What is Business Intelligence System?
Business intelligence also is known as (BI) is a set of techniques used to enhance verdict pronouncement in the world of business by utilizing technology driven or fact-founded electronic support systems (Olszak & Ziemba 2007, p. 135). It is presenting actionable facts to assist organizations in managing and honing business information to make effective assessments.BI uses mathematical and statistical intelligence, huge database analysis and data mining to extract calculated business information to support management decisions. (ElBashir & Collier 2008, p 135)
The core of BI is to gather, analyze and distribute data and information. However the secondary purpose of BI is to support the manipulation, and use of data gathered to enhance strategic decision making and improve an enterprise’s performance (Watson & Wixom 2007, p. 40). Business intelligence is a major way for an organization to be ahead of others in a free market competitive environment.
The effective execution of BIS necessitates the applicable use of all the data produced by the company to improve the managerial capacity for changing statistical facts to information and then information to awareness then later into competitive gain.
BIS architecture is structured into the following components;
- Decisions
- Optimization
- Data mining
- Data exploration
- Data warehouse
Key physiognomies of a well-organized BI system in a corporate environment.
BI system features can be presented and analyzed about a technological view. Since BI systems comprise of a set of technologies, tools and software products that explain an assortment of varied data from integrated sources, its characteristics emanate from its purposes. Definitions reveal the general characteristics of BIS, which is, they realize the process of analyzing large volumes of historical corporate data, managing business performance and extracting trends and guidelines for better management decisions. The characteristics define the opportunities that a BI system should offer. Below is an extensive summary of Business Intelligence System characteristics (Bauer 2005, p. 64)
For an effective Business Intelligence Program, distinct accountability and ownership must be held by an organizations performance alongside its objectives (Hackney & Coelho 2012, p. 727). There should also be penalties for non-performance, which would be a motivation system towards rewarding ambitious aims.
An efficient business intelligence system should be value driven. It should pay attention to the basic operational and financial goals that lead to change by driving value. The system should include primary and lagging pointers and be fortified with penalties for accomplishing results that are not acceptable (Bauer 2004, p. 26)
Business intelligent systems should be enforced and sustained with user-friendly interfaces, systems that are fairly simple to use and manipulate and instinctive design and methods that permit insight to be easily gained by management from the data processed (Wu & Barash 2007, p. 270). Part of this entails providing information at the right time so that it can be used.
Core Components and Purpose of Business Intelligence System
This means creating an extensive process that calculates the efficacy of the systems used and collecting feedback on ways to improve on the former system established (Zhyalova 2012, p. 40). Where the need arises, an individual or organization can take counteractive action to adapt the system with time, more significantly when external improvements take place that might influence system applications.
A Business Intelligence System should show and represent one version of the truth. This entails providing transparency and consistency and balancing elasticity with standardization for ultimate success.
Advantage opportunities for enterprises created by Business Intelligent characteristics.
Business Intelligent Systems come with many advantages in the business world for a lot of organizations and enterprises. Below are some of the competitive advantage opportunities resulting from BIS
When employees in a company have access to too much data or detailed and irrelevant data, a lot of time and resources end up being wasted. Business Intelligent Systems make it easy for data to be operative by dispensing information through incorporated representations of data. They also give information that is precise and up to speed in actual time reports which indicate the condition of the enterprise at the moment. They allow users to look up and create reports independently rather than relying on the companies specialists (Olszak and Ziemba 2007, p. 135). They provide solid documentation helping the user comprehend the information and also show only data that is relevant to a specific user in a role-based environment. The system also shows data on a high aggregated level making it easy to spot overall trends.
BI systems permit extraction of reports by end users when needed instead of depending on the information specialists. The systems do this by providing individual role-based dashboards which gather primary data for daily procedures. It also allows any given user the power to scrutinize and corroborate data and also generate fresh sights of data as required.
Through BI systems, a lot of labor and time is saved in the production and organization of reports. It isn’t the biggest benefit but easily tangible.BI systems reduce labor costs by, automating data collection and aggregation (Olszak and Ziemba 2007, p.135), computerizing report production, providing report design tools that make programming simpler and most importantly reducing the training that is needed for developing and maintaining reports.
Decision making is an important and highly significant matter, especially in managerial business positions. Decisions can be varied, either good or bad each of which has a specific impact on an organization. BI systems enhance decision making by giving the decision makers rich and accurate information. It also allows the users to plunge further into statistics for more investigation.
Characteristics of Business Intelligence System
When relevant information is received early enough faster, decisions can be made and implemented. This is important because it allows an organization to be more responsive to opportunities and threats. It’s also important because it shortens the time between thought and action. The decision maker doesn’t lose their train of thought as a result of waiting. The BI systems enable faster decisions by combining multiple and varied resources of data thus cutting the time needed to manually combine the data (Zhyalova 2012, p. 54). The system also gives analytical reporting capabilities enabling users to get varied data sets as needed.
Business intelligent systems aid an organization to align all its parts towards common business objectives by guiding information presentation through enhanced visualizations. They do so also by providing a single source of informational reports and gathering of information from one source (Olszak and Ziemba 2007, p. 135). At the same time, they push selected information throughout the organization by focusing the attention on the employees on the most critical success factors.
Reporting systems that are traditional aim to give users data that’s consistent with fixed predefined structures. This gives organizations very limited and sometimes basic information. The modern Business Intelligence systems offer ad hoc query know-hows which allow the users to tweak data to acquire solutions to whichever questions that come to attention. This lets users reinforce their comprehension of fundamental norms of the business and gather new information into the subtleties leading to success or failure. The analysis can be referred to as online analytical processing.
- Current issues facing Business Intelligent Systems
Business Intelligent Systems are very efficient and come with a lot of advantages in the business industry. However, there are a couple of challenges and current issues facing the BI systems. Below are some of them.
There is a failure to recognize Business Intelligence systems as cross-organized business enterprises and to appreciate that they are separate from stand-alone answers. Business Intelligence is the subsequent phase to attaining a cross-organizational perspective. It is capable of delivering a generous amount of payback; however, it demands extraordinary collaboration (Watson and Wixom 2007, p. 40). BI systems are not just limited to departmental organizations; they also require to be integrated with customers and other information at every level. To thrive at Business Intelligence, an organization is required to foster across organizational common proficiency where every person works and grasps towards the same tactical vision.
Another challenge Business Intelligence systems face is the lack of engaged sponsors who are genuinely interested in their performance. The sponsors enjoy the very little authority of the enterprise. Without a committed business sponsor, Business Intelligence projects struggle and eventually fail (Yager and Zadeh 2012, p. 165). Business sponsors are responsible for ensuring the systems support the strategic vision by creating appropriate objectives and aims. They also provide the business situation evaluation and at the same time help to secure the venture dynamics. If at all the possibilities are extremely wide-ranged, benefactors may opt to draft the deliverables.
Advantages of Business Intelligence System
The lack of available and if at all available then unwilling business representatives is another current issue. The major focus of Business Intelligence projects is mostly technical. IT project managers with minimal knowledge in the business field run most of the projects. This derails the business based benefits that are expected from such projects as they fail to deliver (Olszak and Ziemba 2007, p.135). It’s, therefore, vital that key business representatives and technical assists are identified from the beginning of a project for it to bear maximum results. A BI venture group is required to involve investors starting with, corporate administrators, clients, and business partners, marketing personnel, the finance department, IT support and operations managers.
There is the lack of qualified staff, that is to say, there is sub-optimal staff utilization. A Business Intelligence project team that does not have application employment familiarity is a lot likely to flop in providing anticipated results (Negash 2004, p. 7). Since many BI tasks have fixed timelines and very brief consignment periods, an inexperienced lineup is usually a hazard that should be evaded.
Another significant issue is that there is no software release concept, which is, not a development method. In order to succeed, the project must comply with a strategy per concisely stated approaches, intentions and indicators (O’Brien & Marakas 2005, p. 12). Unlike many other undertakings, Business Intelligence developments are not restricted to conventional departmental necessities.
There is no methodology or work breakdown structure for the projects. BI project planning is an imperative process where resources, scopes, timelines, and deliverables are continuously adjusted. This is to say they are not planned as a one-time activity (ElBashir & Collier 2008, p.135). Even so, the primary project plan has to be designed with extensive detail.
Many business experts face encounters when it gets to pointing out commercial concerns that are linked to BI application purposes. The biggest hurdle encountered by every single BI venture is the group’s capability of understanding the dynamics, importance, and determination in producing the needed statistical figures existing for the knowledge of workers.
Amateur project managers in BI are often inclined to base their rough estimates to a number of conversions required (Fuld 2005, p. 21). Underestimating the process of cleansing data is amongst the prevalent explanations why BI fails. Even the best applications lack purpose if there is dirty data.
There is no appreciation of the requisite for the usage of metadata. It is imperative for every business environment to have a clear understanding of the context of clean data as valid data is meaningless if it’s not tied to its meaning. Thus metadata should be accorded high importance because it is essential to the realization of goals and therefore must be recorded to the latter and be kept in metadata depositories.
Logically, there lacks one single technology that is primarily capable of resolving all challenges and achieving a successful goal in BI (Zhyalova 2012, p. 23). That is because Business Intelligence projects have a wide scope and cover big environments and technologies.
Evaluate and compare two or more commercially available BI tools.
There are an amount of BI tools which are available commercially. Below are two major tools that have been evaluated and compared.
OLAP tools aid users in accessing, modeling and analyzing business problems. It is a tool that generates queries which give users the ability to search in-depth and analyze summary and conclusive material from a multidimensional database (Ricci & Shapira 2011, p. 35). Analytic workers also use OLAP for operations like dividing data by various dimensions and then drilling down into source data.
These are majorly liable for the relocation of data from contract systems and the internet to data repositories. These are the software gears which are dedicated to executing in a programmed way in three tasks, that is; mining, alteration and loading of statistical figures into the data repositories (Wu & Barash 2007, p. 279). In the initial phase, data is mined from the accessible core and exterior sources. The second phase is then transformation. This is with the aim of cleaning up the data to advance the value of the data removed from the diverse sources by correcting inconsistencies (Yeoh & Koronios 2010, p.23). Data combination and merging are executed in order to acquire the reviews that will moderate the reaction time prerequisite by consequent inquiries and evaluations for which the data depositories are intended. Lastly, there is the loading phase. Here data is loaded into the data repository tables to make them accessible for analysis and assessment backing applications (Gangadharan & Swami 2004, p. 13).
The main difference between the commercially used tools ETL and OLAP is the purpose for which they are used. While ETL gears are intended for removing data from one or more sources and stacking into the objective tables, OLAP tools are mainly meant for reporting purposes. In ETL tools, before stacking into the targets, it comprises some conversions to apply the business logic and principles (Watson and Wixom 2007, p. 40). There is also a staging area for cleaning the data and then loading the data. An example of ETL tools includes the informatics and data stage. OLAP tools stack up to the mined data that is loaded by ETL from the objective tables into the OLAP repositories and then develops the essential variations for crafting a report from it. These reports are checked by the end use, and they can view their data depository.
Conclusion
The primary aim of the research was related to performing a conclusive and meticulous outline of the contemporary condition of the BI solutions ground. This is in relation to BI benefits and the answers it’s expected to provide.
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