As engagement with customers, suppliers and partners is increasingly conducted through digital channels, ensuring that infrastructure and applications are performing as expected is not just important but mission critical. My colleague, David Menninger, recently explained the increasing importance of observability to enable organizations to ensure that their systems and applications are operating efficiently. Observability has previously been the domain of the IT department but is increasingly important for business decision-makers as organizations combine machine-generated telemetry data with business event data to understand the impact of a system outage or application performance degradation on their ability to conduct digital business. Companies such as Mezmo are responding with observability platforms designed to facilitate the integration of machine and business data and encourage collaboration between business and IT professionals.
Mezmo was founded in 2015. Initially known as LogDNA and focused on log management and analytics, the company rebranded as Mezmo in 2022 to reflect its broader focus and expanded functionality to address observability. Log management remains a core capability of the company’s observability platform, enabling the processing and analysis of log data from servers, networking equipment, internet of things (IoT) and applications. However, logs are just one form of machine-generated telemetry data, alongside traces and metrics, which can be used to identify application and infrastructure problems that can impact quality of service. The primary benefit of observability lies in using telemetry data to reduce the mean time to detection (MTTD) of IT infrastructure issues as well as the mean time to resolution (MTTR) — the time it takes to make the necessary changes to resolve them. The importance of machine-generated data is increasingly being recognized outside the IT operations department given the potential benefits for development and DevOps, as well as security and compliance, of correlating telemetry data with business event data to better understand the business risks associated with IT incidents. Almost one-third of participants (31%) in Ventana Research’s Analytics and Data Benchmark Research believe machine data is important for their organization’s analytics activities, while a similar proportion of participants (32%) in Ventana Research’s Data Governance Benchmark Research are managing or planning to manage machine data with their data governance policies. Generating value from telemetry data is not simply about combining it into a single, centralized platform for analysis by IT professionals, therefore. Mezmo’s Telemetry Pipeline provides an environment for ingesting telemetry data from multiple sources before connecting, transforming and enriching it, and then routing the data to multiple destinations, including storage, full-stack cloud-observability platforms, and analytics applications.
The complexity of modern IT infrastructure means telemetry data needs to be ingested and analyzed from an enormous range of computing equipment, sensors and applications, all of which are distributed across on-premises and cloud computing environments. Increasing the volume of telemetry data to be collected and analyzed might lead to greater insight, but it is also likely to lead to increased cost, complexity and management overhead. Mezmo introduced its observability pipeline offering, now known as Telemetry Pipeline, specifically to address this dichotomy by enabling more intelligent and efficient processing of telemetry data. Observability pipelines are designed to improve time to detection and resolution by automating the centralization of telemetry data from multiple sources, with the additional benefit of transforming data prior to routing it to the observability platform to reduce unnecessary costs and time delays.
Observability pipelines also allow data to be routed to other destinations such as data lakes, cloud data warehouses or business intelligence (BI) tools for further analysis and visualization. While many observability
Observability pipelines also potentially provide a foundation for the combination of telemetry data with business events. Translating telemetry data into business decisions requires close cooperation between technology engineers — who have the specialist skills required to understand the dependencies between infrastructure equipment, identify the signal from the noise, and interpret and act upon it — and business analysts and executives, who have the specialist business expertise required to understand the impact on business operations. Mezmo could facilitate the consumption of telemetry data by employees outside the IT department through the inclusion of workflows and templates that utilize best practices to further lower time to insight. Machine-generated data is increasingly critical to business decision-making. I recommend that organizations investing in machine-generated telemetry data evaluate the potential advantages of observability pipelines and include Mezmo in their assessments.
Regards,
Matt Aslett