The Major Components of BI system:
The business intelligence system is a multilayered software system composed of several software components that are duty specialized. These components function in a systematic order. ETL is the first component and it functions in data extraction, transformation, and loading while the data warehouse is the second (Ranjan 2005, p. 61). The third, the fourth and the fifth components are the online analytical processing (OLAP) unit, the data mining unit, and finally the information visualization unit (Ranjan 2005, p. 61). The information visualization unit comprises of the ad hoc inquiry and reporting unit and the report presentation tools. This paper shall discuss the first four components of the business intelligence system in the order listed above in details below.
1. ETL Extract-Transformation-Load:
The extract-transformation-load component provides the mechanism for extracting, transforming, and loading of data from their sources to the warehouse (Olszak & Ziemba 2007, p. 138). It provides the platform for deriving meaningful business information from the huge volumes of data valuable in a business environment . As the name suggests, the ETL component comprises of three stages. In the extraction stage, business data is sourced from transactional systems, business functions, operation processes and the internet . Subsequently, the data sourced in the extraction stage is transformed so that it is compatible with the data warehouse system . This happens at the transformation stage. Finally, the data is loaded to the data warehouse.
2. Data Warehouse:
The data warehouse is the basis of data storage within the businesses intelligence system. Data in the warehouse is majorly oriented and integrated in accordance with the subject (Ranjan 2005, p. 62). The role played by the data warehouse is crucial in the sense that it aggregates data accordingly in their relevance thus minimizing the chances of confusion in information processing and dissemination. The Enterprise Data Warehouse acts as the reception center for all the data received from all units of the business. The data warehouse may be built in a multidimensional architecture and all data received and kept in this component is regarded as trustworthy and reliable (California Department of Technology 2014, p. 10).
3. OLAP:
The online analytical processing is the most vital of the components of the business intelligence system because it is the component in which data is analyzed, and sensible information is drawn (Nedelcu 2013, p. 15). Without it, the data that has been gathered remains irrelevant. Online analytical processing component had its genesis as an easy way of analyzing the huge volumes of data that are continuously encountered in business (Lloyd 2011, p. 54). The amounts of data that needs to be analyzed on a daily basis within a business are always huge and complex. The complexity associated with data analysis and the limitation of time that may be needed in decision making requires a system that can do the processing of such data in the shortest time possible. The online analytical processing unit can help make sensible information from such piles of data in real time and without much difficulty.
4. Data Mining:
Data mining is useful in the automatic detection of variations in normal business processes and transactions (Ong, Siew & Wong 2011, p. 7). These variations are normally primarily presented in data form and thus needs analyzing to interpret. This component of the business intelligence system makes use of statistical techniques to achieve its functionality. Examples of such techniques include classification and clustering (Ong, Siew & Wong 2011, p. 7). Time-series analysis is another statistical technique that may be applied in data mining (Ong, Siew & Wong 2011, p. 7). Because business data collection is always a continuous process and thus means that variations in the business environment may be captured on a continuous basis, the presence of data mining helps to track such variations from their onset. Because variations are immediately noted, speedy and corrective decision-making is made possible (Ong, Siew & Wong 2011, p. 7).
The benefits of BI:
All the components of the business intelligence system are very vital in the efficient management of business operations. These components function together to help the management team to present business information in a more comprehensible manner across the business hierarchical chain (Bălăceanu 2007, p. 67). Apart from the role of making communication effective within the business, these components also assist management in speedy and witty decision-making in the business (Ranjan 2005, p. 61). The use of BI is associated with improved openness and use of information within an organization that in turn improves business functions and processes, business profitability and detects red flags whenever they arise . Some of the benefits of BI are readily visible, easily quantified, and thus tangible. On the other hand, there are those benefits that can neither be seen nor quantified and thus intangible. This study shall explore these tangible and intangible benefits separately.
a) Tangible benefits of BI
The three tangible benefits of business intelligence to an enterprise revealed in many studies include saved time; costs saved and return on investment (ROI) .
Saved Time
The business intelligent system being an automated system prepares, analyzes, and process data in real time thus saving time that would otherwise be greatly be lost by a manual system. BI thus allows for quick information deduction from available data and an expedited process of making business decision. In essence, BI facilitates redirection of personnel time from data analysis and processing to other vital business functions. As a result, it greatly contributes to the overall return on investment of integrating such a system as part of normal business functions . Consequently, BI saves time on activities such as faster generation of reports that may advise in the speedy making of business corrective and helpful business decisions (Hočevar & Jaklič 2010, p. 94).
Cost Saved
Use of an automatic system ensures that the cost incurred in the procurement of such a system is a one-off investment that may be inclusive of such needs as staff training on its use unlike a manual approach where people are involved in manual data processing and analysis (Negash 2004, p. 185). BI avoids the business the resultant the direct recurring costs by information technology (IT) employees or consultants on routine manual data processing and analysis. The deployment of an automated system that is able to multitask also translates to the hiring of fewer employees and as a result reduces employee costs.
Return on Investment
The overall benefits of BI in a business do contribute to return on investment . The functionality of BI in increasing the efficiency of business operations and hence increases profitability contributing increased return on investment in the business. The cost saved on diminished employees head count a guarantees lower expenditures and hence higher return margins. BI allows for early detection of anomalies that would otherwise result to loss of business resources. The advantage of using BI to generate information on competition through competitive intelligence enables the business to take advantage of competition weakness to its benefit .
b) Intangible benefits of BI
Business intelligence can also contribute intangible benefits that can improve the performance and hence profitability of a business. For example, business intelligence is able to accord to a business the knowledge of the environment that encompasses a business.
Advantage over Competition
Through competition intelligence, BI can easily detect competition trends within the business environment (Negash 2004, p. 186). As a result the business management executives can make quick strategic decisions to position the business to take advantage of gaps not filled by the prevailing competition. Again, competition intelligence merits the executives to think ahead of the competition by devising ingenious strategies over the competition.
Business Efficiency and Effectiveness
Business intelligence has also facilitated efficiency of roles and function in organizations making the organizations effective (Hočevar & Jaklič 2010, p. 94). Considering that data processing, analysis, and presentation happen fast, it enhances the speed of making business decisions.
Single version of truth
The system also acts as a single source of information processing, management and analysis thus preventing possible occurrence of overlap and distortion of information that would occur by using several platforms and thus contribute to a clear strategic direction for the business.
Seamless Information Access
A rapid flow of communication across the different quarters of the business environment has been made possible by the timely access to such information by those within the organization that needs it to function effectively (Hočevar&Jaklič 2010, p. 94). Business intelligence also helps in the strategic use of clear business information to drive the business towards profitability and attainment of business objectives and goals (Leat 2007, p. 6).
Business Intelligence Maturity Level “(IEM)”
A status of maturity is granted to a business intelligence model that is termed attained completeness or perfection and ready to be used (Olszak 2013, p. 953). Maturity status in the designing of a business intelligence system is a progressive process and not a none-off hiatus. Being a transformational process, the maturity element of a BI system is guided by such systems that have already attained maturity (Olszak 2013, p. 953). A mature BI system should clearly and succinctly help differentiate which area of the business experiences most analysis and reporting, how the analysis reports and indicators are used and what benefits are realized from its use (Olszak 2013, p. 953). Different maturity models have been devised to guide the transformational process of business intelligence systems. One such example is the Gartner’s Maturity Model. The model gives direction and state of the application of business intelligence in a business (Olszak 2013, p. 953). The five levels that business intelligence system undergoes towards attaining maturity identified by the Gartner’s Maturity Model are: a state of unawareness which then graduates to becoming tactical then attaining focus, then consolidating to becoming strategic and finally pervasive (Olszak 2013, p. 953). Another maturity model is the Information Evolution Model that was proposed by SAS. The model is useful to corporations that want to scrutinize hoe they strategically handle and use information to make their ventures profitable and functioning normally. Business information is a very crucial corporate asset that must be handled with utmost value. It is because its proper use is able to guide business actions and activities that add value and profit to the organization (Leat 2007, p. 6). Olszak, (2013, p. 954) notes five maturity levels proposed by the Information Evolution Model starts with the operation level. The consolidation level is next followed by the integration level. We have the optimization and innovation as the final levels.
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