Definition

business intelligence (BI)

This definition is part of our Essential Guide: An enterprise guide to big data in cloud computing
Contributor(s): Craig Stedman

Business intelligence (BI) is a technology-driven process for analyzing data and presenting actionable information to help corporate executives, business managers and other end users make more informed business decisions. BI encompasses a wide variety of tools, applications and methodologies that enable organizations to collect data from internal systems and external sources, prepare it for analysis, develop and run queries against the data, and create reports, dashboards and data visualizations to make the analytical results available to corporate decision makers as well as operational workers.

The potential benefits of business intelligence programs include accelerating and improving decision making; optimizing internal business processes; increasing operational efficiency; driving new revenues; and gaining competitive advantages over business rivals. BI systems can also help companies identify market trends and spot business problems that need to be addressed.

BI data can include historical information, as well as new data gathered from source systems as it is generated, enabling BI analysis to support both strategic and tactical decision-making processes. Initially, BI tools were primarily used by data analysts and other IT professionals who ran analyses and produced reports with query results for business users. Increasingly, however, business executives and workers are using BI software themselves, thanks partly to the development of self-service BI and data discovery tools.

Business intelligence combines a broad set of data analysis applications, including ad hoc analysis and querying, enterprise reporting, online analytical processing (OLAP), mobile BI, real-time BI, operational BI, cloud and software as a service BI, open source BI, collaborative BI and location intelligence. BI technology also includes data visualization software for designing charts and other infographics, as well as tools for building BI dashboards and performance scorecards that display visualized data on business metrics and key performance indicators in an easy-to-grasp way. BI applications can be bought separately from different vendors or as part of a unified BI platform from a single vendor.

BI programs can also incorporate forms of advanced analytics, such as data mining, predictive analytics, text mining, statistical analysis and big data analytics. In many cases though, advanced analytics projects are conducted and managed by separate teams of data scientists, statisticians, predictive modelers and other skilled analytics professionals, while BI teams oversee more straightforward querying and analysis of business data.

Business intelligence data typically is stored in a data warehouse or smaller data marts that hold subsets of a company's information. In addition, Hadoop systems are increasingly being used within BI architectures as repositories or landing pads for BI and analytics data, especially for unstructured data, log files, sensor data and other types of big data. Before it's used in BI applications, raw data from different source systems must be integrated, consolidated and cleansed using data integration and data quality tools to ensure that users are analyzing accurate and consistent information.

In addition to BI managers, business intelligence teams generally include a mix of BI architects, BI developers, business analysts and data management professionals; business users often are also included to represent the business side and make sure its needs are met in the BI development process. To help with that, a growing number of organizations are replacing traditional waterfall development with Agile BI and data warehousing approaches that use Agile software development techniques to break up BI projects into small chunks and deliver new functionality to end users on an incremental and iterative basis. Doing so can enable companies to put BI features into use more quickly and to refine or modify development plans as business needs change or new requirements emerge and take priority over earlier ones.

Sporadic usage of the term business intelligence dates back to at least the 1860s, but consultant Howard Dresner is credited with first proposing it in 1989 as an umbrella category for applying data analysis techniques to support business decision-making processes. What came to be known as BI technologies evolved from earlier, often mainframe-based analytical systems, such as decision support systems and executive information systems. Business intelligence is sometimes used interchangeably with business analytics; in other cases, business analytics is used either more narrowly to refer to advanced data analytics or more broadly to include both BI and advanced analytics.

Business intelligence vs. advanced analytics
BI vs. advanced analytics

This was last updated in October 2014

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Business intelligence is the only way organization can be ahead of others in a free market competitive environment.
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Business intelligence definitely is a broad topic, but it is so essential in order to thrive and expand. There are quite a few companies out there that provide services covering all the aspects of business intelligence. I believe earlier today I saw that http://www.thinkasg.com/ was celebrating their 20th anniversary and also saw another business intelligence company celebrating an anniversary. Being around for 20 years definitely shows their worth in my opinion. These large corporations out there need time to focus on their business expansion, not IT infrastructure or data management. That's why these business intelligence companies are so important, they take care of the work these large corporations do not have time to focus on.
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What's your secret to success for managing successful business intelligence (BI) programs?
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We need broad availability and detailed analysis. We don't expect every person - I'm primarily talking about marketing and sales - to know exactly how to interpret every bit of data. But we do expect them to use that data. For that, we often bring in outside experts to generate white papers and analysis.

Rather than planning for workers to sort everything out on their own, we try to incorporate BI into our planning and performance meetings. At least one person has to understand and interpret the data even if that means bringing in an off-site expert.

We get smarter the more we work. As BI is incorporated into our marketing and sales strategies, those results become part of the next round. It's easy to see solid results so its self-reinforcing.
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BI will go on refining and enlarging its borders as the business and people needs keep growing.  BI is driven by innovation and providing economically viable solutions with increasing flow of useful results.  BI is border less area wherein every body and any body can grow and get success after success paving way for more challenges.  
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Bi provide huge segments of reports, 
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Business intelligence; this is a gold mine for forward thinking management teams. Am naturally drawn to it. It eases managements work at all levels of the organisation.
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Hi - I am planning to learn BI Tableau course and find the root in this. Can you please some one advise how is the career in data analytic role.
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Hi @Seshaphani, there are wide opportunities in the field of business intelligence & analytics, data analytics professionals are earning huge amounts compared to other fields, if you are interesting to take Training for any BI tool, you can contact Alrasmyat. They are providing training for almost all famous BI tools in different countries.
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When people enter businesses that they know nothing about and are backed by venture funding, it's only then they need external consulting or business intelligence.
An average business man is intelligent anyway.
When somebody starts a business which is backed only by an MBA knowledge, then no amount of BI or external consulting will help. But then that's the case more often.
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Hi
Is this framework must even for smaller business units with turn over of around 300 M USD.  Or can we reduce the overall framework
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Is higher accuracy the primary goal in building a reliable model in BI? Please let me know your thoughts and explain if possible.

Thank you
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There are many organizations that throw away valuable time looking for information from within their different data sources when they need help in gaining a deeper understanding of their business. With a business intelligence services in place, all of the needed data comes from one source and can be accessed from one dashboard and converted into a report
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