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June 6, 2017 By Mark Bloom

Disrupting the Economics of Machine Data Analytics

Mobile, Social, Information, CloudThe power of modern applications is their ability to leverage the coming together of mobile, social, information and cloud to drive new and disruptive experiences…To enable companies to be more agile, to accelerate the pace at which they roll out new code, to adopt DevSecOps methodologies where traditional siloed walls between the teams are disappearing. But these modern applications are highly complex with new development and testing processes, new architectures, new tools (i.e. containers, micro-services and configuration management tools), SLA requirements, security in the cloud concerns, and explosion of data sources, coming from these new architectures as well as IOT.

In this journey to the cloud with our 1500+ customers, we have learned a few things about their challenges:

  • All of this complexity and volume of data is creating unprecedented challenges to enable ubiquitous user access to all this machine data to drive continuous intelligence across operational and security use cases.
  • In this new world of modern applications and cloud infrastructures, they recognize that not all data is created equal. For example, the importance, the life expectancy, the access performance needed, the types of analytics that need to be run against that data. Think IT Operations data (high value, short life span, frequent and high performance access needs) vs. regulatory compliance data (long term storage, periodic searches, esp. at audit times, slower performance may be acceptable).
  • Data ingest in certain verticals such as retail and travel, fluctuate widely and provisioning at maximum capacity loads – with idle capacity the majority of the year – is unacceptable in this day and age.


So if we step back for a moment and look at the industry as a whole, what is hindering a company’s ability to unleash their full data potential? The root of the problem comes from two primary areas:

1. The more data we have, the higher the cost

2. The pricing models of current solutions are based on volume of data ingested and not optimized for varying use cases that we are seeing… it is like a “one size fits all” kind of approach

Unfortunately, organizations are often forced to make a trade-off because of the high cost of current pricing models, something we refer to as the data tax – the cost of moving data into your data analytics solution. They have to decide: “What data do I send to my data analytics service?” as well as “Which users do I enable with access?”

As organizations are building out new digital initiatives, or migrating workloads to the cloud, making these kinds of tradeoffs will not lead to ultimate success. What is needed is a model that will deliver continuous intelligence across operational and security use cases. One that leverages ALL kinds of data, without compromise.

We believe there is a better option – one which leverages our cloud-native machine data analytics platform, shifting from a volume based approach – fixed, rigid, static – to a value based pricing model – flexible and dynamic – aligned with the dynamic nature of the modern apps that our customers are building. One that moves us to a place where democratization of machine data is realized!

democratizing machine data analytics

Introducing Sumo Logic Cloud Flex

As this launch was being conceived, there were four primary goals we set out to accomplish:

  • Alignment: Alignment between how we priced out service and the value customers received from it.
  • Flexibility: Maximum flexibility in the data usage and consumption controls that best align to the various use cases
  • Universal Access: Universal access of machine data analytics to all users, not just a select few
  • Full Transparency: Real-time dashboards on how our service is being used, the kind of searches people are running, and the performance of the system

And there were four problem areas we were trying to address:

  • Data Segmentation: Different use cases require different retention durations
  • Data Discrimination:
    • Not all data sets require the same performance and analytics capabilities
    • Not economical to store and analyze low value data sets
    • Not economical to store data sets for long periods of time, esp. as it relates to regulatory compliance mandates
  • Data Ubiquity: Not economical for all users to access machine data analytics
  • Data Dynamics: Support seasonal business cycles and align revenue with opex

So with this Cloud Flex launch, Sumo Logic introduces the following product capabilities to address these four pain points:

  1. Variable Data Retention
  2. Analytics Profile
  3. Unlimited Users
  4. Seasonal Pricing

If increasing usage flexibility in your data analytics platform is of interest, please reach out to us.

If you would like to get more information on cloud flex and the Democratizing Machine Data Analytics, please read our press release.

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Mark Bloom

More posts by Mark Bloom.