Build Your Own LinkedIn Analytics Part 1: Why It Matters (and What You’ll Learn)

Orginally published on Medium on 7 October 2025

Since starting on my LinkedIn thought leadership initiative 2 months ago, I have been regularly checking in on my performance using the built-in analytics dashboards. While useful, there are several limitations that hinder the view of my overall and post-specifc LinkedIn performance.

As a data professional, I could not let that stand. That’s why I decided to build my own LinkedIn analytics platform. In this first article in a series, I will examine what already exists out there, then explain why I decided to build my own analytics platform on Databricks Free Edition. Subsequent articles will explore how I implement the analytics plaform in a step-by-step fashion.

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TL;DR

• Native LinkedIn Analytics (both dashboards and Excel extracts) have numerous limitations.
• Third-party analytics tools are costly, overkill or both.
• Databricks Free Edition offers a way to implement an end-to-end data product

I. What Already Exists: Native LinkedIn Analytics

The native LinkedIn analytics dashboards provide a clean, modern interface that works well in giving a high-level undestanding of post performance as well as some ability to deep-dive into individual posts. The dashboards also include the ability to export the statistics in Excel file format for further analytics. But there are some serious limitations, as we will see in the following breakdown.

a. Overall analytics dashboard

From the overall analytics, I can see daily and cumulative figures over both pre-defined and custom timeframes (up to 1 year prior). This applies to overall post impressions/engagements as well as audience makeup and growth. I am also able to see a summary of top perfoming posts over the time period, and there is an option for me to see a list of all impressions and engagements over the time period for all posts for which such figures exist.

However, selecting statistics for a single day can only be done for overall analytics, and filtering for the correct date can be a tricky affair (e.g. choosing the from and to date as 2nd October 2025 returns the figures for 1st October 2025).

b. Post analytics dashboard

I can see cumulative figures for individual posts, including views for any associated LinkedIn articles, as well as deep dive into the types of engagements on the posts. On the posts themselves, I can see the number of impressions for individual comments in addition to the types of reactions on the comments.

However, the individual post analytics only has cumulative figures, hence I am unable to see the daily figures and trends for individual posts. The exact dates for the posts are not visible either, only generic ranges such as “2mo” or “3d” ago.

c. LinkedIn Excel File Downloads

LinkedIn does let you download Excel extracts for both the overall and post-specific dashboards, based on the filters selected. I found this to be of limited utility for the following reasons:

• I lose the content of the posts since only the activity-formatted links to the posts are included.
• Daily statistics are only available on an aggregate basis, otherwise all the issues with the lack of daily statistics persist (i.e. no breakdown by posts) unless limited to a single day of download (which is not available for individual posts).
• If I download files on a daily basis, I now have to deal with multiple files in order to get a consolidated view of my analytics.
• Only the top 50 posts for a given time period and statistics are included in the extract. This is not yet a major problem given how recently I started my LinkedIn thought leadership efforts, but could grow to become a bigger issue further down the line.

II. What Already Exists: Third-party Analytics

I reviewed the third-party tools out there that purport to give better analytics performance. Unfortunately, none of them are free (even if they offer free trials that range from 14 days to 1 month), and several cost a pretty penny.

At the same time, most (if not all) are clearly aimed at businesses with their ability to track multiple LinkedIn profiles as well as LinkedIn pages; several cover way more social networks than just LinkedIn.

My LinkedIn and social media presense is not to the extent where I can justify paying for such tools.

III. Opportunity

In June 2025, Databricks launched their Free Edition with the stated aim of providing everyone with “free access to the full capabilities of the Databricks Data Intelligence Platform”. In other words, a leading data platform provider had decided to allow anyone interested to learn about data to build on their platform for free (with certain limitations of course).

If you don’t know what Databricks is, here’s a crash course:

1. It was founded by the creators of Apache Spark, an open-source code library widely used for ingesting huge amounts of data in a massively parallel fashion.
2. It has grown to become one of the leading data platforms in the world, competing with the likes of Snowflake, Google BigQuery and AWS Redshift. No less than Microsoft has paid Databricks the backhanded compliment of creating a data platform (Microsoft Fabric) that in many ways copies the data platform aspects of Databricks. (Even the user interface is similar!)
3. In the last few years, Databricks has been on a tear, acquiring a slew of data and AI startups and rapidly expanding its offerings, both proprietary (e.g. Agent Bricks for GenAI workflows) and open source (e.g. Unity Catalog for data governance). It is also, as of this writing, the only data platform to offer native, governed access to all the major frontier LLMs (from GPT-5 to Gemini 2.5 and Claude 4.5).

In short, knowing Databricks opens doors for any budding data practitioner. And now you can learn it for free.

At the same time, there is demand for reference end-to-end data products from those seeking to become data professionals. I have learned this both from my own research and through feedback that I received from my mentees and advisees. On a more personal note, I remember how hands-on tutorials, including my stint at the Metis Data Science Bootcamp, got me started on my data journey, but there was still plenty that I had to pick up on the job.

This then is an opportunity: to use Databricks Free Edition to build an end-to-end LinkedIn analytics product. This will serve the following objectives:

1. To better understand my LinkedIn statistics
   • Having myself as both the implementor and the end-user is an example of “eating my own dog-food”, or testing our own products. This is something that we as data practitioners need to keep doing so as to not lose sight of our end-user perspectives. In this instance, understanding my LinkedIn statistics will help me understand which of my posts perform better and why, so that I can write posts and articles that provide more utility for my audience.
2. To guide others in analyzing their own LinkedIn statistics
   • Detailing the steps that I took to analyze my LinkedIn statistics will help others interested in deeper analysis to extract more value from their own LinkedIn data.
3. To further explore Databricks’ capabilities and limitations
   • I already use the enterprise version of Databricks on Azure in a work capacity, but there are certain aspects that I have not yet touched, dashboards being chief among them. This is a prime opportunity to explore the strengths and weaknesses of these features, as well as test the boundaries of Databricks Free Edition and evaluate its utility as a teaching tool.
4. To provide a reference end-to-end data product for budding data professionals.
   • I remember how hands-on tutorials, including my stint at the Metis Data Science Bootcamp, got me started on my data journey, but there was plenty that I had to pick up on the job. I have also received feedback from my mentees and advisees on the lack of examples or references that they can use to practice implementing a full-on data product. This project aims to fill that gap with a reference implementation for practical learning.

IV. What’s Next

This is the first in a series of deep-dive articles that will take you step-by-step through the process of designing and building an end-to-end data product. The second article is now up, continue on here!

In an era where actionable insights drive growth and innovation, democratizing analytics skills benefits both professionals and organizations.

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