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Topic Facets: Error Monitoring

July 12, 2018 - Practitioner Marketing - , , , ,
By: Turner Sblendorio, Chris Riley, Yolanda Fintschenko

In the brave new world of DevOps tools, something our customers have come to depend on as part of our practitioner content delivery service is the help we give them developing topics for their blogs and longer-form content marketing assets. We certainly encourage you to develop your own topic strategy. However, part of our partnership with our customers involves helping them generate a topic for each piece of practitioner-written content we deliver.

We write the “Topic Facets” series to help our customers and marketing managers look under the hood to discover how we develop topics using SoV and SoC as metrics. We always begin with SoV calculations for specific conversations, and then use that data to inform our topic selection.

Today we are investigating what is driving the conversation on error monitoring. So, when you say “hello” to continuous integration and continuous delivery (CI/CD), you are also in the position of greeting a ton of errors. Error monitoring lets you monitor in real time so as not to completely foil the agile CI/CD model. Let’s see who’s leading the May 2018 error monitoring conversation and what is driving their conversation share.

May 2018 Error Monitoring — Conversation Topic Interest Over Time by Google Trends

A Metrics-First Approach

Our approach to determining topics within this conversation begins and ends with a share of voice (SoV) calculation, which ultimately gives us an idea of a vendor’s share of this conversation (SoC). Our share of voice methodology is described in some detail in a variety of places, but here is a quick summary:

Share of conversation is the percentage of any specific conversation you own. SoC is more precise because it looks at specific conversations within a market versus focusing only on global SoV compared to competitors. While it’s interesting to know how your brand or product is doing in the world of all products, you can make the greatest impact by going local with specific conversations.

Re:each Conversation SoV Results from May 2018 for Error Monitoring

Fixate’s Re:each platform has algorithms which derive conversation share of voice across traditional and social media. The phases of calculation are data collection, normalization, and interpretation. We can’t give you the secret sauce, but we can give you an idea of how we do it.

Core Calculations

  1. Identify your place: Identify specific keywords and concepts associated with your brand and product based on those concepts that appear the most in all conversations you participate in.
  2. Determine your conversations: From there, the concepts are applied across a body of sources in order to identify the three conversations which are most relevant to you. For each vendor, there are three types of conversations identified:
  • Market
  • Demand Gen
  • Mindshare/Thought Leadership
  1. Find your competition: Competition is derived by identifying the top 4-9 vendors in each conversation based on their SoV in those conversations.
  2. Determining relevant topics: Topic suggestions are derived from entity/concept extraction of content that was most prevalent in each conversation selected over the set period of time. Those concepts that had the greatest reach in that conversation are weighted and end up as the core elements of a suggestion.

Data is collected from traditional social media sources as well as trusted media sources for each broad market. Weight is put on content based on the source it came from using a proprietary algorithm. Currently, calculations are done at the end of each month for the entire month’s worth of data.

Domain Expertise

The machine learning used in SoV is human-supervised (Human-in-the-Loop). SoV calculations can be fully automated; however, topic suggestions are subject to language challenges, and domain expertise based on raw data collection. Domain experts validate SoV calculations, and reformulate raw entity extraction on top-performing content in each conversation to build coherent topic suggestions.

May 2018 Sources that Influenced Topic Selection and SoC for Error Monitoring

High-Impact Industry News

Industry Blogs to Follow

DZone.com published several blogs during the month around error monitoring, highlighting Rollbar and Stackify. The highly technical blog is geared towards developers who use the tools rather than a broader audience.

Don’t miss:

  • Rollbar tutorials
    • The company’s blog has several articles that focus on implementing the software onto your own platform.

Top Social Media Influencers to Follow

Error monitoring is an important but niche conversation for developers—hence the low following of the listed influencers. For more information on the conversation, we would recommend following the social platforms of companies like Sentry, Stackify, Rollbar, Airbrake and Raygun.

  • Jonathan Lehr Twitter
    • Jonathan is an enterprise VC at Work-Bench. He is very active on Twitter with just over 4k followers.
  • Jason Buck Twitter
    • Coder and active Twitter user. Jason has 1k followers.

Practitioner Profile

Errors and exceptions were previously a private thing between the developer and his or her code, but not anymore. With error/exception monitoring tools, there is a new level of transparency which brings the process into the software delivery cycle (and not just on the developer’s machine). Obviously, developers are great practitioners for creating content on error monitoring, but so is anyone on the quality engineering side. Some content from DevOps engineers who can talk about how error monitoring fits into the releases automation process is also really good.


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