Business Intelligence

 

Business intelligence (BI) is a business management term that dates to 1958[1]. It refers to applications and technologies that are used to gather, provide access to, and analyze data and information about company operations. Business intelligence systems can help companies have a more comprehensive knowledge of the factors affecting their business, such as metrics on sales, production, and internal operations, and they can help companies to make better business decisions. Business Intelligence should not be confused with competitive intelligence, which is a separate management concept.

Rationale for using BI

Business intelligence applications and technologies enable organizations to make more informed business decisions. They may afford a company competitive advantage. For example, companies use business intelligence applications and technologies to derive indicators that allow them to forecast the trends in their business sector. Business intelligence is used to improve the timeliness and quality of information and to enable managers to better understand the position of their firm in comparison to its competitors.

Business intelligence applications and technologies help companies analyze changing trends in market share, changes in customer behavior and spending patterns, customers' preferences, company capabilities, and market conditions. Business intelligence can be used to help analysts and managers determine which adjustments are most likely to affect trends.

Business intelligence systems can help companies develop consistent, "data-based" business decisions that produce better results than decisions based on "guesswork." In addition, business intelligence applications can enhance communication among departments, coordinate activities, and enable companies to respond more quickly to changes (e.g., in financial conditions, customer preferences, supply chain operations, etc.) because they are usually supported by a data warehouse in which analytical information about these processes resides. When a BI system is well-designed and properly integrated into a company's processes and decision-making process, it may be able to improve a company's performance. Having access to timely and accurate information is an important resource for a company, in that it can expedite decision-making and improve customers' experiences.

In the competitive customer-service sector, a company should have accurate, up-to-date information on customer preferences, so that it can quickly adapt to changing demands. Business intelligence enables companies to gather information on the trends in the marketplace and to develop innovative products or services in anticipation of customers' changing demands. Business intelligence applications can also help managers to be better informed about actions that a company's competitors are taking. As well, BI can help companies share selected strategic information with business partners. For example, some businesses use BI systems to share information with their suppliers (e.g., inventory levels, performance metrics, and other supply chain data).

BI systems can also be designed to provide managers with information on the state of economic trends or marketplace factors, or to provide managers with in-depth knowledge about the internal operations of a business.

BI technologies

For a BI technology system to work effectively, a company should have a secure computer system which can specify different levels of user access to the data 'warehouse,' depending on whether the user is a junior staffer, a manager, or an executive. As well, a BI system should have sufficient data capacity and a plan for how long data will be stored (data retention). Analysts should set benchmark and performance targets for the system.

Business intelligence analysts have developed software tools to gather and analyze large quantities of unstructured data, such as production metrics, sales statistics, attendance reports, and customer attrition figures. Each BI vendor typically develops Business intelligence systems differently, to suit the demands of different sectors (e.g., retail companies, financial services companies, etc.).

Business intelligence software and applications include a range of tools. Some BI applications are used to analyze performance, projects, or internal operations, such as AQL - Associative Query Logic, Scorecarding, Business activity monitoring, Business Performance Management and Performance Measurement, Business Planning, Business Process Re-engineering, Competitive Analysis, User/End-user Query and Reporting, Enterprise Management systems, Executive Information Systems (EIS), Supply Chain Management/Demand Chain Management, and Finance and Budgeting tools.

Other BI applications are used to store and analyze data, such as Data mining (DM), Data Farming, and Data warehouses; Decision Support Systems (DSS) and Forecasting; Document warehouses and Document Management; Knowledge Management; Mapping, Information visualization, and Dashboarding; Management Information Systems (MIS); Geographic Information Systems (GIS); Trend Analysis; Software as a service (SaaS) Business Intelligence offerings (On Demand) — which is similar to traditional BI solutions, but software is hosted for customers by a provider^[1]; Online analytical processing (OLAP) and multidimensional analysis, sometimes called "Analytics" (based on the "hypercube" or "cube"); Real time business intelligence; Statistics and Technical Data Analysis; Web Mining; Text mining; and Systems intelligence.

Other BI applications are used to analyze or manage the "human" side of businesses, such as Customer Relationship Management (CRM) and Marketing tools and Human Resources applications.Web Personalization For examples of implemented business intelligence systems, see the BI screenshot collection at The Dashboard Spy.

History

Prior to the start of the Information Age in the late 20th century, businesses had to collect data from non-automated sources. Businesses then lacked the computing resources to properly analyze the data, and as a result, companies 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.

Thus we have business intelligence, a term and a definition that date to a seminal October 1958 IBM Journal article by Hans Peter Luhn titled A Business Intelligence System[2] (vol. 2, no. 4, p.314). Luhn wrote,

In this paper, business is a collection of activities carried on for whatever purpose, be it science, technology, commerce, industry, law, government, defense, et cetera. The communication facility serving the conduct of a business (in the broad sense) may be referred to as an intelligence system. The notion of intelligence is also defined here, in a more general sense, as "the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal."

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 these data. Improved Extract, transform, load (ETL) and even recently Enterprise Application Integration tools have increased the speed of collecting the data. OLAP reporting technologies have allowed faster generation of new reports which analyze the data. Business intelligence has now become the art of sifting through large amounts of data, extracting pertinent information, and turning that information into knowledge from which actions can be taken.

Business intelligence software incorporates the ability to mine data, analyze, and report. Some modern BI software allows 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 an umbrella term to describe a set of concepts and methods to improve business decision-making by using fact-based support systems.

The future of business intelligence

In this rapidly changing world, consumers are now demanding quicker, more efficient service from businesses. To stay competitive, companies must meet or exceed the expectations of consumers. Companies will have to rely more heavily on their business intelligence systems to stay ahead of trends and future events. Business intelligence users are beginning to demand Real time Business Intelligence or near real-time analysis relating to their business, particularly in front-line operations. They will come to expect up-to-date and fresh information in the same fashion as they monitor stock quotes online. Monthly and even weekly analyses will not suffice. In his book on the future of Business Intelligence, titled 'In Search of Insight,'^{[citation needed]} Charles Nicholls, CEO of a BI Software company, states, "Business users don't want to wait for information. Information needs to be always on and never out of date. This is the way we live our lives today. Why should business intelligence be any different?"

In the not-too-distant future, companies will become dependent on real-time business information in much the same fashion as people come to expect to get information on the Internet in just one or two clicks. "This instant 'Internet experience' will create the new framework for business intelligence, but business processes will have to change to accommodate and exploit the real-time flows of business data." — Nigel Stokes, CEO, DataMirror Corp., Toronto

"BI 2.0" is the recently-coined term which is part of the continually developing business intelligence industry and which heralds the next step for BI. "BI 2.0" is used to describe the acquisition, provision and analysis of "real-time" data, the implication being that earlier business intelligence and data mining products (BI 1.0?) have not been capable of providing the kind of timely, current data end-users are now clamoring to have. Realizing that hype has historically outpaced reality as business intelligence software companies compete for marketshare, it would be wise to keep in mind the observation of veteran analyst Andy Hayler as vendors now begin to describe their products in terms of the "real-time" and "BI 2.0" nomenclature.

Hayler recently wrote the following in an article titled, "Real Time BI - Get Real": "I permitted myself a wry smile when I first heard the hype about 'real time' business intelligence." Hayler then goes on to explain, "The mismatch between fantasy and reality is driven by two factors. The first is that business rules and structures (general ledgers, product classification, asset hierarchies, etc.) are not in fact uniform, but are spread out among many disparate transaction system implementations... The second problem is that the landscape of business structures is itself in constant flux, as groups reorganize, subsidiaries are sold or new companies acquired".

As long as business intelligence relies upon some kind of data warehouse structure (including web-based virtual data "warehouses"), data will have to be converted into what Hayler calls "a lowest common denominator consistent set." When it comes to dealing with multiple, disparate data sources and the constantly changing, often volatile, business environment which requires tweaking and restructuring of IT systems, getting BI data in a genuinely true, "real-time" format remains, again according to Hayler, "a pipe dream... As long as people design data models and databases the traditional way, you can forget about true 'real-time' business intelligence across an enterprise: the real world gets in the way."

In the near future, business information will become more democratized, so that end users from throughout the organization will be able to view information on their particular segment to see how it's performing. In the future, the capability requirements of business intelligence will increase in the same way that consumer expectations increase. It is therefore imperative that companies increase at the same pace or even faster to stay competitive.

Key intelligence topics

Business intelligence often uses key performance indicators (KPIs) to assess the present state of business and to prescribe a course of action. Prior to the widespread adoption of computer and web applications, when information had to be manually inputted and calculated, performance data was often not available for weeks or months. 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.

Businesses that face higher operational/credit risk loading, such as credit card companies and "wealth management" services, often make KPI-related data available weekly. In some cases, companies may even offer a daily analysis of data. This fast pace requires analysts to use IT systems to process this large volume of data.

Designing and implementing a business intelligence program

When implementing a BI program 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 program. What strategic goal(s) of the organization will the program 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 these 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, and 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 an 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 program to ensure that objectives are being met. Adjustments in the program may be necessary. The program 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?.

Business intelligence in databases

The basic form of data or information is stored in the databases. When the amount of data is significant (i.e. > 10 GB), we transform it into a data warehouse. A data warehouse houses several databases, typically from several engines. For example, a given company might be Oracle-specific, but it might then acquire another company that is SQL-Server-specific, or even half SQL Server and half DB2. Therein lies the problem. A data warehouse ideally stores all these data, resolves these differences in format, and so on.

When a database application is created and attached to a database, certain properties are kept in mind. Within any given application, different users are created and given certain permissions: Admin, Director, Manager, SalesPerson, Temp, etc. Some of them are made administrators, while others are made users. We create a general application, but then we refine its abilities to suit the roles defined (director, manager, etc.). The overall application handles different tasks, depending on the role of the current user — certain parts are exposed and enabled, while others are not.

See also

• Analytics
• Business intelligence tools
• Collective intelligence
• Competitive intelligence
• Digital dashboard
• Economic Espionage Act of 1996
• Environmental scanning
• Intelligent document
• List of management topics
• Location intelligence
• Online analytical processing
• OODA Loop
• OLAP
• Predictive analytics
• Reverse engineering

References

External links

• Screenshots of business intelligence dashboards

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