Effectively reporting on community metrics is a continued challenge for many Jive Social Business customers. We’ve taken A new approach to using big data analytics tools that we normally use for IT data, and have seen some amazing results. Skip to the “Playing with data” section and absolutely watch the video to see the results. If you are interested in my experiences leading up to this, start from the beginning.
Reporting is hard
I’ve been working with Jive Software as a user, consultant and developer for 6 years now, and one of the most persistent topics in my own work and in discussions with other Jive customers is (you guessed it): Reporting and Social Analytics.
When you are running a platform like Jive Software knowing what is happening in your community is relevant on many different levels:
- Community Managers need to know which groups are active and which can be retired to keep the environment relevant and less cluttered
- Your marketing team wants to know which product areas are most frequented and discussed
- The customer support team wants to know if their FAQ articles are found and reach the right audience
- Sales enablement would likely kill if they could find out how a successful sales rep’s usage of Jive differs from an unsuccessful one
The list goes on, and I’m sure anyone who is responsible for a Jive Social Business platform has had similar requests.
Now, all reporting solutions for Jive that I’ve experienced so far (Community Reports, Social Analytics, Business Object powered social analytics, Community Manager Reports, JBA, Resonata and a myriad of home grown solutions customers have built) have one thing in common:
They make assumptions on what data you need/want.
Don’t get me wrong, I am a big fan best practices in the form of out of the box reports. However, it always seems that in my day to day work with our own data and customers, something is missing. So it seems I’m constantly exporting data to Excel or running database queries to get to the needed.
Example: The places activity CMR is great, but if I need to know who is using a group that I want to retire, I can assume it’s the group owner and ask them (more often than not, they don’t know either), or I can start running SQL queries against the Jive database. Not ideal, especially if this is a recurring task.
Likewise: The new Jive Analytics looks awesome, but what if my customers have a different opinion on success criteria?
A different approach
My path to Jive reporting and Jive analytics nirvana (at least for me) started when we started working with the new data export API available in Jive 7 and Cloud. We ported the functionality to a plugin for a customer running an air gapped on premise installation of Jive Social Business platform.
To work with and compare the exported activity data I used one of the tools we use in our IT operations, called Splunk.
If you are not familiar with Splunk, it is a data analytics tool, typically used in IT and security operations to manage large amounts of machine data. It is very intelligent in regards to making sense of unstructured data and works amazingly well even with large datasets (think billions of records and terabytes of data not uncommon in IT).
So after creating a data connector between Splunk and the Jive data export service, it became clear very quickly that this was the answer to the vast majority of my reporting and analytics requirements.
Playing with data
So let’s get started. Here we have an overview dashboard that presents some community stats for me.
A global map of community activity, a high level health indicator (based on the level of activity compared to the average level of activity), and an overview report showing which activity happens where.
There are two things about Splunk that have changed how I go about working with community data dramatically:
- You always have access to the underlying raw data and you can always drill down to it
- You can always change the source data for a report on the fly, no need for report building, SQL and Excel sheets
I hope this was of interest to you. The examples I’ve included are clearly only scratching the surface of what’s possible, but as I mentioned above, my approach on how to report and analyze Jive community metrics has improved dramatically. We’ve also started enriching the Jive data.
We are currently working to package this, so it can be shared. If you are interested in leveraging this integration for yourself, please feel free to reach out to me.
I’ve started using Splunk to analyze and report on Jive data. It is awesome.