6 Major Business Advantages Of Real-Time Data Analytics
Organizations may benefit from real-time data analytics applications such as faster decision-making, increased business agility, better customer service, and more.
When consulting with clients a few years ago, I would have stressed the importance of distinguishing between real-time and right-time data. Why deploy expensive real-time data analytics tools to deliver updates every few seconds if your business processes can be served with hourly updates? If you only check your dashboard once an hour, you won’t notice that the data has been updated thousands of times.
That was an important point to make at the time. However, while it is still interesting to speculate on what the term “right time” may imply, business is moving much faster now. Right-time is becoming increasingly real-time. To keep up, more companies are investing in real-time data analytics to analyze, interpret, and visualize data as it is created or changes in their source systems.
With these considerations in mind, what are the business benefits of real-time data for analytics applications and other applications? Here are 6 of the best.
1. Make decisions at the speed of your business.
Even with the most advanced data science and analytics tools, much of what we do in data management, BI, and data analytics still falls under the umbrella of that old-term decision support. It’s an accurate description: the software assists you in making better decisions.
As I mentioned earlier, better decisions must be made faster as the pace of business picks up. When even small businesses are trading globally online, waiting for an overnight data management process to reconcile the data warehouse to deliver standardized reports is no longer acceptable. Business teams working remotely or internationally require the most up-to-date information continuously, often shown in the form of a dashboard with instant insights.
So, the first — and for many the primary — benefit of real-time data in the enterprise is simply being able to support decisions whenever and wherever they are required.
Read more: What is Advanced Analytics?
2. Improve business agility and efficiency
It’s easy to believe that faster decision-making equates to a more agile business. However, business agility is more than just making decisions; it also includes your strategic and tactical business objectives. Choosing between options takes time to make different kinds of choices.
Forming small, well-informed, tightly focused teams known as squads is one approach to business agility that has proven very successful in several industry sectors. Retailers, for example, may use squads to focus on produce, home goods, or other specific merchandise categories, allowing them to make decisions quickly and directly that would have previously required extensive management review. Manufacturers may have teams dedicated to maintenance or safety.
Empowering squads to act quickly and close to your operations enables faster and, hopefully, smarter responses to a changing business environment. However, this approach can only be effective if a squad has the necessary data, which must be kept up to date to match the team’s urgency. That’s an excellent application for real-time data.
3. Quickly Identify and address operational issues
Squads originated in the technology industry but have since spread to sectors ranging from retail to telecom and, more recently, healthcare – all of which are dealing with rapidly changing markets and cost pressures while maintaining a focus on profit margins.
You don’t need a squad to improve business operations with real-time data. For several years, companies have been using data from IoT sensors, or video feeds to monitor production lines for stoppages and backlogs and to run predictive maintenance applications. It is a prime example of real-time operational improvement in action, and it has proven to be highly effective in reducing downtime in manufacturing plants.
Similar approaches can be used in a variety of scenarios. For example, real-time traffic and weather reports can help logistics companies route delivery trucks more effectively. If the trucks are refrigerated, onboard temperature sensors can also monitor and send real-time alerts for issues that require immediate attention or rerouting.
Companies also use real-time analytics to monitor the balance of incoming orders and product or part availability so that they can quickly replenish supplies that are running low and detect the need for short-term contract labor if production, packaging, or shipping is falling behind schedule.
4. Recognize and respond to short-term market changes
Stock trading is an obvious example of an industry sensitive to rapid market fluctuations. In such cases, real-time data is critical to business survival.
Other industries, such as airlines and hotel chains, manage prices and availability in response to current events, weather, oil prices, and other volatile factors. With so much of the retail experience online these days, retailers must also react quickly to changing demand, costs, and customer trends.
In all of these scenarios, real-time data is invaluable. Slower movement is possible if your inventories and margins allow you to take a deep breath and ride out some disruptions. However, only some businesses nowadays have this luxury. Instead, we’ve had to become much smarter about how we use data to enable faster and more efficient market monitoring.
5. Personalize the customer experience for online marketing
Online retail is an excellent example of real-time data allowing for new and more effective customer experiences. In the old days of the physical store, attentive staff would recognize, greet, and guide regular and best customers. Today, you’re more likely to be recognized by a bot with real-time access to data from your online behavior. While the bot will not greet you, it will ensure that the homepage, special offers, recommendations, and, in some cases, even the color scheme reflect what it has learned about you over multiple sessions.
Some people believe this attentiveness and automated customization is “creepy” and a little too focused for comfort. However, many organizations use real-time technology to personalize their websites and online advertising for individual customers, without people realizing it is working behind the scenes to provide what they perceive to be the everyday customer experience.
6. Improve customer service by providing updated information
Calling customer service at your utility company, cable provider, mobile network operator, or airline should be a better experience now than it was a few years ago. Why? Because all these industries and many others have made significant investments in real-time data integration for their call center operations.
When call center agents look up a customer’s record, they should be able to see information about the local outage, faulty equipment, unusually high bill, canceled flight, or other issues that prompted the call right then and there. This type of insight, enabled by real-time applications, is now commonplace.
Real-time data collection and management
It’s difficult to summarize all of the processes involved in real-time data management. However, it’s important to distinguish between data collection and management approaches, micro-batching, and streaming.
Most traditional data repositories, such as data warehouses and operational databases, load data in batches and respond to analytical queries with data sets themselves batches. There is a beginning and an end to batch processing. The most common type of data integration is an extract, transform, and load; I’ve seen batch ETL processes that ran for 12 hours, loading a data warehouse with millions of records. That was a large batch, and it was always a relief when it was completed successfully.
If we need near-real-time data collection, such as performance data, we can process micro-batches, sometimes only one record at a time. If the data management environment can handle it, these micro-batches can be processed quickly into and out of the analytics system, simulating real-time data at the source. Nonetheless, micro-batches have a beginning and an endpoint. They should have some of the benefits of traditional data integration techniques, such as robust transaction handling in the event of a failure.
Like a flowing river, streaming data has no clear beginning or end. Streaming is particularly popular for collecting real-time data from sensors, such as those found in IoT devices. Still, streams can also come from transaction logs, activity logs, and other sources.
Streaming may be a good option for simple types of analytics, such as reporting on current conditions, monitoring for exceptions and outages, or optimizing business processes in response to real-time activity. A sound streaming system can handle large amounts of data, making it suitable for data science applications.
However, if integrating real-time data from one source system with transaction data from another is critical, micro-batching is likely a better solution. For example, handling airline tickets and re-bookings in the event of flight delays involves significant amounts of rapidly changing data. Still, the airline must ensure that each change in the system is safely committed with a transaction. Micro-batching is helpful in these situations.
Real-time data analytics challenges
For many businesses, real-time data analytics has real advantages and benefits. However, you should be aware of some potential pitfalls. Even though most real-time data is now processed and stored in the cloud, the sheer volume of data that is frequently involved necessitates special data storage planning. Indeed, a large portion of big data, including structured and unstructured data, is generated by sources that have real-time power analytics, such as web traffic logs and manufacturing equipment.
You should also consider your data archiving strategy if the data-driven decisions must be audited or reviewed for governance and compliance. And you must plan for problems such as system outages, late-arriving data, and other real-time processing issues operationally, tactically, and strategically.
Nonetheless, with many leading analytics vendors now offering effective real-time technologies, the real-time stream is rapidly becoming mainstream.