IntroductionAs a complex area of corporations’ information system management, information exchange between the developer and the user is ideally important. However, various methods of data warehousing and mining promulgate great inefficiency and unworthy in the mining and warehousing than what was conventionally made to be. By its definition, data mining implies the process with which data is analyzed through different perspectives which can therefore be summarized into useful information that is incorporated in increasing corporate revenue and cutting the level of costs.Data mining is done through the use of appropriate software where such data is analyzed via different perspectives which are finally categorized and summarized into different relationship.

It therefore represents the process of creating patterns and correlations between various corporate fields which occur in large databases. Elsewhere, data warehousing is the process of retrieving and managing data through a centralized point. It is aimed at maintaining a central point of data repository for an organization.The essence of data mining and warehousing is used in creating operational advantages to organization which are involved in providing services like marketing, financial, retailing and communication. It is a tool aimed at determining the fundamental relationship that would exist between various internal and external factors allied to an organization.

The basic essence of data mining and warehousing is creating comparative advantage to organizations through the services towards customer satisfaction, increasing sales and maximizing the corporate profits. However, the idea behind this process can sometimes be unmet when it fails to serve the ideal purpose for which it was made for .The challenge for the activity in not meeting its worth is a broad precept of complimentary factors that make its implications relevant to the basic users. Basically, business information and data is a compound of complex algorithmic variables developed via complex technology. Such complexity in the data serves as a benchmark of problems for which the data should serve. Methods to maintain the worth of data warehousing and mining(i) Database integrationHowever, stopping the inadequacies that fuel complexity in the worth behind data mining and warehousing is a phenomena that can be achieved through various methods.

Perhaps however, database integration would highly help to provide a framework support towards efficiency in data mining and use by corporate users. Through relational intergration processes users can be able to access the most adequate data, which corresponds to specific requirements. This helps to limit the inadequacies provided by complex data which is not easily understood by the users.(ii) Automated scoring modelsThe use of automated model score would perhaps help to promote efficiency in data use.

Automated scoring models helps to score the different results of the results produced in the data mining and warehousing process. Automated scoring models helps to relieve the inefficiencies that may be created in the application of warehousing and data mining by its users. Data scoring has made it easier and enjoyable to the users as well as being a cost effective tool to the corporation. It has been used in reducing data processing time as well as giving out the most update data where scoring engines are build in the system of their mining process. (John, 2005)(iii) Usability of business templatesElsewhere, corporation should use the most adequate business templates that increase the user valuability than solving ideal statistical models. Business data should be tools for increasing the level of efficiency in the user- business relationship.

However, some business data templates have only been tools of solving statistical models for application in the business process. However, the worth of data mining and warehousing requires business templates that provide valuability to its users than mere solving of its business statistical models.(iv) Incorporation of financial statementsSince most of the users of corporate data are stakeholders, an importance of attaching financial information would promote the reliability of the data to its users. The efforts towards data mining and warehousing has its basic compliment in financially related areas such as credit scores, management of risks and marketing. Such variables should be emphasized in the models for data mining. Many users of these data are concerned with profit maximization.

Therefore, the parameters that form part of financial data such as expected revenue and costs increase data worth and valuability to its users. Therefore, they should form part of the data mining inputs for application.(v) Use of time-series dataAs an import tool, organizations should incorporate time series data than time-based data. Generally, time series data is worth more valuable to the user as it states the relative flow of organizations transactions that would ultimately motivate the users.

Time series data is more worth since it shows a review of past data chronologies to the user. Consequently, more information can be mined from them than when in time based form. (Kurt, 1997)(vi) Data wizardsElsewhere, the use of data wizards helps in improving the experience of the users. Thereby, the data mining process is simplified which therefore reduces various inefficiencies related to human errors in the mining and warehousing process.

ConclusionWith these applications, the data mining and warehousing is made simpler to the user which promotes the level of the information got from them. Elsewhere, corporate goals are promoted where users sufficiently get the information which would be the most appropriate in understanding the projections of the organization.