Intelligent Data Summit

Analytics, Apps & Data for Success in the Digital Enterprise

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As data continues to pile up in infinite amounts and complexity, every company must now operate like a flash trader, quickly responding to business and customer needs to stay competitive. 

Reducing latency and response time has become imperative to gain a stronghold in the marketplace. Processing new data at the speed in which it is generated is becoming the norm as businesses operate in a completely new time scale.

That’s why in this new age, microseconds matter. 

Several factors in 2020 will amplify this requirement -- escalating connectivity between data sources and systems, exponentially increasing compute power and continuous streams of available data. 

Add all these influences together. You’ll understand why I believe the blueprint for success has matured. It’s all about low latency. 

This isn’t just my gut feeling; we have evidence. 

A recent survey of IT decision-makers conducted by Hazelcast (and in collaboration with Intel) found that 58% of companies measure performance in mere microseconds and milliseconds, versus 39% of companies measure in seconds. 

Moreover, 25% of companies that measure latency in seconds admit they are having an “extremely difficult” time managing advances in technology speed, especially compared to those firms that measure in smaller time scales.

So, the signs are here. Latency – not merely data and analytics -- will separate the business leaders from the laggards. As organizations tackle new frontiers, particularly artificial intelligence (AI) and machine learning (ML), latency will prove to be a crucial differentiator.  

So, what should enterprise IT leaders be doing and thinking about? Here are some suggestions for how to envision lower latency – and ways to take advantage of these capabilities in this New Year. 

Suggestion #1: Harness Technology Advancements

Data shows that defeating time has become the crux of an organization’s ability to build the capabilities required to unlock future technologies. 

Ultra-low latency and split-second processing speeds will enable organizations to tackle new frontiers in AI, IoT, 5G, edge computing and more, leading to better decision-making and improving the customer experience. 

Based on our [Hazelcast / Intel] research, IT decision-makers ranked AI and ML first among technologies expected to be the most disruptive. 

Similarly, AI and automation of processes were tied for second as the biggest drivers of change in the next two years across all industries. Though differences in microseconds versus seconds may seem minuscule to us mere humans - for context it takes 300 milliseconds or 300,000 microseconds to blink - it’s monumental for machines. 

Organizations must adopt new technologies that can handle vast amounts of data -- yet eliminate latency and manage advances in technology speed.

Suggestion #2: Identify Risks Sooner

On top of competitive advantage via innovation, lower latency can help enterprises identify potential risks and ultimately impact the bottom line. 

Consider this: Payment processors typically only have milliseconds to match account information and detect fraud. Any delays for businesses can mean billions in undetected payment fraud, missed e-commerce transactions or even failing to predict an industrial disaster. In fact, over 80% of financial services respondents in the survey said access to data was “extremely important.” 

Quicker access to data informs business decisions, and enterprise leaders increasingly need technology that can help them transform and ingest data in microseconds. 

Suggestion #3: Enhance the Customer Experience

Finally, low latency and high performance in handling data and transactions at scale are essential to improving and maintaining a seamless customer experience. In 2018 alone, Amazon made $7,385 in revenue every second, $7.38 every millisecond, and three-quarters of a penny every microsecond. Consider the impact if one of Amazon’s payment processors were plagued by latency issues – both unhappy customers and lost revenue. 

When consumers go online to make purchases during a busy period, they don’t expect to spend a lot of time in a virtual line and will jump to another site if facing any delays. Applications that are revenue-critical cannot be constrained by the slow journey across the network to retrieve information necessary to process a payment. Knocking out latency helps businesses deliver better experiences for customers and directly drive higher profits.

Where to Start in Cutting Down Latency

So now we know low latency is the core ingredient in 2020. It will fuel a company’s upside, tighten relationships with partners and customers, and protect against multiple downside risks. 

Viewed through the lens of low latency, new data-centric applications can deliver transformative business power – so long as they meet today’s requirement of immediacy. 

Companies need to look at every process and system to determine ways to eliminate the drag that latency puts on business progress. Minimizing latency across the entire hardware and software stack should be a top priority for organizations over the next decade to meet the new demands of response time. 

However, it is important to note that the battle against latency is never fully won and always present. Companies that are able to defeat latency will be the ones that have a leg up in the digital era, embracing data-intensive applications like AI/ML and edge computing, which require instant insight. 

In 2020, businesses must stay vigilant as technologies advance, complexity increases and uncertainty persists. Being mindful of how low latency plays in these dimensions will help companies make the best of opportunities and better manage any unforeseen risks. 

Dale Kim is the senior director of product marketing ​at Hazelcast and is responsible for product and go-to-market strategy for the in-memory computing platform. His background includes technical and management roles at IT companies in areas such as relational databases, search, content management, NoSQL, Hadoop/Spark, and big data analytics.