Description
Business Intelligence (BI) is today seen as a basis for intelligent business and enterprise systems development and design.
The Evolution of Business Intelligence:
From Historical Data Mining to Mobile and Location-based Intelligence
ZELJKO PANIAN
The Graduate School for Economics and Business
University of Zagreb
J.F. Kennedy Sq. 6
CROATIA
[email protected]http://www.efzg.hr/zpanian
Abstract: - Business Intelligence (BI) is today seen as a basis for intelligent business and enterprise systems
development and design. But, from the time of its birth in late eighties until nowadays business intelligence has passed
a long way. The primary interest of scientists in this field was knowledge discovery. Although the first efforts of
scientists were originally oriented towards general knowledge discovery principles and methods soon it became
obvious that application of these principles and methods in business environment is perhaps the most promising
development perspective. The strong impetus for further research came from Howard Dresner who proposed ‘Business
Intelligence’ (BI) as an umbrella term to describe concepts and methods to improve business decision making by using
fact- and knowledge-based support systems. From that time Business Intelligence rapidly evolved through several
stages depending on technology used. Improvements in subsequent phases do not derogate those of previous ones but
rather complement them, so that BI becomes more and more complex and sophisticated.
Key-Words: - Business intelligence, data mining, OLAP, balanced scorecard, Web mining, Web analytics, dashboards,
mobile business intelligence, location-based business intelligence, big data.
1 Introduction
Seeing, understanding and acting in real time is what
defines the ‘Intelligent Enterprise’. And enterprise
agility – the ability to change business and adapt quickly
to changing conditions – often may be the difference
between organizational success and failure [1].
In the past, enterprise agility has been exceedingly
difficult to achieve because viewing all the critical data
streaming through the systems, applications, and
processes that make up an enterprise’s transaction and
information data flow, could not be done in cost
effective manner [2].
But, things are changing dramatically. Now business
information that can be understood in its business
context is flowing between applications – and even
between our organizations and those of our business
partners, customers, and suppliers.
In these circumstances, Business Intelligence (BI) is
playing a critical role and must also be available in real
time [3].
Business Intelligence rapidly evolved through several
stages depending on technology used. Improvements in
subsequent phases do not derogate those of previous
ones but rather complement them, so that BI becomes
more and more complex.
2 The Evolutionary Path of BI
At the very beginning, historical data mining methods
and tools were used for strategic managerial reporting
purposes.
The second evolutionary stage is characterized by
On-Line Analytic Processing (OLAP) technologies and
dimensional analysis of data stored in data warehouses
and data marts.
In the third stage Balanced Scorecard methodology is
used as a means of Business Intelligence creation.
With the emergence and growing popularity of E-
Business and other Internet applications and services the
new stage of BI appeared since Web analytics and Web
mining as a form of BI began to attract the wide
professional attention.
The fifth development stage started when usage of
Business Dashboard technology became a core
component of alerting and alarming systems in business
decision-making supported by BI.
Finally, nowadays we are witnessing the era of
mobile and location-based Business Intelligence founded
on appropriate mobile and location-aware technologies.
As far as it can be seen from today’s perspective, the
further development in the near future can be expected in
the field on unstructured content and so-called big data
analysis as a form of sophisticated Business Intelligence.
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3 Stage I: Data Mining
3.1 The Three Parents of Data Mining
Data mining as a methodology has three parents:
statistics, computer science, and database/data
warehouse management. In the early 1980s, statistics
contributed methods such as recursive partitioning and
non-parametric regression, and tools such as the
bootstrap and cross-validation.
At approximately the same time, computer science
developed neural network models and new algorithms
for rapid execution of traditional statistical analyses on
large data sets, such as clustering and smoothing; they
also coined the phrase ‘data mining’.
And database management researchers developed
sequential query procedures and relational data bases, as
well as the concept of data warehouse. The confluence
of these ideas led to the expansion of inferential science
to larger and more complex data sets.
3.2 Business Data Mining
Data mining has been very effective in focused areas,
such as medical diagnosis, scientific research, and
behavioral profiling since the mid-1980s. But, data
mining technology has also journeyed into the business
world where it has added the new dimension of
predictive analysis.
Data mining is a powerful technology that converts
detail data into intelligence that businesses can use to
predict future trends and behaviors [4]. Some vendors
define data mining as a tool or as the application of an
algorithm to data.
The truth is data mining is not just a tool or
algorithm. Data mining is a process of discovering and
interpreting previously unknown patterns in data to
solve business problems. Data mining is an iterative
process, which means that each cycle further refines the
result set. This can be a complex process, but there are
tools and approaches available today to help business
user navigate successfully through the steps of data
mining projects.
From an IT perspective, the data mining process
requires support for the following activities:
Exploring the data
Creating the analytic set data
Building and testing the model
Integrating the results obtained into business
applications.
Therefore, the IT organization must provide an
environment capable of addressing the following
challenges:
Exploring and pre-processing large data
volumes
Providing sufficient processing power to
efficiently analyze many variables (columns)
and records (rows) in a timely manner
Integrating data mining results into the business
process
Creating an extensible and manageable data
mining environment
For years, businesses have relied on reports and ad
hoc query tools to glean useful information from data.
However, as data volumes continue to increase, finding
valuable information becomes a daunting task. Data
mining technology was designed to sift through detailed
historical data to identify hidden patterns that are not
obvious to humans or query tools. Many of these
previously hidden patterns reveal intelligence that can be
integrated into business processes to provide predictive
capabilities for improving strategic business decision
making.
To be effective in the business world, the data mining
process had to be adapted to deliver models in a time-
sensitive manner. Today, with the advent of in-database
data mining techniques, businesses have finally found it
possible and affordable to benefit from the advanced
capabilities of this powerful technology.
Data mining makes analytical business applications
smarter by providing insights into many new areas of the
business that would otherwise go unnoticed. By making
business applications smarter, data mining translates into
a higher return on business investment.
3.3 The Way Data Mining Is Deployed
An organization cannot simply buy a data mining
product, apply it to data and expect to generate a
meaningful model [5]. Data mining models are built as
part of a data mining process – an ongoing process
requiring maintenance throughout the life of the model.
The data mining process is not linear, but an iterative
process where you loop back to the previous phase. For
example, the initial model you create may lead to insight
requiring you to return back to the data pre-processing
phase to create new analytical variables. The data mining
process contains four high-level steps [6]:
Define the business problem
Explore and pre-process the data
Develop the data model, and
Deploy knowledge.
Although each of these steps is important, most of
time will be spent in the data exploration and pre-
processing phase. A well structured data warehouse can
significantly reduce the pain felt in this phase.
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4 Stage II: On-Line Analytical
Processing (OLAP)
4.1 OLAP Basics
OLAP means many different things to different
people, but the definitions usually involve the terms
‘cubes’, ‘multidimensional’, ‘slicing & dicing’ and
‘speedy-response’ [7]. OLAP is all of these things and
more, but it is also a misused and misunderstood term, in
part because it covers such a broad range of subjects.
OLAP is an acronym, standing for ‘On-Line
Analytical Processing’. This, in itself, does not provide a
very accurate description of OLAP, but it does
distinguish it from OLTP or ‘On-Line Transactional
Processing’.
It is easy to question the need for OLAP. If an end
user requires high-level information about their
company, then that information can always be derived
from the underlying transactional data, hence we can
achieve every requirement with an OLTP application.
Were this true, OLAP would not have become the
important topic that it is today. OLAP exists and
continues to expand in usage because there are
limitations with the OLTP approach. The limits of OLTP
applications are seen in three areas.
OLAP applications differ from OLTP applications in
the way that they store data, the way that they analyze
data and the way that they present data to the end-user. It
is these fundamental differences that allow OLAP
applications to answer more sophisticated business
questions.
OLAP applications present the end user with
information rather than just data. They make it easy for
users to identify patterns or trends in the data very
quickly, without the need for them to search through
mountains of ‘raw’ data. Typically this analysis is driven
by the need to answer business questions such as ‘How
are our sales doing this month in South-Eastern Europe?’
or ‘From which supplier, X, Y or Z, we have ordered the
largest quantities of goods needed?’[8].
From these foundations, OLAP applications move
into areas such as forecasting and data mining, allowing
users to answer questions such as ‘What are our
predicted labor costs for next year?’ and ‘Show me our
most successful salesman’.
4.2 Multidimensionality
Although different OLAP tools use different underlying
technologies, they all attempt to present data using the
same high-level concept of the multidimensional cube.
Cubes are easy to understand, but there are fundamental
differences between cubes and databases that can make
them appear more complicated than they really are.
The cube is the conceptual design for the data store at
the center of all OLAP applications. Although the
underlying data might be stored using a number of
different methods, the cube is the logical design by
which the data is referenced.
The axes of the cube contain the identifiers from the
field columns in the database table. Each axis in a cube
is referred to as a ‘dimension’. The basic logical
construct is a simple two-dimensional cube. Although
useful, this cube is only slightly more sophisticated
than a standard database table. The capabilities of a
cube become more apparent when we extend the
design into more dimensions. Multidimensionality is
perhaps the most ‘feared’ element of cube design as it is
sometimes difficult to envisage.
Although the word ‘cube’ refers to a three-
dimensional object, there is no reason why an OLAP
cube should be restricted to three dimensions. Many
OLAP applications use cube designs containing up to ten
dimensions, but attempting to visualize a
multidimensional cube can be very difficult. The first
step is to understand why creating a cube with more than
three dimensions is possible and what advantage it
brings.
4.3 The Key Differences between OLAP and
Data Mining
OLAP is a Business Intelligence tool that allows a
business person to analyze and understand particular
business drivers in ‘factual terms’. Typically, a specific
‘descriptive’ or factual question is formulated and either
validated or refuted through ad hoc queries. OLAP
results are also factual results.
Data mining, on the other hand, is a form of
discovery-driven analysis where statistical and machine-
learning techniques are used to make predictions or
estimates about outcomes or traits before knowing their
true values. With data mining, predictions are
accompanied by specific estimates of the sources and
number of errors that are likely to be made. Estimates of
errors translate directly to estimates of risk.
Consequently, with data mining, making business
decisions in the presence of uncertainty can be done with
detailed and reliable information about associated risks.
Data mining techniques are used to find meaningful,
often complex, and previously unknown or hidden
patterns in data.
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5 Stage III: Balanced Scorecards
Robert S. Kaplan and David Norton, co-creators of the
Balanced Scorecard Method, wrote their first book in
1996 [9].They looked at the Balanced Scorecard as a
Performance Management system that could be used in
any size organization to align vision and mission with
customer requirements and day-to-day work, manage
and evaluate business strategy, monitor operation
efficiency improvements, build organizational capacity,
and communicate progress to all employees. The
scorecard allows an organization to measure financial
and customer results, operations, and organizational
capacity.
The Balanced Scorecard (BSC) has migrated over
time to become a full Performance Management system
applicable to both private sector and public (and not-for-
profit) organizations. And the emphasis has shifted from
just the measurement of financial and non-financial
performance, to the management (and execution) of
business strategy. In this sense, BSC became a new style
of Business Intelligence.
BSC systems can be the heart of a corporate
performance system. They provide the ability to view
three different dimensions of an organization’s
performance: Results (financial and customer),
Operations, and Capacity.
The components of a fully developed scorecard
system are:
Business Foundations, including vision,
mission, and values;
Plans, including communications,
implementation, automation, and evaluation
plans, to build employee buy-in and
communicate results;
Business Strategies and Strategic Maps, to chart
the course and define the logical decomposition
of strategies into activities that people work on
each day;
Performance Measures, to track actual
performance against expectations;
New Initiatives, to test strategic assumptions;
Budgets, including the resources needed for new
initiatives and current operations;
Business and Support Unit Scorecards, to
translate the corporate vision into actionable
activities for departments and offices; and
Leadership and Individual Development, to
ensure that employee knowledge, skills and
abilities are enhanced to meet future job
requirements and competition.
In BSC language, vision, mission, and strategy at the
corporate level are decomposed into different views, or
perspectives, as seen through the eyes of business
owners, customers and other stakeholders, managers and
process owners, and employees. The owners of the
business are represented by the Financial perspective;
customers and stakeholders (customers are a subset of
the larger universe of stakeholders) are represented by
the Customer perspective; managers and process owners
by the Internal Business Processes perspective; and
employees and infrastructure (Capacity) by the Learning
and Growth perspective.
But, building and implementing a scorecard system is
one thing; turning the scorecard into a used and useful
BI system is something else entirely.
The key to transforming a scorecard into a BI system
is to start at the right level of granularity and ‘connect
the dots’ among the components of strategy (mission,
vision, values, pains, enablers, strategic results and
themes, and strategic objectives) and the components of
operations (projects, processes, activities, and tasks),
and the budget formulation and cost reporting processes
[10]. Performance measures tie the parts together, and
give an organization a way to measure how successful
they are at achieving their goals.
6 Stage IV: Web Mining and Web
Analytics
With the explosive growth of information sources
available on the World Wide Web, as well as various E-
Business activities, it has become increasingly necessary
for users to utilize automated tools in finding the desired
information resources, and to track and analyze their
usage patterns. These factors give rise to the necessity of
creating server-side and client-side intelligent systems
that can effectively mine for knowledge. That is the
reason why a plenty of Web Mining and Web Analytics
tools are developed.
6.1 Web Mining
Web mining can be broadly defined as the discovery and
analysis of useful information from the World Wide
Web [11]. This describes the automatic search of
information resources available on-line, i.e. Web content
mining, and the discovery of user access patterns from
Web servers, i.e., Web usage mining.
6.1.1 Web Content Mining
The lack of structure that permeates the information
sources on the World Wide Web makes automated
discovery of Web-based information difficult.
Traditional search engines such as Lycos, Alta Vista,
WebCrawler, and others provide some comfort to users,
but do not generally provide structural information nor
categorize, filter, or interpret documents.
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In recent years these factors have prompted
researchers to develop more intelligent tools for
information retrieval, such as intelligent Web agents,
and to extend data mining techniques to provide a higher
level of organization for semi-structured data available
on the Web. We summarize some of these efforts below:
1. Agent-based Approach – Generally, agent-based
Web mining systems can be placed into the
following three categories:
a. intelligent search agents
b. information filtering/categorization
c. personalized Web agents
2. Database Approach – Focused on techniques
for organizing the semi-structured data on the
Web into more structured collections of
resources, and using standard database querying
mechanisms and data mining techniques to
analyze it:
a. multilevel databases
b. Web query systems
6.1.2 Web Usage Mining
Web usage mining is the automatic discovery of user
access patterns from Web servers. Organizations collect
large volumes of data in their daily operations, generated
automatically by Web servers and collected in server
access logs. Other sources of user information include
referrer logs which contain information about the
referring pages for each page reference, and user
registration or survey data gathered via CGI scripts.
Most existing Web analysis tools provide
mechanisms for reporting user activity in the servers and
various forms of data filtering. But, in recent times more
sophisticated systems and techniques for discovery and
analysis of patterns are also emerging. These tools can
be placed into two main categories, as listed below:
1. Pattern Discovery Tools – The emerging tools for
user pattern discovery use sophisticated
techniques from AI, data mining, psychology, and
information theory, to mine for knowledge from
collected data.
2. Pattern Analysis Tools – Once access patterns
have been discovered, analysts need the
appropriate tools and techniques to understand,
visualize, and interpret these patterns.
One of the open issues in data mining, in general, and
Web mining, in particular, is the creation of intelligent
tools that can assist in the interpretation of mined
knowledge. Clearly, these tools need to have specific
knowledge about the particular problem domain to do
any more than filtering based on statistical attributes of
the discovered rules or patterns.
In Web mining, for example, intelligent agents could
be developed that based on discovered access patterns,
the topology of the Web locality, and certain heuristics
derived from user behavior models, could give
recommendations about changing the physical link
structure of a particular site.
6.2 Web Analytics
Not very long ago, who was visiting Web sites and why
was essentially a mystery. Web masters put counters on
Web pages to track how many times people ‘hit’ the
page –that is, visited, downloaded a file, or some other
activity – but that was the extent of the insight. True
Web analytics capabilities were limited to large
corporations that could afford to spend thousands of
dollars per month on software to track and report on web
activity [12].
Today, there is a wide range of Web metrics
measuring and tracking applications available, making
analytics one of the most talked about topics both online
and off. Although some of these tools are still expensive,
a number of analytics programs available now are
completely free – and just as effective.
Simply put, Web analytics involves measuring,
collecting, analyzing, and reporting Web site traffic and
behavior with the end goal of optimizing the success of
the Web site.
All Web analytics tools work by collecting raw data
about Web site visitors and organizing it in a way that is
easier to view and understand. Some programs, called
log analyzers, use server logs (data files collected by
web servers) to provide information about visitors. Then
there are other programs – analytics applications –
which use bits of code installed on a Web site to gather
information about web activity and generate reports.
Generally, log analyzers are considered more
technical and the raw information they provide may be
hard to understand, especially for people who are
unfamiliar with Web metrics. In this case, it is probably
a better idea to stick with an analytics program.
No matter which Web analytics tool is used, their
users are going to be presented with a robust array of
metrics. From page views and unique visitors, to
referrers and average time on site, there are endless
amounts of data to sift through. But, focusing on the
following key metrics will tell users almost everything
they need to know [13]:
Visitors – The number of visitors to Web site will
give a general idea of how well the Web site
owner is getting the word out about his business.
Page Views – Looking at page views can tell what
content on the site is the most popular.
Referring Sites – Looking at referring sites will
give an excellent snapshot of the type of people
who are visiting the particular site.
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Bounce rate and Exit pages – A bounce rate
measures something different than an exit page,
but both can give important insights into why
people are leaving the site. In most analytics
programs, a “bounce” is recorded when a person
visits and leaves within a second or two, usually
before the page is even done loading. Top exit
pages show which pages people visit immediately
before they leave.
Keywords and Phrases – Keywords and phrases
let the Web site owner know what terms people
are using to find his site in search engines. This
can give him/her some idea of how to add
different content to appeal to even more
customers.
Though still a relatively new invention, Web
analytics is becoming an increasingly popular – and
effective – Web site optimization tactic used by online
business owners. By providing deep insight into the
who, what, when, why and how of web site traffic and
visitor behavior, Web analytics tools can help you
improve the usability of the site and boost its
effectiveness.
7 Stage V: Business Dashboards
Business Dashboards are becoming the new face of
business intelligence (BI) at the very beginning of the
21
st
century. While on the surface, Executive
Information Systems (EIS) from the 1980s had a similar
look and served a similar purpose, modern Dashboards
are interactive, easier to set up and update to changing
business needs, and much more flexible to use. This,
plus their ability to present data and information at both
a summary and detailed level, makes them one of the
most powerful tools in the business user’s kit.
To be useful, however, a Business Dashboard must
be implemented around the needs of the business [14].
Its functions should not be dictated by technology or by
the whims of the end users. Also, a Business Dashboard
should be implemented so that it gets used – and so that
the decision-makers employing it can act on the
information the Dashboard presents.
7.1 Business Dashboards vs. Spreadsheets
Along with modern Dashboards evolving from the old
EIS tools, another BI tool has been with us for a while:
the spreadsheet. Most often in the form of Microsoft
Excel, the spreadsheet has an intuitive interface and is
easy to learn, at least as far as its most basic functions. It
provides detailed numbers, which users can analyze
adding their own calculations.
However, while the spreadsheet is easy to use and
understand, it is often too detailed to give a quick and
comprehensive overview of business data. Furthermore,
users are likely to reformat this business data in other
spreadsheets, adding calculations and aggregations. This
will create yet more cells of important business data.
Although it is possible to create complementary charts
in most spreadsheets, this is a time consuming, manual
activity that lends itself to easily-made mistakes.
Nonetheless, many business users stick with
spreadsheets because they feel comfortable with them
and are reluctant to change to another model. The reality
is that not everything can be done efficiently in the
spreadsheet; and one should not get stuck with them
simply because that is what they have or have been
using. This can lead to a situation where it’s the
limitations of the program – rather than business needs –
that determine the scope of reporting and analysis.
With the right underlying technology, today’s
Business Dashboards stand out from the spreadsheet,
which nevertheless remains the most used BI interface
today. Dashboards allow for a quick and easy-to-
personalize overview of critical business data in a timely
fashion. This added value turns today’s Business
Dashboards into the new face of BI.
7.2 Business Dashboards vs. Scorecards
In many cases, the terms Dashboard and Scorecard are
used almost interchangeably. But, although Dashboards
and Scorecards have much in common, there are the
differences between the two [15]. On the one hand,
executives, managers, and staff use Scorecards, and
particularly Balanced Scorecards, to monitor strategic
alignment and success with strategic objectives and
targets.
On the other hand, Business Dashboards are used at
operational and tactical levels. Managers, supervisors,
and operators use operational Dashboards to monitor
detailed operational performance on a weekly, daily, or
even hourly basis. In the same vein, managers and staff
use tactical Dashboards to monitor tactical initiatives.
7.3 Benefits of Business Dashboards
Deployment
Business Dashboards help organizations reach stated
goals by leveraging information and analytics. They
provide alignment, visibility, and collaboration across
the organization by allowing business users to define,
monitor, and analyze business performance via key
performance indicators (KPIs). Whether organizations
choose to implement strategic or tactical performance
management initiatives, dashboards can provide the
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foundation for enabling organizations to more
effectively align their business strategy with execution.
In defining, tracking, and analyzing performance
indicators, Business Dashboards can provide users with
the following capabilities:
Root-cause analysis – They provide the ability to
drill down on a Key Performance Indicators
(KPIs) to a more detailed report revealing the
underlying business activity driving the higher-
level indicator output. This permits analysis of
causative factors and enables corrective action.
Time-series analysis – Dashboards provide the
ability to track and analyze key metrics over time
and to identify trends and exceptions.
Rules, alerts and alarms – They provide the
ability to track and monitor a plenty of business
processes and receive real-time notifications when
they are out of alignment. Once a notification has
been received, business users can examine the
irregularity, perform proactive root-cause
analysis, and take corrective action.
Predictive analysis – Dashboards provide the
ability to forecast, model, and analyze complex
relationships. Predictive analysis is necessary to
better understand the future impact of decisions
and the key influencers of future business
behaviors (e.g., churn and repeat purchase).
Segment analysis – Business Dashboards provide
the ability to define, manage, and understand the
behavior of business groupings such as strategic
customer segments, departments, and regions.
Segmentation can be used in defining metrics and
in providing root-cause analysis.
Statistical process control – They provide the
ability to monitor and track variables via control
charts and statistical analysis, commonly used in
quality control programs such as Six Sigma and
Total Quality Management.
Many organizations also want to deploy the Business
Dashboard in more sophisticated and intricate
application contexts such as with an analytic application
deployment or as an extranet that reaches beyond the
corporate boundaries. According to Carotenuto [16],
synergies between Business Dashboards and other BI
applications lead to improvements in both.
8 Stage VI: Mobile and Location-based
Intelligence
8.1 Mobile Intelligence
Computing is entering its new era with desktop Internet
applications giving way to a new generation of Mobile
Internet applications. The use of the Internet on
smartphones and other mobile devices has changed the
way people communicate and consume information,
creating an exponential rise in the acceptance, adoption,
and usage of data [17]. With the ability to access
information at any time, in any location, on a hand-held
device, consumers can now make more and more
decisions quickly and easily.
As consumers capitalize on the power of mobile
devices, the same transformation is occurring in
business. Business applications that were mildly
successful when used on a desktop, have become highly
effective and valuable when consumed on the go,
whenever and wherever business is conducted.
The revolutionary impact of Mobile Intelligence is
evidenced by three major drivers [18]:
1. Mobile Intelligence expands the user population
by a factor of 10 – Mobile devices will
significantly surpass the impact and number of
desktop Internet devices. The range and number
of mobile devices is showing explosive growth
and the boundaries between these devices is
blurring. Mobile computing devices now range
from smartphones and tablets to handheld game
consoles and fully functional in-car computers.
For all their differences, these mobile device
types harmonize across themes of connectivity,
mobility, and information delivery.
2. Mobile intelligence expands information
opportunities by at least a factor of 10 – As
mobile computing becomes pervasive in both
personal and professional lives, people are
discovering more and more opportunities to
make complete use of these powerful devices.
From the moment they wake, they can use
applications that not only enhance their personal
lives but also make them more productive and
effective at work. The ability to access
information at anytime in any location, easily in
the palm of a hand, allows immediate decision-
making.
3. Mobile intelligence expands personal query
relevance by a factor of 4 – Today’s mobile
computing devices are revolutionizing how
information is deposited into applications. Using
a keyboard and a mouse is now outdated. A
natural user interface allows users to point at
what they want, touch where they want to go,
and move the device to indicate how they want
to explore the information. Mobile computing
devices respond to how users move their fingers
and arms, and understand their location, the
direction they are moving, and how fast. Mobile
devices use these natural actions as inputs.
Touch screens dynamically change into
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convenient input controls to meet the user’s
needs, such as a keyboard, a calculator, a map,
and a data visualization control. As a result, the
user’s inputs are faster and cover a greater range
of options, all while being more intuitive.
The ongoing impact of the evolution in device inputs
and natural interfaces is to make BI applications faster,
easier, and more natural to use, leading to greater usage
and a higher user adoption rate.
8.2 Location-based Intelligence
Almost all organizations give at least passing attention
to the characteristics of location, whether in evaluating
traffic patterns in choosing a factory location,
determining optimal travel routes, or calculating market
wages in deciding where to site an industrial plant.
There is certainly benefit even in these isolated, often
unstructured observations. But assessing the impact of
location in this way – call it ‘location inference’ [19] – is
a little like stargazing without a telescope.
Although less familiar than giant telescopes, the
software and analytical tools necessary for
systematically probing location-based data closer to
home are just as well developed, and offer willing
companies a far richer and more informed perspective
on their physical operating environment than is possible
with more casual analyses.
These tools allow companies not only to observe and
collect data describing even the hidden, business-
relevant features of their location, but also to probe and
deploy this data in a way that greatly enhances
understanding of the impact of location and, ultimately,
enables organizations to dramatically reduce costs,
increase revenues, and boost profits. Such tools thus
help to translate the notational “location inference” into
a much more powerful form of location-based
knowledge called Location-based Intelligence (LBI).
Conceptually, LBI bears many similarities to the
customer intelligence concept that grew to prominence
during the 1990s and that underlies such well-known
technology solutions as customer relationship
management software, more commonly known as CRM.
The core premise of customer intelligence and CRM
software in particular was that, if a company knew more
about a particular customer’s demographics,
preferences, and buying habits over time, it could tailor
marketing offers and customer interactions in a way that
would increase the customer’s propensity to buy and, in
general, boost the customer’s overall lifetime value.
As noted, LBI has also been part of business
operations for decades, at least in a rudimentary form.
For instance, long before the advent of computers,
delivery firms planned pick-ups and drop-offs so as to
minimize travel time and fuel use. Retailers and service
franchise owners like supermarkets and car repair shops
typically have taken a number of factors into account
before deciding where to locate their businesses. And, of
course, real estate agents have long known that home
values are determined primarily by three factors:
‘location, location, and location’.
As obvious as these examples are, they represent
only a fraction of the actionable intelligence inherent in
a company’s location, and a small portion of the value
that can be obtained today from sophisticated LBI tools.
Location and its business-relevant implications, in fact,
infuse nearly all business operations: every organization
with a physical presence exists somewhere, and the
same is true of nearly all of that organization’s
customers and suppliers.
These and many comparable examples confirm that
LBI is just what it appears to be: invaluable
organizational intelligence, drawn from both the
organization’s and customers’ locations that can
enhance the understanding of the organization’s
operating environment, and so be used to increase
revenues, reduce costs, and improve profits. It is the
same kind of value that CRM-style analytical solutions
began bringing to customer-facing organizations a
decade before.
And like those customer intelligence solutions, which
depended heavily on advanced information technologies
for their analytical and data-management power, so too
are LBI solutions now being powered, not by gut
instinct and consensus “guessing,” but by advanced
analytical and data-processing tools that can detect
patterns, risks, and opportunities that otherwise would
be invisible to human ‘eyeball’ analysis.
9 The Future: Business Intelligence from
Big Data
For decades, companies have been making business
decisions based on transactional data stored in relational
databases. Beyond that critical data, however, is a
potential treasure trove of nontraditional, less structured
data – Web logs (blogs), social media, e-mail, sensors,
and photographs – that can be mined for useful
information.
Decreases in the cost of both storage and computing
power have made it feasible to collect this data, which
would have been thrown away only a few years ago. As
a result, more and more companies are looking to
include nontraditional (yet potentially valuable) data
with traditional enterprise data in their business
intelligence analysis.
‘Big data’ typically refers to nontraditional data,
which can be characterized by four parameters: volume,
variety, velocity, and value [20]. Typically, big data is
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generated in much greater quantities than traditional
enterprise data, is produced on a more frequent basis and
in a wider range of ever-changing formats, and will vary
greatly in its economic value.
When big data is distilled and analyzed in
combination with traditional enterprise data, companies
can develop a more thorough and insightful
understanding of their business, which can lead to
enhanced productivity, a stronger competitive position,
and greater innovation – all of which can have a
significant impact on a company’s bottom line.
Big data by itself, regardless of the type, is worthless
unless business users do something with it that delivers
value to their organizations. That is where Business
Intelligence comes in. Although organizations have
always run reports against data warehouses, most have
not opened these repositories to ad hoc exploration.
A valuable characteristic of big data is that it
contains more patterns and interesting anomalies than
‘small’ data [21]. Thus, organizations can gain greater
value by mining large data volumes than small ones.
While users can detect the patterns in small data sets
using simple statistical methods, ad hoc query and
analysis tools or by eyeballing the data, they need
sophisticated techniques to mine big data.
Big data BI does not change data warehousing or
traditional BI architectures; it simply supplements them
with new technologies and access methods better
tailored to meeting the information requirements of
business analysts and data scientists.
The biggest change in the new BI architecture is that
the data warehouse is no longer the centerpiece. It now
shares the spotlight with systems that manage structured
and unstructured data. The most popular among these is
Hadoop, an open source software framework for
building data-intensive applications.
Hadoop runs on the Hadoop Distributed File System
(HDFS), a distributed file system that scales out on
commodity servers. Since Hadoop is file-based,
developers don’t need to create a data model to store or
process data, which makes Hadoop ideal for managing
semi-structured Web data, which comes in many shapes
and sizes. But because it is ‘schema-less’, Hadoop can
be used to store and process any kind of data, including
structured transactional data and unstructured audio and
video data [22].
However, the biggest advantage of Hadoop right now
is that it is open source, which means that the up-front
costs of implementing a system to process large volumes
of data are lower than for commercial systems.
However, Hadoop does require companies to purchase
and manage tens, if not hundreds, of servers and train
developers and administrators to use this new
technology.
The BI architecture of the future will incorporate
traditional data warehousing technologies to handle
detailed transactional data and file-based and non-
relational systems to handle unstructured and semi-
structured data. The key is to integrate these systems
into a unified architecture that enables casual and power
users to query, report and analyze any type of data in a
relatively seamless manner.
This unified information access is the hallmark of the
next generation BI architecture.
10 Conclusion
The world of business is constantly changing and
Business Intelligence solutions are trying to keep pace
[23]. In 1980s, when first BI solutions appeared, the
focus was on mining historical data in attempts to
recognize important patterns which could make business
more important. From that time BI has passed a long
way.
From today’s point of view we can indentify at least
six evolutionary stages in the field of Business
Intelligence: the first one in which data mining emerged,
the second one which is characterized by OLAP
techniques invention and implementation, the third one
when Balanced Scorecards methodology was developed,
the fourth stage in which proliferation of E-Business
activities made it possible to implement efficient Web
mining and Web analytics methods, the fifth one in
which Business Dashboards shaped the face of BI, and,
finally, the sixth stage when mobile technologies
promoted Mobile and Location-based Intelligence.
And today, we are entering the further development
stage in which the major source of Business Intelligence
will be unstructured information content and so-called
big data.
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2011
Recent Researches in Business and Economics
ISBN: 978-1-61804-102-9 127
doc_767227303.pdf
Business Intelligence (BI) is today seen as a basis for intelligent business and enterprise systems development and design.
The Evolution of Business Intelligence:
From Historical Data Mining to Mobile and Location-based Intelligence
ZELJKO PANIAN
The Graduate School for Economics and Business
University of Zagreb
J.F. Kennedy Sq. 6
CROATIA
[email protected]http://www.efzg.hr/zpanian
Abstract: - Business Intelligence (BI) is today seen as a basis for intelligent business and enterprise systems
development and design. But, from the time of its birth in late eighties until nowadays business intelligence has passed
a long way. The primary interest of scientists in this field was knowledge discovery. Although the first efforts of
scientists were originally oriented towards general knowledge discovery principles and methods soon it became
obvious that application of these principles and methods in business environment is perhaps the most promising
development perspective. The strong impetus for further research came from Howard Dresner who proposed ‘Business
Intelligence’ (BI) as an umbrella term to describe concepts and methods to improve business decision making by using
fact- and knowledge-based support systems. From that time Business Intelligence rapidly evolved through several
stages depending on technology used. Improvements in subsequent phases do not derogate those of previous ones but
rather complement them, so that BI becomes more and more complex and sophisticated.
Key-Words: - Business intelligence, data mining, OLAP, balanced scorecard, Web mining, Web analytics, dashboards,
mobile business intelligence, location-based business intelligence, big data.
1 Introduction
Seeing, understanding and acting in real time is what
defines the ‘Intelligent Enterprise’. And enterprise
agility – the ability to change business and adapt quickly
to changing conditions – often may be the difference
between organizational success and failure [1].
In the past, enterprise agility has been exceedingly
difficult to achieve because viewing all the critical data
streaming through the systems, applications, and
processes that make up an enterprise’s transaction and
information data flow, could not be done in cost
effective manner [2].
But, things are changing dramatically. Now business
information that can be understood in its business
context is flowing between applications – and even
between our organizations and those of our business
partners, customers, and suppliers.
In these circumstances, Business Intelligence (BI) is
playing a critical role and must also be available in real
time [3].
Business Intelligence rapidly evolved through several
stages depending on technology used. Improvements in
subsequent phases do not derogate those of previous
ones but rather complement them, so that BI becomes
more and more complex.
2 The Evolutionary Path of BI
At the very beginning, historical data mining methods
and tools were used for strategic managerial reporting
purposes.
The second evolutionary stage is characterized by
On-Line Analytic Processing (OLAP) technologies and
dimensional analysis of data stored in data warehouses
and data marts.
In the third stage Balanced Scorecard methodology is
used as a means of Business Intelligence creation.
With the emergence and growing popularity of E-
Business and other Internet applications and services the
new stage of BI appeared since Web analytics and Web
mining as a form of BI began to attract the wide
professional attention.
The fifth development stage started when usage of
Business Dashboard technology became a core
component of alerting and alarming systems in business
decision-making supported by BI.
Finally, nowadays we are witnessing the era of
mobile and location-based Business Intelligence founded
on appropriate mobile and location-aware technologies.
As far as it can be seen from today’s perspective, the
further development in the near future can be expected in
the field on unstructured content and so-called big data
analysis as a form of sophisticated Business Intelligence.
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ISBN: 978-1-61804-102-9 118
3 Stage I: Data Mining
3.1 The Three Parents of Data Mining
Data mining as a methodology has three parents:
statistics, computer science, and database/data
warehouse management. In the early 1980s, statistics
contributed methods such as recursive partitioning and
non-parametric regression, and tools such as the
bootstrap and cross-validation.
At approximately the same time, computer science
developed neural network models and new algorithms
for rapid execution of traditional statistical analyses on
large data sets, such as clustering and smoothing; they
also coined the phrase ‘data mining’.
And database management researchers developed
sequential query procedures and relational data bases, as
well as the concept of data warehouse. The confluence
of these ideas led to the expansion of inferential science
to larger and more complex data sets.
3.2 Business Data Mining
Data mining has been very effective in focused areas,
such as medical diagnosis, scientific research, and
behavioral profiling since the mid-1980s. But, data
mining technology has also journeyed into the business
world where it has added the new dimension of
predictive analysis.
Data mining is a powerful technology that converts
detail data into intelligence that businesses can use to
predict future trends and behaviors [4]. Some vendors
define data mining as a tool or as the application of an
algorithm to data.
The truth is data mining is not just a tool or
algorithm. Data mining is a process of discovering and
interpreting previously unknown patterns in data to
solve business problems. Data mining is an iterative
process, which means that each cycle further refines the
result set. This can be a complex process, but there are
tools and approaches available today to help business
user navigate successfully through the steps of data
mining projects.
From an IT perspective, the data mining process
requires support for the following activities:
Exploring the data
Creating the analytic set data
Building and testing the model
Integrating the results obtained into business
applications.
Therefore, the IT organization must provide an
environment capable of addressing the following
challenges:
Exploring and pre-processing large data
volumes
Providing sufficient processing power to
efficiently analyze many variables (columns)
and records (rows) in a timely manner
Integrating data mining results into the business
process
Creating an extensible and manageable data
mining environment
For years, businesses have relied on reports and ad
hoc query tools to glean useful information from data.
However, as data volumes continue to increase, finding
valuable information becomes a daunting task. Data
mining technology was designed to sift through detailed
historical data to identify hidden patterns that are not
obvious to humans or query tools. Many of these
previously hidden patterns reveal intelligence that can be
integrated into business processes to provide predictive
capabilities for improving strategic business decision
making.
To be effective in the business world, the data mining
process had to be adapted to deliver models in a time-
sensitive manner. Today, with the advent of in-database
data mining techniques, businesses have finally found it
possible and affordable to benefit from the advanced
capabilities of this powerful technology.
Data mining makes analytical business applications
smarter by providing insights into many new areas of the
business that would otherwise go unnoticed. By making
business applications smarter, data mining translates into
a higher return on business investment.
3.3 The Way Data Mining Is Deployed
An organization cannot simply buy a data mining
product, apply it to data and expect to generate a
meaningful model [5]. Data mining models are built as
part of a data mining process – an ongoing process
requiring maintenance throughout the life of the model.
The data mining process is not linear, but an iterative
process where you loop back to the previous phase. For
example, the initial model you create may lead to insight
requiring you to return back to the data pre-processing
phase to create new analytical variables. The data mining
process contains four high-level steps [6]:
Define the business problem
Explore and pre-process the data
Develop the data model, and
Deploy knowledge.
Although each of these steps is important, most of
time will be spent in the data exploration and pre-
processing phase. A well structured data warehouse can
significantly reduce the pain felt in this phase.
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4 Stage II: On-Line Analytical
Processing (OLAP)
4.1 OLAP Basics
OLAP means many different things to different
people, but the definitions usually involve the terms
‘cubes’, ‘multidimensional’, ‘slicing & dicing’ and
‘speedy-response’ [7]. OLAP is all of these things and
more, but it is also a misused and misunderstood term, in
part because it covers such a broad range of subjects.
OLAP is an acronym, standing for ‘On-Line
Analytical Processing’. This, in itself, does not provide a
very accurate description of OLAP, but it does
distinguish it from OLTP or ‘On-Line Transactional
Processing’.
It is easy to question the need for OLAP. If an end
user requires high-level information about their
company, then that information can always be derived
from the underlying transactional data, hence we can
achieve every requirement with an OLTP application.
Were this true, OLAP would not have become the
important topic that it is today. OLAP exists and
continues to expand in usage because there are
limitations with the OLTP approach. The limits of OLTP
applications are seen in three areas.
OLAP applications differ from OLTP applications in
the way that they store data, the way that they analyze
data and the way that they present data to the end-user. It
is these fundamental differences that allow OLAP
applications to answer more sophisticated business
questions.
OLAP applications present the end user with
information rather than just data. They make it easy for
users to identify patterns or trends in the data very
quickly, without the need for them to search through
mountains of ‘raw’ data. Typically this analysis is driven
by the need to answer business questions such as ‘How
are our sales doing this month in South-Eastern Europe?’
or ‘From which supplier, X, Y or Z, we have ordered the
largest quantities of goods needed?’[8].
From these foundations, OLAP applications move
into areas such as forecasting and data mining, allowing
users to answer questions such as ‘What are our
predicted labor costs for next year?’ and ‘Show me our
most successful salesman’.
4.2 Multidimensionality
Although different OLAP tools use different underlying
technologies, they all attempt to present data using the
same high-level concept of the multidimensional cube.
Cubes are easy to understand, but there are fundamental
differences between cubes and databases that can make
them appear more complicated than they really are.
The cube is the conceptual design for the data store at
the center of all OLAP applications. Although the
underlying data might be stored using a number of
different methods, the cube is the logical design by
which the data is referenced.
The axes of the cube contain the identifiers from the
field columns in the database table. Each axis in a cube
is referred to as a ‘dimension’. The basic logical
construct is a simple two-dimensional cube. Although
useful, this cube is only slightly more sophisticated
than a standard database table. The capabilities of a
cube become more apparent when we extend the
design into more dimensions. Multidimensionality is
perhaps the most ‘feared’ element of cube design as it is
sometimes difficult to envisage.
Although the word ‘cube’ refers to a three-
dimensional object, there is no reason why an OLAP
cube should be restricted to three dimensions. Many
OLAP applications use cube designs containing up to ten
dimensions, but attempting to visualize a
multidimensional cube can be very difficult. The first
step is to understand why creating a cube with more than
three dimensions is possible and what advantage it
brings.
4.3 The Key Differences between OLAP and
Data Mining
OLAP is a Business Intelligence tool that allows a
business person to analyze and understand particular
business drivers in ‘factual terms’. Typically, a specific
‘descriptive’ or factual question is formulated and either
validated or refuted through ad hoc queries. OLAP
results are also factual results.
Data mining, on the other hand, is a form of
discovery-driven analysis where statistical and machine-
learning techniques are used to make predictions or
estimates about outcomes or traits before knowing their
true values. With data mining, predictions are
accompanied by specific estimates of the sources and
number of errors that are likely to be made. Estimates of
errors translate directly to estimates of risk.
Consequently, with data mining, making business
decisions in the presence of uncertainty can be done with
detailed and reliable information about associated risks.
Data mining techniques are used to find meaningful,
often complex, and previously unknown or hidden
patterns in data.
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5 Stage III: Balanced Scorecards
Robert S. Kaplan and David Norton, co-creators of the
Balanced Scorecard Method, wrote their first book in
1996 [9].They looked at the Balanced Scorecard as a
Performance Management system that could be used in
any size organization to align vision and mission with
customer requirements and day-to-day work, manage
and evaluate business strategy, monitor operation
efficiency improvements, build organizational capacity,
and communicate progress to all employees. The
scorecard allows an organization to measure financial
and customer results, operations, and organizational
capacity.
The Balanced Scorecard (BSC) has migrated over
time to become a full Performance Management system
applicable to both private sector and public (and not-for-
profit) organizations. And the emphasis has shifted from
just the measurement of financial and non-financial
performance, to the management (and execution) of
business strategy. In this sense, BSC became a new style
of Business Intelligence.
BSC systems can be the heart of a corporate
performance system. They provide the ability to view
three different dimensions of an organization’s
performance: Results (financial and customer),
Operations, and Capacity.
The components of a fully developed scorecard
system are:
Business Foundations, including vision,
mission, and values;
Plans, including communications,
implementation, automation, and evaluation
plans, to build employee buy-in and
communicate results;
Business Strategies and Strategic Maps, to chart
the course and define the logical decomposition
of strategies into activities that people work on
each day;
Performance Measures, to track actual
performance against expectations;
New Initiatives, to test strategic assumptions;
Budgets, including the resources needed for new
initiatives and current operations;
Business and Support Unit Scorecards, to
translate the corporate vision into actionable
activities for departments and offices; and
Leadership and Individual Development, to
ensure that employee knowledge, skills and
abilities are enhanced to meet future job
requirements and competition.
In BSC language, vision, mission, and strategy at the
corporate level are decomposed into different views, or
perspectives, as seen through the eyes of business
owners, customers and other stakeholders, managers and
process owners, and employees. The owners of the
business are represented by the Financial perspective;
customers and stakeholders (customers are a subset of
the larger universe of stakeholders) are represented by
the Customer perspective; managers and process owners
by the Internal Business Processes perspective; and
employees and infrastructure (Capacity) by the Learning
and Growth perspective.
But, building and implementing a scorecard system is
one thing; turning the scorecard into a used and useful
BI system is something else entirely.
The key to transforming a scorecard into a BI system
is to start at the right level of granularity and ‘connect
the dots’ among the components of strategy (mission,
vision, values, pains, enablers, strategic results and
themes, and strategic objectives) and the components of
operations (projects, processes, activities, and tasks),
and the budget formulation and cost reporting processes
[10]. Performance measures tie the parts together, and
give an organization a way to measure how successful
they are at achieving their goals.
6 Stage IV: Web Mining and Web
Analytics
With the explosive growth of information sources
available on the World Wide Web, as well as various E-
Business activities, it has become increasingly necessary
for users to utilize automated tools in finding the desired
information resources, and to track and analyze their
usage patterns. These factors give rise to the necessity of
creating server-side and client-side intelligent systems
that can effectively mine for knowledge. That is the
reason why a plenty of Web Mining and Web Analytics
tools are developed.
6.1 Web Mining
Web mining can be broadly defined as the discovery and
analysis of useful information from the World Wide
Web [11]. This describes the automatic search of
information resources available on-line, i.e. Web content
mining, and the discovery of user access patterns from
Web servers, i.e., Web usage mining.
6.1.1 Web Content Mining
The lack of structure that permeates the information
sources on the World Wide Web makes automated
discovery of Web-based information difficult.
Traditional search engines such as Lycos, Alta Vista,
WebCrawler, and others provide some comfort to users,
but do not generally provide structural information nor
categorize, filter, or interpret documents.
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In recent years these factors have prompted
researchers to develop more intelligent tools for
information retrieval, such as intelligent Web agents,
and to extend data mining techniques to provide a higher
level of organization for semi-structured data available
on the Web. We summarize some of these efforts below:
1. Agent-based Approach – Generally, agent-based
Web mining systems can be placed into the
following three categories:
a. intelligent search agents
b. information filtering/categorization
c. personalized Web agents
2. Database Approach – Focused on techniques
for organizing the semi-structured data on the
Web into more structured collections of
resources, and using standard database querying
mechanisms and data mining techniques to
analyze it:
a. multilevel databases
b. Web query systems
6.1.2 Web Usage Mining
Web usage mining is the automatic discovery of user
access patterns from Web servers. Organizations collect
large volumes of data in their daily operations, generated
automatically by Web servers and collected in server
access logs. Other sources of user information include
referrer logs which contain information about the
referring pages for each page reference, and user
registration or survey data gathered via CGI scripts.
Most existing Web analysis tools provide
mechanisms for reporting user activity in the servers and
various forms of data filtering. But, in recent times more
sophisticated systems and techniques for discovery and
analysis of patterns are also emerging. These tools can
be placed into two main categories, as listed below:
1. Pattern Discovery Tools – The emerging tools for
user pattern discovery use sophisticated
techniques from AI, data mining, psychology, and
information theory, to mine for knowledge from
collected data.
2. Pattern Analysis Tools – Once access patterns
have been discovered, analysts need the
appropriate tools and techniques to understand,
visualize, and interpret these patterns.
One of the open issues in data mining, in general, and
Web mining, in particular, is the creation of intelligent
tools that can assist in the interpretation of mined
knowledge. Clearly, these tools need to have specific
knowledge about the particular problem domain to do
any more than filtering based on statistical attributes of
the discovered rules or patterns.
In Web mining, for example, intelligent agents could
be developed that based on discovered access patterns,
the topology of the Web locality, and certain heuristics
derived from user behavior models, could give
recommendations about changing the physical link
structure of a particular site.
6.2 Web Analytics
Not very long ago, who was visiting Web sites and why
was essentially a mystery. Web masters put counters on
Web pages to track how many times people ‘hit’ the
page –that is, visited, downloaded a file, or some other
activity – but that was the extent of the insight. True
Web analytics capabilities were limited to large
corporations that could afford to spend thousands of
dollars per month on software to track and report on web
activity [12].
Today, there is a wide range of Web metrics
measuring and tracking applications available, making
analytics one of the most talked about topics both online
and off. Although some of these tools are still expensive,
a number of analytics programs available now are
completely free – and just as effective.
Simply put, Web analytics involves measuring,
collecting, analyzing, and reporting Web site traffic and
behavior with the end goal of optimizing the success of
the Web site.
All Web analytics tools work by collecting raw data
about Web site visitors and organizing it in a way that is
easier to view and understand. Some programs, called
log analyzers, use server logs (data files collected by
web servers) to provide information about visitors. Then
there are other programs – analytics applications –
which use bits of code installed on a Web site to gather
information about web activity and generate reports.
Generally, log analyzers are considered more
technical and the raw information they provide may be
hard to understand, especially for people who are
unfamiliar with Web metrics. In this case, it is probably
a better idea to stick with an analytics program.
No matter which Web analytics tool is used, their
users are going to be presented with a robust array of
metrics. From page views and unique visitors, to
referrers and average time on site, there are endless
amounts of data to sift through. But, focusing on the
following key metrics will tell users almost everything
they need to know [13]:
Visitors – The number of visitors to Web site will
give a general idea of how well the Web site
owner is getting the word out about his business.
Page Views – Looking at page views can tell what
content on the site is the most popular.
Referring Sites – Looking at referring sites will
give an excellent snapshot of the type of people
who are visiting the particular site.
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Bounce rate and Exit pages – A bounce rate
measures something different than an exit page,
but both can give important insights into why
people are leaving the site. In most analytics
programs, a “bounce” is recorded when a person
visits and leaves within a second or two, usually
before the page is even done loading. Top exit
pages show which pages people visit immediately
before they leave.
Keywords and Phrases – Keywords and phrases
let the Web site owner know what terms people
are using to find his site in search engines. This
can give him/her some idea of how to add
different content to appeal to even more
customers.
Though still a relatively new invention, Web
analytics is becoming an increasingly popular – and
effective – Web site optimization tactic used by online
business owners. By providing deep insight into the
who, what, when, why and how of web site traffic and
visitor behavior, Web analytics tools can help you
improve the usability of the site and boost its
effectiveness.
7 Stage V: Business Dashboards
Business Dashboards are becoming the new face of
business intelligence (BI) at the very beginning of the
21
st
century. While on the surface, Executive
Information Systems (EIS) from the 1980s had a similar
look and served a similar purpose, modern Dashboards
are interactive, easier to set up and update to changing
business needs, and much more flexible to use. This,
plus their ability to present data and information at both
a summary and detailed level, makes them one of the
most powerful tools in the business user’s kit.
To be useful, however, a Business Dashboard must
be implemented around the needs of the business [14].
Its functions should not be dictated by technology or by
the whims of the end users. Also, a Business Dashboard
should be implemented so that it gets used – and so that
the decision-makers employing it can act on the
information the Dashboard presents.
7.1 Business Dashboards vs. Spreadsheets
Along with modern Dashboards evolving from the old
EIS tools, another BI tool has been with us for a while:
the spreadsheet. Most often in the form of Microsoft
Excel, the spreadsheet has an intuitive interface and is
easy to learn, at least as far as its most basic functions. It
provides detailed numbers, which users can analyze
adding their own calculations.
However, while the spreadsheet is easy to use and
understand, it is often too detailed to give a quick and
comprehensive overview of business data. Furthermore,
users are likely to reformat this business data in other
spreadsheets, adding calculations and aggregations. This
will create yet more cells of important business data.
Although it is possible to create complementary charts
in most spreadsheets, this is a time consuming, manual
activity that lends itself to easily-made mistakes.
Nonetheless, many business users stick with
spreadsheets because they feel comfortable with them
and are reluctant to change to another model. The reality
is that not everything can be done efficiently in the
spreadsheet; and one should not get stuck with them
simply because that is what they have or have been
using. This can lead to a situation where it’s the
limitations of the program – rather than business needs –
that determine the scope of reporting and analysis.
With the right underlying technology, today’s
Business Dashboards stand out from the spreadsheet,
which nevertheless remains the most used BI interface
today. Dashboards allow for a quick and easy-to-
personalize overview of critical business data in a timely
fashion. This added value turns today’s Business
Dashboards into the new face of BI.
7.2 Business Dashboards vs. Scorecards
In many cases, the terms Dashboard and Scorecard are
used almost interchangeably. But, although Dashboards
and Scorecards have much in common, there are the
differences between the two [15]. On the one hand,
executives, managers, and staff use Scorecards, and
particularly Balanced Scorecards, to monitor strategic
alignment and success with strategic objectives and
targets.
On the other hand, Business Dashboards are used at
operational and tactical levels. Managers, supervisors,
and operators use operational Dashboards to monitor
detailed operational performance on a weekly, daily, or
even hourly basis. In the same vein, managers and staff
use tactical Dashboards to monitor tactical initiatives.
7.3 Benefits of Business Dashboards
Deployment
Business Dashboards help organizations reach stated
goals by leveraging information and analytics. They
provide alignment, visibility, and collaboration across
the organization by allowing business users to define,
monitor, and analyze business performance via key
performance indicators (KPIs). Whether organizations
choose to implement strategic or tactical performance
management initiatives, dashboards can provide the
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foundation for enabling organizations to more
effectively align their business strategy with execution.
In defining, tracking, and analyzing performance
indicators, Business Dashboards can provide users with
the following capabilities:
Root-cause analysis – They provide the ability to
drill down on a Key Performance Indicators
(KPIs) to a more detailed report revealing the
underlying business activity driving the higher-
level indicator output. This permits analysis of
causative factors and enables corrective action.
Time-series analysis – Dashboards provide the
ability to track and analyze key metrics over time
and to identify trends and exceptions.
Rules, alerts and alarms – They provide the
ability to track and monitor a plenty of business
processes and receive real-time notifications when
they are out of alignment. Once a notification has
been received, business users can examine the
irregularity, perform proactive root-cause
analysis, and take corrective action.
Predictive analysis – Dashboards provide the
ability to forecast, model, and analyze complex
relationships. Predictive analysis is necessary to
better understand the future impact of decisions
and the key influencers of future business
behaviors (e.g., churn and repeat purchase).
Segment analysis – Business Dashboards provide
the ability to define, manage, and understand the
behavior of business groupings such as strategic
customer segments, departments, and regions.
Segmentation can be used in defining metrics and
in providing root-cause analysis.
Statistical process control – They provide the
ability to monitor and track variables via control
charts and statistical analysis, commonly used in
quality control programs such as Six Sigma and
Total Quality Management.
Many organizations also want to deploy the Business
Dashboard in more sophisticated and intricate
application contexts such as with an analytic application
deployment or as an extranet that reaches beyond the
corporate boundaries. According to Carotenuto [16],
synergies between Business Dashboards and other BI
applications lead to improvements in both.
8 Stage VI: Mobile and Location-based
Intelligence
8.1 Mobile Intelligence
Computing is entering its new era with desktop Internet
applications giving way to a new generation of Mobile
Internet applications. The use of the Internet on
smartphones and other mobile devices has changed the
way people communicate and consume information,
creating an exponential rise in the acceptance, adoption,
and usage of data [17]. With the ability to access
information at any time, in any location, on a hand-held
device, consumers can now make more and more
decisions quickly and easily.
As consumers capitalize on the power of mobile
devices, the same transformation is occurring in
business. Business applications that were mildly
successful when used on a desktop, have become highly
effective and valuable when consumed on the go,
whenever and wherever business is conducted.
The revolutionary impact of Mobile Intelligence is
evidenced by three major drivers [18]:
1. Mobile Intelligence expands the user population
by a factor of 10 – Mobile devices will
significantly surpass the impact and number of
desktop Internet devices. The range and number
of mobile devices is showing explosive growth
and the boundaries between these devices is
blurring. Mobile computing devices now range
from smartphones and tablets to handheld game
consoles and fully functional in-car computers.
For all their differences, these mobile device
types harmonize across themes of connectivity,
mobility, and information delivery.
2. Mobile intelligence expands information
opportunities by at least a factor of 10 – As
mobile computing becomes pervasive in both
personal and professional lives, people are
discovering more and more opportunities to
make complete use of these powerful devices.
From the moment they wake, they can use
applications that not only enhance their personal
lives but also make them more productive and
effective at work. The ability to access
information at anytime in any location, easily in
the palm of a hand, allows immediate decision-
making.
3. Mobile intelligence expands personal query
relevance by a factor of 4 – Today’s mobile
computing devices are revolutionizing how
information is deposited into applications. Using
a keyboard and a mouse is now outdated. A
natural user interface allows users to point at
what they want, touch where they want to go,
and move the device to indicate how they want
to explore the information. Mobile computing
devices respond to how users move their fingers
and arms, and understand their location, the
direction they are moving, and how fast. Mobile
devices use these natural actions as inputs.
Touch screens dynamically change into
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convenient input controls to meet the user’s
needs, such as a keyboard, a calculator, a map,
and a data visualization control. As a result, the
user’s inputs are faster and cover a greater range
of options, all while being more intuitive.
The ongoing impact of the evolution in device inputs
and natural interfaces is to make BI applications faster,
easier, and more natural to use, leading to greater usage
and a higher user adoption rate.
8.2 Location-based Intelligence
Almost all organizations give at least passing attention
to the characteristics of location, whether in evaluating
traffic patterns in choosing a factory location,
determining optimal travel routes, or calculating market
wages in deciding where to site an industrial plant.
There is certainly benefit even in these isolated, often
unstructured observations. But assessing the impact of
location in this way – call it ‘location inference’ [19] – is
a little like stargazing without a telescope.
Although less familiar than giant telescopes, the
software and analytical tools necessary for
systematically probing location-based data closer to
home are just as well developed, and offer willing
companies a far richer and more informed perspective
on their physical operating environment than is possible
with more casual analyses.
These tools allow companies not only to observe and
collect data describing even the hidden, business-
relevant features of their location, but also to probe and
deploy this data in a way that greatly enhances
understanding of the impact of location and, ultimately,
enables organizations to dramatically reduce costs,
increase revenues, and boost profits. Such tools thus
help to translate the notational “location inference” into
a much more powerful form of location-based
knowledge called Location-based Intelligence (LBI).
Conceptually, LBI bears many similarities to the
customer intelligence concept that grew to prominence
during the 1990s and that underlies such well-known
technology solutions as customer relationship
management software, more commonly known as CRM.
The core premise of customer intelligence and CRM
software in particular was that, if a company knew more
about a particular customer’s demographics,
preferences, and buying habits over time, it could tailor
marketing offers and customer interactions in a way that
would increase the customer’s propensity to buy and, in
general, boost the customer’s overall lifetime value.
As noted, LBI has also been part of business
operations for decades, at least in a rudimentary form.
For instance, long before the advent of computers,
delivery firms planned pick-ups and drop-offs so as to
minimize travel time and fuel use. Retailers and service
franchise owners like supermarkets and car repair shops
typically have taken a number of factors into account
before deciding where to locate their businesses. And, of
course, real estate agents have long known that home
values are determined primarily by three factors:
‘location, location, and location’.
As obvious as these examples are, they represent
only a fraction of the actionable intelligence inherent in
a company’s location, and a small portion of the value
that can be obtained today from sophisticated LBI tools.
Location and its business-relevant implications, in fact,
infuse nearly all business operations: every organization
with a physical presence exists somewhere, and the
same is true of nearly all of that organization’s
customers and suppliers.
These and many comparable examples confirm that
LBI is just what it appears to be: invaluable
organizational intelligence, drawn from both the
organization’s and customers’ locations that can
enhance the understanding of the organization’s
operating environment, and so be used to increase
revenues, reduce costs, and improve profits. It is the
same kind of value that CRM-style analytical solutions
began bringing to customer-facing organizations a
decade before.
And like those customer intelligence solutions, which
depended heavily on advanced information technologies
for their analytical and data-management power, so too
are LBI solutions now being powered, not by gut
instinct and consensus “guessing,” but by advanced
analytical and data-processing tools that can detect
patterns, risks, and opportunities that otherwise would
be invisible to human ‘eyeball’ analysis.
9 The Future: Business Intelligence from
Big Data
For decades, companies have been making business
decisions based on transactional data stored in relational
databases. Beyond that critical data, however, is a
potential treasure trove of nontraditional, less structured
data – Web logs (blogs), social media, e-mail, sensors,
and photographs – that can be mined for useful
information.
Decreases in the cost of both storage and computing
power have made it feasible to collect this data, which
would have been thrown away only a few years ago. As
a result, more and more companies are looking to
include nontraditional (yet potentially valuable) data
with traditional enterprise data in their business
intelligence analysis.
‘Big data’ typically refers to nontraditional data,
which can be characterized by four parameters: volume,
variety, velocity, and value [20]. Typically, big data is
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generated in much greater quantities than traditional
enterprise data, is produced on a more frequent basis and
in a wider range of ever-changing formats, and will vary
greatly in its economic value.
When big data is distilled and analyzed in
combination with traditional enterprise data, companies
can develop a more thorough and insightful
understanding of their business, which can lead to
enhanced productivity, a stronger competitive position,
and greater innovation – all of which can have a
significant impact on a company’s bottom line.
Big data by itself, regardless of the type, is worthless
unless business users do something with it that delivers
value to their organizations. That is where Business
Intelligence comes in. Although organizations have
always run reports against data warehouses, most have
not opened these repositories to ad hoc exploration.
A valuable characteristic of big data is that it
contains more patterns and interesting anomalies than
‘small’ data [21]. Thus, organizations can gain greater
value by mining large data volumes than small ones.
While users can detect the patterns in small data sets
using simple statistical methods, ad hoc query and
analysis tools or by eyeballing the data, they need
sophisticated techniques to mine big data.
Big data BI does not change data warehousing or
traditional BI architectures; it simply supplements them
with new technologies and access methods better
tailored to meeting the information requirements of
business analysts and data scientists.
The biggest change in the new BI architecture is that
the data warehouse is no longer the centerpiece. It now
shares the spotlight with systems that manage structured
and unstructured data. The most popular among these is
Hadoop, an open source software framework for
building data-intensive applications.
Hadoop runs on the Hadoop Distributed File System
(HDFS), a distributed file system that scales out on
commodity servers. Since Hadoop is file-based,
developers don’t need to create a data model to store or
process data, which makes Hadoop ideal for managing
semi-structured Web data, which comes in many shapes
and sizes. But because it is ‘schema-less’, Hadoop can
be used to store and process any kind of data, including
structured transactional data and unstructured audio and
video data [22].
However, the biggest advantage of Hadoop right now
is that it is open source, which means that the up-front
costs of implementing a system to process large volumes
of data are lower than for commercial systems.
However, Hadoop does require companies to purchase
and manage tens, if not hundreds, of servers and train
developers and administrators to use this new
technology.
The BI architecture of the future will incorporate
traditional data warehousing technologies to handle
detailed transactional data and file-based and non-
relational systems to handle unstructured and semi-
structured data. The key is to integrate these systems
into a unified architecture that enables casual and power
users to query, report and analyze any type of data in a
relatively seamless manner.
This unified information access is the hallmark of the
next generation BI architecture.
10 Conclusion
The world of business is constantly changing and
Business Intelligence solutions are trying to keep pace
[23]. In 1980s, when first BI solutions appeared, the
focus was on mining historical data in attempts to
recognize important patterns which could make business
more important. From that time BI has passed a long
way.
From today’s point of view we can indentify at least
six evolutionary stages in the field of Business
Intelligence: the first one in which data mining emerged,
the second one which is characterized by OLAP
techniques invention and implementation, the third one
when Balanced Scorecards methodology was developed,
the fourth stage in which proliferation of E-Business
activities made it possible to implement efficient Web
mining and Web analytics methods, the fifth one in
which Business Dashboards shaped the face of BI, and,
finally, the sixth stage when mobile technologies
promoted Mobile and Location-based Intelligence.
And today, we are entering the further development
stage in which the major source of Business Intelligence
will be unstructured information content and so-called
big data.
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