real-time business intelligence (RTBI)
What is real-time business intelligence (RTBI)?
Real-time business intelligence (RTBI) combines data analytics and various data processing tools to enable access to the most relevant, up-to-the-minute data and visualizations. RTBI leverages smart data storage solutions like real-time data warehouses and business intelligence (BI) systems to help organizations make smarter decisions.
Data virtualization, data federation, enterprise information integration, enterprise application integration (EAI) and service-oriented architectures (SOA) are technologies that enable RTBI. Complex event processing tools analyze data streams in real time and either trigger automated actions or alert users to patterns and trends.
To maximize their RTBI tools, organizations need robust infrastructure built to store and process data. They must also have processes in place to help understand the value of their data and have a strategy to collect and analyze the most suitable data types. Strategies may involve building and using a data lake, data warehouse or data mart, or even a combination of these, depending on business needs.
What are the benefits of real-time business intelligence?
RTBI tools come with several benefits that are helpful, out of the box, for different industry verticals. These benefits include the following.
Instant decision-making. RTBI supports instant decision-making, which is necessary, for example, if a company sells clothing online. The company's website and representatives at the company's call center need to have the same up-to-the-minute data regarding inventory levels. If a customer places an order and a particular size or color is sold out, the customer can be notified and redirected to another, similar item.
Competitive advantage. As consumer expectations grow higher each day, making intelligent decisions based on real-time data is increasingly critical to business relevance. RTBI provides the information organizations need, including forecast analysis and trend analysis, to make tactical decisions that help them take advantage of events as they occur. By using enterprise data to improve customer engagement, productivity and efficiency, businesses can gain competitive advantage and increase revenue. For example, RTBI at Continental Airlines helped transform its industry status from "worst to first" and from "first to favorite." As the Continental Airlines example underscores, organizations cannot ignore the importance of RTBI in overall customer satisfaction.
Rapid detection of critical issues. RTBI isn't just about improving your bottom line. Businesses can also use it as a service intelligence tool to optimize maintenance protocols and prevent potential downtime.
Given the many uses and benefits of RTBI, it's wise to apply a multipronged approach. Some other RTBI application areas include the following:
- application performance monitoring (APM)
- customer relationship management (CRM)
- data security monitoring
- data validation
- demand sensing
- dynamic pricing and yield management
- fraud detection
- operational intelligence (OI) and risk management
- systems monitoring
RTBI in supply chain analytics
RTBI can help supply chain managers and staff generate reports and personalized dashboards and alerts to monitor performance against objective goals and key performance indicators (KPIs) for supply chain management. Using RTBI tools, they can quickly find solutions to problems, test hypotheses by analyzing patterns and trends in the data and prepare for future demands while averting disruptions.
However, real-time data analytics with BI is not required for every part of a company's operations. Most BI users can meet their business goals by looking at weekly or monthly performance numbers and long-term trends such as year-over-year comparisons. Similarly, finance groups may not need real-time data to analyze financial metrics or compare actual budgets to forecasts.
Because RTBI implementations can increase the overall cost of a BI system, the best practice for organizations is to deploy real-time BI technology only when it's absolutely required.
See also: real-time analytics