AGILE BI – THE FUTURE OF BI

Abstract

In a rapidly changing economy, Business Intelligence solutions have to become more agile. This paper attempts to discuss some questions which help in creating an agile BI solution such as: What is Agile? Why agile is so well suited for BI? Which are the key elements that promote an agile BI solution? Also, this paper briefly looks at technologies that can be used for enabling an agile BI solution

Full text

114 Informatica Economică vol. 17, no. 3/2013
DOI: 10.12948/issn14531305/17.3.2013.10
Agile BI – The Future of BI
Mihaela MUNTEAN, Traian SURCEL
Department of Economic Informatics and Cybernetics
Academy of Economic Studies, Bucharest, Romania
munteanm@ie.ase.ro, tsurcel@ase.ro
In a rapidly changing economy, Business Intelligence solutions have to become more agile.
This paper attempts to discuss some questions which help in creating an agile BI solution
such as: What is Agile? Why agile is so well suited for BI? Which are the key elements that
promote an agile BI solution? Also, this paper briefly looks at technologies that can be used
for enabling an agile BI solution.
Keywords: Agile Business Intelligence, Agile Business Analytics, Agile Development
Methodologies, In-Memory Bi Approaches, Data Virtualization Server
Introduction
Business Intelligence (BI) was defined in
different ways. The Data-Warehousing
Institute has defined Business Intelligence as
“the tools, technologies and processes
required to turn data into information and
information into knowledge and plans that
optimize business actions” [6]. Turban has
defined BI as “a broad category of
applications and techniques for gathering,
storing, analyzing and providing access to
data to help enterprise user make better
business and strategic decisions.” [23]. The
range of capabilities that can be defined as
business intelligence is very broad. The
spectrum of BI technologies is presented in
[16].
Most enterprises have hundreds of internal
and external data sources such as: databases,
e-mail archives, file systems, spreadsheets,
digital images, audio files and more. 80% of
the organizational data are unstructured and
semi-structured data.
Traditional Business Intelligence systems use
a small fraction of all the data available.
Also, traditional BI systems use only
structured data. The core components of a
traditional BI architecture are: ETL tools, an
enterprise data warehouse with metadata
repository and business analytics (Figure 1).
Fig. 1. A traditional BI architecture
Traditional BI systems use ETL tools for
extracting data from multiple sources and
temporarily storing those datasets at a staging
area. Organizations use data warehouses to
aggregate cleaned and structured data.
Business analytics/BI tools include enterprise
reporting tools, ad hoc query tools, statistical
analysis tools, OLAP tools, spatial-OLAP
analysis tools, dashboards, scorecards and
advanced analytics. Advanced analytics
1
Legacy
systems
ERP/CRM
Flat files
Metadata
repository
Datawarehouse
ETL
Business
analytics
/BI tools
Other OLTP
systems

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typically refer to: data mining tools, text
mining tools, predictive analytics, artificial
intelligence, and natural language processing.
But this architecture is unable to get adapted
to change. A study by Aberdeen Group [24]
showed that “this style of BI is predominantly
controlled, driven and delivered by corporate
IT. Often, only static views of data are
available and any changes or enhancements
must be made by the IT organization”. These
characteristics are in contradiction with
frequently changing business requirements
and “big data”. Big data typically refers to
the following types of data: semi-structured
data (XML and similar standards),
unstructured data, Web data (social data,
Web logs) and real-time data (event data,
spatial data, machine-generated data).
Table 1 briefly summarizes the main
disadvantages of traditional BI systems.
Table 1. The main disadvantages of traditional BI systems
Disadvantages
Problems
huge amount of
duplicate data
- every change already made requires an extra change of duplicate data
- data inconsistencies
- data quality risks
use different tools
for different tasks
- non-shared metadata specifications
- inconsistent results
rigid relational or
multidimensional
data models
- limited flexibility to changing
- limited support for analysis on unstructured and external data
waterfall
approach
- the long development lifecycle and less visibility to user
- users are not involved in the development cycles
- inflexible to analytical requirements modifications
- testing at the end of the development cycle
How to eliminate these problems? By
building an agile BI solution. A study by
TDWI Research [22] showed that many
traditional business intelligence systems are
not agile:
 “33% of the organizations needed more
than three months to add a new data
source to an existing business intelligence
system”
 “developing a complex report or
dashboard with about 20 dimensions, 12
measures and 6 user access rules took on
average 7 weeks in 2011”
The next section presents briefly the concept
of agile BI and the key elements that together
promote an agile BI solution.
2 Agile BI
Agile means the ability to be adaptable.
Agile BI was defined in different ways. The
Forrester Research defines agile BI as ”an
approach that combines processes,
methodologies, organizational structure,
tools and technologies that enable strategic,
tactical and operational decision –makers to
be more flexible and more responsive to the
fast pace of changes to business and
regulatory requirements” [9]. According to
Data Warehousing Institute agile BI
“addresses a broad need to enable flexibility
by accelerating the time it takes to deliver
value with BI projects. It can include
technology deployment options such as self-
service BI, cloud-based BI, and data
discovery dashboards that allow users to
begin working with data more rapidly and
adjust to changing needs.”
[tdwi.org/portals/agile-bi.aspx].
In conclusion, an agile BI solution should
provide access to accurate information in the
right format to the right person at the right
time. Below it identifies the key components
that together promote an agile BI solution
(Figure 2):
1. Agile development;
2. Agile business analytics;
3. Agile information infrastructure.

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Fig. 2. The key components that promote an agile BI solution
2.1 Agile Development
An agile BI solution must be implemented
quickly. According to Forrester Research the
purpose of agile BI solution: “is to 1) get the
development done faster and 2) react more
quickly to changing business requirements”
[8].
Two distinct approaches are relevant in the
context of development of BI solutions:
waterfall development and agile
development. A waterfall approach is
illustrated in Figure 3 (adapted from [7],
[20]).
Fig. 3. Waterfall approach to the development of BI solution
But waterfall approach is poorly suited for
BI. The main problems of this approach are:
 the long times between the system request
and the delivery of the BI solution;
 users are not involved in the analysis
phase, design phase, development phase
and testing phase;
 it is inflexible to analytical requirements
changes;
 testing at the end of the development life
cycle.
So a different approach is needed to make BI
applications more flexible and able to react
faster to changing business requirements.
The way to achieve agility in BI development
is the usage of agile development
methodologies. Agile development
methodologies refer to a group of software
development methodologies based on the
following characteristics: collaboration
between cross functional teams, iterative
development and tolerance for changes [1],
[3], [4]. There are different agile
agile BI
agile
developm
ent
agile
information
infrastructure
agile BA
No user interaction
requirements
User input
analysis
design
development
release
User review
testing

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first
release
second
release third
release
fourth
release final
release
agile BI
solution
Sprint 1
Build
something
Sprint 2
Add more
functionality
Sprint 3
Add more
functionality
Sprint n
Keep
evolving
development methodologies such as: Scrum,
Extreme Programming, Crystal, Dynamic
Systems Development, Lean and others.
Many of agile software development
characteristics can be applied to BI projects
from team structure, project management, BI
system design, BI system development and
analytical techniques [5]. The most popular
agile development methodologies for BI
projects are: Scrum, Extreme Scoping and
Agile Data Warehousing.
The main concepts of Scrum [21] are: user
story, product backlog, sprint backlog, sprint
and daily scrum. In this methodology the BI
requirements are divided into small “user
stories”. An agile BI project consists of a
collection of “user stories”. Each story is
then designed, developed, tested and released
to the users. One sprint is a full life cycle of
understanding the BI requirements, analysis,
design, development and user testing. Each
sprint lasts for 1-2 weeks. Users are involved
in sprint steps. User stories need to be
categorized in one of two ways: product
backlog and sprint backlog. Sprint backlog is
a list of tasks the team expects to do during
the sprint. At the end of each sprint, the
business has a deliverable such as a new
report or dashboard. Product backlog is a list
of all requirements ordered by highest
priority of what is needed. The user is
responsible for ranking the features on the
product backlog. Another concept is the daily
scrum that is a short meeting in which every
member of the team answers three questions:
What did you do yesterday? What will you
do until our next meeting? Do you have any
problems?
Extreme Scoping and Agile Data
warehousing are well suited if BI solution
includes a data warehouse. Agile Data
warehousing is defined as “the application of
two agile development approaches –Scrum
and Extreme programming – to the specific
challenges of data warehousing and BI” [11].
Extreme scoping is an agile enterprise data
warehousing approach that includes the
business integration activities. Also, this
approach uses agile principles. The BI
solution is separated into multiple releases
for iterative development [17].
Figure 4 shows a typical agile BI cycle.
Fig. 4. A typical agile BI cycle
2.2 Agile Business Analytics (BA)
Besides developing a business intelligence
system with agile design methodologies, it’s
also recommended adopting agile BA [2].
Both the design methodology and the tools
have to be agile. Agile BA must enable BI
users to become less dependent on IT. Also,
agile BA must be easier to be used by all
types of users. So agile BA should provide at
least office suite integration, a business
glossary and advanced visual features such as
interactive dashboards and drill-down
capabilities. A recent study by Forrester
showed that agile BA should be integrated
Product backlog
Sprint backlog

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with all parts of the “information workplace”
such as: spreadsheet, presentation, word
processing software, email, search portals,
collaboration platforms and social
communities [10]. There are several
technologies to support agile BA such as:
SaaS (Software-as-a-Solution) BI and in-
memory BI technology. SaaS BI includes:
 packaged software-as-service BI
applications that can be deployed in a
cloud environment;
 SaaS BI tools that can be used to develop
BI applications for deployment in a cloud
computing;
 on-premises environment and data
warehousing in the cloud.
The primary goal of the in-memory BI
technology is to eliminate traditional disk-
based BI solutions which are relational or
OLAP-based. In-memory BI technology can
save significant development time by
eliminating the need to store pre-calculated
data in OLAP cubes or aggregate relational
tables.
The common characteristics of in-memory BI
approaches are: easy to use, visual interface,
dashboards, self-service, in memory
processing, speed of response, low costs, and
quickly to deploy.
We see that in-memory BI technology has
the potential to help BI systems to become
more agile. Table 3 presents a SWOT
analysis for implementation of in-memory BI
solutions.
Table 3. A SWOT analysis for implementation of in-memory BI solutions
S (STRENGTHS)
W (Weaknesses)
 in-memory processing
 faster speed of response, rapid access to reports, analysis and
business metrics
 improving self service through analytic flexibility
 allows companies to integrate data from transactional systems,
external data sources, spreadsheets or data warehouses
 quickly to deploy
 eliminates the need to store pre-calculated data in OLAP cubes or
aggregate relational tables
 visual interface, dashboards
 ease of use for end users
 low costs
 a limited metadata
management
 limited by physical memory
 data quality
 limited ETL
 sometimes requires
multidimensional data
modeling
O (Opportunities)
T (Threats)
 allows for real time business intelligence without a DW
 eliminates the need for a pre-built OLAP cube or data mart
 cloud computing
 not a real-time analysis
because data is analyzed in
memory, not in the data store.
There are different in-memory BI approaches
such as: IBM Cognos TM1, MicroStrategy,
Microsoft PowerPivot and QlikView. For
example, QlikView originally called
“QuikView” as in “Quality, Understanding,
Interaction, Knowledge” uses AQL
(associative query logic) technology.
QlikView holds all data in memory with
every association between data points
defined [19]. Table 4 presents few in-
memory BI approaches with their
characteristics.
Table 4. In-memory BI approaches
Approach
Characteristics
Examples
In-memory OLAP

- MOLAP cube loaded entirely in memory
- accessible by MDX tools
- requires multidimensional data modeling
- limited by physical memory
IBM Cognos-
Applix(TM1)
Actuate BIRT
In-memory ROLAP
-ROLAP metadata loaded in memory
-requires multidimensional data modeling
MicroStrategy

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 -not limited by physical memory
A columnar database
-load and store data in a columnar database
 -less modeling required than an OLAP based solution
-limited by physical memory
Tableau Software
In memory spreadsheet
-spreadsheet loaded into memory
 -no modeling required
-access by third-party tools.
Microsoft PowerPivot
In memory “associative”
data model
-loads and store all data in an “associative” data model
(array) that runs in memory.
-all joins and calculations are made in real time.
 -less modeling required than an OLAP based solution
 -limited by physical memory
 -some scripting required to load the data
QlikView
2.3 Agile Information Infrastructure
True agility is reached by making all parts of
a BI system agile. An agile BI solution can
be seen as consisting of two layers: an agile
information infrastructure layer and an agile
analytic layer (figure 5).
Information infrastructure addresses how the
data architecture and data integration
infrastructure ensure agility to react to
changing business requirements. An agile
information infrastructure must be able to
extract and combine data from any data
sources, internal and external sources
including relational, semi-structured XML,
multidimensional and “Big Data”. How to
get an agile information infrastructure? By
using data virtualization.
Data virtualization is “the process of offering
data consumers a data access interface that
hides the technical aspects of data stores,
such as location, storage structure, API,
access language, and storage technology”
[12]. According to [13], [14] data
virtualization “is the technology that offers
data consumers a unified, abstracted, and
encapsulated view for querying and
manipulating data stored in a heterogeneous
set of data stores. Data virtualization means
on-demand data transformation, on-demand
data integration, and on-demand data
cleansing”.
Data virtualization can be implemented in
many ways such as: using a data
virtualization server or placing data sources
in the cloud.
Traditional BI systems use ETL tools for
extracting data from multiple sources and
temporarily storing those datasets at a staging
area. As opposed to ETL tools, data
virtualization server:
 allows the source data to remain in their
original locations;
 eliminates staging of the data;
 abstracts source data, resolving structural
and semantic issues;
 generates business views and/or data
services that provide data required.
A business view is conceptually equivalent
to a relational view. The views can read data
from multiple data sources including:
relational databases, multidimensional
databases, text files, XML documents,
spreadsheets, HTML pages, NoSQL
databases, and so on. Applications access
source data through the business views/data
services using different interfaces such as:
JDBC with SQL, ODBC with SQL,
SOAP/XML and MDX.
Data virtualization server offers:
 data modeling capabilities;
 data profiling capabilities;
 data transformation capabilities;
 on-demand data integration capabilities
which result in more agile BI systems.
[15], [18].

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Fig. 5. A high level architecture of an agile BI system
Many data virtualization servers are currently
available such as: Composite Information
Server, Denodo Platform, IBM InfoSphere
Federation Server, Informatica Data
Services, and so on.
A high-level architecture of an agile BI
system is illustrated in Figure 5. This
architecture is based on data virtualization
server. Data virtualization server examines
the data source structure and the resulting
metadata is stored in metadata repository.
Then you can create business views or data
services using the data source’s metadata
In conclusion, an agile BI solution requires:
an agile development methodology, agile BA
and an agile information infrastructure
(Figure 6). Also, Figure 6 briefly summarize
strengths of an agile BI solution.
Data
warehouse
Data virtualization server
data services
business
view
ETL
Other OLTP
systems
Multidimensional
databases
Agile business analytics
Metadata repository
NoSQL
Legacy
systems
ERP
Operational
data store
Agile analytical layer
Agile information infrastructure
mapping
mapping
mapping
business
view
business
view
mappping

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Fig. 6. The key components that promote an agile BI solution
The Table 5 presents a comparative analysis
between traditional BI solution and agile BI
solution using the following criteria: business
requirements, integration approach, data
timeline, data refresh, information delivery,
data source format, development
methodology, development cycle and BA.
Table 5. Traditional BI versus agile BI
Criteria
Traditional BI
agile BI
Business requirements
- the customer knows what he needs
- well defined
- not change significantly
- the customer discovers during the project
what he needs
- change frequently
Integration approach
- ETL tools
- moves/copies data from data sources
to stage area
- replicated data
- data virtualization
- data remains stored at the source and a
conceptual view is materialized on
demand
Data timeline
historical data
on real-time data
Data refresh
end on day /end of last load
on real-time/near real time
Information delivery
takes too long to deliver
faster
Data source format
- structured data (relational databases),
- limited semi-structured data,
- Excel files,
- multidimensional databases
- structured data,
- semi-structured data
- unstructured data, Big data
Development
methodology
waterfall methodology
agile development methodologies
Development cycle
- too slow
- too inflexible for BI
- during the project nothing changes
- faster
- there is a lot of changes during the
project
Type of Business
analytics
traditional BA
agile BA
an agile BI solution
agile development
methodology
fast implementation
flexible to change
iterative
development
users involvement
easy to manage
accelerated BI solution
delivery
BI solution evolves in small
and manageable chunks
agile BA
easy to use
interactiv and
visual
graduated
capabilities
supporting basic to
advanced BI
customizable and
reusable BI
components
integrated with
"Information
workplace"
agile
information infrastructure
easy and universal access across
internal, external, Web, semi-
structured data, unstructured data, big
data
agile integration
improvement of data quality
flexible to change
optimized for heterogeneous sources
and targets

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Also Figure 7 shows a traditional BI
workflow versus an agile BI workflow. In a
traditional BI workflow the business analyst
gathers user analytics requirements. He
decides how to interpret requirements and
then delivers them to the data warehouse
architect. The data warehouse architect
defines cubes, facts, dimensions and
granularity of facts/dimensions. Then the
data warehouse modeler decides how the
dimensions and facts should be integrated
into data warehouse. Also, he develops data
models for staging area, warehouse database
and cubes. The ETL developer develops ETL
code to load data. Then, the BI developer
develops cubes and dashboards. The Data
quality analyst verifies the quality of
dashboards. Then dashboards are published
to server and the customer uses these
dashboards.
The BI consultant advises customers in the
fields of information management and in
selecting the most suitable BI solution. He is
the first point of contact for the customer. He
has experience with modern business
intelligence techniques, data modeling, ETL
tools, software development cycles, and so
on. In short a BI consultant is responsible for
the requirements gathering, the design and
the development of BI solutions.
Fig. 7. A traditional BI workflow versus an agile BI workflow
4 Conclusion
In conclusion, the main reasons for
implementing agile BI are:
 constantly changing business
requirements;
 inability of IT to meet business user
demands;
 slow access to information.
Agile BI solutions enable organizations to
adapt to changing market conditions. This
paper has identified the key elements that
together promote an agile BI solution. There
are plenty of technologies that can make an
agile BI. This paper briefly looked at
technologies that can be used for enabling an
agile BI solution such as: in-memory
Business
analyst
Data
warehouse
architect
Data
warehouse
modeler
ETL
developer BI
developer
Data
quality
analyst
user/custo
mer
BI consultant
BI
analyst
Data
architect
Data
modeler ETL
developer
BI
developer
Data
quality
analyst
Feedback/
iterate
User/customer

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technology and data virtualization.
Deploying of both technologies in a BI
system results in an agile BI architecture.
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Mihaela MUNTEAN is associate professor in Economic Informatics
Department, Faculty of Economic Cybernetics, Statistics and Informatics,
Academy of Economic Studies of Bucharest. She received her doctoral
degree in Economics in 2003. Since 1997 she is teaching in Academy of
Economic Studies, in Economic Informatics Department. She is interested in
Databases, Information Technology &Communication, OLAP technology,
Business Intelligence Systems and Economic Information Systems Design.
She published over 50 articles in journals, over 30 scientific papers presented at national and
international conferences, symposiums and workshops and she was member over nine
research projects. She is the author of two books and she is coauthor of seven books.
Traian SURCEL is Professor at Academy of Economic Studies Bucharest,
Faculty of Economic Cybernetics, Statistics and Informatics, Department of
Informatics in Economy, PhD in Economic Cybernetics from 1987. He
coordinates the Fundamentals of IT&C for Business Management professors
group and also PC Laboratories for Faculty of, Marketing, Commerce and
International Business and Economics. He is Internal Auditor for the ASE
Bucharest. His main research areas are: ERP and Information System and
Database analyze and design, IT Systems Audit, e-Learning applied methodology, IT&C for
Communications and Business Management.