Layoffs have hit big tech companies—Amazon, Meta, Google and Microsoft, to name a few—amidst an uncertain economy, and interest rate increases (paywall) have made previous debt loads and borrowing habits unsustainable for many companies.

But we should not equate the fates of certain big tech companies with a full tech slide back. In fact, there are areas of tech that are ripe for accelerated growth. As the CEO of a company that offers a real-time SQL database that powers real-time analytics and applications, I believe data, and in particular, real-time data and analytics, will generate efficiencies across all sectors of the economy in a way that is urgently needed right now.

Companies will likely continue to invest in real-time analytics because analyzing data in real time is the foundation of the modern world. The total data volume worldwide today is exponentially higher than it was in 2010. The velocity of data, the speed and scale of data and consumer demand for the application of data are unprecedented.

Real-time analytics has changed the way we operate in the world and the way companies are responding to this environment. This type of data analytics occurs within seconds and could be a bright spot amidst the tech darkness.

The use cases for real-time data, from detecting fraud in finance to finding our favorite TV shows and many more, continue to make real-time solutions ever-relevant.

I’m reminded of the recent PwC 2022 Global Economic Crime and Fraud survey that highlighted alarming results. Of the companies surveyed, 51% of them reported experiencing fraud in the last 24 months. And 40% of organizations that encountered fraud said they had experienced platform fraud. What is often left out of the conversation is that companies can leverage real-time analytics as a stop-gap against fraudulent incidents like these.

For example, a financial services company might have just one second from the time customers swipe their cards to approve or refuse their payment. In those milliseconds, the business must run a sophisticated set of queries to make that determination. Companies can use real-time analytics to quickly ingest data, run a large set of queries and, in the process, decide whether or not to approve the transaction.

Companies use real-time analytics for more than just protection. Whether it’s streaming applications or scrolling through social media, many applications capture and consolidate large amounts of data to suggest your next favorite TV show or a new bathing suit right as you plan a beach vacation.

Speed is crucial for customers—and has arguably never been more critical for the tech industry. It's also important that organizations continue to improve their models over time across use cases. That growth and learning are vital because you can’t prevent fraud or find your next favorite show in real time if you must accumulate feature records for each of your customers, analyze that batch data every night and then try to identify whether or not scoring was correct.

How To Prepare For And Integrate Real-Time Solutions

To enable real-time solutions, organizations should make some tweaks to their overall data architecture: Make sure to implement low latency streaming data ingestion, higher query concurrency requirements and low latency requirements or strict service-level agreements (SLAs), to name a few. Having effective pipelines, supporting a rich set of indexing techniques and separating storage and compute functions are also baseline requirements.

The engineers in the room are probably nodding their heads in agreement with what I just said; the nonengineers are probably wondering if I am still speaking English. But these changes are not as drastic as they may sound.

We’re already seeing the move to cloud applications, and as organizations continue to look for cloud solutions and real-time architectures, my first piece of advice is to find the most streamlined application possible. Simplicity and speed go hand in hand. Do an audit of your current systems: Can you scale down the number of databases you have? Think through where you are spending your resources, both monetarily and in terms of staffing, and look for solutions that will provide improvements in these areas. As we move toward the future, I believe companies that provide these core requirements for real-time analytics well, rather than those that provide a bunch of applications haphazardly, are going to be the ones that succeed.

After you decide how to change your data architecture, you need to audit your team to see where more (or less) staffing is needed. Returning to the cloud example, operating on a public cloud necessitates different skills than operating on an on-premise system. It is your responsibility as a leader to look at your IT team and confirm that they have the knowledge necessary to oversee your data when it's stored in a different way. For example, cost management can be a more prominent aspect of a data manager's job when data is stored in the cloud. Data security needs for these two systems also differ. You have the responsibility to help your team get the training they require to operate successfully or look to hire new team members who can help your current team members get up to speed with new systems.

The prevalence of real-time analytics in our modern world makes it clear that this is a continued area of opportunity, even in a broader moment of challenges for the tech industry at large. Data fueled our economy’s 21st-century advancement, and we can leverage it to continue to drive the tech industry and all of us forward.

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