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Topic Facets: Data Visualization

May 10, 2018 - influencer content marketing, Influencer marketing, Practitioner Marketing - ,
By: Turner Sblendorio, Chris Riley, Yolanda Fintschenko

While we specialize in producing expert-written content for highly technical markets, 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 data visualization. Data visualization is a form of visual communication that displays complex data and relationships graphically so data is human interpretable and actionable. It is an important part of data science and a big part of successfully utilizing so-called “Big Data.”

March 2018 Data Visualization

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 Data Visualization

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.

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.

Results that Influenced Topic Selection for Data Visualization in March 2018

Data visualization as a conversation is a bit different than many others we follow because of its breadth. As you can see from the news, social media, and high impact blogs driving the data visualization conversation, the topics and conversations vary widely, as do the industries they intersect.

Industry News


Here are the influencers driving the conversations about data visualization in March 2018 on social media networks:

Highest-Impact Blogs

Due to the broad spectrum from which these stories come from and the lack of one dominant blog, the top blogs driving the data visualization conversation in March 2018 were the AWS Big Data Blog and the Google Big Data and Machine Learning Blog.

Practitioner Profile

Technologies related to data are generally more closely aligned to business than you find with other technologies. In the case of data visualization, not only is the business persona important when it comes to content, it’s also important to consider verticals, because how data is used in various industries and use cases directly impacts how it needs to be visualized, which directly impacts the architecture and engineering required to visualize it. So in this case, I’m advocating that you spend time finding practitioners with experience with data in target verticals. In that persona, you will want practitioners who are at the business analyst/architect level. For implementation-level content, you’ll want to look for the data engineer or equivalent.

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