Monday 30 January 2012

Positive Effects of Data Warehousing in Business Intelligence of Enterprises

Contemporary software systems aimed at helping managers in the decision-making process usually incorporate Data warehousing in business intelligence applications developed to identify, extract and analyze business data. High-quality and valuable data can be analyzed in a manner that helps enterprises gain competitive advantage over their rivals, enjoying all benefits predictive analysis can provide. Actually, business intelligence systems rely on historical data in large volumes, thus providing neutral view on information collected, while utilizing such data to forecast trends and estimate future market movements, for example.

A major property of business intelligence is its ability to equip corporate executives with valuable tools that facilitate and enrich decision making through analysis of large volumes of data that is otherwise non-analyzable. Data warehousing plays an important role in this process, providing the basis and means for retrieving such data and loading it into a repository, where the information can be cataloged and analyzed. Professionals and scholars are well aware that the ability to analyze huge volumes of data can produce amazing results in terms of future trends predictability and customer behavior forecasting. Data warehousing and business intelligence, in a broader context, provide the essential principles on which modern-day systems facilitating decision-making are made.

Using data warehousing and business intelligence tools, managers and business owners can track how projects and business programs are progressing and compare their progress against a previously adopted broader corporate strategy. Predictive analytics and predictive modeling have proved to be invaluable instruments when the matter in question is to design a working sales strategy or a targeted marketing campaign, for example. Business process modeling is another technique used in business intelligence, which also relies on data warehousing to collect enough historical data on which models are based.

Collaboration was the word of the day in the past decade, with a growing number of software vendors offering business intelligence systems that feature collaboration capabilities through data sharing tools and other instruments designed to provide collaborative virtual workspace. Even the rapidly gaining popularity of social networking platforms rely on data warehousing technologies; and a number of software vendors and developers offer business intelligence platforms that run entirely online, software-as-a-service, or SaaS, where business users can benefit from social networking functionality, in addition to traditional functionality characterizing such systems.

Usually, business intelligence systems are highly configurable reflecting the need to deal with different data and regulations across various industries. Data protection is another well-developed feature of most business intelligence systems due to the sensitive nature of business data gathered and managed through data warehousing techniques and systems. Access of third parties to such data can give them marked competitive advantages; thus, software developers pay special attention to data protection and security in the field of business intelligence systems.

Data warehousing is well known for adding value to other systems as well, most notably to customer relationship management (CRM) systems. Another valuable benefit is the ability of such systems to integrate data from multiple sources and across different systems into a single database, allowing data warehousing and business intelligence techniques to be used to give a broader perspective on what is really going on within an enterprise.

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