What is Business Intelligence? This question is frequently asked by many companies and individuals. It is an increasingly important aspect of managing a company or small business. In today’s ever-changing corporate world, it is more important than ever to keep abreast of current trends and developments. By keeping abreast of industry trends, managers are able to respond quickly and efficiently to emerging issues or challenges.
Business intelligence covers the methods and tools used by companies for the data mining of company data. BI technologies provide both current and historical views of real-time business data. Managers who implement this method rely on it to make critical business decisions based on available data sources.
One way to implement business intelligence is through predictive analytics, which seeks to provide information about patterns from past data. Predictive analytics makes use of statistical tools to examine past customer behavior to forecast future behavior. Most of these tools are capable of providing results within one or two months. As organizations and businesses develop more sophisticated predictive analytics, they can use business intelligence to gain a competitive advantage through timely and accurate reporting.
Business intelligence can also be implemented through the creation of analytic solutions.
These are business objective-oriented applications that use the data mining capabilities of BI technologies in order to support strategic decision making. Analytical solutions include such tools as problem-solving, data mining, process modeling, decision support and project management solutions. The primary objective of these tools is to support business objectives by effectively identifying and solving business problems. Some of the tools even make use of financial modeling, operational forecasting and decision support to support organizational objectives.
There are several types of actual tools that make use of the business intelligence concept.
One type of tool is the data source tool. This is the most widely used tool and it involves the outsourcing of data processing. This outsourcing enables data scientists and IT professionals to access the same information through different sources. Another type of tool is the data mining tool and it entails the search for relevant data sources from varied data sources.
Data descriptive analytics is an important sub-theme of predictive analytics. This type of analytic technique makes use of descriptive data in order to make statistical inferences and predictions. Data descriptive analytics attempts to minimize the usage of statistical methods in order to save time, resources and labor. Another type of tool that makes use of descriptive analytics is data mining. This type of tool makes use of large databases in order to conduct in-depth research on a particular business issue.
What is business intelligence in the context of pervasive biotechnology?
This question was addressed by Kevin Costner, who is a professor at the University of North Carolina at Chapel Hill. Costner called for more study on what is business intelligence as it pertains to biological applications. In his view, what is business intelligence has been largely ignored by biotechnological researchers. In Costner’s opinion, biotechnological companies should be held accountable for their actions. He believes that business intelligence should include a framework that involves both financial, managerial, marketing, and policy frameworks.
The above overview gives us a glimpse of what is business intelligence. These topics include data processing, predictive analytics, business intelligence, and the history of analytics. Given these broad definitions, one can see how the term is used across disciplines and industries.
- Data Sources: Data sources include operational and analytical tools, including those developed by the likes of IBM, Google, Yahoo!, and Palantir. Examples of data sources can also include operational systems developed internally (for example, those used for supply chain management), public data sources, and commercial databases. Examples of predictive analytics tools include what is known as an event-triggered service, such as Hotlist, which allow users to pre-empt the opening of locks, and event-triggered services such as eBay’s “hotlist.” Examples of data processing tools include neural networks (such as those developed by Google Brain), recurrent networks (such as those used at Facebook), and distributed computing (such as what is used at Netflix). Examples of data warehouses include existing product information, market information, and new product development information.
- Business Intelligence Metrics: KPIs or key performance indicators are statistical measurement systems that provide managers with quantitative insights into the business and its key stakeholders. Common metrics included in a data management system include customer satisfaction (KPIs like satisfaction score), profit margin (KPIs like gross and net profit margin), and average order value (KPIs that measure how orders are actually received and transferred through channels). There are other types of KPI that are becoming more common, including customer loyalty and brand loyalty metrics.
- Transformative & Real-Time Metrics: Using KPIs and other techniques, business intelligence can provide managers with valuable insights into their strategic business relationships. The key to transforming raw data into business intelligence is to pair it with the appropriate business intelligence tools. Data transformation tools provide managers with the ability to transform data quickly and efficiently, so they can make quick and accurate decisions. Examples of these tools include transformation engines, data-warehousing schemas, and algorithms that reduce the time needed to conduct analysis and provide comprehensive insights.