Business Development Science and Technology

Data Has A Shelf Life: Why You Should Be Thinking About Real-Time Analytics

Streaming data

By Bernard Marr 

We are now living in a world where real-time and streaming data analytics are becoming increasingly important.

Most companies recognize that data has a shelf life, and the sooner they can use the data and turn it into insight, the more profitable it will be for their businesses.

The Biggest Benefits of Real-Time and Streaming Data

Equalum is a big data ingestion and integration platform which enables real-time analytics, and I recently spoke with their, Co-Founder and CPO, Erez Alsheich. He said:

“Analytics is about getting insights from your data – but the vast majority of companies are using batch technologies to load data into their analytic platforms. Every night, they pull a bunch of data all at once, then load it into the analytical environment. The next day, the rest of their team can do analytics on that data, but the problem is that the information is already outdated. It’s yesterday’s data.”

The volume and velocity of data are ever-increasing, causing strain on legacy architectures as they attempt to process it both efficiently and effectively. The complications of ingesting data from operational sources in near real-time, transformed and optimized, do not come without complexity, but the true gateway to streaming analytics lies in modern, multi-modal change data capture.

Change Data Capture (CDC) is a low overhead and low latency method of extracting data, compared to traditional batch processes, limiting intrusion into the source and continuously ingesting and replicating data by tracking changes to that data. When designed and implemented effectively, CDC is the most efficient method to meet today’s scalability, efficiency, real-time, and low overhead requirements. You can push changes as they happen to your streaming analytics systems to enable faster and better business decisions, create smarter products and services, design recommendation services for customers and improve and automate business processes.

While streaming real-time data is a vital component of any modern architecture, there will most likely still be a place for batch data processing in the years to come. Monthly reports, data with minimal changes, historical assessments, and more can still be processed using a batch approach and may not need to be delivered in real-time. Additionally, many organizations that have invested in a CDC streaming ingestion tool will need data replication abilities as well that their current technology cannot accommodate. On the flip side, those organizations that have invested in a CDC replication tool will often find deficits with real-time transformation, data manipulation, aggregations, and correlation capabilities within the ingestion pipe.

This leads to multiple tools, high cost, architectural complexity, and a real barrier to achieving streaming analytics and a streamlined data architecture that can scale.

Finding a Data Integration solution that offers CDC Replication, Streaming ETL, and Batch in one single pane of glass platform is the ideal scenario as you look to incorporate streaming into your Data Architecture to drive streaming analytics.

Once Streaming Data Is In Place, What Can Companies Do with Real-Time Data Analytics?

Real-time data analytics are already used by banks to detect potential fraud. Financial institutions monitor how and where customers use their cards, and if something doesn’t look quite right, artificial intelligence algorithms will automatically detect the potential fraud and send out an alert.

Websites and apps also use real-time data to monitor prospects’ locations and offer appropriate products and services. If you’re at an airport, for example, it might be the perfect time to show you an ad for travel insurance.

Erez Alsheich also notes, “Organizations can offer immediate, detailed credit assessments, so they can reject or approve loans in real-time. This could revolutionize the consumer loan market.”

Education is also an industry that is ripe for disruption. Once we move to a hybrid education model in which a portion of the curriculum is delivered by AI-enabled systems, we can personalize the education journey with real-time data processing. Companies can customize the sequence and selection of math and language questions that are served to students based on their individual needs and previous responses.

How Organizations Can Get Ready for Real-Time Data Analytics

Choose the right use case. Pick one specific use case in your organization that could benefit immediately from having real-time data. Where would you get a high ROI from having the lowest possible latency in near real-time?

Figure out how you’re going to get data. In order to get the most value from real-time analytics, companies need to have streaming data ingestion with modern, multi-modal change data capture in place.

Design for real-time analytics. “Streaming data analytics requires a paradigm shift,” says Alsheich. Instead of trying to implement older batching-style strategies in real time, you have to design your systems to look for changes in the data in real time. Then you will need to have the right infrastructure in place to process that data as you stream it.

Get Ready for the Real-Time Revolution

As our world becomes more data-driven, streaming and real-time analytics become increasingly important for businesses.

All of the world’s most successful companies (like Google, LinkedIn, Facebook, and Amazon) have integrated real-time analytics systems into their models, so they can leverage big data to connect with their customers and maximize sales.

Your company can be next, and these tips can help you prepare for the real-time revolution.

To learn more about how to get your company ready for streaming analytics, check out my full interview with Erez Alsheich of Equalum.

Learn more about Change Data Capture

‘Top Design and Implementation Challenges with Change Data Capture”

READ MORE – https://hubs.la/H0JYzJ50

About the author

Byron Adonis Mutingwende