Introduction
A system is a set procedure of operations that are linked to each other and contribute to a result. For effective delivery of results, all processes must be in operation and be effectively managed. System safety is a risk management strategy aimed at ensuring that any malfunction of the system has been detected and rectified. It is a control system with the results being high efficiency and effectiveness. A system has some expected, unexpected, and abnormal stress. A good system policy tool should be able to identify and correct all these errors (Weibert & Plunkett, 2006). This paper looks into the use of business intelligence tools for the safety of information in an organization.
Data warehouse
Having good information about the prevailing condition in an economy offers a business a competitive advantage over its competitors since management can make relevant decisions from the information they have. However, the integrity and relevance of data are sometimes influenced by the source at the recording stage. Having a good safety system will assist in detecting any stress in the system.
Business intelligence tools take two angles; custom-built tools and commercial reporting tools. Whichever the category, they are meant to keep custody and give access to certain information from the data warehouse maintained in a company for better decision making. Custom-built software is developed in a company and aims to keep and avail certain information to limited people working in a certain area or organization; commercial reporting tools are developed for sale to help in a specific line of business; they are made in such a way that they can be integrated into the system operating in a business.
Examples of commercial reporting tools are Oracle, Reporting and querying software, and ORAP. Word access (spreadsheets) fall in the category of business tool; it has the widest use. Different industries require different information, thus different systems with varying levels of data rights are used.
Why BI tools are effective in system safety; we will consider a case of an SAP BW System
More companies are embracing the use of Business intelligence tools in their decision-making; there have been consistent problems in almost all companies: lack of information, fragmented and inconsistent information in the database. Most systems use a data warehouse or a data mart information to provide historical information, offer a detailed analysis of the current trends of business and predictive views.
Despite that not all data warehouses are used for business intelligence tools, the level of their accuracy and fullness is important for a successful BI. Realizing this deficit, SAP developed a data warehouse (in 1997). The system merges external and internal data into one repository. From the repository, organizations can get preconfigured data; the preconfigured data is used for data management and archiving. The approach has a wider base than an individual business database and extends its services to administrators/maintainers of data reserves (Kantardzic, 2002).
SAP BW system is based on five info-objects, which are used to describe business variable relationships. They come in different languages, hierarchies, currencies, weights among others. The five Info-objects are;
- Data Sources: they contain general data, which is related in one way or another. It is data in flat/raw form.
- Info-Sources: they contain data that is general but more focused on business. They retrieve data from previous records mostly online transactional processes (OLTP) and master data
- ODS-objects: this merges data from more than one info source. The data are direct but still in flat form. From them, reports that lead to quality assurance can be derived.
- Info-Cubes: they are multi-dimensional data storage that gives organized data that can be easily analyzed or interpolated from a micro point of view. For example, data can be analyzed on the geographical location of a business.
- Info-Providers: they contain all the data objects found in an SAP BW.
- Multi-Providers: they do not contain any data but are used to combine data from different providers.
Making a decision to have a BI system and putting all the infrastructures required is the beginning but the major work is on when the system has started operating. How will the organization start to gain from its benefits; how will the management cope with the organization’s culture; how will the gains be reflected; and how long the will benefits start to accrue, are among the questions that arise. The first thing that an organization should do is to educate its staff; people react differently to change. The system comes as change that affects normal way of doing things; there should be a mechanism to implement it.
One of the most effective ways of starting a change is addressing the internal customer- employees. In most cases, especially when adopting information technology, employees have a tendency of thinking it has come to replace them. As early as possible these fears should be dealt with, a thing that will translate to increased productivity hence increased revenue. After the staff have relaxed, the next step is integrating it. Here the new and the old system should be run together, this offers staff more time to familiarize and understand the system (Bisaccia, Vonderheid & Geskin, 2009).
How to Implement the System
Before implementing a system, there should be set parameters like averages, means, system approaches, and standard deviation that are more likely to prevail in an ideal situation. After these parameters are set, the system is set in such a way that any information that does not fall within this defined range is rejected by the system (Nafday, 2008).
After the department/team using BI tools has set to go, there are areas that will come out either because they require improvements or are redundant. They should be addressed before they paralyze the whole system. Feedback follows; this may be internal or external, short-term and/or long-term. Short term and long term are relative since in some projects it may take three years for a benefit to be felt while others may take just a few hours.
In short term, some costs will be saved as a result of increased efficiency or recognition of areas that were previously erroneously done. The financial impact of this will be reflected on monthly or quarterly yearly reports that will show increased profit margin not necessarily from increased sales but from low production costs. At this point expenses brought about by the system are considered keeping in mind that being a new system, its costs are higher at this stage. In most cases you find those employees who were stubborn when the system was being rolled out are comfortable mostly if they are assured of their job security and have seen gains from the system.
This is a stage where organizational culture has changed to the benefit of BI tools adopted. If the BI tool was not enrolled in an entire organization, this is the time to move it to the next department because chances of its acceptability are high.
Medium and long-term effects of the system should also be looked into. Since the system was developed to do certain functions, the level that satisfies the function should be interpolated. This can be in yearly reports (Kersten, Mikolajuk, & Yeh, 2004). It is not always that a system after implementation benefits an organization; there are times it may not fit in an organization. If it had been enrolled well and staffs are not the problem, some options available include changing the system altogether, reverting back to the old system as a new one is a sort of improvement on the tool adopted. Any BI tool which does not positively affect decision-making should not be adopted.
Conclusion
System safety is of great importance in ensuring that a certain project/process is a success. Adopting the right system safety approach helps a company in attaining its set objectives and goals. Information is important in a business as it assists in decision-making. To have a good data management system, there is a need to adopt effective system safety tools which will detect the slightest valuation of data and advice the management. These checks and balances include the use of averages, means, system approaches, standard deviation, and other scientific risk identification methods.
References List
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Kantardzic, M. (2002). Data Mining: Concepts, Models, Methods and Algorithms. New York: J. Wiley & Sons.
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