Trends in Data Warehousing - Research Paper Example

Trends in Data Warehousing INTRODUCTION It is a unanimous understanding of enterprise executives the world over that the timely and accurate use of knowledge is imperative for the existence and success of any organization. This is because timely and accurate knowledge means improved business performance. The knowledge that is at hand, if handled properly, both in the quantitative and in the qualitative manners, it brings value to the organization’s decision makers. With the evolution of business processes and also with the continuously evolving business requirements the priorities for data warehousing are continuously on a rise. Integration of data is essential such that it can be readily available for instant retrieval, business processing and then embedding back into the business application. DATA WAREHOUSING- TECHNICALLY The models used to implement data warehouses are multi-dimensional database models comprise of hyper cubes and materialized views (sometimes referred to as data marts. These database models are erected on the basis of archival data gathered from the operational databases. The term OLAP (Online Analytical Processing) is associated with these database models. ROLAP is the RDBMS based implementation of multi-dimensional database models and MOLAP is refers to a whole new design of implementation. The multi-dimensional databases are used for trend analysis, business intelligence, forecasting and time series analysis. Data warehousing provides strong basis for the techniques like knowledge management, enterprise data warehousing, business forecasting and business intelligence that assist in the providence of the value of knowledge. An introduction to the basic concept of above mentioned trends would assist in the further proceedings of the paper. BUSINESS INTELLIGENCE: Business Intelligence refers to a very comprehensive terminology, often narrated as a combination of ‘data’, ‘information’ and ‘knowledge’. This key business terminology has more than just one meaning associated with it. The various meanings it holds correspond to the numerous means in which it can be deployed. Each business holds this entity as its integral part without the inclusion of which it may be potentially impossible to run a business at all. In order to demonstrate the variations in its meaning two of its various meanings are demonstrated as under. “Business intelligence (BI) is a broad category of application programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. BI applications include the activities of decision support, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting, and data mining.” (Sauder, 2011) BI and OLAP: The advent of information technology and its embedding into the corporate environment proved to be a turning point for the business arena. This coupled with the advent of technological frameworks and the ever expanding World Wide Web has resulted in the input of un-administrable amounts of information into a business. It is essential for every business to adhere to the information that is coming into it as this is vital for its up-to-date process framework and infrastructure. The handling of large amounts of data and the selection of what is relevant and what is not is an imperative task that every organization needs to take care of irrespective of its volume of business. This handling is done by storing the incoming and the generated data both it into large data banks. The systematic storage of data enables its timely retrieval by both business units and workers connected to the organization. This, in turn, enhances the way this information is used to become building blocks of the business itself. The process of systematic and intelligent storage of data is referred to as data warehousing. Assisting data warehousing is another interactive software based application. Known as OLAP (Online Analytical Processing), this tool has been designed to respond to multiple and complex resolution of queries simultaneously. Being multidimensional in nature, OLAP responds efficiently to complex analytical queries. On a broader scale, OLAP is considered as an integral part of business intelligence as it also gives the provision of relational reporting and data mining. SALES FORECASTING: Sales forecasting is a well-known business terminology. Integral to its existence this business process involves an insight into the future prospects of a company’s sales. These sales may be spanned over any specific amount of time may it be weekly, monthly or yearly. The importance of this vital business process can be assessed by the fact that the prospective production strategies and those involving inventory are all based upon the feedback given to the business by it sales forecasting process. It is impossible that without having an idea of the sales of a business the designing of its inventory be done. Estimates regarding the cash flow in a business and its growth plan are also based upon knowledge input from the sales forecasting business process. Thus, it can very truly be said that information used to make intelligent business processes is gained from sales forecasting only. The procedural steps for forecasting sales of an existing business are far more convenient and simpler than forecasting the sales patterns of a new or proposed system. This is owing to the fact that for an existing business sales forecasting can be done by using trends of sales that existed in the same time month of the last financial year and combining this information with the current existing economic and business trends. KNOWLEDGE MANAGEMENT: This business process refers to a series of steps that involve the creation, spreading out and utilization of knowledge. The term knowledge here refers to the intellectual assets of an organization that are used to enhance its performance and competing power. Two virtual steps constitute the business process of knowledge management. Firstly, the capturing and scripting of knowledge and secondly, its proper distribution into the business fractions concerned so as to gain full advantage of it. EDW (Enterprise data warehousing): The most notable trend in data warehousing is the establishment of standardized customization of usage of data for an enterprise. The notable features of enterprise data warehousing are, The integration of diverse systems. Support of different topologies. Development of topologies. Easy information access through layered methodology. Physical and virtual data Integration facility with real time access. Support for operational reporting and strategic analyses. The two IT scenario variants of Enterprise Data Warehousing are, Scenario variant for design time. Scenario variant for runtime. The variant for design time addresses the issues of modeling and implementation. The functions include, Data Modeling Source Definition Data distribution transformation Facilitation of meta data management. The run-time scenario variant is responsible for monitoring and administration issues. The functions include, Control of data flow. Performance optimization using performance analytics. Monitoring and administration. Management of information life cycle. User management facility. These two scenarios enable the user to design highly reliable, robust, scalable and flexible BI solution. CONCLUSION Data Warehousing and its trends are the integral components of an organization and tis integrity enabling the business to make the most of the organization and its resources. REFERENCES: American Marketing Association AMA. (2011). Introduction to Marketing Research. Retrieved from http://www.marketingteacher.com/lesson-store/lesson-marketing-research.html. Centre for Advanced Analysis and Business Studies. (2011) Differences Between Knowledge management and Business Intelligence. Retrieved from http://caabi.ba.ttu.edu/Paper/Del/BI%20and%20KM%20differences.htm CodeIdol. (2009). SAP NetWeaver Business Intelligence. Retrieved from http://codeidol.com/other/designing-it/Supporting-Composite-Applications/SAP-NetWeaver-Business-intelligence/ Csumb. (2011). Business Intelligence. Retrieved from it.csumb.edu/departments/data/glossary.html Kahn, B. K., Adams, E. M. Kenneth (2001) Sales forecasting as a knowledge management process. The Journal of Business Forecasting, Retrieved from http://bus.utk.edu/ivc/forecasting/articles/Sales_Forecasting_as_a_Know ledge_Management_Process.pdf Sauder. (2011). Business Intelligence. Retrieved from www.sauder.ubc.ca/cgs/itm/itm_glossary.html Read More