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The core value of business intelligence solutions is to convert data into insights. This enables businesses to use a clear decision making process and ultimately make better decisions, faster. The benefits can be equally as valuable for executives making strategic decisions, as it can be for middle management, business analysts or operations staff making tactical decisions. A business intelligence solution must be able to integrate historical business data from multiple business applications into a single database which is optimized for responsive querying and analysis. To ensure that the solution is utilized long-term, desktop applications must provide users with flexibility and the ability to perform complex analysis without a steep learning curve.

OLAP Office Explorer™ and SQL Server offer a complete business intelligence (BI) platform that provides the features, tools and functionality to build both classic and innovative kinds of analytical and business intelligent applications. From data warehouse generation to reporting, OLAP Office™ and SQL Server provide the tools you need to build scalable, secure and comprehensive BI solutions.

The most common bottleneck to making good decisions is the ability to quickly and routinely measure business results and perform complex analysis. This bottleneck can be the result of information being too slow to obtain, too complicated to access or too much to analyze.

• Too slow. Standard business reports are typically dependent on operational cycles and may not be produced for days or weeks after the close of a period. Manual intervention may also be required to produce final formatted reports.
• Too complicated. Data from multiple corporate database systems is often not integrated and usually only specialized technicians know how to get to the data.
• Too much. The sheer volume of data can make analysis overwhelming. Data often requires organization to make sense.

Today's medium sized businesses capture data about their business in many forms. Data sources could include Web site statistics from the corporate Internet or various intranet sites, financial and customer data in Enterprise Resource Management (ERM) and Customer Relationship Management (CRM) databases as well as any number of custom solutions that store data in text files, desktop productivity software or corporate databases. The sheer volume of this data can become overwhelming. Yet merely possessing the data, in itself, does not give the organization a competitive advantage.

The value of data is realized by putting it in the hands of business decision makers, so that they can analyze it, put it in context and turn it into actionable intelligence. To do this you need a solution with four main elements:

• Aggregation. Pull the data together from the many different sources and formats. Ensure that it is consistent and accurate.
• Storage. The data must then be stored in a format that makes efficient querying possible. It also needs to be managed and kept up-to-date.
• Modelable. The data must be modelable to produce additional business critical information that is not present in the original data source.
• Analysis. Make the data available to decision makers through sophisticated, yet simple to use, analysis tools.

The combination of these four elements make up what is called a business intelligence solution. A BI solution provides knowledge workers with the data they need to make informed business decisions. Over time, these decisions can have a dramatic bottom-line impact on the business. For example, with a business intelligence solution, an organization can analyze:

• Product performance. Understand sales trends, product mix and seasonality issues, individual product profitability and product life cycle issues. Determine which products will bring the greatest opportunity for return on investment (ROI).
• Customer trends. Understand customer buying habits. Shift sales and support focus on customers that will bring the greatest ROI, and devise strategies to grow the customer base or retain existing customers.
• Marketing campaigns. Reduce the cost per lead and improve the quality of the marketing pipeline by measuring the success of individual marketing campaigns.
• Sales performance. Measure sales interactions to help managers identify low-performing areas and spotlight the most successful sales channels.
• Customer Service incidents. Analysis of customer service data may focus on evaluating efficiency of the customer service center. It may be desirable to analyze cost per request, customer, product or customer service representative. This analysis can help management determine if changes are required, such as additional headcount, improved documentation, increased training, changes in pricing structure for service calls or customer self-help options.
• Order and shipment trends. Optimize production through more efficient inventory management, reduce or eliminate shipping backlogs, help sales set accurate expectations for product delivery and negotiate lower shipping costs by understanding order and shipment trends.
• Inventory Levels. Reduce inventory costs by more accurately anticipating inventory needs. Measure seasonal and sales trends to forecast future demand.
• Web site statistics. Correlate the volume and origin of Web site visits with marketing and advertising campaigns. Optimize navigation by analyzing traffic patterns.
• Point-of-Sale data. Analysis of retail sales data may focus on store operation, including measures and key performance indicators (KPIs) to support sales performance, fraud detection, store performance and marketing campaign analysis.

To learn more about the many uses for Business Intelligence, please contact OLAP Office™.