We specialize in producing expert-written content for highly technical markets, but perhaps the greatest service we provide our customers is developing topics for their blogs and longer-form content marketing assets. While it is certainly possible to create your own topic strategy, normally, part of our partnership with our customers involves helping them generate a topic for each piece of practitioner-written content we deliver to them.
We’ve written this series to help our customers and marketing managers look under the hood to discover how we develop topics. Examined here is Artificial intelligence for IT operations, or AIOps. AIOps is a term that embraces the various technology platforms that automate and enhance IT operations by using data analytics and machine learning to analyze Big Data collected from IT operations tools and devices to automatically detect and respond to IT operations issues in real time.
April 2018 AIOps – Conversation Topic Interest Over Time by Google Trends
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 (or conversation share of voice) is the percentage of any specific conversation you own. Conversation share of voice 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 topic areas.
The Re:each Share of Conversation Calculation for AIOps for April 2018
Below, we will dig deeper into why the results were the way they were.
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.
- 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.
- 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:
- Demand Gen
- Mindshare/Thought Leadership
- Find your competition: Competition is derived by identifying the top 4-9 vendors in each conversation based on their SoV in those conversations.
- 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.
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.
Results that Influenced Topic Selection for AIOps in April 2018
In April 2018 industry news, Splunk was a major conversation driver as it closed its acquisition of Phantom, expanding its AIOps capabilities through Phantom’s SOAR (Security Orchestration, Automation and Response). Developers are always interested in acquiring new expertise, so it is unsurprising the the CA Technologies AIOps Virtual Summit on June 20 is capturing conversation share.
Evan Kirstel Twitter: Influencer in the IoT and Cloud space
Eric T. Tung Twitter: Really! When you get past his profile and through all the weird stuff, he does a popular podcast for IT Chronicles.
Rackspace Facebook UK: Focusing on IT transformation makes it impossible to avoid AIOps.
Solutions Review and BMC Software both showed up in April 2018 as capturing the conversation. In April, Splunk didn’t show up nearly as much, but it is a blog to watch for AIOps, and often has a larger SoC in this topic area.
Splunk and Sumo Logic are two commercial log analysis platforms with the largest install base outside of open source. They’ve talked about anomaly detection, and other machine-learning tools to enhance infrastructure monitoring. But the phrase “AIOps” in April 2018 had not yet been used. What AIOps brings in theory on top of smarter analytics is execution—things like automated remediation and guidance. So while AIOps (the term) is new, the idea is not. And this is good and bad when it comes to finding practitioners. The good news is that they are the same user base of original log analysis platforms—individuals who are already using similar monitoring tools but are opportunistic about what the next generation can bring.
Right now the concepts are so new that there has not been enough time for people to really understand how AIOps will impact their delivery chain, jobs, and how AIOps will be utilized. This means that the types of topics you cover are important. These practitioners can’t pretend to be experts in implementing AIOps, because no one is. So some of the content needs to focus on the opportunity, and touch on implementation when possible. But the majority of the content will be tangential, probably talking about the issues with current methodologies and the weak spots that need to be addressed.