The paper discusses Business Intelligence Organization Modeling as a concept along with practical implementation aspects with reference to Analytics and Business Intelligence Strategy in large enterprises. BI organization modeling revolves around the ability to model the patterns of BI prevalent within a corporate structure to assess organizational capability and maturity, and there by contributing towards BI strategy development and implementation. The paper also details Analytics & BI organization modeling in a predominantly SAP based enterprise ecosystem and is demonstrated with BI systems based on the SAP NetWeaver Business Warehouse (BW) using data discovery and machine learning techniques. The data discovery process for Analytics & BI organization modeling is carried out using SAP Lumira Data Visualization tool connected to an SAP NetWeaver BW based Global Enterprise Data Warehousing and Reporting System.

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Abstract — The paper discusses Business Intelligence
Organization Modeling as a concept along with practical
implementation aspects with reference to Analytics and Business
Intelligence Strategy in large enterprises. BI organization
modeling revolves around the ability to model the patterns of BI
prevalent within a corporate structure to assess organizational
capability and maturity, and there by contributing towards BI
strategy development and implementation. The paper also details
Analytics & BI organization modeling in a predominantly SAP
based enterprise ecosystem and is demonstrated with BI systems
based on the SAP NetWeaver Business Warehouse (BW) using
data discovery and machine learning techniques. The data
discovery process for Analytics & BI organization modeling is
carried out using SAP Lumira Data Visualization tool connected
to an SAP NetWeaver BW based Global Enterprise Data
Warehousing and Reporting System.
I. INTRODUCTION
HE recent times have witnessed the huge amount of
investments that large organizations worldwide have
undertaken in the domain of Analytics and Business
Intelligence. It can be observed that most of these investments
in business intelligence, in the initial days, and even till
yesterday, were related to implementation of data marts,
enterprise data warehouses (EDW) and automation of
management information and reporting systems. Despite
having spent substantial amount of financial, technical,
material and human resources, enterprises have only realized
on the nature of continued spend in this domain. The goalposts
have been constantly been changing, and the domain of data,
analytics and business intelligence is evolving each day
unleashing opportunities like never before. Subject areas such
as advanced analytics comprising of data mining and
predictive analytics, computational and machine learning, big
data, data science and visualization have opened up new
challenges and expectations, with some of them even
becoming independent areas of study and interest. This is
upheld even by the 2015 Gartner CIO Agenda Survey where
Business Intelligence and Analytics tops the list for the year
2015 as it did in 2014, only with an increased share of wallet
on the overall Information Technology (IT) spend and
budgets.
Vijay Raj manages Business Analytics and Technology with SAP at
Insights & Data, Capgemini. He has led and been a part of multiple Analytics,
BI & Digital transformation programs and strategy engagements for enterprise
IT ecosystems with substantial SAP footprint. A detail of his profile can be
accessed here with.
The year on year spend on IT, especially Business Intelligence
and Analytics, spreading across a span of years highlights the
question on strategy and direction of the overall portfolio and
the tangible returns yielded on investment.
Figure: 1 - Top technology investment priorities from the Gartner CIO
Agenda Survey 2015[1]
At the same time the entire domain of business intelligence
and analytics is at a point of inflection, where in technology
experts predict the far reaching consequences that Internet of
Things (IoT), Digitalization, Big Data, In-memory Computing
and drive for real-time reality awareness can have. Traditional
business intelligence and analytics which largely relied on data
marts, enterprise data warehouses, reporting tools, analytics
applications etc. will undergo a radical paradigm shift in terms
of its transformation to the modern context analytics centered
around volume, variety, velocity, visualization and value of
data to produce real-time, situational and on-demand insights.
The modern day BI is at its infancy with limitless possibilities
yet to be explored and opportunities yet to be tapped.
Enterprises have started to revisit their BI strategy, assess their
current BI capability and maturity levels, and validate the
current spend to be in the right direction and thereby determine
a BI roadmap to channelize future BI initiatives and funding to
help achieve operational, tactical and strategic vision and
mission. One of the stepping stones in defining a BI Strategy
depends on the organizations current state of affairs when it
comes to BI and Analytics and also its history. A clear-cut
assessment of the organization’s as-is state of BI is crucial for
the development of a road map for transforming the
organizations BI capability to meets desire to be state outlined
as a part of the strategy. The course of steps, programs and
Analytics & BI Organization Modeling for
Maturity Assessment & Strategy Development
Vijay Raj, Manager, Insights & Data, Capgemini
T
November, 2015

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projects required under the strategic BI portfolio for enterprise
wide BI transformation is determined by a realistic assessment
of the as-is state. The paper explores Analytics & BI
organization modeling as a scientific technique to carry out a
realistic assessment of as-is and historical state of BI in an
organization.
II. BI STRATEGY
A Business Intelligence Strategy is a quintessential element of
any organizations BI, Data and Analytics Strategic Portfolio.
The BI strategy is a framework that ensures that the data
available in an enterprise’s ecosystem is identified, captured,
transformed and churned into information to provide timely
and actionable insights required for fulfilling an organizations
business strategy in absolute alignment, from time to time,
meeting its vision and mission for existence.
The BI strategy at a given point of its revision describes the BI
goals for the organization, BI value proposition, the as-is or
current state of the BI, gaps within the current BI
organizational state, be it people, process or technology, and
the initiatives to be undertaken under the BI portfolio to attain
the goals defined in the first place. It is important to
periodically review the BI strategy and ensure that the BI
strategy remains hand in hand with business strategy to
empower, enable and support the latter. Any new initiative or
project undertaken in the BI space in the organization needs to
be in coherence with the overall framework outlined by the BI
strategy.
We will, from this juncture, focus on the BI strategy that an
organization with a predominant SAP business applications
base looks forward to. These types of enterprises have nearly
50-60 percentage of their core business processes mapped to
SAP based applications, and they form the primary source for
capturing data related to the underlying business process.
There will also be many other applications and technology
platforms in the overall enterprise architecture which works in
unison with the SAP based applications to support core
business processes. Hence organizations prefer to adopt the
SAP vision and roadmap to de-risk enterprise architecture
integration by adopting a risk transfer strategy or sharing the
risk with a strategic partner. This addresses problems arising
out of heterogeneous technology landscapes in terms of
integration, technology obsolescence, compatibility etc. Since
larger organizations tend to partner with SAP as a strategic
technology partner, it is a natural consequence that SAP based
products like, SAP NetWeaver BW, SAP Business Objects BI
solutions, SAP IQ based BI solutions, SAP HANA and
Business Planning & Consolidation applications gets
implemented as a part of the immediate BI projects.
We will now discuss the scope and elements of BI strategy.
The need for a BI strategy, and the elements constituting the
BI strategy needs to be well defined and communicated among
the key stakeholders in the organization. Executive and
leadership buy-in is detrimental for the conception, design,
development and implementation of the BI Strategy.
Information centric approach to the development of BI
strategy is essential for ensuring all data and information
demands in an organization is successfully envisioned and
fulfilled.
Figure: 2 – Information centric approach to Analytics & BI strategy
The scope of Analytics & BI Strategy
In a typical organization, the business intelligence portfolio
covers an umbrella of different, yet integrated specializations.
These can be broadly grouped into Enterprise Information
Management (EIM), Enterprise Data Warehousing (EDW),
Information Lifecycle Management (ILM), Enterprise
Business Intelligence (EBI) and Enterprise Performance
Management (EPM). Enterprise Information Management can
further comprise of sub areas like Master Data Management
(MDM), Master Data Governance (MDG), Extraction,
Transformation and Loading (ETL), Data Quality
Management (DQM) etc. Similarly, Enterprise Data
Warehousing deals with modeling transforming and storing to
enable enterprise wide consumption and consists of data and
information modeling, ETL integration, reporting integration
etc. Information Lifecycle Management deals with managing
data and information throughout its lifecycle, starting from
creation or generation of data, its storage and consumption, to
its archiving and destruction. Use cases like retention,
temporary archiving, near line storage, cold archiving etc.
covered under the spectrum of data archiving processes
(DAP). fits into this context. Enterprise Business Intelligence
revolves around how individuals in the organization interact
with data and information. It caters to use cases for reporting
and analysis, dash boarding and applications, data discovery
and visualization, predictive modeling and analysis and
enterprise self-service.

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Figure: 3 – Building blocks of a comprehensive Analytics & BI Strategy
Enterprise business intelligence sits on top of core business
process applications, enterprise data warehousing solutions,
planning and consolidation applications etc. and the tools
deployed as part of this use case provides data and information
to users. Enterprise Business Intelligence will play a crucial
role in successfully transforming an organization into an
analytic organization because of its importance for BI
adoption. Enterprise Business Intelligence, unlike other
foundation building blocks of enterprise information
management, data warehousing, or information life cycle
management is responsible for rendering the outcomes to the
business user, and has constant interaction from stakeholders
in the various functions of business. User experience is one of
the core elements that needs to be taken into account and
addressed in enterprise business intelligence use cases. When
the desired level of user experience is not met, the business
users according to their needs, nature and behavior, work
around and outside the deployed BI applications and systems
to extract, manipulate and churn data into information there by
disrupting BI adoption within the organization. Upon the event
of such occurrences, many a times, IT as a function reacts to
these situations by perceiving such problems to that of non-
adherence to standards and principles and puts in place
solutions in the form of governance. The root cause of the
issue here is largely the way IT perceives the problems and
requirements of the business when it comes to data and
information, and the level of user experience that the deployed
solutions were able to achieve, to meet the so called articulated
requirements. And when business discovers that the deployed
BI solutions do not meet their intended ask, frustration builds
among business users, and due to criticality of data and
information needs within their respective business functions,
the business ends up working around and outside the
Enterprise BI Deployments for getting data and information.
One such example that I have come across in many large BI
transformation programs that often cripples the business is
when IT adopts the principle of single version truth as
universal or even as an implicit business requirement. The
celebrated principle of single version of truth, articulated by
Bill Inmon [2], drawing its roots to the framework of
enterprise data warehousing, is a technical concept that all
enterprise data warehousing applications needs to adhere to.
To put this into context, it is a principle for enterprise data
warehousing applications, and thus can be recognized as a
principle for applications but never as a business principle.
From a business perspective, the single version of truth is too
ideal of a thought, as business truths are often contextual than
absolute. For e.g. revenue recognized by one division in an
organization can be revenue to it, but cost to another. For e.g.
sales department tend to recognize at a point in time when
sales is complete, whereas finance would do so once the
account receivables are processed. Similarly, in an
organization having a parts division and an equipment
division, it is quite likely that the equipment division sells
equipment to the end customer with free of cost (FOC) parts to
boost its sales. In this case parts division might recognize this
sale as revenue, and the equipment division or finance will
treat this as cost of sales. And revenue, as a metric in the
organization is looked upon different levels pertaining to
various contexts. There are so many other examples that
surface out during a requirements workshop as business finds
it challenging and difficult to agree up on the definitions for
single version of truth. And often these are driven by IT
functions and departments resulting from an arguable
misconception of a technical concept as universal.
Business requirements, in its state of affairs seem simpler.
Business expect IT systems to capture the reality associated
with business process, and anticipate BI systems to provide a
reflection of reality at desired levels, and also wants the BI
systems to be able to help them access the base data, visualize
it, create view points and perspectives of the reality. The
reflection of reality is used for managing a business process,
and will have measures that drive the key performance
indicators of the underlying business process. Viewpoints and
perspectives of reality have an element of human judgment and
are supported by tools capable of providing the desired levels
of user experience when it comes to ad-hoc analysis.
It is when the expectation of business users are not met by the
BI tools with due user experience that the business users tend
to develop alternate BI solutions which function as cottage BI
factories within departments as opposed to the mainstream BI
function.
Thus, it is very important that the Enterprise Business
Intelligence addresses these major, but subtle, aspects in a
strategy development exercise.
Over the top of the other BI streams, there lies Enterprise
Performance Management. Enterprise Performance
Management deals with managing the enterprise at a corporate
group level and supports various functions of strategy
formulation, balanced scorecards, business planning and
forecasting, financial consolidation and management, supply
chain effectiveness etc. The help optimize the value chain.
These are the subject areas for the umbrella of scope that
needs to be covered by a corporate BI strategy.

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Overpowering the data beast
Time has arrived for organization to re-visit the Analytics and
Business Intelligence Strategy before it is too late. Data in the
recent times have grown beyond imagination and this has led
to newer challenges and increased expectations. Business
functions are looking forward to leverage the newer
opportunities that the massive volumes of data have brought
about and IT departments have to cater the newer variety of
demands. But with data growing like never before, it is
essential for business that it doesn’t become too big of a beast
that it overpowers the organizations competitive advantage. To
tackle growing data is thus an essential objective in the current
juncture as far as enterprises are concerned and hence should
be drawn down to the Analytics & Business Intelligence
strategy. It may or may not be likely that the organizations
current Analytics and Business Intelligence Strategy can
withstand and cater to the data explosion witnessed by advent
of internet of things, digitalization and big data. One of the
prime reasons why the strategy is discussed in detail at this
point of time is to ensure that the organization takes the data
aspect into account, and methodically approaches the new
challenge and turn it into their advantage. Overpowering the
data beast is thus an important objective that should be called
out while outlining the analytics and business intelligence
strategy.
III. THE REFINERY MODEL - ANALYTICS & BI ARCHITECTURE
The refinery model for Analytics & BI Architecture is based
on the role played by data in various sub-systems constituting
the overall architecture. The flow of data and information is
treated as the central aspect in synthesis of the Analytics & BI
Architecture, and other aspects, including technology, process
and people perform roles to assist the flow required to achieve
business objectives. In contrast with traditional models,
interaction of all aspects is taken into account to ensure that
data and information demands in the organization are met. The
systems where data is created is the source with reference to
the BI Architecture. The systems holding the data is thus
called as source systems. Data can be created both manually
with human interaction and also by systems programmatically,
interfaces driven by devices capable of recording business
transactions. Source systems mostly ensure that validations are
in place for capturing the data at the right quality. This is a
largely a function of master data management responsible for
ensuring that every entity participating in a business
transaction is standardized, uniquely maintained, codified and
consumed. However, recent times have witnessed the
enormous growth of data, and one of its characteristics is the
variety. With variety, all forms of data can be anticipated to be
generated. Therefore, data can be assumed to be essentially
crude. For example, “I love Coke” and “I <3 Coke” both are
acceptable data. A traditional BI audience may disagree with
the previous statement and high light the problem back to data
quality; however the future state of BI will witness tools
capable of interpreting all varieties of data. Machine learning,
capable of intelligently interpreting the wide variety of data, in
the future will be a part of master data management and
enterprise information management tools. It is very similar to
how Google directs a user to the right search results despite a
variant entry in the search box. A typical Analytics & BI
Architecture is comprised of three elements depending on the
function of the component in the overall architecture.
Upstream BI comprises of the major functions of extraction,
transformation and loading of data. This involves refining the
crude data to a consumable format for the other components in
the Analytics & Business Intelligence Architecture. This space
is occupied by the various Enterprise Information Management
Tools. A seamless integration of these Enterprise Information
Management tools is expected with the Master Data
Management (MDM) function within the enterprise landscape.
These upstream components of the BI Architecture is mostly
hidden from the end user and major stakeholders but plays a
vital role in ensuring that the rules are in place for
transforming the data for further processing by the mid-stream
and downstream components of the Analytics & BI
Architecture. Products like the SAP Business Objects Data
Services, Data Quality Manager etc. gets positioned in the
Enterprise Information Management space. The footprint of
systems in the upstream space is often centrally owned by the
IT department.
The mid-stream BI is responsible for holding and storing the
data in a manner which is consumption friendly. Consumers of
analytics and BI solutions range from C-level executives to
line managers of departments. In the recent times it can be
observed that the consumers of analytics have even become
the operational workforce who leverages the power of real
time information provided by these systems to influence
business process and to improve operational effectiveness.
Democratization of data in enterprises is soon turning out to be
a business reality. For instance, a credit card operations
executive can alert a customer on a potential threat which a
predictive analytics solution can provide on the unusual patter
of usage of a customer’s credit card. The data and information
required by these business users are stored in enterprise data
warehouses, data marts, big data analytic platforms, in-
memory computing machines, multiple parallel processing
(MPP) databases etc. It is these systems that are responsible
for the mid-stream BI in an analytics & BI architecture. The
systems have their own databases holding large volumes of
information, and data often gets modeled based on the
concepts of multi-dimensional modeling (MDM), may it be the
star schema, extended star schema, snow flake schema etc. Off
late the big data and in-memory computing platforms have
created their own space in the mid-stream Analytics & BI
architecture. SAP, as a key partner for enterprise solutions for
organizations, have also positioned a range of its products
from SAP NetWeaver Business Warehouse, Sybase IQ, SAP
High Performance Analytic Appliance etc.

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An
Figure: 4 – Analytics & BI Reference Architecture – Refinery Model

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Though large enterprise data warehouses are often owned by
central IT, there can be small local data marts, for e.g. access
data bases, within the functions of business. They may or may
not be visible to IT in general. However, identification of these
small downstream components is of importance from an
Analytics & BI Architecture. The existence of the systems can
be of help in determining right decisions on the architecture,
especially when it comes to the agility and pervasive self-
service that the Analytics & BI strategy can offer. Agility and
self-service are often reasons for existence of these mid-stream
components with-in business departments.
Now finally there is the downstream segment of the
Analytics & BI Architecture. Downstream consists of the
various components responsible for delivering the information
to the business users. They can be web tools, reporting tools,
analysis tools, modeling and visualization tools, analytic and
mobile applications and even office tools that are capable of
delivering data and information to the business users. Today,
there exist a wide variety of tools existing in this space. SAP,
after its acquisition of Business Objects, KXEN and Sybase,
have also inherited a wide range of tools in the Analytics &
Business Intelligence downstream space in addition to the
classical Business Explorer tools. The customers partnering
with SAP perhaps started feeling at crossroads with the advent
of these new generation tools and SAP, arguably, to a large
extent has addressed this challenge with its BI Statement of
Direction [3] focusing around radical simplification and
convergence of the BI portfolio. The downstream BI is where
there is active involvement of business users interacting with
both data and information, and for that reason there is a social
and behavioral element driving its structure and organization.
Community of business users exchange data and information
among themselves, within and outside departments, and
therefore act as brokers of data and information within
organizations. Every organization, therefore, knowingly or
unknowingly maintains a BI Social Network. A successful
Analytics & BI Strategy also is responsible for engineering,
enabling and empowering an efficient and effective BI Social
Network designed to meet the organization objectives. With
the advent of internet of things (IoT) consumers of data and
information can also be networked devices in addition to the
social network of people.
IV. BI MATURITY & TRANSFORMATION MAP
The Analytics & BI Transformation Map bridges the as-is state
of Analytics and BI within an enterprise with the envisaged to-
be state by a series of projects forming the transformation
program aligned with the Analytics & BI strategic portfolio.
The transformation map at this juncture is important as it is
responsible for the smooth transition from traditional BI to
advanced data driven analytics, and enforces overall fitment to
emerging technology aspects like that of big data and internet
of things.
Figure: 5 – Sample Analytics & BI Transformation Map
The Analytics and BI transformation map is responsible for
achieving the BI strategy outlined by the organization. The
transformation map is built up by business cases for Analytics
and BI at portfolio, program and project levels taking into
account funding reconciliation with year over year IT budgets.
It is also assessed and updated throughout the transformation
phase, and can adopt updates to technology trends. BI
Maturity models from Gartner and SAP are cited below.
Figure: 6 – BI Maturity - ITScore Overview for Business Intelligence [4]
Figure: 7 – SAP’s BI Maturity Model [5]

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The organization as it moves along the transformation path, it
improves the Analytics & BI maturity of the organization.
Various maturity models exist in the industry which assesses
enterprise BI maturity, which aids in assessing maturity levels
and devising strategy.
V. ORGANIZATION MODELING - ANALYTICS & BI STRATEGY
Organization modeling for Analytics & BI deals with
understanding the behavior and pattern of data and information
consumption by users and user groups within business.
Consumption by business users can range from a mere
download of data from a system or a file to access of complex
reports, application and dashboards deployed and published by
a central IT organization. There has always been an attempt by
central IT departments of large organizations to achieve a
balance and tradeoff between IT deployed Analytics and BI,
and enterprise self-service for data and information. A large
enterprise witnesses use of data and information by its various
constituents and organization units, viz. company codes,
business areas, functions, departments, sectors, divisions,
profit centers, cost centers etc. All these different entities have
different information needs, and often information accessed
needs to be valuated in terms of the value they bring across.
Different techniques can be used to identify the value that the
information or the report provides to the organization. Scoring
methods, information value assignments etc. are helpful in
quantifying and ranking reports used by business as part of
their management information framework, and are derived
during a transformation consulting exercise.
VI. CASE STUDY
The case study illustrates Analytics and Business Intelligence
Organization Modeling carried out as a part of the BI maturity
assessment during an Analytics Transformation Consulting, BI
Strategy and Roadmap development exercise. A large global
fortune 500 enterprise which has a predominant SAP based
business application footprint in its enterprise architecture is
the candidate for the case study. The organization uses SAP
NetWeaver Business Warehouse based global enterprise data
warehouse for the purpose of management information
reporting.
The system has been in place for several years and caters to
management information reporting. There are a variety of
reports that are deployed on the system which are accessed by
users across various department and functions within the
business. Reports are also prepared manually by business users
by extraction of data from the source systems and there also
exist multiple spread marts across the business functions.
Majority of these off-system spread marts are due to historical
reasons due to limitation imposed by centrally deployed
management information systems and some of them are ideal
candidates for a potential enterprise self-service use case.
The SAP NetWeaver BW system in the form of its technical
content deployed in the system holds enormous amount of
information which can provide insights into information
consumption patterns and behavior demonstrated by business
users which it comes to analytics and BI.
Figure: 8 – Representative Analytics & BI Architecture for Case Study
In order to understand and visualize the information hidden in
the usage statistics held by the SAP NetWeaver BW 7.x
enterprise data warehouse, SAP Lumira 1.28 is used as a data
discovery tool. The SAP Lumira data discovery tool is
connected to the SAP BW statistics information models.
Organizational modeling of information access data held by
the SAP BW statistics cube is linked with the SAP Employee
Master data and its organizational attributes during the data
preparation phase in the data visualization tool.
Figure: 9 – Data Visualization Architecture for Case Study

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The following are some examples of the models created during
the maturity assessment phase.
a) Information Consumption - Global View
The model visualizes analytics & information consumption
patterns across the globe for a large enterprise. When
combined with information & report scoring data sheet created
as a part of the assessment exercise, the extent of
standardization and localization can be clearly obtained and
even benchmarked.
Figure: 10 – Information Consumption - Global View
b) Global View - Consumer vs. Power User
The model visualizes the actual analytics & information
consumption pyramid within the enterprise. A clear
distribution of information consumers and power users within
the business is obtained. It is also possible to deduce
Advanced information consumers or analysts by modeling
further patterns of actual usage, say the threshold number of
navigations etc.
Figure: 11 – Global View - Consumer vs. Power User
c) Global Information Consumption by Cost Center Groups
The model visualizes analytics & information consumption by
cost center groups. This can also be drilled down to cost center
level, and aggregated at respective profit center levels.
Indicative values can be assigned to the information & report
scoring data sheet, thereby quantifying and ranking
information value. This is extremely useful as an input for
deriving funding models for IT, both in centralized and de-
centralized IT organizations.
Figure: 12 – Global Information Consumption by Cost Center Groups
d) Information Consumption by Function & Company
The model visualizes analytics & information consumption
across functions within various companies belonging to group
structure as a heat map. It in an indication of companies and
functions championing analytics and BI, and also provides
input for assessing the extent of actual BI saturation vs.
planned. Low ranked functions and companies, in terms of %
BI pervasiveness, may be potential candidates with a BI
demand pipeline.
Figure: 13 – Information Consumption by Function & Company
e) BI Capability Across the Globe
The model visualizes analytics & BI capability across various
regions of the enterprise. It provides an assessment of presence
of business users who champion BI within the regions. This is
important for planning BI capability development initiatives,
and also for optimizing spend depending on both skill and
availability across regions.
Figure: 14 – BI Capability across the Globe

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f) Information Consumption by Management Level
The model visualizes analytics & information consumption
summary by management level. Drill downs of consumption
patterns by level can be helpful in Stakeholder Assessment
acting as a key input for large Analytics & BI transformation
programs.
Figure: 15 – Information Consumption by Management Level
g) Business User & Report Footprint
The model visualizes the actual footprint of BI Reports &
Users by core functions within the enterprise as a tree map. A
transformation consulting exercise will also provide current
business maturity levels for BI and will also devise the to-be
footprint along with the desired levels of maturity.
Figure: 16 – Business User & Report Footprint
h) Information Consumption by Job – Tag Cloud
The model visualizes the job roles in the organizations who
contribute and champion BI. It can also be used as an input for
skill development, and identification of potential roles who can
contribute towards the organization’s BI transformation.
Figure: 17 – Information Consumption by Job – Tag Cloud
i) The BI Social Network
The model visualizes the BI social network, which is crucial in
analyzing how people are related and how interactions occur
among business users with reference to business information.
Figure: 18 – The BI Social Network
VII. CONCLUSION
The paper BI Organizational Modeling for Maturity
Assessment and Strategy Development demonstrate how data
and information consumption patterns within in an
organization can be leveraged as an input to perform BI
maturity assessments, and there by assist roadmap
development for Analytics and BI transformation programs.
Practical aspects signifies BI Organization modeling as an
effective scientific technique for organizational BI
benchmarking in the present day context where BI as a
strategic portfolio is witnessing ground breaking evolutions
with reference to big data, internet of things etc.

10. 10
VIII. REFERENCES
[1] Gartner Executive Programs: Flipping to Digital
Leadership - Insights from the 2015 Gartner CIO Agenda
Report:
http://www.gartner.com/imagesrv/cio/pdf/cio_agenda_insights
2015.pdf
[2] The Single Version Of The Truth: http://www.b-eye-
network.com/view/282: The article respects and accepts the
concept of single version of truth, but merely re-emphasizes
it’s boundary conditions for practical purposes.
[3] SAP BI Statement of Direction - https://www.sapbi.com/bi-
statement-of-direction/
[4] ITScore Overview for BI and Analytics -
https://www.gartner.com/doc/3136418/itscore-overview-bi-
analytics
[5] SAP’s BI Strategy White Paper -
https://www.sapbi.com/wp-
content/themes/sapbi/library/images/bistrategy/BI%20Strategy
.pdf
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