What are the core tenets of a social analytics data stack?

Daniel Buchuk
rivery-blog
Published in
3 min readJun 3, 2020

There is a bewildering amount of content from a growing number and variety of platforms. Every marketing team in the world is familiar with handling and analyzing social data to decipher insights that will help them drive the decision-making process.

The power of these platforms is colossal, and so is their complexity. They generate data that takes into account both the content but the reactions triggered by it. The true power of social analytics is the ability to sift through these diverse data formats, outputs, and relationships to deliver meaning that justify strategic business choices.

On a recent webinar, our team took a deep dive into these data challenges and explored strategies to address social data analysis head on. If you missed the event, you can still watch this social analytics masterclass, which includes an in-depth look at Social Analytics as a Service for Looker. This new plug-and-play solution from Rivery and Looker contains data pipelines and visualizations to track and analyze key engagement metric trends and growth across social platforms, specifically Facebook, Instagram, and Twitter.

The challenge of making sense of social insights

Each platform’s reporting functionality is structured differently, and outputs data in completely different ways. This means data prep and transformation required to merge data from multiple sources and formats can become very cumbersome. In addition, files are often saved locally which makes it virtually impossible to have a single source of truth.

Some companies have initiatives in place to automate data ingestion processes by coding their own social APIs. However, they require a lot of upkeep to maintain these APIs up to date with the latest versions — and with each new platform added to the marketing mix needing development and maintenance efforts for each connector can become a significant strain in terms of resources.

Best of breed stack to orchestrate social analytics

There are three essential steps any company planning to align all their social analytics needs to consider:

  1. Automating data ingestion

Make it code-free and user friendly! Not only should the initial creation of these processes be code-free, but should also take into account future API updates and maintenance, as well as new social platforms that could be adopted in the future.

2. Combine & centralize your data

Get your data into one central repository in the cloud. This will be the place where all your data is aligned — regardless of the initial data format. Access this unitary source of truth. Harness the cloud warehouse capabilities to combine, transform, and normalize data in one format. For example, fields from daily organic impressions could look different across different social APIs where we pull data from.

3. Democratize data access & visualization

Once we have all of our data in one format we can now easily use this data for exploration, to create business insights. Whether this is achieved through a BI tool & analytics tool, through data modelling or other third party applications — this last step is how you’re able to share your insights and democratize the accessibility to social data across your organization.

Have you experienced these challenges when trying to orchestrate and align your social data? Which solutions do you use to aggregate, orchestrate, and visualize your social insights? I would love to hear key challenges, tips or hacks from data experts managing social data. You can also watch the Rivery’s full webinar about social analytics now for more in-depth discussion!

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