Scroll down for the list of data science influencers.
My methodology for identifying and measuring influence differs slightly from others in the industry. For the most part influence is measured using three core social data points-reach, relevance and resonance:
- Reach: Community size. Essentially it is an aggregate sum of an influencers audience across all channels.
- Topical Relevance: Volume. In other words, how often is the influencer talking about and mentioning the core topics across his or her social channels.Was it a mention in a YouTube video three years ago or do they consistently talk about it in the channels where they participate the most.
- Resonance: Engagement. When they do mention the topics in question, are there audiences engaging with the content? Is it resonating with them?
I’d like to add an additional layer of influencer analytics, but it’s somewhat of a manual process. I like to call it a reference and it answers the following questions:
- Is the influencer referenced by other influencers?
- Is the influencer referenced by traditional media?
- Is the influencer mentioned by a specific audience that is important to the brand?
Unfortunately some of this analysis has to be done manually but this is a good thing. It requires spending some time in the data and provides contextual insights as to why you were choosing this influencer.
I created a Twitter list of the 10 data science influencers for your convenience.
Here are a few other influencer lists in case you are interested:
- AI Influencers
- Machine Learning Influencers
- Blockchain Influencers
- IoT Influencers
- 5G Influencers
- Digital Transformation Influencers
- Clubhouse Influencers
I talk a lot about managing and deploying influencer marketing programs on my YouTube channel and it’s not just theory or ideas. It’s all based on real life experience, testing and learning, and sometimes failing when working with various influencer groups.