History

The earliest known use of the term "Business Intelligence" is by Richard Millar Devens in the ‘Cyclopædia of Commercial and Business Anecdotes’ from 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.” (Devens, (1865), p. 210).

The ability to collect and react accordingly based on the information retrieved, an ability that Furnese excelled in, is today still at the very heart of BI.business intelligence.

In a 1958 article, IBM researcher Hans Peter Luhn used the term business intelligence.

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.

From DSS, data warehouses, Executive Information Systems, OLAP and business intelligence came into focus beginning in the late 80s.

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.

"Business Intelligence"

Critics see BI as evolved from mere 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.

Data discovery

Data discovery is a buzzword in BI for creating and using interactive reports and exploring data from multiple sources. The market research firm Gartner promoted it in 2012.

Although there is no official Data Discovery definition, an accepted one is an user-driven process of searching for patterns or specific items in a data set. Data discovery applications use visual tools such as geographical maps, pivot tables, and heat maps to make the process of finding patterns or specific items rapid and intuitive.

However, since the human brain is not fit for this task, at the end data mining must be employed to accomplish these goals.

There is a growing understanding Business Intelligence is a field where data is applied to strategic thinking, whereas the more mundane need of data to solve daily problems must be addressed by a different set of process and tools, colectivelly known as

[Operational Intelligence].

It is to Operational Intelligence the concept of Data Discovery seems to be more properly attached.

Data warehousing

To distinguish between the concepts of business intelligence and data warehouses, Forrester Research defines business intelligence in one of two ways:

1. Using a broad definition: "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."

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4. Under this definition, business intelligence also includes technologies such as data integration, data quality, data warehousing, master-data management, text- and content-analytics, and many others that the market sometimes lumps into the "Information Management" segment.

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7. Therefore, Forrester refers to data preparation and data usage as two separate but closely linked segments of the business-intelligence architectural stack.

1. Forrester defines the narrower business-intelligence market as, "...referring to just the top layers of the BI architectural stack such as reporting, analytics and dashboards."

Comparison 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.

Comparison with business analytics

Business intelligence and business analytics are sometimes used interchangeably, but there are alternate definitions.

One definition contrasts the two, stating that the term business intelligence refers to collecting business data to find information primarily through asking questions, reporting, and online analytical processes.

Business analytics, on the other hand, uses statistical and quantitative tools for explanatory and predictive modelling.

In an alternate definition, 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.

Applications in an enterprise

Business intelligence can be applied to the following business purposes, in order to drive business value:

1. Measurement – program that creates a hierarchy of performance metrics (see also Metrics Reference Model) and benchmarking that informs business leaders about progress towards business goals (business process management).

1. Analytics – program that builds quantitative processes for a business to arrive at optimal decisions and to perform business knowledge discovery.

2. Frequently involves: data mining, process mining, statistical analysis, predictive analytics, predictive modeling, business process modeling, data lineage, complex event processing and prescriptive analytics.

1. Reporting/enterprise reporting – program that builds infrastructure for strategic reporting to serve the strategic management of a business, not operational reporting.

2. Frequently involves data visualization, executive information system and OLAP.

1. Collaboration/collaboration platform – program that gets different areas (both inside and outside the business) to work together through data sharing and electronic data interchange.

1. Knowledge management – program to make the company data-driven through strategies and practices to identify, create, represent, distribute, and enable adoption of insights and experiences that are true business knowledge. Knowledge management leads to learning management and regulatory compliance.

In addition to the above, business intelligence can provide a pro-active approach, such as alert functionality that immediately notifies the end-user if certain conditions are met.

For example, if some business metric exceeds a pre-defined threshold, the metric will be highlighted in standard reports, and the business analyst may be alerted via e-mail or another monitoring service.

Prioritization of projects

It can be difficult to provide a positive business case for business intelligence initiatives, and often the projects must be prioritized through strategic initiatives.

BI projects can attain higher prioritization within the organization if managers consider the following:

• As described by Kimball, the BI manager must determine the tangible benefits such as eliminated cost of producing legacy reports.

• Data access for the entire organization must be enforced. In this way even a small benefit, such as a few minutes saved, makes a difference when multiplied by the number of employees in the entire organization.

• As described by Ross, Weil & Roberson for Enterprise Architecture, managers should also consider letting the BI project be driven by other business initiatives with excellent business cases.

• To support this approach, the organization must have enterprise architects who can identify suitable business projects.

• Using a structured and quantitative methodology to create defensible prioritization in line with the actual needs of the organization, such as a weighted decision matrix.

Success factors of implementation

According to Kimball et al., there are three critical areas that organizations should assess before getting ready to do a BI project:

1. The level of commitment and sponsorship of the project from senior management.

1. The level of business need for creating a BI implementation.

1. The amount and quality of business data available.

Business sponsorship

The commitment and sponsorship of senior management is according to Kimball et al., the most important criteria for assessment. This is because having strong management backing helps overcome shortcomings elsewhere in the project.

However, as Kimball et al. state: “even the most elegantly designed DW/BI system cannot overcome a lack of business [management] sponsorship”.

It is important that personnel who participate in the project have a vision and an idea of the benefits and drawbacks of implementing a BI system.

The best business sponsor should have organizational clout and should be well connected within the organization. It is ideal that the business sponsor is demanding but also able to be realistic and supportive if the implementation runs into delays or drawbacks.

The management sponsor also needs to be able to assume accountability and to take responsibility for failures and setbacks on the project. Support from multiple members of the management ensures the project does not fail if one person leaves the steering group.

However, having many managers work together on the project can also mean that there are several different interests that attempt to pull the project in different directions, such as if different departments want to put more emphasis on their usage.

This issue can be countered by an early and specific analysis of the business areas that benefit the most from the implementation.

All stakeholders in the project should participate in this analysis in order for them to feel invested in the project and to find common ground.

Another management problem that may be encountered before the start of an implementation is an overly aggressive business sponsor. Problems of scope creep occur when the sponsor requests data sets that were not specified in the original planning phase.

Business needs

Because of the close relationship with senior management, another critical thing that must be assessed before the project begins is whether or not there is a business need and whether there is a clear business benefit by doing the implementation.

The needs and benefits of the implementation are sometimes driven by competition and the need to gain an advantage in the market.

Another reason for a business-driven approach to implementation of BI is the acquisition of other organizations that enlarge the original organization it can sometimes be beneficial to implement DW or BI in order to create more oversight.

Companies that implement BI are often large, multinational organizations with diverse subsidiaries. They may go through the implementation of a Business Intelligence Competency Center (BICC).

A well-designed BI solution provides a consolidated view of key business data not available anywhere else in the organization, giving management visibility and control over measures that otherwise would not exist.

Amount and quality of available data

Without proper data, or with too little quality data, any BI implementation fails; it does not matter how good the management sponsorship or business-driven motivation is. Before implementation it is a good idea to do data profiling.

This analysis identifies the “content, consistency and structure [..]” of the data. This should be done as early as possible in the process and if the analysis shows that data is lacking, put the project on hold temporarily while the IT department figures out how to properly collect data.

When planning for business data and business intelligence requirements, it is always advisable to consider specific scenarios that apply to a particular organization, and then select the business intelligence features best suited for the scenario.

Often, scenarios revolve around distinct business processes, each built on one or more data sources. These sources are used by features that present that data as information to knowledge workers, who subsequently act on that information.

The business needs of the organization for each business process adopted correspond to the essential steps of business intelligence.

These essential steps of business intelligence include but are not limited to:

1. Go through business data sources in order to collect needed data

2. Convert business data to information and present appropriately

3. Query and analyze data

4. Act on the collected data

The quality aspect in business intelligence should cover all the process from the source data to the final reporting. At each step, the quality gates are different:

1. Source Data:

   • Data Standardization: make data comparable (same unit, same pattern...)

   • Master Data Management: unique referential

2. Operational Data Store (ODS)

   :

   • Data Cleansing: detect & correct inaccurate data

   • Data Profiling: check inappropriate value, null/empty

3. Data warehouse

   :

   • Completeness: check that all expected data are loaded

   • Referential integrity: unique and existing referential over all sources

   • Consistency between sources: check consolidated data vs sources

4. Reporting:

   • Uniqueness of indicators: only one share dictionary of indicators

   • Formula accuracy: local reporting formula should be avoided or checked

User aspect

Some considerations must be made in order to successfully integrate the usage of business intelligence systems in a company. Ultimately the BI system must be accepted and utilized by the users in order for it to add value to the organization.

If the usability of the system is poor, the users may become frustrated and spend a considerable amount of time figuring out how to use the system or may not be able to be productive. If the system does not add value to the users´ mission, they simply don't use it.

To increase user acceptance of a BI system, it can be advisable to consult business users at an early stage of the DW/BI lifecycle, for example at the requirements gathering phase.

This can provide an insight into the business process and what the users need from the BI system. There are several methods for gathering this information, such as questionnaires and interview sessions.

When gathering the requirements from the business users, the local IT department should also be consulted in order to determine to which degree it is possible to fulfill the business's needs based on the available data.

Taking a user-centered approach throughout the design and development stage may further increase the chance of rapid user adoption of the BI system.

Besides focusing on the user experience offered by the BI applications, it may also possibly motivate the users to utilize the system by adding an element of competition.

Kimball suggests implementing a function on the Business Intelligence portal website where reports on system usage can be found.

By doing so, managers can see how well their departments are doing and compare themselves to others and this may spur them to encourage their staff to utilize the BI system even more.

In a 2007 article, H. J. Watson gives an example of how the competitive element can act as an incentive.

Watson describes how a large call center implemented performance dashboards for all call agents, with monthly incentive bonuses tied to performance metrics. Also,

Agents could compare their performance to other team members. The implementation of this type of performance measurement and competition significantly improved agent performance.

BI chances of success can be improved by involving senior management to help make BI a part of the organizational culture, and by providing the users with necessary tools, training, and support. Training encourages more people to use the BI application.

Providing user support is necessary to maintain the BI system and resolve user problems. User support can be incorporated in many ways, for example by creating a website.

The website should contain great content and tools for finding the necessary information. Furthermore, help-desk support can be used. The help desk can be manned by power users or the DW/BI project team.