Why Real-Time Data Analytics Is a Must-Have, Not a Nice-to-Have

To keep up with consumers and competitors alike, businesses require real-time data analytics to facilitate informed, rapid-fire decisions. Dynamic pricing, recommendation engines, fraud detection and vehicle routing are just a few applications that are improved, if not enabled, by real-time data analysis.

The increasing accessibility of AI tools and machine learning algorithms is enabling businesses of just about every size to implement real-time data analysis. That’s especially important when you consider industry Goliaths have long been enjoying the benefits of real-time data analytics: Amazon, for instance, is believed to have been making as many as 2.5 million dynamic pricing changes a day for more than a decade.

What Is Real-Time Data Analytics?

Processing data and analyzing it in real time so that organizations can base decisions on the information almost immediately — that is real-time data analytics in a nutshell. It eliminates the lags inherent in traditional batch analysis, in which data is processed and analyzed at regularly scheduled times.

Let’s say visitors to a brand’s website who buy rugs are highly likely also to buy picture frames. The organization discovered this correlation via batch analysis of data from the previous six months. It subsequently used real-time data analytics to identify customers as they put rugs into their cart and then served them a pop-up suggesting frames before they checked out, increasing average order size.

When an organization needs to act immediately — to deliver product suggestions to consumers on their website, to react to trending topics on social media, to detect fraudulent purchases before a transaction is processed — real-time data analysis is essential. However, it’s not ideal for providing a more in-depth understanding of consumer behavior, campaign performance or trends. For those business intelligence applications, batch data analytics is the better option.

The Four Stages of Real-Time Data Analytics 

The real-time data analytics process is akin to a constantly flowing stream with no dams or other impediments. This process can be broken down into four stages.

Stage 1: Data ingestion

Data is collected from multiple sources continuously, as it is generated, then sent onto the next stage in a matter of seconds, or even milliseconds.

Stage 2: Data cleaning and preparation

Machine learning (ML) algorithms enable in-stream data validation, correction and standardization, eliminating the latency inherent in batch processing. As with data ingestion, this occurs continuously and instantly so the data can immediately proceed to the next step.

Stage 3: Real-time data analysis and visualization

Rather than sitting in a database waiting to be analyzed as part of a large dataset, the data is processed and analyzed in-stream by AI algorithms. The outcomes are fed just as quickly to the relevant dashboards, charts and other visualization tools.

Stage 4: Acting on analytic outcomes

Visualization tools can be programmed to deliver immediate alerts regarding certain outcomes — if inventory is projected to slip below a certain threshold within a predetermined period, for instance, or if the abandonment rate of a webpage exceeds an acceptable level. This enables an organization to consider appropriate actions as the events are occurring, rather than when it might be too late to take advantage of an opportunity or to remedy the situation. Sometimes these triggers do not require human response — ad buying software, for example, is often programmed to automatically make bids when prices fall to specified levels, acting faster than a person monitoring prices might.

How Various Industries Optimize Real-Time Data Analytics 

The e-commerce industry is well known for its use of real-time data analysis to power recommendation engines, dynamic pricing and online personalization. But myriad other sectors also enjoy the benefits of real-time data analytics.

• AI algorithms in conjunction with continual patient monitoring enable hospitals and other healthcare providers to better detect sepsis and other diseases. The real-time data analytics capabilities of wearable devices can alert users to symptoms such as high heart rates, and medical practitioners use clinical — and therefore FDA-approved — wearable devices such as continuous glucose monitors and electrocardiograms (ECGs) as diagnostic and disease-management tools. Real-time data analytics also enables healthcare organizations to anticipate outbreaks of infectious diseases, allowing for better allocation of resources.

• Real-time data analysis has dramatically improved the speed and accuracy of fraud detection, but that’s not the only way it is used in the finance In-stream compliance checks and regulatory reporting can be programmed into the data preparation process, streamlining reporting and minimizing errors. Setting up alerts in real-time dashboards also helps traders and managers take a more proactive approach to investment decisions.

• In facilities management, real-time data analytics alerts teams to subtle changes in equipment performance that indicate the need for maintenance well before a failure occurs, reducing unplanned downtime. It also helps managers schedule employees more efficiently, as well as to monitor and optimize energy usage.

• Real-time data analytics has improved myriad links within supply chains. Manufacturers can see on demand when materials will arrive, just as their customers have immediate visibility into where their orders are at any given time. Real-time analytics also alerts manufacturing and operations managers of emerging bottlenecks before they disrupt schedules. Transportation firms use real-time data, as well, to determine the most efficient delivery routes.

• In marketing, programmatic advertising would be impossible without real-time data analytics. Beyond automating the process of buying ads at optimal prices to reach highly targeted audiences, real-time analysis enables marketers to monitor response to live campaigns so that they can make almost-immediate changes to boost performance. It also allows marketers to offer personalized promotions based on where consumers are in the purchase funnel, lifting sales and customer satisfaction. In addition, real-time data analysis regarding individuals’ attrition risk and lifetime value empowers customer service teams to offer promotions that will increase retention without eating into profitability.

Reaping the Benefits of Real-Time Data Analytics

A survey of more than 1,200 companies found that 80% saw revenue increase — by an average of 17.5% — after implementing real-time data analytics. By enabling faster, more-informed decisions, anticipating production challenges, facilitating on-the-fly changes to resource allocations and improving the detection of fraud and product quality anomalies, real-time data analysis offers benefits to just about every area of business.

That said, it’s not a substitute for traditional batch data analysis. The most effective organizations use both traditional and real-time data analytics for different purposes. Understanding when to apply each and establishing an architecture that accommodates both can be challenging, however.

The data strategy services team at Material has helped many organizations rise to this challenge. We’ve designed data ecosystems that align with individual businesses’ needs, established data governance frameworks and activated segmentation and other data-driven programs, among other strategic and tactical projects. Contact us today to learn how we can improve your data analytics programs — and with them, your organization’s performance.

FAQs

What are the differences between real-time data analytics and batch processing?

In real-time data analytics, data is processed and analyzed instantly, and the outcomes of that analysis are subsequently presented in a matter of seconds. It’s best used for time-sensitive applications such as fraud detection, programmatic advertising and dynamic advertising. Traditional batch-processed data analysis gathers and stores data to be processed and analyzed at predetermined times, such as every hour or every day at midnight. Batch processing is typically more cost-efficient than real-time data analysis and allows for analysis of large datasets, but it also creates a lag time between when the data is collected and when the analysis is presented. For those reasons, it’s best used for analyzing historic trends and generating regular reports.

What technologies, tools or platforms are used for real-time data analytics?

Always-on data ingestion tools, streaming platforms and processing engines are foundational to real-time data analytics. These need to be integrated with business intelligence (BI) dashboards or other visualization tools that make the analytic outcomes visible and actionable to end users.

What are the main challenges of real-time data analytics?

Ensuring scalability to accommodate high data volume is a major challenge of real-time data analysis. A system that works perfectly for a retailer 11 months of the year might slow significantly or crash during peak season, for instance. Other challenges include ensuring data security and compliancy, maintaining data quality and monitoring model and algorithm effectiveness.

What key steps should a business take before implementing real-time data analytics?

Determining how the organization will use the outcomes and defining KPIs is essential to ensure that the real-time data framework can support them. The next step entails identifying all the data sources so that the architecture can accommodate them. Then it’s a matter of selecting the technology stack, including the visualization tools, and integrating the analytics feeds into the relevant existing systems. During this process, the organization must establish data encryption and compliance systems, along with training employees.