In celebrating my recent one-year anniversary as CEO of Sumo Logic, I feel very proud of the growth and progress we’ve experienced as a company. What gets me especially excited is knowing that while the big data market will exceed $50 billion in 2016 according to analysts, the disruptive power of machine data analytics, which is the fastest growing category within big data with a CAGR greater than 1000%, is only in its infancy. This gives Sumo Logic – and our customers – the opportunity to shape the direction of what we believe will be the most critical and essential category within the big data market.
Why? Because machine data sits at the intersection of the most powerful trend in technology, and I’d argue – business in general: digital transformation. As digital transformation grows within organizations, so does their reliance on new software and architectures. Today, software is not only driving business processes, but entire business models, and the need to manage, monitor and troubleshoot applications in real-time has never been more critical. Thus, the need for full-stack visibility, analyzing data at scale and real-time insights – what we call “continuous intelligence” – is the true business demand underpinning the growth of machine data analytics.
Since Sumo Logic delivers the only cloud-native, advanced analytics platform to address the need for real-time, continuous intelligence, through our customers we have a front-row seat to the transformative power of real-time insights on digital business transformation. From that vantage point, we see five major trends impacting and shaping the world of machine data analytics:
- The maturation of DevOps and DevOps tools: It’s no secret that cloud will continue to grow at an unabated pace, as organizations look to new technologies to increase their business speed, agility and competitive edge. This is also resulting in the need for new tools that help them break down traditional silos between developers and IT operations teams to innovate more continuously and scale as fast as their business. As organizations embrace the DevOps approach to application development, they face new challenges that simply can’t be met with traditional on premise and antiquated monitoring tools. In 2016, we’ll see DevOps adopt a new breed of next-generation log and machine data analytics services that run at cloud-scale, employ predictive algorithms, and can be seamlessly integrated with a host of DevOps tools across the entire pipeline, not just server container or infrastructure data, in order to dramatically improve the continuous integration and continuous deployment processes.
- CISO and security operations to invest more in system intelligence: For years, companies have understood the value of using big data to gain actionable insights for business decisions. Now, through advancements in technology such as machine learning, data analytics are providing business insights at a granular layer in the systems infrastructure not yet experienced by most companies. For security teams, analytics are giving rise to new, faster intelligence around system and user anomalies, threat detection and breach alerts that will not only improve mean time to investigate (MTTI) / mean time to recovery (MTTR) speed, but change the way security leaders think about security systems architectures for years to come. Moreover CISO’s and security teams will join forces to partner with DevOps teams to help secure new application architectures, with embedded security capabilities leveraging integrated machine analytics.
- Log management will be a huge opportunity for IT operations and customer support teams: Thinking about the value of log data may seem too far in the weeds for most tech industry professionals. However, using analytics to monitor, manage and gather insights from user, application or infrastructure logs will be the only near-perfect way to address the growing complexity of cloud and hybrid-cloud infrastructures. We’ve already seen vendor consolidation and are likely to see new vendors try and move into the logs analytics space. As such, it’s anticipated this will increase organizational awareness of the value of log management to support application development, security and IT operational success, and will be the first sign the “democratization of analytics.”
- The rise of “Extreme Architectures”: Historically, advancements in the capacity and processing power of microprocessors has served as a common metric for software advancement (e.g., Moore’s Law, in which microprocessor power doubles every two years.) However with today’s cloud infrastructure, the ability to “string together” thousands of microprocessors via virtualized servers obviates Moore’s Law. Thus, the most innovative CTOs will push for new, “extreme” software-based architectures to harness the processing power inherent in public or private cloud infrastructures to lessen traditional in-house data center constraints (performance, management and maintenance) while increasing focus at the application layer to drive the functionality needed for differentiated customer experiences.
- Business intelligence value shifts from rear-view to continuous: As technology infrastructures shift to cloud-based platforms to increase business speed and agility, it begs the question, “wouldn’t you want your business intelligence to do the same?” Therefore, 2016 will be the year that the definition for business intelligence shifts, from on-premise solutions delivering rear-window insights to solutions that deliver continuous intelligence in real-time. By harnessing the insights inherent in real-time log data analytics, companies will have faster access to operational and customer data that can enable 24/7 innovation and sustain competitive edge. Hence, for companies betting their business on software/application platforms, continuous intelligence will not be a nice-to-have, but a must-have.
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