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Quick Contact BoxData Quality ServicesThe importance of data management is often overlooked by many companies. They frequently underestimate the important contribution data management makes to the success or failure of their operations. Data quality is vital to business intelligence. Companies typically spend thousands and even millions of dollars setting up business intelligence systems to improve their operations, but the results generated by these efforts are only as good as the data that is fed into them.Many fall short of their expectations because of poor data quality issues. Contradictory, inconsistent or inaccurate information exposes companies to many business risks that lead to increased costs, customer dissatisfaction, poorer decision making and lost business. Clean, high quality data helps company decision makers to accurately and correctly assess their business activities and avoid potential pitfalls that can significantly impair a company's profitability. At Excel Business Solutions we offer companies with data cleansing, data integration, data enrichment and data mining services in support of accurate reporting, analysis and business decisions; and consequenentially, minimize risk and cost, enhance business opportunity and increase returns. More Info |





Database marketing emphasizes the use of statistical techniques and data analyses to develop models of customer behavior, which are then used to select customers for communications. The benefit of database marketing is the ability to target marketing efforts. Companies can concentrate their marketing effort on customers that are most likely to buy. Through continually gathers, refines, and analyzes data about the customers, their buying history, prospects, past marketing efforts, demographics, data can be turned into information that supports all marketing and sales programs.
Effective database marketing requires extracting useful information from your marketing data.
Our reporting consulting services can create a view of your dataset and categorize
your data into groups and summarize them into meaningful information.
One of the key issues raised by data mining technology is data integrity. Clearly, data analysis can only be as good as the data that is being analyzed. A key implementation challenge is integrating conflicting or redundant data from different sources. For example, a bank may maintain credit cards accounts on several different databases. The addresses (or even the names) of a single cardholder may be different in each. Data cleansing processes must translate data from one system to another and select the address most recently entered.