When businesses capture and analyze data as it's generated, they can respond quickly to events and changing conditions.
Up-to-the-minute insights into customer behavior, market trends and operational performance create business value through quick and confident decision-making. Real-time analytics advancements make this approach possible.
The real-time analytics space is evolving rapidly with improvements in automation, machine learning, edge computing, data lakehouses and serverless computing. A variety of industries use real-time analytics to improve operations, as detailed in the following examples.
Real-time analytics advancements
Real-time analytics has benefitted from a range of technology improvements. Some, such as edge computing, are directly related to real-time data. Others, such the growth of serverless computing and automation, are broadly applied in the IT industry but have benefits for real-time analysis.
Automation of analytics. Analytic process automation is a critical development. Algorithms can process large quantities of data more efficiently than ever before and deliver useful results within seconds rather than hours or days. By automating aspects of the analytical processes, such as detecting trends and outliers, businesses benefit from faster turnaround times on key decisions, improved efficiency and cost savings. They also improve accuracy and reliability compared with human-run analysis alone.
Machine learning models. In real-time data analysis, machine learning identifies patterns and anomalies, and predicts future events, such as equipment failures. Similarly, machine learning models can identify potential customer churn. Machine learning is often automated in these scenarios. Businesses can personalize marketing campaigns or automate customer support. Large language models, such as GPT-3, are AI algorithms that can summarize, translate and even predict text, enabling the tools to generate sentences that replicate human speech.
Edge computing. Edge computing is a way to put distributed processing power close to where the action happens rather than sending large volumes of data offsite for computation. Computing at the edge could mean in endpoints such as mobile devices, IoT sensor nodes, or even internet routers. This significantly reduces latency in analysis and decision-making.
The data lakehouse. Yes, it's a terrible name, but the convergence of data lakes and data warehouses into a single repository called a data lakehouse helps with real-time insights from their combined data sets. The unified platform can store both structured and unstructured data in one place, making it accessible without having to move large volumes of data between different systems. As a result, in the data lakehouse, real-time data can integrate with authoritative corporate data without additional processing.
Serverless computing. Serverless platforms help real-time analytics by eliminating the need for organizations to maintain their own servers or always-running cloud instances, instead using serverless services from Amazon Web Services (AWS), Microsoft Azure, Google Cloud or other providers. Companies can access a range of data tools without having to provision or manage physical hardware or pay for idle cloud instances.
Furthermore, these platforms provide scalability on demand so that organizations can increase processing power when needed. This is particularly useful for real-time analysis where volumes of data can vary considerably over time as events change.
Real-world use cases for real-time data
With these technological developments in mind, evaluate scenarios where real-time data analysis makes a difference to the business.
Manufacturing. Perhaps more than another industry, manufacturers rely on real-time analytics. With access to quick insights, manufacturers can identify problems or predict when equipment needs servicing before it fails. This is known as predictive maintenance, which helps eliminate downtime and reduce costs associated with unplanned repairs.
Furthermore, analysis can identify opportunities for improvement within the production process itself, such as adjusting machine settings. With optimization in real time, operations managers improve quality control measures and are continuously ready for total quality management audits. Real-time analytics can monitor production processes, identify bottlenecks and optimize workflow and staffing.
Retail. Retailers use real-time analytics to track inventory levels and sales data instantly, across stores or product categories. They can then quickly respond to changes in demand and adjust their ordering and stock accordingly.
Similarly, retailers can optimize pricing and promotions after monitoring how a price structure performs relative to competitors.
Increasingly, product owners can use real-time data analysis tools such as natural language processing models for sentiment analysis of customer reviews and feedback from social platforms like Twitter or Facebook. These insights not only yield a better understanding of their target market's preferences, but also help customize marketing campaigns. Indeed, with machine learning models applied against purchase patterns, marketers can create offers specifically for individual shoppers as they browse.
Finance. In financial services, real-time analytics monitor financial markets, identify trends and inform trades. However, real-time data can benefit the financial services industry in other ways.
For example, financial institutions can rely on real-time insights into customer behavior and market trends to improve risk management. By monitoring transactions closely as they happen, managers, auditors and regulators have visibility into potential money-laundering activities or other suspicious behaviors.
Similarly, banks can use predictive algorithms based on data streams, such as stock prices or deposit levels, for efficient capital allocation decisions in their operations.
Healthcare. People imagining real-time analytics in healthcare picture the heart monitor beeping in the corner of a hospital room. Hospitals can indeed monitor patient health and respond to alerts thanks to real-time data, but with analytics they can also predict potential issues before they occur. They may identify patterns that suggest a health condition earlier than traditional diagnosis can.
In addition to helping with diagnostics, this technology can also reduce operational costs by streamlining processes such as tracking inventory levels of drugs and supplies.
Meanwhile, we have all seen during the COVID-19 pandemic how real-time data can be used in public health scenarios ranging from modeling epidemics to contact tracing of individual cases.
Energy. Real-time analytics technologies are useful for tracking renewable energy sources, such as solar or wind power generation, where weather conditions affect production. As a result of real-time monitoring and analysis, operators know how much electricity the renewable system will produce at any given moment.
Analysis of electrical grid operations in real time with sensors also can identify faults in transmission lines quickly, improving safety across networks while avoiding outages.
In the energy market, analytics contributes more accurate pricing models based on current values combined with historical trends. This integration of data sources for analysis is an excellent use case for the data lakehouse.
Transportation. Just like a car driver uses a navigation app, commercial systems use real-time information about traffic and weather to detect potentially troublesome or dangerous road conditions. Transportation companies increasingly use automated sensors, not just GPS, built into vehicles and shipping containers to monitor conditions such as temperature and humidity.
Automated sensors on public transit vehicles track their status, location data and passenger load, providing operators with real-time insights into how resources are best allocated across routes during times of heavy demand.
In trucking, real-time tracking monitors vehicle performance data, such as fuel economy and route efficiency. Indeed, some companies have -- controversially -- gamified this information to encourage truckers to be more efficient and cost-effective.
Telecommunications. Telecom companies use real‑time data to get an accurate picture of customer usage and behavior. This helps optimize their network and plan for future changes or upgrades. They also use real-time analytics for fraud detection and signal quality optimization, both critical tasks for the security and reliability of services.
Of course, analytics are helpful when it comes to marketing campaigns. Telecoms tailor offers based on consumer purchase patterns. It's surprising how real-time that analysis is, but telecoms aim to be able to serve offers and advertisements based on someone's location, such as near a sports stadium, cinema or shopping mall.
These are only some of the many examples of real-time analytics. Many more will emerge as both business and personal devices become increasingly connected. Optimization and efficiency drive use cases in diverse industries. There are also great benefits in improving the safety, reliability and security of systems everyone relies on.