This sponsored post is produced by Sumo Logic.
In the future, we may look back on 2015 as the year that machine data analytics emerged as a true market disruptor, emanating from the mega market trends of Big Data, Cloud, DevOps, and the Internet of Things. Ironically, these mega trends have played a dual role in this market disruption: both as the creator of the problem — the volume, velocity, and variety of data — and source of the solution — a cloud-native, service platform to provide the scale, security, and rich analytics to transform real-time log streams into powerful operational and customer insights for business growth and success.
Although the big data market will be nearly $50B by 2019 according to analysts, what’s most exciting is that the disruptive power of machine data analytics is only in its infancy. Machine analytics will be the fastest growing area of big data, which will have CAGR greater than 1000%. As digital transformation grows, so does the 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 speed, full-stack visibility, and agility — all in real-time — is the true business demand underpinning the growth of machine data analytics.
From this vantage point, below are five trends that will continue into 2016 that will have significant impact to the world of machine data analytics:
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.
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.
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, we anticipate that this will increase organizational awareness of the value of log management to support application development, security and IT operational success. This will be the first sign of the “democratization of analytics.”
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 datacenter constraints (performance, management and maintenance) — while increasing focus at the application layer to drive the functionality needed for differentiated customer experiences.
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 their 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.
It will be interesting to look back 12 months from now to see what unfolds, but no matter what happens, one thing is certain: we’re in a whole new world and data is in the driver’s seat.
Ramin Sayar is President and CEO of Sumo Logic.
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