What is Real-Time Analytics?

Real-time analytics is the process of analyzing large volumes of data as soon as it's generated. It is a type of data analytics that uses analytics tools and techniques to provide immediate insights. It supports quicker decision-making than with traditional analytics, which involves manual inspection and analysis of historical data using statistical methods.

With real-time analytics, businesses can get immediate insights into their operations, customer behavior, and market conditions. This allows them to react quickly to the most relevant, up-to-date data. This better helps them manage changing conditions, take advantage of opportunities, and address problems as they arise. 

The real-time analytics process depends on several components all operating in real time. Once the business goals are identified, the continuous process works accordingly.

Identify and collect useful, relevant data

Identifying and collecting useful, relevant data for real-time analytics involves gathering data that can provide immediate insights and support decision-making. Look at your business goals and objectives, then decide what types of information are most useful. This typically includes organizational data like social media engagement and sales as well as external data around your industry or region.

Combine data from multiple sources

To get the most effective results, you’ll need to combine data from multiple sources. Pulling from a variety of sources including e-commerce customer activity, customer support, sales data, and financial trends can help ensure you’re getting the right information to have a well-rounded analysis.

Perform analytical queries

Performing analytical queries with real-time analytics involves running data queries on live data streams to gain immediate insights. Once you’ve chosen the real-time analytics platform you want to use, prepare queries that are compatible with that platform.

Run continuous or windowed queries based on time requirements. Query latency is the amount of time it takes between executing a query and receiving the result. Queries can include filtering data, aggregation, trend analysis, event detection, and more.

Create data visualizations and dashboards

Share analytics through data visualizations and give teams access to dashboards that update in real-time. Methods of sharing information like these ensure complex analyses are explained clearly and that non-technical team members are able to understand results.

Extract actionable insights

Extracting actionable insights from real-time analytics involves identifying key patterns, anomalies, and trends in live data streams and making data-driven decisions that can positively impact operations, customer experience, or business strategies. When employing on-demand real-time analytics, insights are only generated after a user submits a request. When employing continuous real-time analytics, responses are generated proactively in other applications as data is generated. Plan how to act on these insights in order to meet your goals.

Pair real-time and traditional analytics

Transactional data is the current data being used in the real-time analytics process. Analytical data is recent and historical data that underwent a traditional process. To create a complete analysis, real-time analytics should be paired with traditional analytics. The clearest picture comes from looking at both transactional and analytical data. Looking at current data can help you understand what’s happening now but historical data can help you plan for the future by looking at what happened this time last year, for example.

Businesses and other organizations can benefit with the use of real-time analytics as it enables them to be more agile and replace manual tasks with data query, extraction, cleaning, and report generation.

Work faster

From enabling faster decision-making, to implementing more agile business processes, and responding more rapidly to market trends, real-time analytics can help you work faster. In traditional data analytics, data is often collected at set intervals, then batch reports are periodically created. With real-time analytics, decision-makers don’t need to wait for periodic reports or run separate analyses. Real-time dashboards display current data, enabling teams to make informed decisions quickly.

Better customer service

Real-time analytics allows businesses to personalize customer interactions instantly based on live data, improving engagement. This creates more personalized and more dynamic interactions with customers. Better interactions can lead to higher customer satisfaction and faster conversions.

Quick fixes of operational issues

Automated reporting can help you take immediate action. Real-time analytics tools can trigger automated alerts when specific limits are met or anomalies are detected, which allows teams to react as soon as the problem appears.

Predictive and immediate support

This process can help monitor applications to prevent downtime or offer predictive maintenance. By employing real-time analytics, one can detect fraud faster and improve prevention by blocking fraudulent transactions as soon as they happen.

Once you identify your business goals and what data you want to analyze, it’s time to choose your tools. You’ll want to choose scalability tools that can grow with your business. In some cases, the use of real-time analytics presents challenges or can complicate processes. When implementing real-time analytics, consider what tools or processes you want to use and if any are easy to pair with existing software.

High cost of implementation

There are many real-time analytics tools and technologies to choose from at different price points. One way to save costs is to analyze the existing systems you have at hand and see what technology is compatible.

Data volume

The real-time analytics process looks at large amounts of data. Dealing with high volume and velocity of data can be complex, creating latency or performance issues. Data latency is the amount of time it takes for data to be processed or received after it has been sent or generated.

Data privacy and security

Data security in real-time analytics is critical to protecting sensitive information, ensuring compliance with regulations, and safeguarding systems from potential threats. As real-time analytics involves processing personal, financial, and operational information, it is essential to choose a tool with robust security measures and ensure data governance in your organization. Major analytics tools offer data encryption and data masking in order to prevent intervention.

When deploying real-time analytics, find a balance between achieving business goals, controlling costs, and managing data integration complexities. Companies need to focus on aligning real-time analytics with core business goals, managing the associated costs, and ensuring smooth data integration for the best outcomes.

Business goals

Analytics should be employed to support business goals. Common business goals that can be reached faster with the help of real-time analytics include enhancing customer experience, improving operational efficiency, risk management and fraud detection, and increasing profitability. Consider immediate and long-term business goals when choosing a real-time analytics tool.

Cost

As we mentioned earlier, the cost of implementing real-time analytics can be significant. You’ll want to consider infrastructure, data processing and storage, licensing, headcount, implementation, and maintenance costs. Some businesses may choose to work with external partners and all-in-one tools for a quicker, easier, and potentially more expensive implementation. Others may choose to train employees, pair existing technology, and piecemeal software to save costs, but this may take longer.

Data integration

Real-time analytics requires the seamless integration of diverse data sources into a unified system that supports continuous analysis. You’ll want to consider:

• Integration of multiple data sources
• Data volume
• Data quality and governance
• Latency

Augmented analytics is a class of analytics powered by artificial intelligence (AI) and machine learning (ML) that expands a human’s ability to interact with data at a contextual level. When AI chatbots that automate conversations with customers employ real-time augmented analytics, they can have more productive conversations.

Predictive analytics determines the likelihood of future outcomes using techniques like data mining, statistics, data modeling, artificial intelligence, and machine learning. When predictive analytics is used to predict outcomes based on real-time data insights, organizations are able to act faster.

As an example, let's look at social media where trends expire quickly. If the marketing team realizes a viral template they used in a video is trending on social media, they will know to make more videos using that template later that day. With traditional analytics, which makes insights on historical data, the team may have to wait until the next meeting to see which videos performed best. By that time, the template may not be viral anymore.

Harness the power of real-time analytics for your business and start your free Tableau trial. With Tableau’s real-time analytics capabilities, get the most relevant insights as soon as you need them. Become more agile when you use real-time analytics to automate manual processes and make data-driven decisions even faster.