Description
Evidence of the competitive value of business intelligence (BI) and analytics solutions is growing.
WHI TE PAPER
I mpr ovi ng Or gani z a t i ona l Pe r f or ma nce Manageme nt
Thr ough Pe r va si ve Bus i ness I nt e l l i gence
Sponsored by: SAP AG
Dan Vesset Brian McDonough
March 2009
EXECUTI VE SUMMARY
Evidence of the competitive value of business intelligence (BI) and analytics solutions
is growing. Fact-based decision making is spreading throughout commercial,
nonprofit, and public sector organizations. The economic downturn is spurring
organizations to examine ways of retaining customers, spending capital and operating
budgets, and complying with regulations. However, over the long term, BI solutions
will continue to be applied to optimize a wide array of processes in an effort to
improve performance management and organizational competitiveness.
An increasing number of organizations are making BI and analytics functionality more
broadly available to all decision makers inside and outside the organization.
Internally, more pervasively available BI solutions lead to greater accountability by all
employees and greater consistency in performance management. Externally,
relationships with supplier and partners can be strengthened through effective sharing
of key performance indicators (KPIs). However, having pervasive BI means more
than having the appropriate BI tools distributed to all stakeholders. In pursuit of
pervasive BI, organizations should focus on the five key factors that can be directly
influenced to increase diffusion of BI. They are:
! Degree of training on the data, tools, and analytic techniques
! Design quality of the BI solution
! Prominence of data governance
! Nonexecutive involvement in promoting the design and use of BI solutions
! Prominence of a performance management methodology
These factors have to do as much with BI and analytics technology as they do with
the related professional services for BI strategy and solution development,
deployment, and maintenance.
G
l
o
b
a
l
H
e
a
d
q
u
a
r
t
e
r
s
:
5
S
p
e
e
n
S
t
r
e
e
t
F
r
a
m
i
n
g
h
a
m
,
M
A
0
1
7
0
1
U
S
A
P
.
5
0
8
.
8
7
2
.
8
2
0
0
F
.
5
0
8
.
9
3
5
.
4
0
1
5
w
w
w
.
i
d
c
.
c
o
m
2 #217286 ©2009 IDC
I N THI S WHI TE PAPER
In this white paper IDC discusses the growing body of evidence suggesting a direct
link between investment in business analytics solutions and organizational
performance. IDC highlights market trends that point toward more pervasive use of BI
solutions. The recommendations presented in this white paper are based on ongoing
IDC coverage of the BI and analytics solutions market with specific focus on return on
investment (ROI) and diffusion of the business analytics technology and BI
processes.
I NVESTI NG I N BUSI NESS I NTELLI GENCE
The extent of BI pervasiveness is a statistically significant predictor of organizational
competitiveness and performance — this is one of the most significant conclusions
from a recent IDC research project entitled Improving Organizational Decision-
Making Through Pervasive Business Intelligence: The Five Key Factors That Lead to
Business Intelligence Diffusion.
1
The study, based on in-depth interviews of midsize
and large organizations and a survey of 1,141 organizations across 11 countries,
sheds light on the importance of BI solutions as an enabler of competitiveness.
Although both interest and investment in BI and analytics solutions are growing, we
would not anticipate organizations investing in these solutions unless there was some
belief that they would eventually benefit from it. The need for quantifiable benefits is
made even stronger in difficult economic times when discontinuous change pushes
more decision makers to rely on fact-based insight rather than only their experience
or intuition. IDC's market research provides growing evidence of the potential value of
BI and analytics solutions.
! In surveys conducted by IDC throughout 2008, we found that:
# About half of all respondents indicated that BI and analytics was a top
priority for their organizations. In fact, over a six-month period in 2008, the
priority ranking increased.
2,3
# Among 18 application software segments, BI software ranked as the second
highest that organizations expect to purchase, upgrade, or replace over the
next 12 months. The only application software segment expected to garner
more investment in the short term is project and portfolio management.
4
# Surveys conducted from February to October 2008 indicate that a majority of
organizations plan to maintain flat BI budgets, with only 8% of organizations
expecting a decrease in BI budgets over the coming year.
! In 2003, IDC released the results of a study titled Leveraging the Foundations of
Wisdom: The Financial Impact of Business Analytics that was based on in-depth
evaluation of the ROI of business analytics projects at 43 leading organizations in
North America and Western Europe. The median ROI of business analytics
projects was 112%.
5
The extent of BI
pervasiveness is a
statistically significant
predictor of
organizational
competitiveness and
performance.
The median ROI of
business analytics
projects was 112%.
©2009 IDC #217286 3
! Additional research from academic institutions provides further proof points of the
value of business analytics. Examples include a Harvard Business Review article
and a subsequent book entitled Competing on Analytics.
6
The latest developments in the business analytics market, in which about $23 billion
was spent on software alone in 2008, are based on the foundation laid in the industry
over the past 30-plus years. As shown in Figure 1, although BI and analytics solutions
are not new, they are only now entering the mainstream.
F I GURE 1
Bu s i n e s s I nt e l l i g en c e a n d An al y t i c s Mar k e t T r en d s
Internal
Developers
Query, Reporting,
OLAP, Data Mining,
Statistical Analysis
Query, Reporting,
OLAP, Data Mining,
Statistical Analysis
Business Intelligence
Suites and Analytic
Applications
Business Intelligence
Suites and Analytic
Applications
Intelligent
Process
Automation
Intelligent
Process
Automation
Data/Content
Users
Low
High
DW Life-Cycle
Management
DW Life-Cycle
Management
Event
Monitoring
Event
Monitoring
Content
Analysis
Content
Analysis
Ad Hoc
Query and
OLAP
Ad Hoc
Query and
OLAP
Templates
Templates
Collaboration
and
Workflow
Collaboration
and
Workflow
Process
Awareness
Process
Awareness
Static,
Batch
Reporting
Static,
Batch
Reporting
Data
Warehousing
Data
Warehousing
ETL and
Data
Quality
ETL and
Data
Quality
Predictive
Analysis
Predictive
Analysis
Data
Models
Data
Models
Scorecards
Scorecards
Dashboards
and
Visualization
Dashboards
and
Visualization
Alerting
Alerting
1975–1989
1990–2004
2005–2020
Internal
Developers
Query, Reporting,
OLAP, Data Mining,
Statistical Analysis
Query, Reporting,
OLAP, Data Mining,
Statistical Analysis
Business Intelligence
Suites and Analytic
Applications
Business Intelligence
Suites and Analytic
Applications
Intelligent
Process
Automation
Intelligent
Process
Automation
Data/Content
Users
Low
High
DW Life-Cycle
Management
DW Life-Cycle
Management
Event
Monitoring
Event
Monitoring
Content
Analysis
Content
Analysis
Ad Hoc
Query and
OLAP
Ad Hoc
Query and
OLAP
Templates
Templates
Collaboration
and
Workflow
Collaboration
and
Workflow
Process
Awareness
Process
Awareness
Static,
Batch
Reporting
Static,
Batch
Reporting
Data
Warehousing
Data
Warehousing
ETL and
Data
Quality
ETL and
Data
Quality
Predictive
Analysis
Predictive
Analysis
Data
Models
Data
Models
Scorecards
Scorecards
Dashboards
and
Visualization
Dashboards
and
Visualization
Alerting
Alerting
1975–1989
1990–2004
2005–2020
Source: IDC, 2009
The market, which seems to be moving in 15-year cycles, continues to evolve by
incorporating new components. What started out as standalone batch reporting and
statistics tools has matured into broad suites of components that address data
integration, data warehousing, query and reporting, advanced analytic, and other
related components that address organizational needs as diverse as master data
management (MDM) and real-time alerting. Based on these trends, IDC believes that
the market has begun to focus on broader diffusion of BI only during the current 15-
year market cycle. But what does it mean to have pervasive BI?
The market has
begun to focus on
broader diffusion of BI
only during the
current 15-year
market cycle.
4 #217286 ©2009 IDC
PERVASI VE BUSI NESS I NTELLI GENCE
Pervasive BI results when organizational culture, business processes, and
technologies are designed and implemented with the goal of improving the strategic
and operational decision-making capabilities of a wide range of internal and external
stakeholders. IDC has identified six indicators of pervasive BI, as shown on the
horizontal axis of Figure 2. The indicators are as follows:
Degree of internal use by employees at all levels; degree of external use by
stakeholders such as customers, suppliers, and government agencies; percentage
of power users within an organization; number of domains within the primary data
warehouse; appropriateness of data update frequency to support business decision
making; and analytical orientation, an indicator that consists of elements dealing
with information sharing, importance of and reliance on analytics for decision making,
and the influence BI has on an employee's actions.
Organizations embarking on or continuing on their path toward pervasive BI need to
decide how to allocate their scarce human, capital, and IT resources to tasks and
projects that have the biggest impact on increasing the diffusion of BI throughout their
organizations and to external stakeholders. There are potentially large capital and
human costs involved in defining metrics and KPIs; assembling, cleansing, staging,
and analyzing data; and disseminating and presenting information. As organizations
move along the path toward pervasive BI, the needs and requirements of end users
increase, resulting in a widening gap between the demand for and supply of business
analytics solutions (refer back to Figure 1). This gap can be closed by employing
more automation and utilizing external service providers.
IDC has identified five key factors that have the strongest influence on BI
pervasiveness, as shown on the vertical axis of Figure 2. These factors are as
follows:
! Degree of training refers to the satisfaction level with training on the meaning of
data, the use of BI tools, and the use of analytics to improve decision making.
! Design quality refers to the extent to which end users' expectations about the
speed of adding various BI solution components by the IT group are met.
! Prominence of governance refers to the existence of and the importance of a
data governance group and associated data governance policies in BI system
design or enhancement initiatives.
! Nonexecutive involvement refers to the level of nonexecutive management's
involvement in promoting and encouraging the design and use of the BI solution
at the organization.
! Prominence of performance management methodology refers to the
existence of and the level of importance within the organization of a formal
performance management methodology.
The gap between
supply and demand of
business analytics
solutions can be
closed by employing
more automation and
utilizing external
service providers.
©2009 IDC #217286 5
The model shown in Figure 2 depicts the relationship between the six pervasive BI
indicators (dependent variables) and the five key factors leading to pervasive BI
(independent variables). The shading schema identifies independent variables that
have a statistically significant impact on the corresponding dependent variables. The
three levels of shading represent the level to which a unit change in a given
independent variable affects a change in the dependent variable. For example, Figure
2 shows that statistically, the degree of internal use of the BI solution can be affected
most by focusing on deploying and encouraging the use of a performance
management methodology. Analytical orientation can be affected by focusing on all
five factors. The unshaded cells do not indicate that any given factor should be
ignored when trying to influence any of the six indicators — there is simply no
statistically significant relationship based on IDC's chosen analytic technique.
F I GURE 2
T h e F i v e F ac t o r s o f I nf l u e n c e o n P e r v a s i v e Bu s i n e s s
I nt e l l i g e nc e
Pervasive Business Intelligence
(dependent variables)
Degree of
internal use
Degree of
external use
Percentage
of power
users
Number of
domains
Data update
frequency
Analytical
orientation
M
o
s
t
I
n
f
l
u
e
n
t
i
a
l
F
a
c
t
o
r
s
(
i
n
d
e
p
e
n
d
e
n
t
v
a
r
i
a
b
l
e
s
)
Degree of
training
Design quality
Prominence of
governance
Nonexecutive
involvement
Performance
management
methodology
Pervasive Business Intelligence
(dependent variables)
Degree of
internal use
Degree of
external use
Percentage
of power
users
Number of
domains
Data update
frequency
Analytical
orientation
Pervasive Business Intelligence
(dependent variables)
Degree of
internal use
Degree of
external use
Percentage
of power
users
Number of
domains
Data update
frequency
Analytical
orientation
M
o
s
t
I
n
f
l
u
e
n
t
i
a
l
F
a
c
t
o
r
s
(
i
n
d
e
p
e
n
d
e
n
t
v
a
r
i
a
b
l
e
s
)
Degree of
training
Design quality
Prominence of
governance
Nonexecutive
involvement
Performance
management
methodology
Note: The shading schema identifies independent variables that have a statistically significant
impact on the corresponding dependent variable. The three levels of shading represent the level
to which a unit change in a given independent variable affects a change in the dependent
variable.
Source: IDC, 2009
6 #217286 ©2009 IDC
THE I MPACT OF BUSI NESS ANALYTI CS
SERVI CES ON PERVASI VE BI
The IDC pervasive BI model can be used as a guide to help make critical decisions
pertaining to resource allocation for supporting successful deployment of BI solutions.
One of the conclusions we draw from the model is that services are as important to
successful business analytics projects as technology. The five factors with greatest
influence on the pervasiveness of BI are highly dependent on professional services
associated with each of the potential focus areas. Services can provide strategic and
tactical resources to execute on the vision of pervasive BI and organizationwide
performance management.
D e g r e e o f T r a i n i n g
The first key factor leading to more pervasive BI is the degree of training. It refers to
an interrelated set of variables that organizations should consider as part of their
overall BI program. These variables include the following:
! Level of satisfaction with training on the meaning of data, metrics, or KPIs; the
use of the BI tools; the use of analytics to improve decision making
! Level of ease with which end users learn how to use the organization's BI tools
! Level of ease with which data from different organizational domains (e.g.,
finance, manufacturing, and human resources) can be correlated
Recommendations
! Be aware of the positive impact that improving the degree of training, as defined
above, can have on the pervasiveness of BI. Training on the use of data and
training on the BI tools are independently important and additively important.
Organizations have a choice to train users on the use of tools or the use of data.
Although either type of training can increase BI pervasiveness, doing both types
of training can have an even greater positive effect on BI pervasiveness.
! Training does not refer only to classroom or online courses. To enable better
understanding of information, organizations should expose as much BI content
metadata, or information about the data, metrics, and KPIs, as possible directly in
reports and dashboards. This can be accomplished through something as simple
as having highly descriptive report or dashboard titles or through features such
as "pop-up" definitions of each KPI on mouseovers. The metadata can include
the description of KPIs, data lineage, and other relevant descriptors. Assuming
that organizationwide definitions of such BI content exist, exposing the BI content
metadata will assist in eliminating misunderstandings about the information made
available through a BI solution.
Services are as
important to
successful business
analytics projects as
technology.
It is important to
expose as much BI
content metadata, or
information about the
data, metrics, and
KPIs, as possible
directly in reports and
dashboards.
©2009 IDC #217286 7
D e s i g n Q u a l i t y
Design quality is another factor that has a strong effect on pervasive BI.
Design quality refers to the extent that end users' expectations about the speed of
adding various BI solution components by the IT group or a consulting partners
are met. A BI solution must be able to address not only the needs of various
end-user groups but also those of the IT group in its effort to support the ongoing
BI needs of end users. From a system design perspective, the user interface is
especially important to broader use of the BI solution. While logical understanding
of the value of BI exists among most end users, the emotional attachment to a
product (or service) leads to pervasiveness. We observed this phenomenon in
many organizations.
When a BI solution is well designed, it is easier to add new data sources, new
domains, new reports, new metrics, and new data hierarchies to it. The design quality
can be viewed as a proxy for system flexibility and organizational agility in responding
to ongoing decision support demands. Dissatisfaction with an IT group's ability to
rapidly respond to new requests is the primary cause for end users to seek alternative
BI solutions to those provided by central IT resources. Thus, insufficient design
quality often leads to silos of information that don't follow data governance policies,
decentralized purchasing of software by business groups, manual aggregation of data
in spreadsheet with no security or process controls, and the creation of "shadow" IT
groups within business units or departments.
Recommendations
! Work with your services partner to create an enterprisewide BI strategy. Although
BI should be viewed as an ongoing program rather than a one-off project, the
deployment of individual solutions should be done iteratively. A common
characteristic of business analytics system design among leading organizations
is the extensive use of rapid prototyping and the AGILE method of software
development. This seems to be the only effective method to match IT
development plans with frequently changing end-user BI requirements.
! Initiate a requirements-gathering process that is not predicated on asking end
users "What data do you need?" When IT groups deploy BI solutions without
direct business end-user input, they find that these technology deployments
remain idle or substantially underutilized. Asking end users for their BI system
requirements usually results in a question from end users about what data is
available, a wish list of all possible information, or simply a request for electronic
versions of previously available paper reports. Leading organizations evaluate
end-user decision-making processes, not simply data requirements. In other
words, they ask, "What decisions do you make?"
P r o mi n e n c e o f G o v e r n a n c e
Prominence of governance refers specifically to the existence of and the importance
of a data governance group and associated data governance policies in BI system
design or enhancement initiatives. About 10% of organizations in our research do
not have a data governance group or associated data governance policies.
8 #217286 ©2009 IDC
Organizations that have more experience with BI assign more importance to
governance. Also, those organizations that rank themselves as more competitive
within their industry tend to place greater importance on data governance.
The development of agreement on the meaning of data elements and the subsequent
need to train end users on what the data represents are key to the diffusion of BI
solutions. Without governance, there may not be consensus regarding what the data
means, thus guaranteeing BI a noncentral role in decision making. In some sense,
when decision making is based on unarticulated, estimated data, decisions are made
in an environment of strategic ambiguity — decision makers understand each other
less than they think they do.
Recommendations
! There are no easy solutions to data governance issues, and it is important not to
underestimate the time and effort involved in bringing various internal parties into
agreement about the meaning and value of data, metrics, and KPIs. Allocate
sufficient time to this process to resolve data governance, MDM, and data quality
issues. Part of the problem is that in most sizable organizations, the division of
labor has resulted not only in data silos but also in process silos, with no single
person or group responsible for end-to-end processes and associated data.
! Our research suggests that a best practice is to set up a governance body as a
virtual entity that is made up of employees with decision-making authority. Much
of the job of the governance body is to explain, cajole, influence, placate, and
otherwise bring different end-user groups into agreement about a common
language for managing organizational performance. Thus, a governance body
must show leadership in resolving any intergroup conflicts. Many organizations
effectively utilize external consultants as part of the data governance body to
help facilitate communication among internal user groups.
N o n e x e c u t i v e I n v o l v e me n t
Nonexecutive involvement refers to the level of nonexecutive management's
involvement in promoting and encouraging the design and use (separately) of the BI
solution at the organization. As shown in Figure 2, this factor has the highest
influence on the following pervasive BI indicators: data update frequency and
analytical orientation. Organizations that assess themselves as being more
competitive have a higher level of nonexecutive involvement.
One of the common techniques for expanding the use of BI functionality is for the BI
group to seek out a partner in one business group and provide that individual with
information and BI tools that can give that person an advantage over his or her peers
during meetings and collaborative decision-making sessions. There are many examples
where the resulting "BI envy" leads those without the latest information and BI tools to
request it from the BI group. However, it is important to note that the spread of BI tools
and processes in an organization is not "viral," as some pundits would say. Unlike
biological viruses, BI use does not spread simply by association. It requires the
"unaffected" party to consciously agree to start using BI, which is likely to happen only if
that party understands the data, understands the BI tool, and sees value in using both.
Organizations that
rank themselves as
more competitive
within their industry
tend to place greater
importance on data
governance.
Many organizations
effectively utilize
external consultants
as part of the data
governance body to
help facilitate
communication
among internal user
groups.
©2009 IDC #217286 9
Recommendations
! Statistically, nonexecutive management's involvement in BI has more influence
on the pervasiveness of BI than the involvement of executive management. The
existing literature regarding BI and analytics suggests that executives must be
involved in BI initiatives in order for them to lead to analytic organizations.
7
Our
research confirms that executives must be involved, but their involvement should
be different from that of nonexecutive management. The biggest impact of
executives is that they usually initiate and provide funding for BI project, while
nonexecutive management can be more influential in driving these projects once
they have been launched.
! One of the key lessons from our research is the recognition of the importance of
a "champion" to expanding the use of the BI solution throughout the organization.
The "champion" could be a single person, or a small team of employees, with the
vision and expertise to convince key business stakeholders about the potential
positive impact that a BI solution could have on the performance of an
organization. These BI project "champions," who persist in using a BI solution
and encourage colleagues to do the same, most often come from the ranks of
nonexecutive managers. The association of nonexecutive managers in meetings
facilitates information sharing
8
and, subsequently, BI solution diffusion.
P r o mi n e n c e o f P e r f o r ma n c e Ma n a g e me n t
Me t h o d o l o g y
Prominence of performance management methodology refers to the existence of and
the importance of a performance management methodology within an organization.
One of the keys to an effective BI and performance management solution is to ensure
a direct connection between business strategy and actionable KPIs as well as a
subsequent link between strategic and operational KPIs. Such tiered KPIs are usually
established in the context of a performance management methodology. Several
industry-standard performance management methodologies, such as the balanced
scorecard, exist. However, organizations can and also do develop their own
methodologies or look to their IT product and service providers to assist with
developing and deploying such a methodology.
Based on IDC's pervasive BI study, 75% of organizations that rate themselves as
most competitive in their industry use a formal performance management
methodology; this rate drops to 43% for the least competitive organizations. A similar
observation can be made about the importance of the performance management
methodology. The use of the balanced scorecard or a similar methodology
demonstrates how this factor can affect the number of domains pervasive BI
indicator. The cross-domain nature of this methodology forces an organization looking
to automate some aspects of the balanced scorecard to ensure that all organizations'
domains or subject areas are represented in the BI solution.
75% of organizations
that rate themselves
as most competitive in
their industry use a
formal performance
management
methodology; this rate
drops to 43% for the
least competitive
organizations.
10 #217286 ©2009 IDC
Recommendations
! If your organization doesn't already employ a formal performance management
methodology, evaluate your technology vendor's capabilities for recommending
and assisting in deploying such a methodology. Consultants' experiences span
many companies within an industry, and they are able to bring best practices in
performance management to their clients' organizations.
! The success of performance management efforts depends in large part on
expanding accountability within the organization through the availability of
metrics and KPIs for all employees and by tying a portion of compensation to
performance metrics. Services partners can assist in identifying the most relevant
KPIs as part of the performance management methodology deployment.
EVALUATI NG SERVI CES PROVI DERS
As the market research evidence presented demonstrates, professional services,
along with appropriate business analytics technology, can play a key part in enabling
pervasive BI, which in turn can lead to greater competitiveness and improved
performance.
9
When evaluating professional services vendors for BI and analytics projects,
organizations should review several criteria, including the vendors' BI and analytics
methodology, availability of local and off-shore staff, pricing structure (e.g., fixed bid
or hourly time and expense), and range of experts available across the different
technology and business processes involved in a typical BI and analytics project.
Typical BI and analytics project methodologies include steps such as BI strategy
development; requirements gathering; design of the BI and analytics solution
architecture; development of the various data integration, data warehousing, and end-
user query, reporting, and analysis components of the solution; deployment of the
solution; and related training and support.
Although SAP is known primarily for its extensive software portfolio provided by the
SAP BusinessObjects division, the company also provides a range of BI professional
services. These services utilize such SAP methodologies as ASAP Methodology,
SAP Road Map Composer, and SAP Strategic Data Services for SAP NetWeaver
MDM, among others.
SAP BI and analytics professional services place a strong focus on identifying an
organization's strategic and operational goals and linking them to specific KPIs. These
steps not only help identify the needs of individual decision makers but also help
implement a performance management initiative with the right scope and focus. Once
the KPIs are identified, the subsequent services methodology steps address the
requirements for data integration, data quality, data governance, and master data
management. The third major element of SAP's methodology revolves around ensuring
broad adoption of the BI and analytics solution by delivering user-friendly information
access. End-user needs are evaluated based on specific roles, and recommendations
of specific user interfaces for various user groups from the executives to line-of-
business employees are developed prior to technology implementation and deployment.
Although SAP is
known primarily for its
extensive software
portfolio provided by
the SAP
BusinessObjects
division, the company
also provides a range
of BI professional
services.
©2009 IDC #217286 11
OPPORTUNI TI ES AND CHALLENGES
The long-term trends suggest that the market is still in the early stages of a BI
solution adoption cycle that will extend the reach of various decision support and
decision automation solutions to a broad set of new users. These users will span all
levels of an organization and will be involved in a spectrum of strategic and
operational decision-making activities. Some of these activities will be based on
information access through reports, dashboards, or search boxes. Other BI activities
will include advanced analytic techniques for descriptive and predictive analytics.
Organizations investing in BI and performance management have many opportunities
to take advantage of the growing body of evidence suggesting a direct link between
these solutions and organizational competitiveness and performance. These
opportunities must make effective use of both IT products and services as well
business process reorganization and organizational behavior changes necessary to
shift toward more fact-based decision-making processes.
At the same time, organizations will increase their chances of BI project success and
overcome technical and organizational challenges by following methodologies, such
as those presented in this document. Whether an organization chooses to partner
with SAP or another solution provider, IDC, as always, encourages all organizations
to evaluate any IT vendor based on specific technology features and functionality,
services offerings, support structure, expertise within the selected technology or
business area, financial strength, and availability and quality of partners.
RELATED RESEARCH AND REFERENCES
1. Improving Organizational Decision-Making Through Pervasive Business
Intelligence: The Five Key Factors That Lead to Business Intelligence Diffusion,
IDC multiclient study, November 2008.
2. IDC and InfoWorld's Business Intelligence Survey, February 2008.
3. IDC and ComputerWorld's Business Intelligence Survey, July 2008.
4. IDC's quarterly AppStats Survey #7, October 2008.
5. Leveraging the Foundations of Wisdom: The Financial Impact of Business
Analytics, IDC, 2003.
6. Thomas H. Davenport and Jeanne G. Harris, Competing on Analytics, Harvard
Business School Press, 2006.
7. Thomas H. Davenport and Jeanne G. Harris, Competing on Analytics, Harvard
Business School Press, 2006.
8. Sinan Aral, Erik Brynjolfsson, Marshall Van Alstyne, "Productivity Effects of
Information Diffusion in Networks," The MIT Center for Digital Business, July
2007.
12 #217286 ©2009 IDC
9. The research results highlighted in this white paper were the outcome of IDC's
study Improving Organizational Decision-Making Through Pervasive Business
Intelligence: The Five Key Factors That Lead to Business Intelligence Diffusion.
The research (methodology and execution) was completed by IDC in
collaboration with researchers from Boston University School of Management
Systems Research Center and was underwritten by 11 competing business
analytics solution providers, including SAP.
C o p y r i g h t N o t i c e
External Publication of IDC Information and Data — Any IDC information that is to be
used in advertising, press releases, or promotional materials requires prior written
approval from the appropriate IDC Vice President or Country Manager. A draft of the
proposed document should accompany any such request. IDC reserves the right to
deny approval of external usage for any reason.
Copyright 2009 IDC. Reproduction without written permission is completely forbidden.
doc_426809620.pdf
Evidence of the competitive value of business intelligence (BI) and analytics solutions is growing.
WHI TE PAPER
I mpr ovi ng Or gani z a t i ona l Pe r f or ma nce Manageme nt
Thr ough Pe r va si ve Bus i ness I nt e l l i gence
Sponsored by: SAP AG
Dan Vesset Brian McDonough
March 2009
EXECUTI VE SUMMARY
Evidence of the competitive value of business intelligence (BI) and analytics solutions
is growing. Fact-based decision making is spreading throughout commercial,
nonprofit, and public sector organizations. The economic downturn is spurring
organizations to examine ways of retaining customers, spending capital and operating
budgets, and complying with regulations. However, over the long term, BI solutions
will continue to be applied to optimize a wide array of processes in an effort to
improve performance management and organizational competitiveness.
An increasing number of organizations are making BI and analytics functionality more
broadly available to all decision makers inside and outside the organization.
Internally, more pervasively available BI solutions lead to greater accountability by all
employees and greater consistency in performance management. Externally,
relationships with supplier and partners can be strengthened through effective sharing
of key performance indicators (KPIs). However, having pervasive BI means more
than having the appropriate BI tools distributed to all stakeholders. In pursuit of
pervasive BI, organizations should focus on the five key factors that can be directly
influenced to increase diffusion of BI. They are:
! Degree of training on the data, tools, and analytic techniques
! Design quality of the BI solution
! Prominence of data governance
! Nonexecutive involvement in promoting the design and use of BI solutions
! Prominence of a performance management methodology
These factors have to do as much with BI and analytics technology as they do with
the related professional services for BI strategy and solution development,
deployment, and maintenance.
G
l
o
b
a
l
H
e
a
d
q
u
a
r
t
e
r
s
:
5
S
p
e
e
n
S
t
r
e
e
t
F
r
a
m
i
n
g
h
a
m
,
M
A
0
1
7
0
1
U
S
A
P
.
5
0
8
.
8
7
2
.
8
2
0
0
F
.
5
0
8
.
9
3
5
.
4
0
1
5
w
w
w
.
i
d
c
.
c
o
m
2 #217286 ©2009 IDC
I N THI S WHI TE PAPER
In this white paper IDC discusses the growing body of evidence suggesting a direct
link between investment in business analytics solutions and organizational
performance. IDC highlights market trends that point toward more pervasive use of BI
solutions. The recommendations presented in this white paper are based on ongoing
IDC coverage of the BI and analytics solutions market with specific focus on return on
investment (ROI) and diffusion of the business analytics technology and BI
processes.
I NVESTI NG I N BUSI NESS I NTELLI GENCE
The extent of BI pervasiveness is a statistically significant predictor of organizational
competitiveness and performance — this is one of the most significant conclusions
from a recent IDC research project entitled Improving Organizational Decision-
Making Through Pervasive Business Intelligence: The Five Key Factors That Lead to
Business Intelligence Diffusion.
1
The study, based on in-depth interviews of midsize
and large organizations and a survey of 1,141 organizations across 11 countries,
sheds light on the importance of BI solutions as an enabler of competitiveness.
Although both interest and investment in BI and analytics solutions are growing, we
would not anticipate organizations investing in these solutions unless there was some
belief that they would eventually benefit from it. The need for quantifiable benefits is
made even stronger in difficult economic times when discontinuous change pushes
more decision makers to rely on fact-based insight rather than only their experience
or intuition. IDC's market research provides growing evidence of the potential value of
BI and analytics solutions.
! In surveys conducted by IDC throughout 2008, we found that:
# About half of all respondents indicated that BI and analytics was a top
priority for their organizations. In fact, over a six-month period in 2008, the
priority ranking increased.
2,3
# Among 18 application software segments, BI software ranked as the second
highest that organizations expect to purchase, upgrade, or replace over the
next 12 months. The only application software segment expected to garner
more investment in the short term is project and portfolio management.
4
# Surveys conducted from February to October 2008 indicate that a majority of
organizations plan to maintain flat BI budgets, with only 8% of organizations
expecting a decrease in BI budgets over the coming year.
! In 2003, IDC released the results of a study titled Leveraging the Foundations of
Wisdom: The Financial Impact of Business Analytics that was based on in-depth
evaluation of the ROI of business analytics projects at 43 leading organizations in
North America and Western Europe. The median ROI of business analytics
projects was 112%.
5
The extent of BI
pervasiveness is a
statistically significant
predictor of
organizational
competitiveness and
performance.
The median ROI of
business analytics
projects was 112%.
©2009 IDC #217286 3
! Additional research from academic institutions provides further proof points of the
value of business analytics. Examples include a Harvard Business Review article
and a subsequent book entitled Competing on Analytics.
6
The latest developments in the business analytics market, in which about $23 billion
was spent on software alone in 2008, are based on the foundation laid in the industry
over the past 30-plus years. As shown in Figure 1, although BI and analytics solutions
are not new, they are only now entering the mainstream.
F I GURE 1
Bu s i n e s s I nt e l l i g en c e a n d An al y t i c s Mar k e t T r en d s
Internal
Developers
Query, Reporting,
OLAP, Data Mining,
Statistical Analysis
Query, Reporting,
OLAP, Data Mining,
Statistical Analysis
Business Intelligence
Suites and Analytic
Applications
Business Intelligence
Suites and Analytic
Applications
Intelligent
Process
Automation
Intelligent
Process
Automation
Data/Content
Users
Low
High
DW Life-Cycle
Management
DW Life-Cycle
Management
Event
Monitoring
Event
Monitoring
Content
Analysis
Content
Analysis
Ad Hoc
Query and
OLAP
Ad Hoc
Query and
OLAP
Templates
Templates
Collaboration
and
Workflow
Collaboration
and
Workflow
Process
Awareness
Process
Awareness
Static,
Batch
Reporting
Static,
Batch
Reporting
Data
Warehousing
Data
Warehousing
ETL and
Data
Quality
ETL and
Data
Quality
Predictive
Analysis
Predictive
Analysis
Data
Models
Data
Models
Scorecards
Scorecards
Dashboards
and
Visualization
Dashboards
and
Visualization
Alerting
Alerting
1975–1989
1990–2004
2005–2020
Internal
Developers
Query, Reporting,
OLAP, Data Mining,
Statistical Analysis
Query, Reporting,
OLAP, Data Mining,
Statistical Analysis
Business Intelligence
Suites and Analytic
Applications
Business Intelligence
Suites and Analytic
Applications
Intelligent
Process
Automation
Intelligent
Process
Automation
Data/Content
Users
Low
High
DW Life-Cycle
Management
DW Life-Cycle
Management
Event
Monitoring
Event
Monitoring
Content
Analysis
Content
Analysis
Ad Hoc
Query and
OLAP
Ad Hoc
Query and
OLAP
Templates
Templates
Collaboration
and
Workflow
Collaboration
and
Workflow
Process
Awareness
Process
Awareness
Static,
Batch
Reporting
Static,
Batch
Reporting
Data
Warehousing
Data
Warehousing
ETL and
Data
Quality
ETL and
Data
Quality
Predictive
Analysis
Predictive
Analysis
Data
Models
Data
Models
Scorecards
Scorecards
Dashboards
and
Visualization
Dashboards
and
Visualization
Alerting
Alerting
1975–1989
1990–2004
2005–2020
Source: IDC, 2009
The market, which seems to be moving in 15-year cycles, continues to evolve by
incorporating new components. What started out as standalone batch reporting and
statistics tools has matured into broad suites of components that address data
integration, data warehousing, query and reporting, advanced analytic, and other
related components that address organizational needs as diverse as master data
management (MDM) and real-time alerting. Based on these trends, IDC believes that
the market has begun to focus on broader diffusion of BI only during the current 15-
year market cycle. But what does it mean to have pervasive BI?
The market has
begun to focus on
broader diffusion of BI
only during the
current 15-year
market cycle.
4 #217286 ©2009 IDC
PERVASI VE BUSI NESS I NTELLI GENCE
Pervasive BI results when organizational culture, business processes, and
technologies are designed and implemented with the goal of improving the strategic
and operational decision-making capabilities of a wide range of internal and external
stakeholders. IDC has identified six indicators of pervasive BI, as shown on the
horizontal axis of Figure 2. The indicators are as follows:
Degree of internal use by employees at all levels; degree of external use by
stakeholders such as customers, suppliers, and government agencies; percentage
of power users within an organization; number of domains within the primary data
warehouse; appropriateness of data update frequency to support business decision
making; and analytical orientation, an indicator that consists of elements dealing
with information sharing, importance of and reliance on analytics for decision making,
and the influence BI has on an employee's actions.
Organizations embarking on or continuing on their path toward pervasive BI need to
decide how to allocate their scarce human, capital, and IT resources to tasks and
projects that have the biggest impact on increasing the diffusion of BI throughout their
organizations and to external stakeholders. There are potentially large capital and
human costs involved in defining metrics and KPIs; assembling, cleansing, staging,
and analyzing data; and disseminating and presenting information. As organizations
move along the path toward pervasive BI, the needs and requirements of end users
increase, resulting in a widening gap between the demand for and supply of business
analytics solutions (refer back to Figure 1). This gap can be closed by employing
more automation and utilizing external service providers.
IDC has identified five key factors that have the strongest influence on BI
pervasiveness, as shown on the vertical axis of Figure 2. These factors are as
follows:
! Degree of training refers to the satisfaction level with training on the meaning of
data, the use of BI tools, and the use of analytics to improve decision making.
! Design quality refers to the extent to which end users' expectations about the
speed of adding various BI solution components by the IT group are met.
! Prominence of governance refers to the existence of and the importance of a
data governance group and associated data governance policies in BI system
design or enhancement initiatives.
! Nonexecutive involvement refers to the level of nonexecutive management's
involvement in promoting and encouraging the design and use of the BI solution
at the organization.
! Prominence of performance management methodology refers to the
existence of and the level of importance within the organization of a formal
performance management methodology.
The gap between
supply and demand of
business analytics
solutions can be
closed by employing
more automation and
utilizing external
service providers.
©2009 IDC #217286 5
The model shown in Figure 2 depicts the relationship between the six pervasive BI
indicators (dependent variables) and the five key factors leading to pervasive BI
(independent variables). The shading schema identifies independent variables that
have a statistically significant impact on the corresponding dependent variables. The
three levels of shading represent the level to which a unit change in a given
independent variable affects a change in the dependent variable. For example, Figure
2 shows that statistically, the degree of internal use of the BI solution can be affected
most by focusing on deploying and encouraging the use of a performance
management methodology. Analytical orientation can be affected by focusing on all
five factors. The unshaded cells do not indicate that any given factor should be
ignored when trying to influence any of the six indicators — there is simply no
statistically significant relationship based on IDC's chosen analytic technique.
F I GURE 2
T h e F i v e F ac t o r s o f I nf l u e n c e o n P e r v a s i v e Bu s i n e s s
I nt e l l i g e nc e
Pervasive Business Intelligence
(dependent variables)
Degree of
internal use
Degree of
external use
Percentage
of power
users
Number of
domains
Data update
frequency
Analytical
orientation
M
o
s
t
I
n
f
l
u
e
n
t
i
a
l
F
a
c
t
o
r
s
(
i
n
d
e
p
e
n
d
e
n
t
v
a
r
i
a
b
l
e
s
)
Degree of
training
Design quality
Prominence of
governance
Nonexecutive
involvement
Performance
management
methodology
Pervasive Business Intelligence
(dependent variables)
Degree of
internal use
Degree of
external use
Percentage
of power
users
Number of
domains
Data update
frequency
Analytical
orientation
Pervasive Business Intelligence
(dependent variables)
Degree of
internal use
Degree of
external use
Percentage
of power
users
Number of
domains
Data update
frequency
Analytical
orientation
M
o
s
t
I
n
f
l
u
e
n
t
i
a
l
F
a
c
t
o
r
s
(
i
n
d
e
p
e
n
d
e
n
t
v
a
r
i
a
b
l
e
s
)
Degree of
training
Design quality
Prominence of
governance
Nonexecutive
involvement
Performance
management
methodology
Note: The shading schema identifies independent variables that have a statistically significant
impact on the corresponding dependent variable. The three levels of shading represent the level
to which a unit change in a given independent variable affects a change in the dependent
variable.
Source: IDC, 2009
6 #217286 ©2009 IDC
THE I MPACT OF BUSI NESS ANALYTI CS
SERVI CES ON PERVASI VE BI
The IDC pervasive BI model can be used as a guide to help make critical decisions
pertaining to resource allocation for supporting successful deployment of BI solutions.
One of the conclusions we draw from the model is that services are as important to
successful business analytics projects as technology. The five factors with greatest
influence on the pervasiveness of BI are highly dependent on professional services
associated with each of the potential focus areas. Services can provide strategic and
tactical resources to execute on the vision of pervasive BI and organizationwide
performance management.
D e g r e e o f T r a i n i n g
The first key factor leading to more pervasive BI is the degree of training. It refers to
an interrelated set of variables that organizations should consider as part of their
overall BI program. These variables include the following:
! Level of satisfaction with training on the meaning of data, metrics, or KPIs; the
use of the BI tools; the use of analytics to improve decision making
! Level of ease with which end users learn how to use the organization's BI tools
! Level of ease with which data from different organizational domains (e.g.,
finance, manufacturing, and human resources) can be correlated
Recommendations
! Be aware of the positive impact that improving the degree of training, as defined
above, can have on the pervasiveness of BI. Training on the use of data and
training on the BI tools are independently important and additively important.
Organizations have a choice to train users on the use of tools or the use of data.
Although either type of training can increase BI pervasiveness, doing both types
of training can have an even greater positive effect on BI pervasiveness.
! Training does not refer only to classroom or online courses. To enable better
understanding of information, organizations should expose as much BI content
metadata, or information about the data, metrics, and KPIs, as possible directly in
reports and dashboards. This can be accomplished through something as simple
as having highly descriptive report or dashboard titles or through features such
as "pop-up" definitions of each KPI on mouseovers. The metadata can include
the description of KPIs, data lineage, and other relevant descriptors. Assuming
that organizationwide definitions of such BI content exist, exposing the BI content
metadata will assist in eliminating misunderstandings about the information made
available through a BI solution.
Services are as
important to
successful business
analytics projects as
technology.
It is important to
expose as much BI
content metadata, or
information about the
data, metrics, and
KPIs, as possible
directly in reports and
dashboards.
©2009 IDC #217286 7
D e s i g n Q u a l i t y
Design quality is another factor that has a strong effect on pervasive BI.
Design quality refers to the extent that end users' expectations about the speed of
adding various BI solution components by the IT group or a consulting partners
are met. A BI solution must be able to address not only the needs of various
end-user groups but also those of the IT group in its effort to support the ongoing
BI needs of end users. From a system design perspective, the user interface is
especially important to broader use of the BI solution. While logical understanding
of the value of BI exists among most end users, the emotional attachment to a
product (or service) leads to pervasiveness. We observed this phenomenon in
many organizations.
When a BI solution is well designed, it is easier to add new data sources, new
domains, new reports, new metrics, and new data hierarchies to it. The design quality
can be viewed as a proxy for system flexibility and organizational agility in responding
to ongoing decision support demands. Dissatisfaction with an IT group's ability to
rapidly respond to new requests is the primary cause for end users to seek alternative
BI solutions to those provided by central IT resources. Thus, insufficient design
quality often leads to silos of information that don't follow data governance policies,
decentralized purchasing of software by business groups, manual aggregation of data
in spreadsheet with no security or process controls, and the creation of "shadow" IT
groups within business units or departments.
Recommendations
! Work with your services partner to create an enterprisewide BI strategy. Although
BI should be viewed as an ongoing program rather than a one-off project, the
deployment of individual solutions should be done iteratively. A common
characteristic of business analytics system design among leading organizations
is the extensive use of rapid prototyping and the AGILE method of software
development. This seems to be the only effective method to match IT
development plans with frequently changing end-user BI requirements.
! Initiate a requirements-gathering process that is not predicated on asking end
users "What data do you need?" When IT groups deploy BI solutions without
direct business end-user input, they find that these technology deployments
remain idle or substantially underutilized. Asking end users for their BI system
requirements usually results in a question from end users about what data is
available, a wish list of all possible information, or simply a request for electronic
versions of previously available paper reports. Leading organizations evaluate
end-user decision-making processes, not simply data requirements. In other
words, they ask, "What decisions do you make?"
P r o mi n e n c e o f G o v e r n a n c e
Prominence of governance refers specifically to the existence of and the importance
of a data governance group and associated data governance policies in BI system
design or enhancement initiatives. About 10% of organizations in our research do
not have a data governance group or associated data governance policies.
8 #217286 ©2009 IDC
Organizations that have more experience with BI assign more importance to
governance. Also, those organizations that rank themselves as more competitive
within their industry tend to place greater importance on data governance.
The development of agreement on the meaning of data elements and the subsequent
need to train end users on what the data represents are key to the diffusion of BI
solutions. Without governance, there may not be consensus regarding what the data
means, thus guaranteeing BI a noncentral role in decision making. In some sense,
when decision making is based on unarticulated, estimated data, decisions are made
in an environment of strategic ambiguity — decision makers understand each other
less than they think they do.
Recommendations
! There are no easy solutions to data governance issues, and it is important not to
underestimate the time and effort involved in bringing various internal parties into
agreement about the meaning and value of data, metrics, and KPIs. Allocate
sufficient time to this process to resolve data governance, MDM, and data quality
issues. Part of the problem is that in most sizable organizations, the division of
labor has resulted not only in data silos but also in process silos, with no single
person or group responsible for end-to-end processes and associated data.
! Our research suggests that a best practice is to set up a governance body as a
virtual entity that is made up of employees with decision-making authority. Much
of the job of the governance body is to explain, cajole, influence, placate, and
otherwise bring different end-user groups into agreement about a common
language for managing organizational performance. Thus, a governance body
must show leadership in resolving any intergroup conflicts. Many organizations
effectively utilize external consultants as part of the data governance body to
help facilitate communication among internal user groups.
N o n e x e c u t i v e I n v o l v e me n t
Nonexecutive involvement refers to the level of nonexecutive management's
involvement in promoting and encouraging the design and use (separately) of the BI
solution at the organization. As shown in Figure 2, this factor has the highest
influence on the following pervasive BI indicators: data update frequency and
analytical orientation. Organizations that assess themselves as being more
competitive have a higher level of nonexecutive involvement.
One of the common techniques for expanding the use of BI functionality is for the BI
group to seek out a partner in one business group and provide that individual with
information and BI tools that can give that person an advantage over his or her peers
during meetings and collaborative decision-making sessions. There are many examples
where the resulting "BI envy" leads those without the latest information and BI tools to
request it from the BI group. However, it is important to note that the spread of BI tools
and processes in an organization is not "viral," as some pundits would say. Unlike
biological viruses, BI use does not spread simply by association. It requires the
"unaffected" party to consciously agree to start using BI, which is likely to happen only if
that party understands the data, understands the BI tool, and sees value in using both.
Organizations that
rank themselves as
more competitive
within their industry
tend to place greater
importance on data
governance.
Many organizations
effectively utilize
external consultants
as part of the data
governance body to
help facilitate
communication
among internal user
groups.
©2009 IDC #217286 9
Recommendations
! Statistically, nonexecutive management's involvement in BI has more influence
on the pervasiveness of BI than the involvement of executive management. The
existing literature regarding BI and analytics suggests that executives must be
involved in BI initiatives in order for them to lead to analytic organizations.
7
Our
research confirms that executives must be involved, but their involvement should
be different from that of nonexecutive management. The biggest impact of
executives is that they usually initiate and provide funding for BI project, while
nonexecutive management can be more influential in driving these projects once
they have been launched.
! One of the key lessons from our research is the recognition of the importance of
a "champion" to expanding the use of the BI solution throughout the organization.
The "champion" could be a single person, or a small team of employees, with the
vision and expertise to convince key business stakeholders about the potential
positive impact that a BI solution could have on the performance of an
organization. These BI project "champions," who persist in using a BI solution
and encourage colleagues to do the same, most often come from the ranks of
nonexecutive managers. The association of nonexecutive managers in meetings
facilitates information sharing
8
and, subsequently, BI solution diffusion.
P r o mi n e n c e o f P e r f o r ma n c e Ma n a g e me n t
Me t h o d o l o g y
Prominence of performance management methodology refers to the existence of and
the importance of a performance management methodology within an organization.
One of the keys to an effective BI and performance management solution is to ensure
a direct connection between business strategy and actionable KPIs as well as a
subsequent link between strategic and operational KPIs. Such tiered KPIs are usually
established in the context of a performance management methodology. Several
industry-standard performance management methodologies, such as the balanced
scorecard, exist. However, organizations can and also do develop their own
methodologies or look to their IT product and service providers to assist with
developing and deploying such a methodology.
Based on IDC's pervasive BI study, 75% of organizations that rate themselves as
most competitive in their industry use a formal performance management
methodology; this rate drops to 43% for the least competitive organizations. A similar
observation can be made about the importance of the performance management
methodology. The use of the balanced scorecard or a similar methodology
demonstrates how this factor can affect the number of domains pervasive BI
indicator. The cross-domain nature of this methodology forces an organization looking
to automate some aspects of the balanced scorecard to ensure that all organizations'
domains or subject areas are represented in the BI solution.
75% of organizations
that rate themselves
as most competitive in
their industry use a
formal performance
management
methodology; this rate
drops to 43% for the
least competitive
organizations.
10 #217286 ©2009 IDC
Recommendations
! If your organization doesn't already employ a formal performance management
methodology, evaluate your technology vendor's capabilities for recommending
and assisting in deploying such a methodology. Consultants' experiences span
many companies within an industry, and they are able to bring best practices in
performance management to their clients' organizations.
! The success of performance management efforts depends in large part on
expanding accountability within the organization through the availability of
metrics and KPIs for all employees and by tying a portion of compensation to
performance metrics. Services partners can assist in identifying the most relevant
KPIs as part of the performance management methodology deployment.
EVALUATI NG SERVI CES PROVI DERS
As the market research evidence presented demonstrates, professional services,
along with appropriate business analytics technology, can play a key part in enabling
pervasive BI, which in turn can lead to greater competitiveness and improved
performance.
9
When evaluating professional services vendors for BI and analytics projects,
organizations should review several criteria, including the vendors' BI and analytics
methodology, availability of local and off-shore staff, pricing structure (e.g., fixed bid
or hourly time and expense), and range of experts available across the different
technology and business processes involved in a typical BI and analytics project.
Typical BI and analytics project methodologies include steps such as BI strategy
development; requirements gathering; design of the BI and analytics solution
architecture; development of the various data integration, data warehousing, and end-
user query, reporting, and analysis components of the solution; deployment of the
solution; and related training and support.
Although SAP is known primarily for its extensive software portfolio provided by the
SAP BusinessObjects division, the company also provides a range of BI professional
services. These services utilize such SAP methodologies as ASAP Methodology,
SAP Road Map Composer, and SAP Strategic Data Services for SAP NetWeaver
MDM, among others.
SAP BI and analytics professional services place a strong focus on identifying an
organization's strategic and operational goals and linking them to specific KPIs. These
steps not only help identify the needs of individual decision makers but also help
implement a performance management initiative with the right scope and focus. Once
the KPIs are identified, the subsequent services methodology steps address the
requirements for data integration, data quality, data governance, and master data
management. The third major element of SAP's methodology revolves around ensuring
broad adoption of the BI and analytics solution by delivering user-friendly information
access. End-user needs are evaluated based on specific roles, and recommendations
of specific user interfaces for various user groups from the executives to line-of-
business employees are developed prior to technology implementation and deployment.
Although SAP is
known primarily for its
extensive software
portfolio provided by
the SAP
BusinessObjects
division, the company
also provides a range
of BI professional
services.
©2009 IDC #217286 11
OPPORTUNI TI ES AND CHALLENGES
The long-term trends suggest that the market is still in the early stages of a BI
solution adoption cycle that will extend the reach of various decision support and
decision automation solutions to a broad set of new users. These users will span all
levels of an organization and will be involved in a spectrum of strategic and
operational decision-making activities. Some of these activities will be based on
information access through reports, dashboards, or search boxes. Other BI activities
will include advanced analytic techniques for descriptive and predictive analytics.
Organizations investing in BI and performance management have many opportunities
to take advantage of the growing body of evidence suggesting a direct link between
these solutions and organizational competitiveness and performance. These
opportunities must make effective use of both IT products and services as well
business process reorganization and organizational behavior changes necessary to
shift toward more fact-based decision-making processes.
At the same time, organizations will increase their chances of BI project success and
overcome technical and organizational challenges by following methodologies, such
as those presented in this document. Whether an organization chooses to partner
with SAP or another solution provider, IDC, as always, encourages all organizations
to evaluate any IT vendor based on specific technology features and functionality,
services offerings, support structure, expertise within the selected technology or
business area, financial strength, and availability and quality of partners.
RELATED RESEARCH AND REFERENCES
1. Improving Organizational Decision-Making Through Pervasive Business
Intelligence: The Five Key Factors That Lead to Business Intelligence Diffusion,
IDC multiclient study, November 2008.
2. IDC and InfoWorld's Business Intelligence Survey, February 2008.
3. IDC and ComputerWorld's Business Intelligence Survey, July 2008.
4. IDC's quarterly AppStats Survey #7, October 2008.
5. Leveraging the Foundations of Wisdom: The Financial Impact of Business
Analytics, IDC, 2003.
6. Thomas H. Davenport and Jeanne G. Harris, Competing on Analytics, Harvard
Business School Press, 2006.
7. Thomas H. Davenport and Jeanne G. Harris, Competing on Analytics, Harvard
Business School Press, 2006.
8. Sinan Aral, Erik Brynjolfsson, Marshall Van Alstyne, "Productivity Effects of
Information Diffusion in Networks," The MIT Center for Digital Business, July
2007.
12 #217286 ©2009 IDC
9. The research results highlighted in this white paper were the outcome of IDC's
study Improving Organizational Decision-Making Through Pervasive Business
Intelligence: The Five Key Factors That Lead to Business Intelligence Diffusion.
The research (methodology and execution) was completed by IDC in
collaboration with researchers from Boston University School of Management
Systems Research Center and was underwritten by 11 competing business
analytics solution providers, including SAP.
C o p y r i g h t N o t i c e
External Publication of IDC Information and Data — Any IDC information that is to be
used in advertising, press releases, or promotional materials requires prior written
approval from the appropriate IDC Vice President or Country Manager. A draft of the
proposed document should accompany any such request. IDC reserves the right to
deny approval of external usage for any reason.
Copyright 2009 IDC. Reproduction without written permission is completely forbidden.
doc_426809620.pdf