What Is Business Intelligence?
Business intelligence (BI) is an increasingly important way of getting interesting and actionable insights from your organization's big data sets.
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Business intelligence (or BI) is a process used by companies to analyze their data and create actionable takeaways that impact the company's performance. Typically, the process involves gathering your company data into a data warehouse or other repository and using a specially designed tool to analyze the data. For example, you might look at customer online shopping habits, operational costs, or regional sales information. Or, you might compare your business operations against benchmarking standards. Business intelligence is critical to an organization's survival in a competitive business climate. Often, BI applications use data gathered from a data warehouse, and companies typically use ETL (Extract, Transform, and Load) tools to get data from different parts of the organization into the data warehouse. Matching a good ETL solution with a good business intelligence tool can up your business game by leaps and bounds. A study by Nucleus Research showed that business intelligence analytics pay back $13.01 for every dollar spent.
Why Is Business Intelligence Important?
The ability to view and understand current data, to understand the bigger picture of a company's strengths and weaknesses, and to forecast future trends or needs are all essential lifeblood for a business and are among the group of activities that include business intelligence.
The following list shows some common business intelligence activities:
- Reporting. Providing regular summary data to key decision-makers within an organization to support their ability to make business decisions.
- Analytics. Activities that involve finding and understanding patterns in data that can be used to make business decisions.
- Data mining. Activities that involve finding patterns in large data sets.
- Complex event processing. Complex Event Processing (CEP) is an analysis of streaming data in real-time. Streaming data is typically data that is constantly updated, such as stock market feeds, traffic reports, electrical grids with sensors, etc.
- Business performance management. This is a set of analytic processes geared towards analyzing and measuring a specific performance goal (or set of goals) that the organization defines for itself. For example, a business might set a goal of operational excellence defined by on-time shipping and customer satisfaction, and create analytics to measure this.
- Benchmarking. This is a set of analytic processes that attempts to gather performance metrics for an organization and compare them against the best practices as defined for a given industry.
- Predictive analytics. Predictive analytics includes a range of statistical techniques such as data mining, machine learning, and predictive modeling to analyze historical data in order to predict future patterns.
Common Business Intelligence Challenges
Getting quality data is critical to achieving good business analytics. Bad data results in bad business intelligence. Data quality is a challenge for a few reasons:
- Data is outdated. Data becomes outdated easily in a large, complex organization.
- Companies aren't taking the time to practice good data hygiene. To maintain quality data, companies need to take steps to clean and normalize the data regularly.
For more information about data quality, see What is Data Quality?
Critical Data Is Buried in Different Systems
When data is in different systems and inaccessible to other systems, it's called siloed data. The problem with siloed data is that it's inaccessible to the rest of the organization because the software may not be compatible with other systems or the business unit tightly controls user permissions. When this happens, this critical data is locked away, and you only get a partial picture of your data, so your business intelligence is incomplete. Working with a good ETL tool can help you to bring data from different systems together to make the data available for analysis.
Lack of Expertise
Another challenge with business intelligence tools is that they can require a lot of expertise to use them. This means that only a few key people in your organization have the skills to use the business intelligence tools effectively, creating a bottleneck.
Business Intelligence Tools
Business intelligence tools generally fall into three categories: on-premise, open source, and cloud-based tools. The right tool to use depends on your environment and your goals for business intelligence software.
Some popular on-premise tools include Microsoft Power BI, Tableau, and Yellowfin. On-premise tools are designed to be run on your organization's infrastructure and are commonly used with traditional data warehouses that are also running on-premise. They can be less robust and scalable than cloud solutions, however.
Open Source Tools
Open source options are cost-effective, and if they are cloud-based, can also save you money on infrastructure costs. But they still require a level of knowledge and hand-coding to be able to use effectively. Some popular open source tools include Apache Hive and the BIRT Project.
Cloud-based business intelligence tools are especially good at handling streaming data and large volumes of data. They can also be extremely cost-effective because the infrastructure and expertise required to maintain the environment are handled by the vendor. Cloud-based tools include Oracle Netsuite, Birst, GoodData, and Adaptive Insights.
Published at DZone with permission of Garrett Alley, DZone MVB. See the original article here.
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