12 Benefits of Real-Time Analytics for Businesses

Real-time analytics is having a moment. Once the domain of mobile app-based businesses, such as Uber and DoorDash, streaming data and real-time analytics have arrived as a vital operations tool for businesses across industries. In industries as varied as retail and industrial manufacturing, real-time analytics is helping businesses use their data for much more than making better, faster decisions—though that’s part of it. Real-time analytics also lets companies detect operational or market blips in the moment and accurately anticipate the next events, allowing them to design smarter, more personalized products and services and even automate processes to make the business more efficient and less expensive to run. Below, we’ll see many of the creative ways businesses are reaping the benefits of real-time analytics.

Key Takeaways

• Use of real-time analytics is growing because it puts data to use quickly—when it’s most valuable.
• Artificial intelligence and machine learning give businesses the ability to analyze and act on data at speeds and volumes that were previously impossible.
• With real-time analytics, people are empowered to not only make better decisions, but to fully reimagine the way they do business.
• The velocity of real-time analytics leaves little time to move data between databases and separate data analytics tools.

12 Benefits of Real-Time Analytics for Businesses

Unlike traditional data analytics, real-time analytics is about more than informing future decisions; it enables whole new ways of doing business by letting teams take action in the moment.

Three trends have converged to make real-time analytics valuable for more industries.

One is the growing availability of data streams, including those outside a company, such as from social media sites or public data from satellites and government agencies. Another is the growing number of data streams within a company from enterprise applications, such as ERP or CRM systems; internet of things (IoT) devices and sensors; and sources such as emails, text, and videos. Finally, cloud-based software and infrastructure make the technology needed to manage and understand all this data accessible to more companies. Companies use them to deliver insights at what had been unimaginable speed and scale. These technologies include artificial intelligence (AI) and machine learning (ML) as well as emerging technologies that streamline data management and analytics infrastructure.

Here’s a quick look at the creative ways companies are using real-time analytics in their everyday business operations.

1. More relevant data from live dashboards

Live dashboards up the analytics game. Traditional data analytics takes information stored in a data warehouse and moves it in large batches to an analytics system that updates graphs and charts in a dashboard. This helps people see results over the past days, weeks, or months.

Live dashboards, on the other hand, are hooked into data streams that show the business what’s happening right now for immediate action. They help businesses adjust, such as rerouting shipments before a storm hits or servicing a vital machine before it breaks down.

2. Improved logistics visibility

Businesses are using real-time data feeds from public and private services for maps, weather, traffic patterns—even satellite feeds—mixing it with their own real-time data from sensors on the manufacturing floor or offsite at a building site or in trucks, planes, and ships. This gives them an up-to-the-moment understanding of their operations so they can adjust routes, set customer expectations, track progress on a construction project, or proactively order parts for equipment.

3. More agile decision-making

Rather than analyzing past events, real-time data lets a business detect trends or anomalies as they’re happening and react immediately. By connecting to IoT sensors as well as public data feeds from municipalities or weather satellite feeds, for example, a ridesharing or shipping company can see trouble, whether that’s high traffic, bad weather, or other problems, and make immediate changes. Without real-time analytics, they would have no way to understand the problem or react until much later.

4. Fewer operations hiccups

Using machine learning, IoT sensors, and streaming analytics, a company can remotely monitor equipment and foresee mechanical failures, enabling them to proactively run maintenance operations to avoid manufacturing downtime. Or a logistic company can monitor shipments and notify customers in a timely manner if a shipment is delayed.

5. Improved ad campaign performance

Real-time analytics is a game changer in advertising and marketing campaigns. For example, a real-time analytics platform that connects to distributor sites and social media accounts and monitors web traffic can understand which ad platforms are working best and direct spending accordingly. A company called Tetris.co (now NeoDash), for example, unifies data from multiple media sources so front-line analysts can understand trends faster and shift investments into higher-performing channels and away from underperforming platforms.

6. Improved customer experience

By using real-time analytics and providing automated responses to those real-time insights, companies can offer a superior customer experience. In the technology industry, real-time analytics is used to identify cyberattacks and then automate steps to head them off. That benefits everyone.

Top IT service providers are using real-time analytics to go beyond responding to problems and are instead constantly analyzing performance so they can support clients with preventative maintenance, heading off threats before the client knows they exist. In financial services, real-time analytics can help a bank detect possible fraud on a transaction, which can then set in motion an automated notification to a bank card customer and even freeze the account if warranted.

7. Easier adaptations to market volatility

One benefit of real-time analytics is the ability to automate systems so they’re responsive to fast-moving events. As we’ve seen with global supply chain snarls in the past few years, businesses that can react more quickly to bottlenecks can find supplies and keep business flowing. A streaming data analytics platform can connect industry sites, public data, satellites, and a company’s own ERP systems, which can help it visualize and adapt to market volatility more effectively.

8. Faster detection of workflow problems

From manufacturing lines to retail stores, companies that work on tight schedules are integrating data streams with event-processing systems to detect workflow problems before employees or customers start seeing the fallout. The system can, for example, ping an agent if sensors monitoring a complex system at a customer site send data that indicates of a possible breakdown. Some manufacturing and power-generation systems go beyond such alerts to order parts and dispatch a maintenance team—all based on real-time sensing of anomalies in a machine’s sensor outputs. Such a system might require IoT data, data management platforms, and machine learning algorithms that detect minute changes in fast-moving data streams and even analyze long-term operational data to suggest process improvements over time.

9. Improved cost efficiencies

Real-time data analytics allows you to monitor suppliers in real time and automate certain procurement decisions, helping to keep supply costs down. Streaming data and artificial intelligence can also be combined to automate regular business processes, such as an intelligent document flow in a financial transaction or an insurance claim that can handle many steps of a transaction with no human intervention.

10. Enhanced testing capabilities

Software testing and IT management offer time-tested use cases for real-time data and automated response. A good software testing system uses real-time analytics to catch and report errors in data, spot breaks in APIs, and even identify issues with user interfaces. Real-time analytics can also help with maintenance of long, tedious testing scripts, automating validation exercises instead of relying on manual spreadsheet-based validations.

11. Develop better customer profiles

Companies build customer profiles to help them serve offers or content options that resonate with a buyer. Analytics helps marketers know which potential customers are currently online and what products might be of interest to them. But people change constantly—while profiles do not. Unless, that is, they are connected to a real-time analytics system that updates the profile based on connections to not just searches and purchases, but feeds such as social media or web activity that can note life changes and even shifting opinions. The more data inputs, the better the product suggestions, leading to more sales.

12. Track competitors

With machine learning, real-time analytics can be built using big data sources, such as social media feeds. This can help a company keep tabs on its industry. For example, social posts can reveal that a competitor is running a sale or a promotion or is losing goodwill with customers due to a service failure or a promotional gaffe. Companies can then take measures to react in the marketplace.

Get All the Benefits of Real-Time Analytics in One Place with Oracle HeatWave MySQL

Developers love the open source MySQL database. Until now, however, when they wanted to analyze data stored in MySQL, they had to buy additional databases or analytics software and laboriously move—or ETL (extract, transform, and load)—all that transactional data over to the analytical environment. That pretty much ensured that it was no longer real-time data.

Now, developers can use Oracle HeatWave MySQL, which gives them the simplicity of having transactions and real-time analytics in one MySQL database service, where their analytical queries can always access the most up-to-date data. They can use HeatWave MySQL on Amazon Web Services, Microsoft Azure, and Oracle Cloud Infrastructure.

Using HeatWave MySQL lets them access other built-in HeatWave capabilities, too: HeatWave AutoML and HeatWave GenAI enable them to benefit from integrated and automated machine learning and generative AI—without ETL across cloud services. HeatWave Lakehouse lets them build applications to query up to half a petabyte of data in the object store—in a variety of file formats, such as CSV, Parquet, Avro, JSON, and exports from other databases—and optionally combine it with data in MySQL.

If you’re looking to use real-time analytics in your business, HeatWave MySQL will let you say goodbye to the cost, complexity, latency, and security risks of the ETL processes and multiple database environments that once held you back.