Real-Time Analytics

What is Real-time Analytics?

Real-time analytics is the analysis of data in real time. Instead of analysis taking place after the fact, data can be collected, processed and analyzed as it is generated. This gives you a live view of business and customer activity which is useful for making real-time decisions.

Batch vs. Real-time analytics

Batch analytics is the analysis of large groups of historical data. This analysis takes place after data has been collected over a scheduled period of time. Batch analytics is useful for discovering trends or patterns, and making long-term decisions. On the other hand, real-time analytics analyzes data as it enters the system. It’s useful for responding to live events and making quick decisions.

How Does Real-time Analytics Work?

Real-time analytics often uses edge computing so data processing can be done as quickly and efficiently as possible. There are four main components of a real-time analytics system:

  •   Aggregator. An aggregator collects and processes data from different sources.
  •   Broker. A broker manages the flow of data and ensures the right data is available.
  •   Analytics engine. An analytics engine analyzes and produces the data, correlating it from various sources and generates insights.
  •   Stream processor. A stream processor processes data streams as they are generated. It can filter out irrelevant data, add information to improve the accuracy of the analysis and provide a more complete view of the output.

Pros of Real-time Analytics

Real-time analytics introduces new possibilities for your business. You can unlock the benefit of big data and business analytics immediately, without having to wait for data to be collected and batch processed over time.

More Agile Decision Making

Real-time analytics enables you to make immediate decisions on events as they are happening. Rather than making decisions further down the line with batch analysis, real-time analytics lets you act on your data as it enters your systems.

Quick Resolutions to Operational Issues

Real-time analytics can help monitor operations and identify whether they are running smoothly. By being able to pinpoint where problems or inefficiencies are as soon as possible, they can be resolved quickly to minimize impact.

Better Customer Service

Real-time analytics helps with understanding and responding to customer needs as they occur. You can also provide a more personalized customer experience by analyzing customer behavior as it happens. This helps ensure your personalization efforts are as relevant as possible, rather than using historical data which can lead to out-of-date recommendations for your customers.

Cons of Real-time Analytics

Although real-time analytics has clear benefits, there are several things you should consider before implementing it for your business.

System Requirements

Not all systems can handle real-time data analysis, and those that can are costly. You may need to upgrade your analytics systems or invest in a new one entirely. To set up and maintain a real-time analytics system, you may also need to hire the necessary technical expertise.

Internal Process Changes

Real-time analytics can lead to a big shift in internal processes. It can lead to increased pressure to make quick decisions, which can result in errors. Employees may also not accept the changes to internal processes that real-time analytics brings, so the implementation could be met with resistance.

Employee Training

Real-time analytics can generate a lot of data that may be overwhelming for employees to handle. Time and resources may need to be dedicated to training employees on navigating the system and taking full advantage of the insights it provides.

Use Cases for Real-time Analytics

Real-time analytics can be applied to many industries:

  •   Marketing & Sales. Real-time analytics enables businesses to deliver personalized sales efforts to their customers. By using real-time data, you can understand what customers are interested in at that time, to deliver more personalized messaging and ads.
  •   Cybersecurity. Cybersecurity threats can be detected much sooner using real-time analytics. This can help reduce the mean time to react during a cyberattack.
  •   Manufacturing. Manufacturers can use real-time analytics to enact predictive maintenance, monitoring when equipment has a problem or needs servicing before it fails.
  •   Banking & Financial Services. Financial institutions can improve risk management by monitoring customer behavior through real-time analytics. It can also be used to track market trends.
  •   Healthcare. Hospitals can use real-time analytics for resource optimization. This healthcare case study shows how real-time analytics can bring efficiencies to resource management, thereby reducing costs.

The Future of Real-time Analytics

Real-time analytics is growing in popularity, and accommodating this will lead to an increase in its capabilities. We can see real-time analytics being able to ingest greater amounts of data as larger organizations search for continuous and expansive analytics. AI is increasingly used in conjunction with real-time analytics to enable accurate predictive analytics. It can also help analyze customer feedback with natural language processing (NLP), while machine learning (ML) can automate the process of identifying patterns in data.

About Sutherland CX360

Sutherland CX360 is a CX intelligence platform that uses AI to analyze 100% of customer interactions, rather than the mere 3% that gets audited manually. CX360 works to analyze the customer journey, monitor quality and give you predictive outcomes.

Sutherland CX360 features:

  •   Rich insights into agent behaviors.
  •   Sentiment, topic and DSAT analysis.
  •   Omnichannel interaction analysis into customer expectations.
  •   Automated QA reports.
  •   Predictive models for outcomes by interaction, agent and team.

Discover more about Sutherland’s Analytics Services:

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