If we had to ascribe just one definition to the typical business model of the new millennium, it would probably be “data-driven.” It’s the case for all sorts of businesses that cater to the modern-day customer: banks that employ mobile banking apps; retail establishments that use point-of-sale (POS) software both in brick-and-mortar stores and their online counterparts; and social media networks, which engage thousands of users all over the world down to the second.

As businesses scale up, big data moves closer and closer to the fore. Everyday transactions can generate massive volumes of data, and this all goes to a business’s repository of data collected from multiple disparate sources.

Businesses of a bygone era once mined their data repositories only every so often, simply anchoring their decision-making on stale data from past events. But in today’s business environment where the generation of huge volumes of data are so immediate in nature, delaying data analysis becomes all the more risky and disadvantageous for any business. Not capitalizing on opportunities immediately can lead to unnecessary expenses, obsoleteness, or delays in addressing needs and crises.

This is where an approach like real-time data analytics becomes important. What real-time data analytics contributes to business intelligence is a powerful method of processing diverse data that comes from multiple sources and are generated in high volumes. The ability to analyze high-volume data in real time can help in increasing revenues, lowering the cost of doing business, or even preemptively addressing business risks.

Here’s a rundown of how businesses can use advanced data analysis methods to their advantage.

Effectively managing customer experience

Each customer that transacts with a business will have a unique data fingerprint, and this singular fingerprint comprises data of even more disparate types, such those that represent personal information, purchasing behaviors, and preferred methods of transaction. Wise retailers will often use real-time data analytics to build a competitive advantage, utilizing the data they gather to create more personalized customer experiences that better address these people’s needs.

Targeted marketing campaigns

In a similar vein, real-time data analytics can be used to read into emergent market trends. Immediate data can be gathered about the popularity of certain products, the customer profiles of those buying the products, and the rate at which the products are being bought off the shelf. Companies can then use this information to capitalize on what’s in at the moment by creating more targeted marketing campaigns.

Better equipment performance in factories

Data is also starting to encompass a bigger role in manufacturing. For example, pairing real-time data analytics software with sensors in factories can help businesses assess the efficiency of their machines while also informing them of what actions can be taken to improve their performance. Machine operators can collect relevant data in order to quickly determine fixes or firmware updates to the equipment, thus preventing losses from machine error or breakdown.

Mitigating the risks of fraudulent behavior

Real-time data analytics can also be employed to resolve challenges like financial fraud, which is best done immediately as opposed to after the fact. Real-time data analytics and artificial intelligence solutions can recognize patterns of suspicious activities happening across a platform, enabling fraud detection officers to discover criminal activities as they happen or even before they occur. Thus, real-time data analysis plays a big part in letting businesses know where and how to tighten online data security, in quickly acting upon suspicious activities, and in optimizing their fraud prevention measures in the future.

The true worth of adopting real-time data solutions is in being able to gain access to actionable insights. It’s all a race against time to stay updated, relevant, and dynamic—a race that we hope modern businesses can win.

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