Business intelligence (BI) has matured and is almost universally accepted as a critical part of overall IT strategy. It continues to grow in sophistication and technical requirements, and is being used more and more across the enterprise. The real-time analysis it provides enables companies to improve ongoing business processes, maximize revenue and reduce costs. It helps IT departments mitigate risk and develop compliance processes for regulatory mandates such as Sarbanes-Oxley. Most importantly, BI allows for the in-depth analysis of customer data so that management teams can better monitor corporate performance and sales activity. According to Brian Dooley of the Cutter Consortium, "The need to retain information leads to a desire to use it, and data mining moves quickly to data analysis and BI in the quest to leverage information stores."1
As enterprises look for more integrated, comprehensive BI solutions to meet their reporting and data analysis needs, several considerations come into play. Evaluating a BI solution requires a thorough examination of the way the system interacts with data from multiple sources, how it integrates with the existing infrastructure, what kind of security it offers and how the data is presented. Depending on your needs and infrastructure, these requirements weigh heavily on which solution meets your specific business requirements. Implementing a flexible solution that integrates well at all levels and conforms to known standards will simplify the process of adopting BI as part of your overall IT strategy and enable you to make the most of the data across the enterprise. Open source BI systems tend to be more modular and flexible than proprietary systems and thus are often a good fit for companies evaluating BI solutions.
A BI Solution for Every IT Infrastructure
No two infrastructures are the same. Some are organized into silos of information that don't talk to each other at all. Others are centralized, where all the data is fed into one database. Some use an enterprise information integration (EII) system to integrate separate data sources together. Still others rely on a service-oriented architecture (SOA). And that doesn't include the data stored in a hosted software as a service (SaaS) application.
But that's only the tip of the iceberg. A single organization can have a mixture of all of these. For example, there may be a set of servers that feed data into a single database alongside a few systems such an on-demand customer relationship management (CRM), which can run independently. A company might also acquire another company that maintains a completely separate data management system. For a broader view of what's going on across the enterprise, IT personnel must integrate data collected across these multiple infrastructures, which requires tedious data entry, labor-intensive extract, transform and load (ETL) tools and the manual combining of disparate reports. Clearly, this is an inefficient method that wastes time, money and resources.
New BI solutions are aimed at analyzing data across the enterprise by working with and linking all types of environments - the siloed, the centralized and the hosted. Ideally, authorized users should be able to access data from any system across the various infrastructures and obtain an aggregate view of that data for comprehensive analysis. The ability to pull data from all sources across the enterprise offers big rewards, including faster, more thorough analytics, better business agility and reduced costs.
When evaluating BI solutions, consider the following:
- How does the system talk to data across the enterprise
- What type of security infrastructure is supported
- How is data presented
- How does the system integrate with your existing infrastructure, and what are the benefits of purchasing an open source versus a proprietary solution?
Accessing, Integrating and Aggregating Data
The volume of enterprise data is ever increasing. The more capable we are of collecting data, the more we have, and that creates the bigger challenge of organizing and using that data. Especially when systems are operating independent of each other, making sense of collected data can be a logistical nightmare. Often, companies will dedicate staff to data entry. They must gather data from disparate systems and consolidate it manually into Excel spreadsheets or other analysis tools. If resources are not available, the data remains separate. Reports from each of the siloed systems often cannot be combined, and the data cannot be compared and manipulated in an efficient way.
BI addresses this problem by providing the capability to combine data from multiple sources, either as part of a batch process or on demand, so that it can be subject to more accurate, in-depth analysis. For example, sales data can be combined with inventory to examine ratios or establish customer behavior patterns.
Given the variety of data sources in today's complex enterprise IT infrastructures, it's critical that the BI solution implemented be able to talk to various data sources and aggregate data from these sources into a single console or reporting server. Manually creating a data warehouse or BI universe has become an antiquated method as new solutions provide the intelligence to pull data from all connected sources.
Three common BI deployment models help aggregate data across the enterprise. In a siloed infrastructure, where reporting is distributed, a reporting server is embedded into each device and sends data to a single console or browser that aggregates all of the input for presentation. In a data center with centralized reporting, multiple databases can feed into a single reporting server, which then distributes reports to a Web browser or printer, depending on the needs of the user. Finally, in a shared reporting model, multiple application servers share a single reporting server for all clients.
Secure Data Access
Security is crucial, as much of the data that requires analysis in an enterprise is confidential. Any BI system implemented should work with the security processes and standards already in place. For ease of use, the system should support single sign-on - meaning that once a user logs in, he can access the various reporting and analysis tools without having to re-enter his credentials along the way. The BI systems should also support whatever security infrastructure is already deployed, including commercial identity management systems and auditing tools. Look for solutions that include an integrated open source security frameworks such as ACEGI (part of the Spring framework), which offers comprehensive authentication access control and supports a variety of existing security infrastructures, such as JAAS, PAM, CAS and Kerberos.
