What is real-time analytics?

Real-time analytics essentially means that data is provided for analysis almost immediately once it is collected. Users can see, analyze, and understand data from a system in real time. Furthermore, real-time analytics provides insights for making real-time decisions. This allows organizations to draw conclusions from the data and react without the usual delays.

Traditional business data is historical data. Digital business data is constantly changing, in some environments, billions of times a day. To become a digital business, you must see, analyze, and act on data in real time. Real-time technology is not new. For decades, hand-crafted real-time dashboards have been built for operational staff. But those dashboards required months or years of custom development and were designed for niche business areas that required monitoring, not exploration.

Real-time analytics can provide a single, common view of operations and can tremendously improve the way you run your business. Real-time dashboards allow business users and frontline staff to benefit from continuous intelligence. By visualizing and analyzing historic and real-time data together, you can improve your knowledge of what’s happened in the past and better respond to conditions in the moment.

Real-time analytics can let the business know instantly when changes occur by setting alerts for key problems. Users can then drill down into what’s happening in the moment and analyze emerging patterns in the real-time data for high-value insights and opportunities.

On-demand vs. continuous real-time analytics

There are two types of real-time analytics as outlined below. Both are valuable in different situations and can be used simultaneously by the business to improve decision making.

  • On-demand Real-time Analytics: On-demand real-time analytics requires a user or system to request a query of the data for analysis to take place and results to be delivered back to the user or system. This is referred to as a pull approach because the data is pulled to answer a specific question in that moment.
  • Continuous Real-time Analytics: Continuous real-time analytics does not require a query request. Instead, certain events trigger alerts to users or system responses in a more proactive, ongoing manner. This is referred to as a push approach because analytics is constantly running in the background and then pushed out to the organization at intervals that are established in advance.

What are the benefits of real-time analytics?

Most BI and analytics data is analyzed monthly, weekly, or daily. But data is being generated right now, and organizations need to be able to analyze it and act on it in real time. Businesses need to respond quickly to frequent changes in order to benefit from real-time opportunities.

  • Speed to Insight: The primary benefit of real-time analytics is of course speed. It speeds up time to insight and lets businesses work faster to make necessary changes to systems or act on any critical information discovered. This can help organizations not only flag potential problems and mitigate risk, but also seize opportunities when they matter.
  • Customer Experience: Real-time analytics can help businesses anticipate problems and streamline operations to improve the overall customer experience. These on-the-fly adjustments greatly influence customer interactions and can help improve the end-to-end experience.
  • Operational Excellence: Real-time analytics allows organizations to gain a clear view of the business and understand what needs to be done to address potential operational issues. It also allows users to understand what resources are available to make those changes.
  • Deeper Understanding: When there is a need for deeper analytics to make a business decision, real-time analytics can help compare real-time and historical data to inform the decision.

Required capabilities for real-time analytics

  • Continuous Query Engine: For real-time analytics, organizations need a continuous query engine that can process ultra-fast streaming data. This engine should continually push live, real-time data to the business for analysis. Furthermore, it should enable ad-hoc data queries and live data tables.
  • Self-service Analytics: Real-time analytics should be self-service so that business users can easily access live data and interact with it without requiring a data expert. By allowing everyone across the organization to combine real-time and historical data in analyses, businesses can add context to every decision.
  • Data Wrangling: By leveraging in-line data wrangling, organizations can wrangle, clean, transform, and aggregate real-time data easily.
  • IoT and Big Streaming Data: Real-time analytics solutions must be built to handle large, complex data so that businesses can perform continuous, streaming queries on IoT and big data sources.
  • Business Alerts: Systems should be able to send automated alerts and notifications to users based on key business events. This will allow for instant actions to follow the processing of real-time data.

What are some examples of real-time analytics?

Real-time analytics can be beneficial for many companies across a wide range of industries. For the financial industry, real-time analytics can analyze big data in real time to inform important trading decisions. For any company website, developers can use real-time analytics to get notifications if the page load performance drops below set standards. For manufacturing, companies can analyze and monitor machine data in real time to catch any potential malfunctions and reduce machine downtime. And for product releases, organizations may want to use real-time analytics to assess the response to a new product release, tracking current user behavior and making adjustments to improve reception.

  • Marketing Operations: Traditional business intelligence (BI) solutions can only predict customer behavior based on history. Real-time analytics adjust customer engagement based on what the customer is doing in the moment.
  • Industrial IoT Operations: Operational problems are predicted with traditional BI based on historical data using supervised and unsupervised machine learning (ML). Real-time analytics adjust operations based on live conditions and dynamic learning.
  • Security Operations: Security with traditional BI bases forensics on historical data. Real-time analytics analyzes and stops security breaches before they happen in real time.
  • Financial Operations: Financial forecasts based on historical data are all that traditional BI can offer. Real-time analytics offers the ability to optimize pricing and incentives on the fly based on 360-degree views of operations.

Real-time analytics industry use cases

Whenever there is a need to respond quickly to frequent changes and/or to compare real-time and static/historic data, real-time analytics enables better decision making across multiple industries.

  • Manufacturing: High-tech yield optimization
  • Logistics: Real-time track and trace
  • Retail: Continuous live sales operations for peak sales times (Black Friday, Cyber Monday), inventory management, sentiment analysis, and alerting
  • Energy: Wind turbine operations and maintenance, oil and gas drilling and pumping, predictive analytics
  • Transportation: Airport ground staff operations, servicing, and scheduling
  • Finance: Fraud detection
  • Capital Markets: Trade flow monitoring and surveillance, FX liquidity analysis, risk management, P&L management
  • Cross-industry: IT infrastructure monitoring
Real time analytics diagram

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