Business intelligence (BI) comprises the strategies
and technologies used by enterprises for the
data analysis of business information.
BI technologies provide historical, current
and predictive views of business operations.
Common functions of business intelligence
technologies include reporting, online analytical
processing, analytics, data mining, process
mining, complex event processing, business
performance management, benchmarking, text
mining, predictive analytics and prescriptive
analytics.
BI technologies can handle large amounts of
structured and sometimes unstructured data
to help identify, develop and otherwise create
new strategic business opportunities.
They aim to allow for the easy interpretation
of these big data.
Identifying new opportunities and implementing
an effective strategy based on insights can
provide businesses with a competitive market
advantage and long-term stability.Business
intelligence can be used by enterprises to
support a wide range of business decisions
ranging from operational to strategic.
Basic operating decisions include product
positioning or pricing.
Strategic business decisions involve priorities,
goals and directions at the broadest level.
In all cases, BI is most effective when it
combines data derived from the market in which
a company operates (external data) with data
from company sources internal to the business
such as financial and operations data (internal
data).
When combined, external and internal data
can provide a complete picture which, in effect,
creates an "intelligence" that cannot be derived
from any singular set of data.
Amongst myriad uses, business intelligence
tools empower organizations to gain insight
into new markets, to assess demand and suitability
of products and services for different market
segments and to gauge the impact of marketing
efforts.Often BI applications use data gathered
from a data warehouse (DW) or from a data
mart, and the concepts of BI and DW combine
as "BI/DW"
or as "BIDW".
A data warehouse contains a copy of analytical
data that facilitate decision support.
== History ==
The earliest known use of the term business
intelligence is in Richard Millar Devens'
Cyclopædia of Commercial and Business Anecdotes
(1865).
Devens used the term to describe how the banker
Sir Henry Furnese gained profit by receiving
and acting upon information about his environment,
prior to his competitors:
Throughout Holland, Flanders, France, and
Germany, he maintained a complete and perfect
train of business intelligence.
The news of the many battles fought was thus
received first by him, and the fall of Namur
added to his profits, owing to his early receipt
of the news.
The ability to collect and react accordingly
based on the information retrieved, Devens
says, is central to business intelligence.When
Hans Peter Luhn, a researcher at IBM, used
the term business intelligence in an article
published in 1958, he employed the Webster's
Dictionary definition of intelligence: "the
ability to apprehend the interrelationships
of presented facts in such a way as to guide
action towards a desired goal."
Business intelligence as it is understood
today is said to have evolved from the decision
support systems (DSS) that began in the 1960s
and developed throughout the mid-1980s.
DSS originated in the computer-aided models
created to assist with decision making and
planning.In 1989, Howard Dresner (later a
Gartner analyst) proposed business intelligence
as an umbrella term to describe "concepts
and methods to improve business decision making
by using fact-based support systems."
It was not until the late 1990s that this
usage was widespread.Critics see BI merely
as an evolution of business reporting together
with the advent of increasingly powerful and
easy-to-use data analysis tools.
In this respect it has also been criticized
as a marketing buzzword in the context of
the "big data" surge.
== Definition ==
According to Forrester Research, business
intelligence is "a set of methodologies, processes,
architectures, and technologies that transform
raw data into meaningful and useful information
used to enable more effective strategic, tactical,
and operational insights and decision-making."
Under this definition, business intelligence
encompasses information management (data integration,
data quality, data warehousing, master-data
management, text- and content-analytics, et
al.).
Therefore, Forrester refers to data preparation
and data usage as two separate but closely
linked segments of the business-intelligence
architectural stack.
Some elements of business intelligence are:
Multidimensional aggregation and allocation
Denormalization, tagging, and standardization
Realtime reporting with analytical alert
A method of interfacing with unstructured
data sources
Group consolidation, budgeting and rolling
forecasts
Statistical inference and probabilistic simulation
Key performance indicators optimization
Version control and process management
Open item managementForrester distinguishes
this from the business-intelligence market,
which is "just the top layers of the BI architectural
stack, such as reporting, analytics, and dashboards."
=== 
Compared with competitive intelligence ===
Though the term business intelligence is sometimes
a synonym for competitive intelligence (because
they both support decision making), BI uses
technologies, processes, and applications
to analyze mostly internal, structured data
and business processes while competitive intelligence
gathers, analyzes and disseminates information
with a topical focus on company competitors.
If understood broadly, business intelligence
can include the subset of competitive intelligence.
=== Compared with business analytics ===
Business intelligence and business analytics
are sometimes used interchangeably, but there
are alternate definitions.
Thomas Davenport, professor of information
technology and management at Babson College
argues that business intelligence should be
divided into querying, reporting, Online analytical
processing (OLAP), an "alerts" tool, and business
analytics.
In this definition, business analytics is
the subset of BI focusing on statistics, prediction,
and optimization, rather than the reporting
functionality.
== Components of the BI landscape ==
The Business Intelligence landscape reflects
the complex system which data goes through
in order to get processed into information.
One of the first steps of starting a BI program,
is to understand all components of this landscape.
The particularities of this system tend to
differ based on the industry and organization,
but at a macro level, all BI landscapes have
the same format.
It’s usually composed of five pillars and
five foundation blocks:
The five pillars:
Data source(s)
Data integration
Data management
Reports
Information disseminationThe five foundation
blocks:
Information security
Data quality
Metadata management
Data governance
People & culture
== Data ==
Business operations can generate a very large
amount of information in the form of e-mails,
memos, notes from call-centers, news, user
groups, chats, reports, web-pages, presentations,
image-files, video-files, and marketing material.
According to Merrill Lynch, more than 85%
of all business information exists in these
forms; a company might only use such a document
a single time.
Because of the way it is produced and stored,
this information is either unstructured or
semi-structured.
The management of semi-structured data is
an unsolved problem in the information technology
industry.
According to projections from Gartner (2003),
white collar workers spend 30–40% of their
time searching, finding, and assessing unstructured
data.
BI uses both structured and unstructured data.
The former is easy to search, and the latter
contains a large quantity of the information
needed for analysis and decision making.
Because of the difficulty of properly searching,
finding and assessing unstructured or semi-structured
data, organizations may not draw upon these
vast reservoirs of information, which could
influence a particular decision, task or project.
This can ultimately lead to poorly informed
decision making.Therefore, when designing
a business intelligence/DW-solution, the specific
problems associated with semi-structured and
unstructured data must be accommodated for
as well as those for the structured data.
=== Unstructured data vs. semi-structured
data ===
Unstructured and semi-structured data have
different meanings depending on their context.
In the context of relational database systems,
unstructured data cannot be stored in predictably
ordered columns and rows.
One type of unstructured data is typically
stored in a BLOB (binary large object), a
catch-all data type available in most relational
database management systems.
Unstructured data may also refer to irregularly
or randomly repeated (nonrepetitive) column
patterns that vary from row to row within
each file or document.Many of these data types,
however, like e-mails, word processing text
files, PPTs, image-files, and video-files
conform to a standard that offers the possibility
of metadata.
Metadata can include information such as author
and time of creation, and this can be stored
in a relational database.
Therefore, it may be more accurate to talk
about this as semi-structured documents or
data, but no specific consensus seems to have
been reached.
Unstructured data can also simply be the knowledge
that business users have about future business
trends.
Business forecasting naturally aligns with
the BI system because business users think
of their business in aggregate terms.
Capturing the business knowledge that may
only exist in the minds of business users
provides some of the most important data points
for a complete BI solution.
=== Limitations of semi-structured and unstructured
data ===
There are several challenges to developing
BI with semi-structured data.
According to Inmon & Nesavich, some of those
are:
Physically accessing unstructured textual
data – unstructured data is stored in a
huge variety of formats.
Terminology – Among researchers and analysts,
there is a need to develop a standardized
terminology.
Volume of data – As stated earlier, up to
85% of all data exists as semi-structured
data.
Couple that with the need for word-to-word
and semantic analysis.
Searchability of unstructured textual data
– A simple search on some data, e.g. apple,
results in links where there is a reference
to that precise search term.
(Inmon & Nesavich, 2008) gives an example:
“a search is made on the term felony.
In a simple search, the term felony is used,
and everywhere there is a reference to felony,
a hit to an unstructured document is made.
But a simple search is crude.
It does not find references to crime, arson,
murder, embezzlement, vehicular homicide,
and such, even though these crimes are types
of felonies.”
=== Metadata ===
To solve problems with searchability and assessment
of data, it is necessary to know something
about the content.
This can be done by adding context through
the use of metadata.
Many systems already capture some metadata
(e.g. filename, author, size, etc.), but more
useful would be metadata about the actual
content – e.g. summaries, topics, people
or companies mentioned.
Two technologies designed for generating metadata
about content are automatic categorization
and information extraction.
== Applications ==
Business intelligence can be applied to the
following business purposes:
Performance metrics and benchmarking inform
business leaders of progress towards business
goals (business process management).
Analytics quantify processes for a business
to arrive at optimal decisions, and to perform
business knowledge discovery.
Analytics may variously involve data mining,
process mining, statistical analysis, predictive
analytics, predictive modeling, business process
modeling, data lineage, complex event processing
and prescriptive analytics.
Business reporting can use BI data to inform
strategy.
Business reporting may involve data visualization,
executive information system, and/or OLAP
BI can facilitate collaboration both inside
and outside the business by enabling data
sharing and electronic data interchange
Knowledge management is concerned with the
creation, distribution, use, and management
of business intelligence, and of business
knowledge in general.
Knowledge management leads to learning management
and regulatory compliance.
== Marketplace ==
In a 2013 report, Gartner categorized business
intelligence vendors as either an independent
"pure-play" vendor or a consolidated "megavendor".
In 2012 business intelligence services received
$13.1 billion in revenue.
=== Historical predictions ===
A 2009 paper predicted these developments
in the business intelligence market:
Because of lack of information, processes,
and tools, through 2012, more than 35 percent
of the top 5,000 global companies regularly
fail to make insightful decisions about significant
changes in their business and markets.
By 2012, business units will control at least
40 percent of the total budget for business
intelligence.
By 2012, one-third of analytic applications
applied to business processes will be delivered
through coarse-grained application mashups.A
2009 Information Management special report
predicted the top BI trends: "green computing,
social networking services, data visualization,
mobile BI, predictive analytics, composite
applications, cloud computing and multitouch".
Research undertaken in 2014 indicated that
employees are more likely to have access to,
and more likely to engage with, cloud-based
BI tools than traditional tools.Other business
intelligence trends include the following:
Third party SOA-BI products increasingly address
ETL issues of volume and throughput.
Companies embrace in-memory processing, 64-bit
processing, and pre-packaged analytic BI applications.
Operational applications have callable BI
components, with improvements in response
time, scaling, and concurrency.
Near or real time BI analytics is a baseline
expectation.
Open source BI software replaces vendor offerings.Other
lines of research include the combined study
of business intelligence and uncertain data.
In this context, the data used is not assumed
to be precise, accurate and complete.
Instead, data is considered uncertain and
therefore this uncertainty is propagated to
the results produced by BI.
According to a study by the Aberdeen Group,
there has been increasing interest in Software-as-a-Service
(SaaS) business intelligence over the past
years, with twice as many organizations using
this deployment approach as one year ago – 15%
in 2009 compared to 7% in 2008.An article
by InfoWorld's Chris Kanaracus points out
similar growth data from research firm IDC,
which predicts the SaaS BI market will grow
22 percent each year through 2013 thanks to
increased product sophistication, strained
IT budgets, and other factors.An analysis
of top 100 Business Intelligence and Analytics
scores and ranks the firms based on several
open variables
== See also
