Business intelligence

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Template:Wikify-date The term business intelligence (BI) typically refers to a set of business processes for collecting and analyzing business information. This includes the technology used in these processes, and the information obtained from these processes.

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BI business processes

Organizations typically gather information in order to assess the business environment, and cover fields such as marketing research, industry or market research, and competitor analysis. Competitive organizations accumulate business intelligence in order to gain sustainable competitive advantage, and may regard such intelligence as a valuable core competence in some instances.

Generally, BI-collectors glean their primary information from internal business sources. Such sources help decision-makers understand how well they have performed. Secondary sources of information include customer needs, customer decision-making processes, the competition and competitive pressures, conditions in relevant industries, and general economic, technological, and cultural trends. Industrial espionage may also provide business intelligence by using covert techniques. A gray area exists between "normal" business intelligence and industrial espionage.

Each business intelligence system has a specific goal, which derives from an organizational goal or from a vision statement. Both short-term goals (such as quarterly numbers to Wall Street) and long term goals (such as shareholder value, target industry share / size, etc.) exist .

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BI technology

Some observers regard BI as the process of enhancing data into information and then into knowledge. Persons involved in business intelligence processes may use application software and other technologies to gather, store, analyze, and provide access to data, and present that data in a simple, useful manner. The software aids in Business performance management, and aims to help people make "better" business decisions by making accurate, current, and relevant information available to them when they need it.

Some people use the term "BI" interchangeably with "briefing books" or with "executive information systems", and the information that they contain. In this sense, one can regard a business intelligence system as a decision-support system (DSS).

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BI software types

People working in business intelligence have developed tools that ease the work, especially when the intelligence task involves gathering and analyzing large quantities of unstructured data. Each vendor typically defines Business Intelligence their own way, and markets tools to do BI the way that they see it.

Business intelligence includes tools in various categories, including the following:

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History

An early reference to non-business intelligence occurs in Sun Tzu's The Art of War. Sun Tzu claims that to succeed in war, one should have full knowledge of one's own strengths and weaknesses and full knowledge of one's enemy's strengths and weaknesses. Lack of either one might result in defeat. A certain school of thought draws parallels between the challenges in business and those of war, specifically:

  • collecting data
  • discerning patterns and meaning in the data (generating information)
  • responding to the resultant information

Prior to the start of the Information Age in the late 20th century, businesses sometimes struggled to collect data from non-automated sources. Businesses then lacked the computing resources to properly analyze the data, and often made business decisions primarily on the basis of intuition.

As businesses started automating more and more systems, more and more data became available. However, collection remained a challenge due to a lack of infrastructure for data exchange or to incompatibilities between systems. Analysis of the data that was gathered and reports on the data sometimes took months to generate. Such reports allowed informed long-term strategic decision-making. However, short-term tactical decision-making continued to rely on intuition.

In modern businesses, increasing standards, automation, and technologies have led to vast amounts of data becoming available. Data warehouse technologies have set up repositories to store this data. Improved Extract, transform, load (ETL) and even recently Enterprise Application Integration tools have increased the speedy collecting of data. OLAP reporting technologies have allowed faster generation of new reports which analyze the data. Business intelligence has now become the art of sieving through large amounts of data, extracting pertinent information, and turning that information into knowledge upon which actions can be taken.

Business intelligence software incorporates the ability to data mine, analyze, and reporting. Some modern BI software allow users to cross-analyze and perform deep data research rapidly for better analysis of sales or performance on an individual, department, or company level. In modern applications of business intelligence software, managers are able to quickly compile reports from data for forecasting, analysis, and business decision making.

In 1989 Howard Dresner, a Research Fellow at Gartner Group popularized "BI" as a umbrella term to describe a set of concepts and methods to improve business decision-making by using fact-based support systems. Dresner left Gartner in 2005 and joined Hyperion Solutions as its Chief Strategy Officer.

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Key performance indicators

BI often uses Key performance indicators (KPIs) to assess the present state of business and to prescribe a course of action. More and more organizations have started to make more data available more promptly. In the past, data only became available after a month or two, which did not help managers to adjust activities in time to hit Wall Street targets. Recently, banks have tried to make data available at shorter intervals and have reduced delays.

The KPI methodology was further expanded with the Chief Performance Officer methodology which incorporated KPIs and root cause analysis into a single methodology.

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KPI example

For example, for businesses which have higher operational/credit risk loading (for example, credit cards and "wealth management"), A large multi-national bank makes KPI-related data available weekly, and sometimes offers a daily analysis of numbers. This means data usually becomes available within 24 hours, necessitating automation and the use of IT systems.

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Designing and implementing a business intelligence programme

When implementing a BI programme one might like to pose a number of questions and take a number of resultant decisions, such as:

  • Goal Alignment queries: The first step determines the short and medium-term purposes of the programme. What strategic goal(s) of the organization will the programme address? What organizational mission/vision does it relate to? A crafted hypothesis needs to detail how this initiative will eventually improve results / performance (i.e. a strategy map).
  • Baseline queries: Current information-gathering competency needs assessing. Does the organization have the capability of monitoring important sources of information? What data does the organization collect and how does it store that data? What are the statistical parameters of this data, e.g. how much random variation does it contain? Does the organization measure this?
  • Cost and risk queries: The financial consequences of a new BI initiative should be estimated. It is necessary to assess the cost of the present operations and the increase in costs associated with the BI initiative? What is the risk that the initiative will fail? This risk assessment should be converted into a financial metric and included in the planning?
  • Customer and Stakeholder queries: Determine who will benefit from the initiative and who will pay. Who has a stake in the current procedure? What kinds of customers/stakeholders will benefit directly from this initiative? Who will benefit indirectly? What are the quantitative / qualitative benefits? Is the specified initiative the best way to increase satisfaction for all kinds of customers, or is there a better way? How will customers' benefits be monitored? What about employees,... shareholders,... distribution channel members?
  • Metrics-related queries: These information requirements must be operationalized into clearly defined metrics. One must decide what metrics to use for each piece of information being gathered. Are these the best metrics? How do we know that? How many metrics need to be tracked? If this is a large number (it usually is), what kind of system can be used to track them? Are the metrics standardized, so they can be benchmarked against performance in other organizations? What are the industry standard metrics available?
  • Measurement Methodology-related queries: One should establish a methodology or a procedure to determine the best (or acceptable) way of measuring the required metrics. What methods will be used, and how frequently will the organization collect data? Do industry standards exist for this? Is this the best way to do the measurements? How do we know that?
  • Results-related queries: Someone should monitor the BI programme to ensure that objectives are being met. Adjustments in the programme may be necessary. The programme should be tested for accuracy, reliability, and validity. How can one demonstrate that the BI initiative (rather than other factors) contributed to a change in results? How much of the change was probably random?.
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See also

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Related subjects

es:Inteligencia empresarial fr:Intelligence économique it:Business intelligence he:מודיעין עסקי nl:Business intelligence pl:Business intelligence pt:Business intelligence fi:Business intelligence zh:商业智能

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