We are seeing today widespread and explosive use of database technology to manage large volumes of business data. The use of database systems in supporting applications that employ query based report generation continues to be the main traditional use of this technology. However, the size and volume of data being managed raises new and interesting issues. Can we utilize methods wherein the data can help businesses achieve competitive advantage, can the data be used to model underlying business processes, and can we gain insights from the data to help improve business processes? These are the goals of Business Intelligence (BI) systems, and Data Mining is the set of embeddable (in BI systems) analytic methods that provide the capabilities to explore, summarize, and model the data. Before applying these methods to data, the data has to be typically organized into history repositories, known as data warehouses. Data warehousing may require integration of multiple sources of data, which may involve dealing with multiple formats, multiple database systems, and distributed databases, cleaning the data, and creating unified logical view of the underlying non-homogeneous data. Data mining analytics try to go beyond OLAP by providing abilities for discovering insights that are computer driven and not end-user driven. Data size is increasing at a rate far exceeding any rates that end-users can cope with. Providing solutions when end-users cannot reasonably supply all possible aggregates to pre-compute, or when it is not possible to express an insight as a pre-computed aggregate, is the goal of data mining analytics.
Business Intelligence refers to computer-based techniques used in identifying, extracting and analyzing business data, such as sales revenue by products and/or departments, or by associated costs and incomes. The term ‘business intelligence’ (BI) is used to describe the technical architecture of systems that extract, assemble, store and access data to provide reports and analysis. It can also describe the reporting and analysis applications or performance management tools at the top of this ‘stack’. Confusingly, BI can also be used to describe just front end reporting and analysis tools. However, BI is not just about hardware and software. It requires cultural change and company wide recognition that a company’s data is an important strategic asset that can yield valuable management information. It is about using this information to improve decision making Business intelligence is about improving decision making. It involves developing processes and systems that collect, transform, cleanse and consolidate organization wide and external data, usually in an accessible store (a data warehouse), for presentation on users’ desks as reports, analysis or displayed on screens as dashboards or scorecards. However, BI is broader than technology. It is about using the information available to a business to improve decision making. With technology, the right information can be accessed, analyzed and presented at the right time to the right people in the right format. The information then needs to be used to inform evidence based decision making. This requires leadership and cultural change.
Effective decisions are those that achieve impact. An effective decision making process has three key elements:
1. How strategic decisions are informed, considered and communicated.
2. How performance and risk are assessed and managed.
3. How routine operational decisions are guided, made and governed so the intended impact is actually achieved.
Management accountants who can combine financial expertise with business understanding have the potential to support decision making in a wide range of roles throughout this process. Providing evidence in the form of financial and management information has long been the basis for accountants’ role in the decision making process....
Please join StudyMode to read the full document