mode (according to Forrester); and human
resources, where SAP and Oracle made an almost $6 billion investment between
the two of them, not only to take over the already wide customer base of the two
rising stars, Success Factors and Taleo, but to also try to protect their existing base
from Workday’s spectacular ascent.
So, why did business intelligence remain on the sidelines of this evolution? Was it a
matter of time? We could reasonably think as much in light of recent developments,
both in terms of supply and demand.
The market seems to be opening up to three BI sub-segments. In order of maturity,
they are: performance management applications, the focus of the first part of this
white paper; departmental BI applications, which will be discussed in the second
part; and finally, even though this segment is the least mature of the three but none
the less very promising, enterprise BI and big data, the subject of the last part.

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Cloud BI and
Performance Management
A Renewed Interest?
In many companies, performance management (or EPM for
enterprise performance management) is a discipline in itself,
separate from business intelligence. It is generally used
exclusively by the office of the CFO in order to cover key
processes such as budget planning, «fast close,» statutory
consolidation, and cost and profitability analysis. But EPM
does not only apply only to finance.
Cloud BI and Performance Management

2013 WHITE PAPER
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It also helps implement best practices for forecasting, simulation, planning,
analysis, strategy management, and regulatory and management reporting.
And these practices are relevant across all business activities. For example,
sales managers need to plan their sales targets, define sales territories and
then assign those territories to the sales force so that they are as balanced
as possible. They then need to measure the objectives achieved by each
of their sales representatives. Marketing needs to plan campaigns, to
allocate budgets and resources and ensure the return on investment of
its campaigns; human resources need to plan payroll expenses and then
define a performance measure framework that will apply to a majority of
employees.
Unfortunately, these increasingly time-consuming activities are often
performed using barely formalized processes and Excel. Several factors
single out the cloud as a suitable model for implementing performance
management applications.
First, the current EPM market revolves around three leading players that
account for 70% of market share when it comes to mature solutions. This
low competition in the market creates opportunities for innovative start-ups
that naturally opt for the software-as-a-service (SaaS) model to penetrate
the market.
Then, EPM applications are process-oriented and, by nature, closer
to software package solutions than development platforms. Yet, Saas
applications have more success in the Cloud. Moreover, they are usually
‘‘The EPM market, led by three main players, is not very
competitive. Young promising start-ups launching Saas
based suites are disrupting and shaking this market with
innovation’’

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chosen by functional departments, that would more likely choose Cloud,
rather than by IT, which is another one of the many appealing attributes of
SaaS solutions.
Finally, EPM is an application that is relatively isolated from the rest of the
information system. Exchanging data with the core information system is
indeed necessary, but the amount of data involved is relatively small and
rarely requires real time sharing, making this cloud limitation irrelevant in
this context.
All of these favorable conditions explain the emergence of numerous EPM
SaaS solutions. Some, such as Anaplan, Host Analytics, Adaptive Planning
and Tidemark, offer a generic platform that can be adapted across business
activities.
With its recent EPM on-demand announcement, SAP has also jumped on
the bandwagon. Oracle also announced the long-awaited Planning and
Budgeting Cloud Service to be available by the end of 2013, providing
budgeting, planning, collaborative forecasting and reporting as a service.
IBM also released its BM Cognos TM1 on Cloud solution in September
2013. Others, like CallidusCloud for sales, have chosen to focus on one
specific activity in the company. This category of highly specialized players
is currently the target of the market’s major providers, as evidenced by
recent mega-vendor acquisitions of those players that have developed
the performance management feature of their offerings. This includes
SuccessFactors by SAP and Taleo by Oracle for human resources, Ariba by
SAP for spend performance management (purchasing), Eloqua by Oracle
Cloud BI and Performance Management
Example of an Anaplan sales dashboard

2013 WHITE PAPER
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for revenue performance management (marketing) and Varicent by IBM
for sales performance management (sales and incentive compensation
management).
For this solution segment, though, the main drawback of the SaaS mode is
that it accentuates the dividing line between performance management and
business intelligence. And regarding specialized applications for HR, sales,
marketing, etc., the SaaS model also strengthens activity-based silos. Let
us point out that the offerings mentioned in this respect cover more than just
performance management; they are often selected for a wider scope that
encompasses process execution, planning and optimization.
Security is another frequently mentioned concern. But most of the mentioned
offerings, some of which are already mature, include solid security
infrastructure. Besides, performance management is no more a delicate
matter than customer relationship or human resources management,
segments where cloud adoption is now a popular practice.
Solutions dedicated to Cloud EPM
Purchasing
Intelligence
Marketing perfor-
mance management
Sales performance
management
Human resources
intelligence
Global Solutions for EPM in the Cloud
EPM on demand Planning and budgeting
Cloud Services
EPM on demand
EPM actors in the Cloud classification

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Departmental Bi in the Cloud
Departmental BI
in the Cloud
Why did BI remain hermetic
against the Cloud success?
Despite numerous attempts from providers, the cloud failed
until now to secure a spot in the business intelligence market.
However, there now is an increasingly pressing demand for
it, particularly in the departmental applications area. The
arrival of two new cloud specialists in the Gartner 2013 BI
Magic Quadrant pinpoints the rise of those offerings.

2013 WHITE PAPER
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Analytical Applications for each functional department
If there was ever proof of cloud BI’s worth, it is web analytics for clickstream
analysis. According to Gartner, it would seem that 98% of such applications
are deployed in the cloud. Web analytics is an ideal match for deployment
in the cloud since data is sourced from outside the company, its format
is standardized and the company usually wishes to cross-reference data
gathered through its own website with external data from the Web.
This success has led some of the BI market players to position themselves
in analytics at the intersection of departmental BI and performance
management applications. At the core of their solutions is a library of key
performance indicators. Those indicators are categorized by activities and
are often associated with connectors that source the content needed to
calculate the value of those indicators from popular-off-the shelf enterprise
applications packages, such as SAP, Oracle Applications or Salesforce.
com.
Some examples of such players are mirror42 (offering a library of more
than 6,000 indicators on the KPIlibrary.com site) and GoodData. Also, IBM
recently announced Analytic Answers, a predictive analytics offering for
small and medium-sized companies, with sector ranges such as insurance,
retail or education. Hub’Scan, a Google certified analysis tool for websites,
‘‘AccordingtoGartner,98%ofWebAnalyticsapplications
are in the Cloud.’’

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published and sold by Hub’Sales – Group Business & Decision, is another
perfect example of web analytics in the Cloud. It enables web to make
diagnosis of websites errors in order to correct them very fast. The web
analytics example has shown the appeal of the cloud model when data is
sourced within the cloud itself. For the same reasons, cloud-based analytics
solutions are particularly well suited to those whose enterprise applications
are in the cloud.
Providers of Enterprise Apps delivered SaaS mode are thus in theory ideally
positioned to combine them with analytical applications. But, as is the case
for the SaaS world leader, Salesforce.com, their offerings are sometimes
weak in the analytics area, which leaves a white space for third-party
solutions.
More Agile «Departmental» BI Applications
The underlying infrastructure needed to deliver on the promises of business
intelligence is still a primary concern in many organizations. Indeed, legacy
architectures struggle to cope with the exploding volume of data and the
ever-increasing user demand for empowerment. New technologies, such
as in memory, do bring relevant solutions for this type of problem, but they
require a constant readjustment of the infrastructure to suit uses. As opposed
to transactional environments in which demand forecasts are controlled, BI
Example of the WebAnalytics solution, Hub’Scan

2013 WHITE PAPER
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demand is more volatile and challenging to predict.
Moreover, business intelligence requires considerable agility. There is
currently a high demand for the setup of «data labs» to satisfy the individual
needs of small groups of users over a sometimes short period of time, or to
extend the reach of existing systems to a wider population. Similarly, it is
often necessary to recover data in more detail or more frequently, and this
may disrupt existing infrastructures. Standardization has caused business
intelligence to lose some agility, and this is mostly due to its underlying
technological infrastructure.
The cloud model, and its renowned flexibility, is particularly suited to this
kind of demand. Some players, such as BIME or Birst, specialize in cloud
solutions.
It should be noted that this sub-segment, as with performance management,
is not necessarily limited to players positioned exclusively in the SaaS
market. Traditional enterprise software vendors have recently clarified their
cloud strategy, including for BI.
MicroStrategy, for instance, provides a cloud version of its tools enriched by
third-party databases and integration tools. On its side, SAP multiplies its
initiatives : it just released a cloud version of its latest BI self-service offering
called Lumira (and by the way, completed its offerings) and partnered with
Example of BIME dashboard

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Amazon to make HANA accessible through the cloud with a pay-per-use
model – an offering that unfortunately is limited (with a database capacity
of only a few dozens of gigabytes), but nonetheless appealing if only to
jump-start new projects. Similarly, at its 2013 Sapphire conference, SAP
announced Hana Enterprise Cloud, a complete platform to run both analytical
and transactional applications, including “legacy” SAP applications such as
BW or ECC, off premises in a cloud environment. Oracle, during its annual
convention, OpenWorld, excitedly announced its new database version,
now better suited to the cloud and its flexible nature.
After a timid entry on the subject, since several months passed before
a small button appeared in the BI platform Windows Azure Cloud with
Windows Azure SQL Reporting component, Microsoft made the leap in July
2013. The announcement was made not in the context of Windows Azure
but within Office 365. It is a complete package for self-service BI, including
the integration and modeling of various sources and kind of data, data
discovery and visualization including the form of maps or through to mobile
devices. There are still too few details about the features, the pricing or
functions related to the safety and management of large volumes of data for
this offer to be released in the second half of 2013. But what is striking is the
innovative side and break with traditional BI technologies from Microsoft,
previously centered on the basis of SQL Server, Windows and equipment
Solutions dedicated to Cloud BI
EPMBI Mobile Data Discovery
Generic BI Software in the Cloud
SAP Lumira
BI Actors in the Cloud Classification

2013 WHITE PAPER
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PC.
Following the same line of reasoning, some hosting or cloud specialists
are providing standardized, dedicated solutions for some of the market’s
BI platforms. At Business & Decision, we used our Eolas subsidiary’s
infrastructure as a foundation to design our Qloud Services offering for
QlikView as well as dedicated offerings for SAP and Oracle planning and
financial consolidation platforms.
This model’s limits? If source data is not already located within the cloud,
companies may be reluctant to transport (and eventually store, depending
on the cloud BI architecture) it externally. This is one limitation that must
be closely examined and dealt with on a case-by-case basis. Most service
providers know how to integrate a virtual private network to take care of
that problem. Another limitation is the volume of data to be transferred,
which explains why this sub-segment should be positioned for departmental
BI applications or for enterprise BI in companies that do not have to deal
with massive volumes of data or do not have to transfer data that is too
complex. Finally, the last limitation relates to the vendor’s license model:
if the cloud offering is provided through a third party and not by the vendor
itself and if this third party cannot offer the software by subscription as part
of its services, the company must buy a perpetual license in order to use
the service. Expenses must then be treated as an investment (CAPEX) and
not as solely operation-related (OPEX), and this can become a constraint
in terms of project financing. Additionally, the third-party service may not
include automatic application of release notes or upgrades to the latest
version.

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Enterprise BI in the Cloud
Cloud On Demand leads to new BI use-cases
The relationship between the cloud and BI is obviously
gradually growing stronger, with immediate implications for
performance management applications, and departmental
BI application. But are companies ready to take the ultimate
leap of faith and opt for an enterprise data warehouse or big
data in the cloud?

2013 WHITE PAPER
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The cloud may be gradually creeping into BI through the sidelines – for niche
applications or areas neglected by traditional BI, but does this necessarily
mean that it will be shaking BI’s very foundations as it did for CRM?
There can be no definitive answer at this stage. However, three disruptive
innovations are becoming increasingly distinct possibilities. The first relates
to database technology and its so-called elasticity property, which allows
adaptation to the strong demand fluctuations that characterize some
analytical applications. The second is a shift in the economics dimension.
And the third, now that BI can no longer be limited to pre-structured and
internal company data and must help communicate with people other than
employees within the company, is in terms of functionality.
Datawarehouse in the Cloud
SearchIn memory Big Data
Traditional Databases in the Cloud
Data Management actors in the Cloud Classification

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First Disruptive innovation: Technological Breakthrough
The digital world has helped bring innovation back to databases. Indeed,
large websites found themselves facing big data issues well before enterprise
applications. These issues include the need to manage streaming data as it
comes, sometimes in real time without even time for storage or association
with a structure or controls; the variety of processing to be applied to data
based on their nature; and demand unpredictability imposing fully elastic
mechanisms to assign IT resources to application needs, etc.
This «On Demand» principle, upon which the cloud is based, opens
business intelligence to new uses. For instance, the American company
Netflix, a multiple-platform provider of on-demand Internet streaming media
and DVDs by mail, started storing increasingly detailed information from
its customers’ terminals (decoder, television, mobile device), even before
knowing precisely why it was doing so. This approach helped it gradually
process growing volumes of data, which grew from a few terabytes to several
petabytes of «useful» data, without having to anticipate the load increase.
Another example is that of a major retailer using this elasticity property to
offload data from centralized systems or very big BI databases and then
distribute the data to a multitude of servers, requisitioned only for a few
hours before being reassigned to other tasks. This retailer can, for instance,
readjust its selling prices regularly according to market demand and further
adjust its pricing strategy to each trade area, store, etc.
While the BI field learned to work with costly, hence limited, resources
requiring upstream allocation, the Internet chose a totally different
approach. It endeavored to trivialize the contribution of the underlying
material resources as much as possible and make them available in self-
service mode. It also reinvented applications for dynamic use by making
them capable of extreme processing parallelization as well as dynamic and
automatic resources allocation and deallocation.

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Adapting this approach to BI is, however, no simple task. By nature, it goes
against all the best practices that have been acquired over the past years.
But big data is demanding the change: data warehouses must gradually
evolve from a centralized and homogenous architecture to a distributed and
protean one. The cloud is ideally suited to these new architectures, and as
such has a card to play here, even if only as a complement to existing data
warehouse architectures (to deal with specific data or processing types).
Another area causing much trouble for BI environments, to the point of the
subject being now considered sensitive, is the ability to integrate new data
sources on the fly. While progress has been made to allow users to explore
data on non predefined paths, the integration of any new data sources
after upstream project phases remains generally impossible. Sometimes,
the integration of new data streams even requires architecture redesign.
And yet, it is a fact that our environment is creating new data at an ever-
increasing pace. Just like e-merchants who managed to considerably
extend their catalogs – and turnover – by setting up external marketplaces,
should BI systems not set up their own data marketplaces in order to rapidly
integrate new data sources without having to pass through the multiple
layers of traditional warehouses? If the answer is yes, then the cloud is
bound to become a major element of the equation.
Second Disruptive Innovation: Costs
At the heart of the BI market is the database. And out of all information system
components, the database is probably the most difficult to replace. General-
purpose relational databases were indeed a breakthrough in the 1990s,
but since then they have not conceded much ground to any alternatives.
Although some vendors like Teradata have become key players of the
high-end market, they only represent a few percentage points of market
share. The same applies for dedicated business intelligence databases like

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Sybase IQ or for OLAP bases such as Oracle Essbase. Today, a new and
strong wind of innovation is blowing over the database market. But will it be
enough to cause disruption at the heart of existing information systems?
Well, disruption is possible, but backed-up with a radical change in the
economic equation! Indeed, costs associated with business intelligence
databases remain high and are often a deterrent when it comes to processing
the business data «long tail» resulting from aggregating all the data made
potentially available or generated by sensors or online channels interactions.
Amazon’s Redshift announcement is currently causing a sensation in
the business intelligence sphere even though this offering, based on the
ParAccel database, like several other cloud offerings on the market is
not a revolutionary one. With a quoted price of $1,000 per terabyte, i.e.
– according to Amazon – a tenth of competing solutions’ cost, Redshift’s
low cost positioning intrigues. Admittedly, the offering needs to tackle some
new, still poorly managed challenges. Security, of course, is one of them,
but also data transfers and the billing model whose flexibility is not without
drawbacks and must be thoroughly dissected for the purpose of comparison
with alternatives (for instance, data transfer cost could turn out to be higher
than storage cost).
Nevertheless, Redshift touches on a sore point for both today’s business
intelligence and tomorrow’s big data. Initial feedback, such as that made
public from AirBnb, confirm that this offering is creating evolutionary
prospects for BI in terms of cost and performance. How will this impact
user enterprises or the provider side of the market? Some players, like
Prevalence
Information available
Information as we know it
- informational assets from the internal company System of Information
- information operated in postponed time
- structured information
Information as we would like
Data is managed as we know it
+ generated by humans (Users Big Data)
+ generated by machines (Monitoring Big Data)
+ real-time or just-in-time information (speed)
+ model, assembled and extendible data (elasticity)
The Long Tail Principle

2013 WHITE PAPER
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Pentaho or Informatica, have already jumped on the bandwagon, not only
by announcing Redshift-dedicated versions of their respective offerings, but
also by modifying their economic model to align it with the cloud’s pricing
model.
Redshift was only mentioned here as an example. The lesson to be
learned is that the cloud will likely democratize high-end solutions, until
now the prerogative of major corporate groups, by making them available
to populations that did not previously have access to them, such as small
and medium enterprises as well as functional departments (for example, the
marketing division), which in exchange for a relatively low hourly rate will
be granted highly flexible access to significant processing capacity. Now it
remains to master this capacity and understand how to best use it over time.
Third Disruptive Innovation: Widely Shared Information
According to Gartner, 30% of companies are expected to monetize their
information assets by 2016. Even though information sharing is not necessarily
associated with financial transactions, this is a trend that we, as consumers,
have already observed: sporting goods suppliers inserting electronic chips
in their equipment to become our personal digital coach, electricity or gas
providers helping us optimize consumption through analysis of our bill, or local
authorities making available data on the public services that they provide, etc.
The cloud seems a natural solution for these issues. Moreover, it is this use
case that appears to be driving short-term demand, more aggressively than
the two previously mentioned innovation counterparts, since it is more demand
than supply-oriented.

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As we have seen in this 3-part series, the cloud is establishing itself as a
serious deployment option for business intelligence. Unlike in other domains,
it probably won’t take the market by storm, but is gaining ground through
solid use cases backed by innovative and more mature technologies. It is not
a “one size fits all,” but a card we should aspire to have in our game and be
ready to play.
Conclusion
Conclusion

2013 WHITE PAPER
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As Innovation and Solutions Director
for Business & Decision, Jean-Michel
Franco designs and markets value
propositions on an international level.
Franco has dedicated his career
to developing and broadening the
adoption of innovative technologies in
companies.
He started out at EDS by creating and
developing a new business intelligence
(BI) practice. Franco then joined SAP
EMEA to develop the business in the
areas of BI and ERP solutions and
later became Director of Marketing
Solutions in France and North Africa.
Jean-Michel Franco regularly speaks
during conferences dedicated to
management best practices and innovating technologies. He writes paper
published on the web or in the newspapers and specific press. He is also the
author, co-author or contributed to books about
data warehouses and SAP technologies, available in French: « Piloter
l’entreprise grâce au data warehouse » - Eyrolles ; « Dynamique de
l’adaptation » - Village Mondial ; « mySAP ERP pour les nuls » - First Interactive.
He may be contacted at jean-michel.franco@businessdecision.com
Linkedin : www.linkedin.com/in/jeanmichelfranco
Twitter: @jmichel_franco
Slideshare : http://fr.slideshare.net/jmfranco
The Author

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White Paper
Business & Decision is an international Consulting and Systems Integration
(CSI) company. It is a leader in Business Intelligence (BI) and Customer
Relationship Management (CRM), and a major player in e-Business,
Enterprise Information Management (EIM), Enterprise Solutions as well as
Management Consulting. Business & Decision contributes to the success of
customer projects by driving maximum business performance. The company
has a reputation for functional and technological expertise and has forged
partnerships with all of the key technology vendors.
Located in 15 countries, Business & Decision currently employs 2500 people
worldwide.
Additional information is available at www.businessdecision.com
Twitter : http://twitter.com/bd_group
Linked In : http://www.linkedin.com/company/business-&-decision
Viadeo : http://fr.viadeo.com/fr/profile/business.decision
YouTube : https://www.youtube.com/user/BandD75
Business & Decision Group
Conception: Business & Decision - Communication Department
Contact: communication@businessdecision.com
Artistic Direction: Interakting, Groupe Business & Decision

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