Better Decision Making with
Proper Business Intelligence
Quality information is key to making quick, rational business decisions
A
s companies focus on growth and business development, the
availability of quality information is crucial to making quick,
rational business decisions. New technologies, however, can
cause information overload, leaving decision makers buried under a
mass of irrelevant, inadequate, and inconsistent data. Some companies
manage to provide their decision makers with precise, relevant data
in an easy-to-grasp format. These companies have discovered the value
of effective business intelligence capabilities.
For many companies, new technologies are causing
information overload, leaving decision makers
overwhelmed with inadequate, incorrect, inconsistent and misleading information. Indeed, the
various acts of retrieving and processing this
often useless information can tie up numerous
resources. Yet there are companies that manage
to provide their decision makers with processed
and automatically consolidated raw data, presented
in an understandable, easy-to-grasp format. These
companies provide insightful information for
quick, profound decision making. What do these
companies have that the others don’t? Business
intelligence (BI) capabilities and processes.
Business intelligence is a research field that
focuses on theoretical and practical aspects of
achieving a solid information basis for decision
making. This paper summarizes A.T. Kearney’s
experience in helping companies shape their data
processes to obtain the right information for
rational and quick decision making.
What Is Business Intelligence?
Business intelligence focuses on the particular field
of data processing and consolidation to retrieve
information for decision making. The overarching
objective is to provide—via various solutions—
the right knowledge to the right people at the
right time. Doing this requires the right mix of
IT systems, architectures, data structures, datacollection processes, and responsibilities for providing meaningful information. Business intelligence
has a proven impact on key performance indicators (KPIs). For example:
• 60 percent of executive managers state that the
use of a performance management tool has a
positive impact on shareholder value
• Return on equity (ROE) is more than twice
as high in companies that widely use performance management tools compared to those
that do not in the same industry
When assessing BI capabilities, there are four
levels to consider:
Reporting. Reporting is a core functionality
of BI tools as the objective is to create recurring,
standard, reports in an efficient and user-friendly
manner. Reports are predefined and static by
nature, generated either by request of an end-user
or refreshed periodically through an automatic
scheduler (uploaded on Intranet servers or shared
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drives and accessible to a predefined group of corporate users). Key functionality is reduced to data
consolidation and aggregation from various
sources in a repetitive approach (automated, ideally) from trusted data sources.
Dashboards. Dashboards contain high-level,
aggregated strategic company data, inclusive comparable presentations, and consolidated performance indicators. They include both static and
interactive reports with data translated into graphics, charts, gauges and illustrations to simplify the
communication of complex topics. Dashboards
allow basic interactions (such as drill down, sliceand-dice operations to “play with the data”) and
provide various levels of detail to achieve deeper
insights. However, the explanatory power of dashboards relies mostly on users’ interpretations.
Analysis. At the analysis level, BI systems
provide not only consolidated information that
users can detail and filter, but also forecasts and
trend analyses to develop new insights (based on
the raw data).
Analytics. At the top level of a BI system is
automated intelligent data analyses based on
sophisticated “fuzzy” logic and “neuro-fuzzy” systems. Based on user-friendly but powerful functions, the BI system can retrieve meaningful
insights while hiding the underlying complexity
of data interpretation. “What-if ” scenarios and
simulation functionality provide advanced, tailored decision-making support.
The fundamental basis of every BI system,
however, is the data base on which it operates. Basic
systems focus on corporate financial data only,
while advanced systems interconnect internal and
external sources with qualitative and quantitative
data. Advanced systems process the comprehensive
data set using methods perfectly tailored to a firm’s
needs and condense the findings into meaningful
knowledge—hiding the complexity and selecting
2
only the maximum amount of input necessary to
help executives make the right judgments.
Figure 1
Three phases of a business intelligence solution
The Advantages of Business Intelligence
When analyzing a business intelligence solution,
it is important to consider the business benefits,
including improved overall decision making and
increased efficiency for business reporting and
analysis. To this first point, BI offers four important prerequisites for proper decision making:
• Required information is available
• Data is consistent across organizational units
• Information can be easily analyzed using builtin analysis functionality
• Reports are presented in a user-friendly format
A well-designed business intelligence solution
ensures that information across the organization is
available in a consistent, reliable manner. Figures
can be aggregated and compared in different
business units, assuring the validity of like-forlike data comparisons, and that all management
reports provide operations leaders and top management with the information they need to steer
the business properly.
Essentially, BI improves efficiency on both the
information technology (IT) side and the business
side of the organization. On the IT side, workers
are freed from the recurring task of creating and
changing data reports as end-users are able to create
and change their own reports. On the business
side, less time is spent in data analysis and preparation as management reports are created directly
from the BI dashboards. Not only is the data in
these reports more up-to-date and credible, but
also they are easier to read and handle. And, importantly, the information can be downloaded on
smart devices, including the iPhone and iPad. The
sidebar on page 4, What Makes a BI Leader?, offers
a short list of success factors shared by top business
intelligence organizations.
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Phase 1
Phase 2
Phase 3
Define key performance
indicators and level of business
intelligence to be achieved
Collect requirements, build
prototype and conduct
vendor “proof of concept”
Implement solution
and improve tool
according to release plan
Source: A.T. Kearney analysis
Figure 2
The main tasks in Phase 1
Perform qualitative
evaluation
• Evaluate potential key
performance indicators
(KPIs)
• Analyze empiricial findings
related to use of KPIs
Determine value
correlations
• Evaluate simple
profitability metrics
• Compare analysis of
value-add KPIs
• Assess select KPIs for
appropriateness
• Identify potential risks of
making the wrong decision
Make
recommendations
Source: A.T. Kearney analysis
Three Phases of Implementation
A successful corporate BI implementation has
three phases (see figure 1). In implementing BI systems, it is important to begin with business
requirements, because projects that are technically
triggered usually fail.
Phase 1: Define the necessary KPIs on a
management dashboard. A key activity in the
first phase is to define future reports and KPIs,
which often means eliminating some existing
reports, as many are unnecessary and do not truly
reflect the company’s objectives (see figure 2).1
Next, is assessing data availability to calculate
defined KPIs; a sound data basis is a key success
factor for system implementation. External tools
1
Analyze
risks
connecting to existing data warehouses and additional calculations should be kept to a minimum.
Also, the various layers of a BI system are evaluated to define which areas should be addressed
and identify specific parts that should be eliminated (see figure 3 on page 4).
It is important to clarify at the beginning
which areas shall be covered by the project: Is it
a BI system for finance KPIs only or does it also
include supply chain management? Will the project integrate other functional areas, departments
or even specific business lines within operations?
Often human resources and customer relationship
management (CRM) are the next candidates to
improve finance BI and integrate more of the
Phase 1 should always be completed fully before proceeding with the second phase, as the required reporting and the defined KPIs set the framework for tool selection.
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Strategy / objectives management
Collaboration
Life-cycle Mgt.
Localization
Metadata-integration, repositories
Time series
ECM
Guided decisions
OLAP
Usage analytics
QA
BAM / CEP
eLearning
NLP
Predictive analytics
Statistical analysis
Web analytics
Adapters / tool kits
BPM / BRE integration
DQ – cleansing, profiling
MDM
Guided search
Operational DSS
Accelerators / query optimization
Discovery and
integration
Version control
Discovery accelerators
EAI / SOA
EII
ETL / CDC
Integration – third party applications
Operational data stores (ODS), data warehouses (DW), data marts (DM)
Report mining
Services registry and repository
Columnar DBMS
Data
Hierarchical / XML
Multi-dimensional OLAP
Multi-value RDBMS
Streaming DBMS
Infrastructure
In-memory DBMS
RDBMS
Search DBMS
Servers
Network
Storage
Org
Form
factor
PSO
Strategy
Methodology
Scorecards
Governance
Reporting: ad-hoc, analytical, production
Planning
SI
Geospatial
Metrics / KPIs
MSP / apps outsourcing
Search
Advanced data visualization
Center of Excellence
Alerts
Appliance
Interactive voice response, ATM, point-of-sale
Dashboards
Data / text mining
Analytics
Disconnected
BPO
Support
applications
Portals
Mobile
BISaaS
Performance
management
Office suites
Hosted BI (ASP)b-layers)
Reporting
Desktop gadgets
Enterprise apps: ERP, CRM, SCM, ERM
Delivery
Industry vertical applications
Apps
Figure 3
Layers of a business intelligence system
Sources: Forrester, A.T. Kearney analysis
4
• Establish a balanced scorecard
• Define clear data dimensions and
structures
• Develop a master data management plan
• Establish corporate data governance and clear ownership of
master data
• Use report visualization in pilots
• Leverage web technologies
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Phase 2 is not only about collecting requirements regarding the functionality of a future tool
(navigation and analysis deep dives, for example)
but also about report design, dashboards and tool
functionality. One point must be stated very
clearly: A pure management “cockpit” or dashboard cannot replace the internal or external
reporting. As a first step, it can be seen as a second
channel (always available), but with a different
level of detail (management adequate). All management cockpit tools offer a reporting functionality used to print-out the dashboard content.
Replacing the complete paper-based internal (or
external) reporting requires significant efforts as
the detailed design of every single page is outlined
up-front. Both the dashboard screen design and
the report layouts, which cover all of the depicted
content regarding historic numbers and comparisons, among other things, finally get defined
before the detailed concept is handed over to a
system integrator for implementation. The realization of the screens and the corresponding KPI
visualization are the main drivers of the system
implementation and the testing. Therefore stability is required.
Figure 4
The tasks in phase 2
What Makes a BI Leader?
From our experience helping companies implement their business
intelligence projects, we have found
several common characteristics or
“success factors” that differentiate
the BI leaders from the followers.
The BI leaders:
• Define and optimize corporate
report standards
• Focus on the business not the data
business. The link to operations is more difficult
as KPIs from manufacturing, production or logistics are fairly different from finance KPIs and are
often difficult to connect to existing KPI trees.
Even between similar business lines, KPIs are
sometimes slightly different because they represent different business models — for instance,
internal production for intermediate products or
external production for final products. If the longterm strategy is to analyze operational KPIs, this
should be addressed at the outset. Once these
questions are answered and all affected business
areas are addressed, the second phase can begin.
Phase 2: Create a design and navigation
prototype and build a “proof of concept.” Four
tasks are involved in preparation for system implementation. The result is a “proof of concept,”
designed before the comprehensive implementation begins. At this point, software vendor selection is independent of specific design requirements
and often driven by strategic policies or IT landscape requirements. Based on IT architecture
rules, a short list can be devised up-front to select
appropriate tools to fit the company’s requirements, IT strategy and IT landscape (see figure 4).
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• Focus on cross-organizational
efforts
• Distinguish between businessdriven and IT-driven projects
• Define the long-term BI scope,
from the beginning
• Realize a mobile version of
business intelligence
A.T. Kearney
Assessment
• Perform assessment
to ensure the need
for a “cockpit”
• Conduct a brief costbenefit analysis of the
area and other potentially
affected areas such as
procurement
Vendor
selection
• Draw up a short list
of vendors, taking into
account industry reports
and other external
information
• Assess the options from
an internal viewpoint
Requirements
and prototypes
• Complete a list of
business requirements
and build a first simple
PowerPoint prototype
to develop a “look-andfeel” of the final product
Proof of concept
• Begin company-vendor
discussions
• Help the vendor understand
the client and ensure vendor
receives timely feedback so
to make quick improvements
Source: A.T. Kearney analysis
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The proof of concept increases the developers’
understanding of the company’s requirements
before starting the real systems implementation.
In our experience, a quick prototype allows a
company to check the design of all possible preselected tools and test the capability of the potential solution provider. Work packages can also be
tested to assess vendors’ innovation capabilities in
solving design questions. Finally, the firm’s
requirements can be tested and challenged.
Phase 3: Implementation. Implementation is
a typical IT project. However, due to high visibility and management awareness, the implementation should be fast and provide quick solutions.
A release plan ensures first results are delivered
quickly, while the final and comprehensive solution is created in several steps, aligned with reporting cycles and data availability. The first release
should provide all required dashboard functions
(design, navigation and drill-down) and some of
the reporting requirements, which are detailed in
the follow-on implementation phases and can be
delivered sequentially. Typically, first releases lack
the full data set. An essential activity in the BI
project is to cleanse existing data and establish
a process to record new data in a proper way,
which often requires developing the entire data
architecture, including dimensions, hierarchies
Business Intelligence: A Case Study
The management team at a large
German company decided to implement a corporate-wide business
intelligence (BI) process. The company, involved in heterogeneous
businesses, started with pilot units
in its three business concerns, defining financial key performance indicators (KPIs) to manage and direct
the corporation.
The management team knew
that success would depend on corporate-wide definitions for its KPIs
so they could be cascaded down to
all business units.
The project began with development of a basic management dashboard that provided a visual KPI tree
to clarify the logical dependencies of
the two main KPIs —and then illustrating all the subsequent KPIs that
flowed from them —and how they
navigated through the organization.
Next, business-specific operational
KPIs were added to the dashboard,
a simulation tool was developed
to provide units with a businessplanning model to illustrate the
dependencies between KPIs, and
a BI tool-based reporting process
was designed and implemented
The IT work began during the
final definition of corporate-wide
KPIs and reporting, providing information about the availability of existing data within the data warehouse.
This was followed by the vendor
and tool-selection process. A prototype was designed, and software providers were asked to use the design
and business units’ functional
requirements to develop a rough
proof of concept.
Immediately after the proof-ofconcept presentations, the vendorselection process was finalized and
implementation began. Within three
months, the company had its first
release, covering main functionalities, dashboard design and navigation, and first-reporting factors. The
release plan took into account the
main reporting cycles, and two subsequent releases were scheduled
within the planned timeframe.
Soon after introduction, the
management team was delighted as
user acceptance proved stronger than
expected throughout the organization. When asked about the success
factors, team members were quick to
cite the top two: design and ease of
navigation within the BI tool, and
on-time delivery of the major KPIs.
All in all, the company is now
dedicated to using business intelligence to help steer it on a richer
and more successful course.
and formulas. Close interaction between system
provider and business departments (as the final
users and clients of the system) is crucial to the
success of the project. The management cockpit
or dashboard fulfills the design and functional
requirements because it provides clear visibility
to management and the necessary ease of use.
Although all required data or KPIs may not be
included in the beginning, they are provided
throughout the steps.
Governance and the CIO’s Role
Within a BI project, the corporate IT and the
CIO moderate between the different business
departments that are involved, the internal IT
group, and the software provider or implementer.
A BI project is often driven by the finance departFigure 5
Detailed analysis of BI software vendors
Category
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On-board tools
Vendor 1
Vendor 2
Implementation
design
• Only static design possible;
performance critical
• Through TOOL 1 WYSIWYG
the dashboard can be handled
• Possible according to the
demonstration; no information
given on the interface builder;
use of all graphic types possible
Implementation
comments
• Use of standard BDS
associated with
performance issues
• Vendor 1offers functional
building blocks for the
storing of comments in the
SAP BW
• Possible according to the
demonstration
• Comments can only be used in
the machine-three environment
Implementation
simulation
• Generally not simulation
functionality
• Simulations can be saved;
retraction through vendor 1’s
own functionalities into BW
backend
• Possible according to the
demonstration; simulations can
be saved and retracted into the
SAP BW
Transition from
prototype to
release I to II
• Due to low performance on
comments and the illustration
of graphs, no usage possible
in phases 1 to 2
• Look-and-feel remains the
same
• Comments and simulation
can be executed early
• Look-and-feel remains the same
• Comments and simulation can be
executed early
Connectivity to
data
• Executed on machine
one, no export of the cube
data needed
• Either DataMart on machine
two or WebService on
machine one
• According to vendor, it can
communicate with SAP BW 3.5
but not demonstrated
Hardware costs
• None
• BOE Infrastructure for
LiveOffice and reporting tool
(phase2)
• Basic infrastructure on machine
one has to be built anew
Source: A.T. Kearney analysis
6
ment as the key user due to corporate reporting
and corporate management requirements; finance
defines the main KPIs on a corporate level and
ensures standardization across all different business models within the firm.
Operational KPIs are provided by the different operating units and standardized to ensure
that consistent content is reported to top management. The CIO and corporate IT take over the
role of supporting KPI definitions by providing
information about data sources, data availability
and data quality. And they start the analysis on
how a business intelligence tool could be used to
fulfill high-level requirements (see sidebar: Business
Intelligence: A Case Study).
Part of the project involves defining future
BI governance. This often means establishing a BI
Full function
Very limited function
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center of excellence led by the finance department
that includes data governance with BI system
maintenance authority. Corporate IT should be
part of the change advisory board that assesses and
evaluates change requests based on the capabilities
of the system solution, while IT maintains technology authority.
Choosing Tools and an Implementation
Partner
BI tool selection is rarely a “green field” approach.
The corporate IT strategy, system landscape and
the main requirements regarding the BI level to
be achieved are all guidelines in the selection process. Market research and analysts’ estimations
can be used as support documents, but in most
cases these analyses are too general and cannot
be adapted to meet company specifics. Instead,
external reports are typically used afterwards as
support information to justify the short-list vendors or tools.
The software vendor and implementer are the
same in most cases, as only a few companies are
able to provide a full business intelligence suite,
connected or even integrated somehow to the
main enterprise resource planning (ERP) systems,
such as SAP or Oracle.
A company-specific questionnaire and evaluation template allows the efficient gathering of key
requirements. The main questions or key evaluation points should be shared in advance with the
short-listed software vendors to give them an
opportunity to prepare the tool presentation and
to answer all questions completely. The clearer the
requirements are documented and described, the
faster the selection process can be executed (see
figure 5 on page 7).
A rough prototype — such as a PowerPoint
visual of the management cockpit to demonstrate
key navigation functions and design-related
requirements will help both sides understand the
desired outcome and manage expectations. It is
the basis for the later detailed design and concept,
containing the description of each requirement.
This and the more technical description of the
existing landscape and necessary interfaces are
the most relevant documents for the implementation and build up to the kick-off for the systemimplementation phase.
BI: A Must for Business Success
A proper BI solution is a must have in today’s
world. Companies in all industries are using BI
systems for successful decision making. These
companies beat their competition and identify
new opportunities to optimize their businesses.
They also reduce resources for manual effort and
rededicate people to analyzing data and preparing
decision memos. For companies that grasp the
true potential in business intelligence, they should
take action sooner rather than later — time, as
always, is of the essence.
A.T. Kearney is a global management consulting firm that uses strategic
insight, tailored solutions and a collaborative working style to help clients
achieve sustainable results. Since 1926, we have been trusted advisors on
CEO-agenda issues to the world’s leading corporations across all major
industries. A.T. Kearney’s offices are located in major business centers
in 37 countries.
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Authors
Alexander Martin is a principal in the strategic information technology practice. Based in the Düsseldorf office,
he can be reached at alexander.martin@atkearney.com.
Robert Jekel is a consultant based in the Zurich office. He can be reached at robert.jekel@atkearney.com.
Edgar Simons is a consultant in the Düsseldorf office. He can be reached at edgar.simons@atkearney.com.
© 2011, A.T. Kearney, Inc. All rights reserved.
A.T. Kearney Korea LLC is a separate and independent legal entity operating under the A.T. Kearney name in Korea.
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