Business intelligence technologies have become commonplace in the enterprise, and network managers are turning...
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to more data sets to mine for BI. Growth in data volume and actionable intelligence is forcing network and database managers to seek new ways to derive information, a transition that often requires investing in hardware, software and support options that can affect the bottom line.
The magic of enterprise BI
Enterprise business intelligence (BI) doesn't happen overnight; it can require significant resources to pilot and implement. One of the biggest challenges is budgeting, where executives that control the purse strings must be convinced that a project is worth undertaking. Luckily, proving BI's value takes little more than explaining the benefits. For example, a properly implemented enterprise BI project will do the following:
- Deliver value from what is normally considered a business liability -- archived data;
- Reveal patterns and relationships from customer data to create new market opportunities;
- Deliver intelligence about what is happening in the business by examining everything from purchasing to payroll to sales;
- Transform IT from a cost center to an asset by allowing business managers to self-query data sets and create customized reports;
- Centralize definitions of all metrics, calculations and measures to ensure a single version of the truth; and
- Embed business intelligence directly into business process workflows.
All these elements add up to a return on investment that justifies the cost of enterprise BI.
Business intelligence is all about the data
A key element of BI is data. The more data you have, the more intelligence you can mine from it. As the company sees value from BI, IT departments usually have to scale up BI platform capabilities. But the amount of data to process is not directly correlated to the quality of data used. In other words, IT departments need a method to deal with ever-growing data sets that doesn't distort information for analysis.
Further complicating the situation is the type of data involved. Is it structured data, such as you would find in a standard Oracle database? Or is it unstructured or semi-structured data or a basic flat file? Does the data set consist of several smaller pieces of information, or is it primarily stored in large Indexed Sequential Access Method files? All those elements affect scalability and the technology used to handle the data. As businesses leverage multiple types of data and add to that data store, they risk moving into the realm of so-called big data, and standard technologies may no longer be able to handle it.
In a sense, having to manage big data can be considered a win -- it means the business has grown beyond the canned questions that are normally delivered via BI. But most businesses are not ready to make that leap because of technical or applicable knowledge challenges. Nevertheless, that transition can be delayed, if not avoided, by implementing a robust, expandable BI platform, such as Oracle Business Intelligence Suite.
A closer look at Oracle BI
Oracle's BI software offers a way to face BI problems in scalable steps. What's more, the software can become part of a big data platform if needed at a later date. The integrated suites from Oracle deliver important capabilities that are needed by business managers looking to maximize the value of BI.
For example, Oracle Exalytics leverages in-memory processing to deliver answers in real time. As a pre-engineered system, it also simplifies deployment and can provide advanced visualization capabilities.
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Oracle Business Intelligence Publisher is another part of the BI puzzle. It delivers reporting engines and dashboards to make it simple to create easy-to-read reports. It also shifts the burden of extracting information from IT to the business users who need to interpret that data, creating a streamlined approach to data visualization.
Another Oracle BI component is Essbase, which handles online analytical processing (OLAP). Essbase can create a central repository of interrelated data sets to create analytics that expose trends in much larger data sets. Ideally, data analysts can create automated reports, which will expose "what-if" scenarios based on trends and varying conditions.
When implemented properly, a BI platform can deliver intelligence that was once impossible to mine from business processes. That intelligence can expose trends, identify relationships, create "what-if" scenarios and offer a pathway to new types of analytics, revolutionizing the way a business operates. But the real value behind BI comes from knowing what to ask, and Oracle's BI tools can help with that quest as well.