Mastering Complex BI Systems with Real-Time Data Streaming Solutions

Real-time data streaming revolutionizes business intelligence, enabling faster responses, more relevant insights, and micro-optimizations for a competitive edge.

Business intelligence (BI) is a crucial but complex undertaking. Understanding and responding to your organization’s data is key to succeeding in today’s fast-paced environment, but this is often more complicated than it initially seems. Real-time data streaming can help.

Even among companies with strong analytics cultures, only 48% significantly exceed business goals after implementing BI practices. Less than half of these projects are delivering their full value, partly because many don’t account for data’s short life span. That’s where real-time data streaming comes in.

What is Real-Time Data Streaming?

Real-time data streaming ingests and processes information as soon as it’s generated. Conventional BI and artificial intelligence (AI) applications compile data over hours, days, or weeks before analyzing it to produce actionable insights. Streaming, by contrast, analyzes it in real-time.

The overall process looks similar to traditional BI, but no more waiting periods and manual steps exist. Enabling a continuous process requires slightly different architecture — namely, flexible storage and automated data preparation solutions to minimize processing bottlenecks.

This agility is quickly becoming a key differentiator in the BI space. According to a 2024 report, 86% of IT leaders say data streaming is their strategic investment priority. More importantly, 41% of those using the technology see a five-time return on investment or greater.

Also Read: The Future of Business Intelligence: 10 Trends Shaping the Data-Driven Landscape

How Real-Time Data Streaming Improves BI

Data moves quickly, so business intelligence must be just as fast to reach its full potential. Real-time data streaming helps BI projects achieve that goal in several ways.

Faster Responses

The most direct benefit of data streaming on BI is that it enables timely responses to changing situations. Information has a limited useful lifespan, which varies between use cases. You often need to keep research data for three or more years, but customer trends can shift within hours. 

Real-time responsiveness is most impactful in applications where conditions change suddenly and often. Fraud monitoring and customer service automation are only effective if they respond as soon as a suspicious transaction happens or a buyer’s behavior shifts. In a conventional environment, there would be a delay between such processes and a business’s response, but data streaming removes the gap, boosting efficacy.

More Relevant Insights

Analyzing BI data in real time also means you’re always acting on the most up-to-date information. Businesses often make the mistake of not recognizing that all processes today are time-sensitive. Many impactful factors can shift at a moment’s notice, so if you’re basing your strategic moves on weeks-old data, they will not be as relevant or effective as they could be. 

Even valuing or selling a company — a typically long process — benefits from real-time insights. The market approach is one of the most common valuation methods and relies on current trends. Data streaming can provide information on other companies’ valuations up to the minute, ensuring they reflect the moment in time as closely as possible.

Micro-Optimizations

Another benefit of real-time data streaming is that you can make granular improvements to your operations you wouldn’t be able to otherwise. Each of these optimizations will be small on its own — they often involve affecting a single sale or customer — but over time, they add up.

Consider how e-commerce cart abandonment rates range from 60%-80% on average. You can lower that figure and avoid lost sales through data streaming. An AI algorithm could recognize when someone has left items in their cart and immediately respond by reminding them or offering a temporary discount to encourage the purchase. Dynamic pricing and real-time inventory management are other common examples of such micro-optimizations.

Heightened Security

Similarly, data streaming is highly useful in a cybersecurity context. Stopping a security incident or fraud case hinges on quickly recognizing and responding. Consequently, real-time analytics are key to minimizing losses and preventing large-scale disruption.

Automated network monitoring and anti-fraud tools can recognize suspicious activity when it occurs and restrict the account or system in question until staff can respond. This application of real-time BI has helped the U.S. Treasury Department recover $375 million in 2023 alone and can produce similar savings for private businesses.

Also Read: Explained: Predictive Analytics

Real-Time Data Streaming Best Practices

Recognizing such benefits starts with ensuring you have enough timely data to enable streaming. The Internet of Things (IoT) is an invaluable resource here, as these sensors gather and report real-world information as soon as they record it. You must also connect your server logs, CRM platforms, and other data sources to a stream ingestion platform.

Today, many data streaming solutions handle ingestion, storage, and processing. However, each has unique advantages and disadvantages. Choosing the right one begins with understanding your needs. 

What kinds of data will you be analyzing? How much of it do you generate every minute? What destinations must the insights go to? Do you already have an analytical model that can keep up with the volume, or do you need a new one? Questions like these help you narrow down what features and support your solution must offer.

As with any BI application, data quality and agility are key. Still, you cannot sacrifice reliability for speed—remember, decisions based on poor-quality information cost businesses $12.9 million annually. Consequently, your streaming pipeline must include an automated, scalable data-cleansing workflow before the processing and analytics stage.

Take Your Business Intelligence Further

Your BI applications are not as valuable as they could be if you’re not using real-time data streaming. The need for agile responses will only increase as this field grows in scale and complexity. Capitalizing on this opportunity today is key if you hope to remain competitive.