Information System in Organization
Data mining and warehousing technologies use data about past events to inform better decision-making in the future. Do you believe this stifles innovative thinking, causing companies to become too constrained by the data they are already collecting to think about unexplored opportunities? Compare and contrast both viewpoints in your answer. There are both positive and negative reviews from companies toward the data mining technology.
Positive reviews side is the benefits and usefulness of data mining to their business performance, the prediction from the database that holds past information are quite accurate and it’s important for company to refer before making any future decision. While negative reviews stating that company that use data mining will tend to follow exactly with the future trends and patterns predicted by system, it will cause company unable to think out of the box and thus distort their innovative with the unexplored opportunities.
Data mining technology can generate new business opportunities by two systems known as “Automated prediction of trends and behaviors” and “Automated discovery of previously unknown patterns”. Automated prediction of trends and behaviors mean data mining automates the process of finding predictive information in a large database. Questions that traditionally required extensive hands-on analysis can now be directly answered from the data. A typical example of a predictive problem is targeted marketing. Data mining uses data on past promotional mailings to identify the targets most likely to maximize return on investment in future mailings.
Other predictive problems include forecasting bankruptcy and other forms of default, and identifying segments of a population likely to respond similarly to given events. While automated discovery of previously unknown patterns are tools that sweep through databases and identify previously hidden patterns. An example of pattern discovery is the analysis of retail sales data to identify seemingly unrelated products that are often purchased together. Other pattern discovery problems include detecting fraudulent credit card transactions and dentifying anomalous data that could represent data entry keying errors. However, data mining require a company with a complete data warehouse which store all the transaction or purchasing data from the customers. The cost from the complete system sometime reach million to billion Ringgit Malaysia in order to fully deploy it, a good data collector is important as better the data means better results will be produce by data mining. If this expenditure used for product R&D or human resources, it would improve innovation of the company.
Overuse or misuse of data mining might distort innovation of company because it is still a developing technology. Data mining only deal with the past events in company’s data warehouse and predict future trends with sophisticated computing process, the data mining is not complete itself because the prediction and guide given couldn’t take all possible outcome into account. Data mining will also became inaccurate when nature disaster, riot or politic instability happening in the real world, as it only read past events and could not cope with the present events that will surely change the customer behavior.
Data mining itself will weaken the company’s innovativity if only the company rely too deep on this technology, because the main idea of data mining is an extra tool to help companies identify and recognize future trends from the data warehouses of companies. Data mining also can help companies to predict the future trend of the customers with the variable or structural changes from companies, which mean companies could understand the effects before they trying out the new marketing strategy or product changes.
It’s crucial for companies to have data mining technology in order to become innovative towards customer’s needs. A company who start to utilize data mining is an innovative move itself, as not much of the organizations started to deploy them and it is still a new technology in this time period. References: * http://cseserv. engr. scu. edu/StudentWebPages/PTing/f2. htm * Good prospects ahead for data mining. Australian Academy of science (1999) * Wu, J. Business Intelligence: The value in mining data. (6/2003) http://www. dmreview. com/master. cfm * M.
Bensaou and Michael Earl (2003) The right mind-set for managing information technology Definition “Data Warehouses” can be define as a database used for reporting and analysis. Data about each company’s customers and potential customers stored in the data warehouses. A data warehouse stores large quantities of data by specific categories so it can be more easily retrieved, interpreted, and sorted by users. Warehouses enable executives and managers to work with vast stores of transactional or other data to respond faster to markets and make more informed business decisions.
But merely storing data in a data warehouse does a company little good. Companies will want to learn more about that data to improve knowledge of customers and markets. The company got full benefits when meaningful trends and patterns are extracted from the data. Companies will use the “Data Mining” technology in order to extract data from the data warehouses. Data mining is a form of knowledge discovery, it’s a computer-assisted process of and digging through analyzing enormous sets of data and then extracting the meaning of the data.
Data mining tools predict behaviors and future trends, allowing businesses to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that traditionally were too times consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. Data mining derives its name from the similarities between searching for valuable information in a large database and mining a mountain for a vein of valuable ore.
Both processes require either sifting through an immense amount of material, or intelligently probing it to find where the value resides. -missing opportunities of the companies, look backward but not forward -software cost, manpower and time consuming -inaccurate of information causing prediction to be inaccurate too -getting to the best answer involves powerful and comprehensive solutions -getting the information might be not easy, as not all companies have data mining expertise manpower and also a complete database warehouse before mplementing the technology Contrast -data mining gives them the ability to proactively make changes to exceed their goals. -innovative start-ups and progressive-thinking agencies are using data mining successfully to predict and change their futures -to learn not only what happened in your operations, but also why things happened. The results of data mining can easily be deployed to all the decision-makers in your organization, including “virtual” decision-makers such as your Web site and operational systems to improve decisions in real time.
