The primary differences between data warehousing and business intelligence are in function and design. Data warehousing refers to the process, mechanism and infrastructure utilized in the preservation of digital information. Business intelligence on the other hand, is the use and analysis of information, some of which may be obtained via data warehousing. This information is used for the decision making pertaining to the operation of a business.
The State of North Dakota describes business intelligence thoroughly in its 'Business Intelligence Report'. Specifically, the white paper describes business intelligence in terms of technology and infrastructure such as data mining information held within a data warehouse and using the appropriate software to perform data analytics. This helps in designing business models that have basis in logical, mathematical and qualitative assessment of business data.
Data warehousing can be thought of as a component of business intelligence that has wider scope. However, without adequate data warehousing, effective business intelligence may have little to draw valid conclusions from and therefore lead to faulty decision making. Thus, data warehousing ideally possesses qualities such as strong organization, easy access, time sensitivity and relevance. Since data warehousing takes physical space used by hardware storage, practically making use of space, and equipment to store sufficient data also involves prudent determination.
The objectives of data warehousing and business intelligence further delineate differences between the two. For example, business intelligence attempts to define, assess, observe, forecast and calculate for the benefit of a business; this is usually or ultimately financial in nature by design and function of a business. The corresponding function of data warehousing is to serve the interest of business intelligence and other business data storage needs.
Further differences between data warehousing and business intelligence include variation in software applications, employee tasks, cost and hardware needs. For example, an information technology (IT) professional familiar with magnetic, optical and solid storage devices would more likely be involved with data warehousing than an executive marketing manager seeking to make use of marketing research to implement a new product launch.
The service industry itself is differentiated in such a way as to distinguish between data warehousing and business intelligence. This is because managerial science and information technology become specialized and require specific knowledge in data warehousing and business intelligence.
Differences arising out of, and between data warehousing and business intelligence may also have the potential to lead to professional entry barriers or differences in approach to a business operation. To illustrate, an IT professional may install and program a data warehouse to store information with maximum capacity in mind, whereas a professional focusing on business intelligence may emphasize strategic use of information instead.
The State of North Dakota describes business intelligence thoroughly in its 'Business Intelligence Report'. Specifically, the white paper describes business intelligence in terms of technology and infrastructure such as data mining information held within a data warehouse and using the appropriate software to perform data analytics. This helps in designing business models that have basis in logical, mathematical and qualitative assessment of business data.
Data warehousing can be thought of as a component of business intelligence that has wider scope. However, without adequate data warehousing, effective business intelligence may have little to draw valid conclusions from and therefore lead to faulty decision making. Thus, data warehousing ideally possesses qualities such as strong organization, easy access, time sensitivity and relevance. Since data warehousing takes physical space used by hardware storage, practically making use of space, and equipment to store sufficient data also involves prudent determination.
The objectives of data warehousing and business intelligence further delineate differences between the two. For example, business intelligence attempts to define, assess, observe, forecast and calculate for the benefit of a business; this is usually or ultimately financial in nature by design and function of a business. The corresponding function of data warehousing is to serve the interest of business intelligence and other business data storage needs.
Further differences between data warehousing and business intelligence include variation in software applications, employee tasks, cost and hardware needs. For example, an information technology (IT) professional familiar with magnetic, optical and solid storage devices would more likely be involved with data warehousing than an executive marketing manager seeking to make use of marketing research to implement a new product launch.
The service industry itself is differentiated in such a way as to distinguish between data warehousing and business intelligence. This is because managerial science and information technology become specialized and require specific knowledge in data warehousing and business intelligence.
Differences arising out of, and between data warehousing and business intelligence may also have the potential to lead to professional entry barriers or differences in approach to a business operation. To illustrate, an IT professional may install and program a data warehouse to store information with maximum capacity in mind, whereas a professional focusing on business intelligence may emphasize strategic use of information instead.
Sources:
1. http://bit.ly/eABq8x (North Dakota: Business Intelligence)
2. http://bit.ly/2ryqwS (Internet Journal)
3. http://bit.ly/gtLrDV (National Security Agency)
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