Q&A: Cutting-Edge Analytics Technologies Are At the Edge
What technology must be part of your tool kit today, what technology has the greatest potential this year, and where are analytics and data management headed? Aerospike CEO John Dillon shares his perspective.
- By James E. Powell
- March 26, 2019
Is cutting-edge technology located at the edge? We asked John Dillon of Aerospike about the technology that every enterprise should have as part of its data strategy, the emerging tech he's most excited about, and what challenges enterprises face today, and the direction analytics and data management are taking this year.
Upside: What technology or methodology must be part of an enterprise's data strategy if it wants to be competitive today? Why?
John Dillon: In the world of artificial intelligence, IoT, and personalization, data and real-time insights have gained paramount importance within enterprises. Real-time, actionable, "in-the-moment" insights drive better decisions -- all in a split second -- that can greatly improve the customer experience and operational efficiency and open up new business models. These actionable insights are derived from analysis and decisioning based on data from both the transactional and streaming data workloads.
To remain competitive, enterprises need to incorporate a real-time transactional analysis system that combines both streaming data and historical transaction data to provide valuable insights. Examples include anomaly detection for fraud in payments, personalized targeting in ad tech, and recommendation engines for e-commerce and media.
What one emerging technology are you most excited about and think has the greatest potential? What's so special about this technology?
Edge computing is real. It's here, and companies have to have a strategy to handle the enormous influx of data coming in real time from devices globally.
Analysts project there will be 50 billion telematics devices by 2020 and forecast the sum of the world's data will reach 175 zettabytes by 2025. Although edge computing is putting enormous pressure on IT infrastructure -- where legacy systems at the networking, storage, and application layers are straining today -- a new generation of systems is coming to market to help companies deal with the data explosion caused by edge computing.
What is most exciting is the ability these new systems give companies to engage with customers in fundamentally new ways. There are examples of new business models being developed around the edge -- Netflix, Uber, and Amazon are notable examples -- but now many companies can adopt these new business models with next-generation, edge-aware systems emerging today.
What is the single biggest challenge enterprises face today? How do most enterprises respond (and is it working)?
As enterprises continue expanding their digital investments, they are challenged with transforming from a data-driven to an insights-driven strategy. They have collected a lot of data and built data warehouses and data lakes but are struggling to harness the data to gain insights to make better, real-time business decisions.
Enterprises are starting to reinvest in their data platforms. They understand they need to combine streaming and transactional data analysis, but they end up running into technological limitations with legacy systems in place today.
One of the biggest hurdles is handling the sheer amount of data required to harness the power and the promise of today's machine learning-based applications. Machine learning and AI engines perform best when fed sufficient amounts of data that is current enough to make the models sufficiently accurate to work as designed. We call this "Hungry Machine Learning and AI." Companies are challenged today to truly power machine learning and AI-based applications on legacy data systems.
Is there a new technology in data and analytics that is creating more challenges than most people realize? How should enterprises adjust their approach to it?
There have been a lot of advancements in recent years in storage technologies that offer a lot of promise but also a lot of challenges. It's hard to believe, but the traditional spinning disk drive that was the standard storage medium just a few years ago is being rapidly replaced with flash-based solid-state memory. In addition, dynamic memory capacities are increasing at a rapid pace.
As these tiers of storage become more economical, companies have to determine the right mix of storage types to best handle the massive influx of data, as well as deliver the right performance and be persistent to power the new, mission-critical operational applications powered by machine learning and AI. The analytics required on top of these storage tiers and applications is not well supported by legacy BI and reporting architectures.
Companies need to begin reevaluating their approach to both storage and analytics, looking at the hybrid storage architectures and real-time analytics platforms emerging today.
What initiative is your organization spending the most time and resources on today?
We believe there is a large need for a next-generation data and analytics platform to help solve some of the pressing issues companies are faced with as a result of the rise of edge computing. Currently, we help some of the world's largest and most innovative companies manage the hyper-scale data needs for their systems of engagement -- those touching their customers and users directly -- that consume massive amounts of streaming data that get merged with data from back-end systems of record.
Our experience working with these companies is driving an initiative to look at how we can help our customers and prospective customers get better insights in real time using this combination of data sources to make better decisions to drive new business models and gain competitive advantage.
Where do you see analytics and data management headed in 2019 and beyond? What's just over the horizon that we haven't heard much about yet?
Analytics and data management are undergoing a sea of change with the rise at the edge. Data management systems built to manage relatively static data and drive analytics that are updated monthly, weekly, or daily no longer work for many of the mission-critical applications companies deploy today. What is interesting is that applications on the edge, or systems of engagement, themselves embed analytics; they model and analyze massive amounts of data and render decisions without human intervention. In essence, they are mimicking human decision-making as we would in a live interaction.
We see the evolution of data management and analytics supporting real-time decisioning more broadly across companies and within companies.
Tell us about Aerospike's solution and the problem it solves for enterprises.
Aerospike helps leading enterprises around the world build and deploy modern data architecture solutions with confidence. Our enterprise-grade database platform helps companies power mission-critical, strategic operational applications that make digital transformation possible. Powered by a patented Hybrid Memory Architecture and autonomic cluster management, Aerospike is used by enterprises in a variety of industries, from financial services to ad tech, and is well suited for fraud prevention, digital wallets, online brokerages, and other applications that require extreme uptime, performance, and scale.