Business intelligence (BI) is the set of techniques and tools for the transformation of raw data into meaningful and useful information for business analysis purposes. BI technologies are capable of handling large amounts of unstructured data to help identify, develop and otherwise create new strategic business opportunities. The goal of BI is to allow for the easy interpretation of these large volumes of data. Identifying new opportunities and implementing an effective strategy based on insights can provide businesses with a competitive market advantage and long-term stability.
BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics.
BI can be used to support a wide range of business decisions ranging from operational to strategic. Basic operating decisions include product positioning or pricing. Strategic business decisions include priorities, goals and directions at the broadest level. In all cases, BI is most effective when it combines data derived from the market in which a company operates (external data) with data from company sources internal to the business such as financial and operations data (internal data). When combined, external and internal data can provide a more complete picture which, in effect, creates an “intelligence” that cannot be derived by any singular set of data.
According to Kimball et al., there are three critical areas that organizations should assess before getting ready to do a BI project:
- The level of commitment and sponsorship of the project from senior management
- The level of business need for creating a BI implementation
- The amount and quality of business data available.
The quality aspect in business intelligence should cover all the process from the source data to the final reporting. At each step, the quality gates are different:
- Source Data:
- Data Standardization: make data comparable (same unit, same pattern…)
- Master Data Management:unique referential
- Operational Data Store (ODS):
- Data Cleansing:detect & correct inaccurate data
- Data Profiling: check inappropriate value, null/empty
- Data warehouse:
- Completeness: check that all expected data are loaded
- Referential integrity:unique and existing referential over all sources
- Consistency between sources: check consolidated data vs sources
- Uniqueness of indicators: only one share dictionary of indicators
- Formula accuracy: local reporting formula should be avoided or checked
Reference : http://en.wikipedia.org/wiki/Business_intelligence#Data_warehousing
Published at :