BUSINESS INTELLIGENCE AND DATA WAREHOUSING
Retailers, banks, and financial services firms, in particular, have made large investments in business intelligence product suites. The systems were purchased to help uncover customer trends, product development opportunities, and market gaps. Business intelligence solutions are complex, however, and companies have found that they need a great deal of support to manage and optimize. Excel has extensive experience in running and maximizing the major business intelligence solutions from vendors such as Cognos, BRIO, Microstrategy, Informatica, Hyperion, SAS, Information Builders, Business Objects, Applix, and Oracle.
Data warehousing is commonly used by companies to analyze trends over time. In other words, companies may very well use data warehousing to view day-to-day operations, but its primary function is facilitating strategic planning resulting from long-term data overviews. From such overviews, business models, forecasts, and other reports and projections can be made. Retailers, for example, can utilize data warehouses to uncover trends in spending, store traffic, product mix, and other variables which will lead to smarter business decisions about what to sell, to whom, and where. Similar benefits also apply to manufacturers and telecommunications companies. Building a data warehouse is critical for companies in competitive industries. The data warehouses must be flexible and scalable to support millions of terabytes. Users must have fast and easy access to the data to analyze trends and respond quickly as opportunities appear. Try yourself at the free spins no deposit required keep your winnings. Excel has extensive experience in designing, deploying, and managing data warehouses for various industries. With significant domain and technology expertise, Excel helps build and manage data warehouses that can help uncover new customer opportunities and optimize existing customer relationships. Excel takes a strong pragmatic approach, in order to design, deploy, and manage a successful data warehouse and decision support strategy. The process is:
Strategy development and review
Focuses around the strategic and tactical objectives of the organization, what each division needs, and data acquisition opportunities.
Logical and physical architecture design for decision support environments
This is based around building a consistent, useful repository. Excel ensures that the server platform elements, middleware, and client tools all fit in with the existing infrastructure.