Do you think National Hockey League goalies have cat-like quickness? Well they do, quite literally, according to the smart folks at Sport Science. As this episode notes, a goalie is able to respond to a shot in a staggering 1/10 of a second, which is a similar to the reaction time of a cheetah. This incredible feat is achieved based on highly attuned reflexes or an almost automatic response to a common situation, which, in the case of NHL goalies, is a puck hurtling toward them at up to 100 mph.
When I think about big data analytics today, I believe most companies are striving to be faster, but what they really need to be thinking about is becoming NHL-goalie quick. This is particularly true when it comes to customer interactions. With customers, every moment matters, and the time it takes for a company to turn data into action can be the difference between making a sale and losing a customer.
Unfortunately, examples of companies with these fine-tuned reflexes are rare today. Generally, companies like Netflix, Google, and Amazon – massive organizations with large IT budgets and teams – dominate such conversations. These organizations have been able to maintain a competitive advantage by delighting customers and anticipating their needs through predictive search results and personalized offers or recommendations.
But real-time analytics and insights are becoming available to a broader range of companies.
Recently, some key technology ingredients came together to allow a far broader range of companies to create the big data supply chain needed to support real-time big data analytics. At the heart of this is Apache Spark, an open standard for flexible and very fast in-memory data processing. Coupling Spark with analytics tools and optimized solutions from firms like Cloudera, Databricks, Tableau, and Talend allows companies to react to customers at the speed of an NHL goalie’s glove hand.
Speeding Insight with Spark
If you need to make fast decisions, then Spark is the answer. Spark runs functions up to 100 times faster than Hadoop MapReduce in memory and 10 times faster on disk. Data integration platforms built on Spark provide in-memory analytics, machine learning, and caching components, so your big data projects deliver real-time results. Spark jobs can automatically generate fast big data code that can run on Hadoop, standalone, or in the cloud. Platforms that offer support for Spark provide real-time data that give businesses the ability to actively influence customer buying behavior. More and more companies are realizing that this real-time big data insight is needed in order to understand their customers and tailor future campaigns.
This ability to analyze customer data in real time is a substantial market change that has broad consequences. For organizations, it’s interesting to know what their customers were doing last week, but it’s infinitely more meaningful to know what their customers are doing and what they need right now. And it’s even better to be able to respond to customers’ needs in an instant and fundamentally transform their experience for the better.
Ecommerce is an obvious fit for real-time customer insight and management, but there is no shortage of compelling use cases across virtually every market segment – from fraud detection in the financial sector to optimizing IoT-equipped wind turbines in the field based on current weather conditions. Healthcare is another industry ripe for being reshaped by the availability of real-time big data. Whether you look at diagnosis or research and development, faster access to quality data points will help drive better results.
Spark-powered data integration, coupled with Spark-enabled analytics, is a game changer. Companies with the speed and agility of an NHL goalie will have a significant market advantage. Beyond the obvious opportunities around revenue optimization and customer relations, just about every aspect of business – from manufacturing and supply chain management to human resources – can benefit as well.
I look at Spark as a great engine and today, our bet is on Spark running on Hadoop. I see Spark as augmenting Hadoop rather than replacing it. While Spark is one area of the big data market that is growing, another area that customers are increasingly interested in is self-service. There is an interest on the IT side of things in tools that empower the business and gets IT out of the way by doing low complexity, low-value-added work.
Ashley Stirrup joined Talend in 2014 as Chief Marketing Officer. In this role, Ashley is responsible for driving market leadership, global awareness, product management and demand generation. Prior to Talend, Ashley held a number of senior leadership positions in marketing and products at leading cloud and software companies, including ServiceSource, Taleo, Citrix and Siebel Systems.
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