Companies have long considered data collection and analysis to be fundamental activities for long term strategic planning. Before the rise of the Information Age, most decision making was based on guess-work or trial-and-error practices. Businesses seeking to achieve a sustainable advantage over their competition quickly turned to information management systems for detailed data analysis. These systems and methods have evolved to what is now known by the broader term "business intelligence" or "BI". Business intelligence refers to in-depth analysis of company data for better decision-making. The technology and processes that make this analysis possible take unwieldy collections of information and translate them into organized, readily-accessible, human-readable compilations of data. With an effective BI tool, companies can easily track their own operations, their customers' activity patterns, and industry trends. These fact-based assessments help companies work toward specific goals with confidence.
The business intelligence process can be broken down into the following three stages:
Raw data is gathered and processed into information. The information must be filtered and arranged into meaningful patterns. The knowledge drawn from that data analysis helps to form the business intelligence of a corporation.
Business intelligence needs vary across industries. The functional area and particular processes under examination play a large role in the type of data gathered and the range of knowledge sought. Common functional areas include: Sales and Marketing, Human Resources, Operations, and Finance.
Sales and marketing departments track products, customers, demographics, promotions, sales force, order type, and other related fields. Human resources groups often look to measure employee, organizational, and departmental issues. Assembly speed, warehouse stock, manufacturer and supplier cost, and shift productivity are the domain of operations management. Finance departments will closely watch data on topics such as currency standards, account information, and industry trends.
In the field of business intelligence, staff organizational levels also come into play. Those in lower organizational levels are more likely to focus on measures of short-term, correctable performance while more senior employees may measure high-level trends instead of absolutes. Of course, both of these types of measures are important to gauging a company's relative success.
Good business intelligence means balanced information. Too much or too little data is not useful. Corporations can focus on the most crucial improvements by setting reasonable limits on the information gathered, coordinating the efforts around a company-wide strategy, and employing business intelligence systems.
Intelligence support systems can improve day-to-day business decision-making. Once a company has decided to adopt a business intelligence strategy, the first step in the process is to decide on goals for the initiative. After a central goal has been agreed upon, such as providing shareholders with a return above the industry-average, it is important to assess where overall decision making can be improved and to target the most valuable areas.
To facilitate their efforts, businesses may choose from a variety of intelligence support products on the market. Common tools include: data warehouses, business performance management, data mining or KDD (knowledge-discovery in databases), document warehouses, text mining, data visualization, scorecarding, and OLAP (Online Analytical Processing). These products work to sort through raw data and contribute to informed decision making. Some of these tools can produce compelling results when applied in tandem. For example, data and document warehouses used in tracking a store's inventory can be linked to show both company data on past sales trends and external articles containing consumer opinions, thus expanding the pool of information available for decision-support and providing a more complete understanding of the situation.
Software-oriented business intelligence is seen by some as the next phase in the movement. Companies with limited Information Technology (IT) resources or whose data is held in disparate data sources can rely on software such as DecisionCentric® or Decision Analyzer®. Both products are produced by Decision Technology, Inc.. They enable users to analyze data, create reports, and export files to other applications like Acrobat, Word, and Excel. DecisionCentric®, in particular, is a powerful business intelligence application for small to midsize companies. At its heart is an EII engine optimized for small and midsize organizations. This allows organizations to postpone data warehouse implementations or extend the power and reach of any existing data warehouse. Moreover, the software program offers a free-form reporting tool that does the heavy lifting of report design to make the end-user's job easier.
Business intelligence is prevalent in virtually every level of corporate dealings, in every industry. Technological advances require companies to make 'round-the-clock decisions at a moment's notice. The companies that can develop winning strategies in the face of increased competition and mountains of data will triumph over their competitors.
Business intelligence products guarantee companies the confidence of knowing the current fact-based information they need will always be right at their fingertips.
About Decision Technology, Inc. Decision Technology business intelligence software is the ideal solution for organizations with limited IT resources, and whose data are confined in disparate databases or operational systems. Since 1985, Decision Technology has provided organizations with intuitive, information retrieval software for decision support applications. Its newest business intelligence software product, DecisionCentric, provides Enterprise Information Integration with query and reporting tools optimized for small and medium size organizations. It enables users to integrate, publish and analyze enterprise data across disparate data sources - without expensive ETL technology.
About the Author - R.L. Fielding has been a freelance writer for 10 years, offering her expertise and skills to a variety of major organizations in the education, pharmaceuticals and healthcare, financial services, and manufacturing industries. She lives in New Jersey with her dog and two cats and enjoys rock climbing and ornamental gardening.
Copyright © 2018. Owned and Operated by InfoBureau.net Co. All Rights ReservedBack to top