Buyer’s Guide to Processing Automation: Measuring Team Productivity 

In December 2021, we published two articles describing some lessons we’ve learned in the 5 years of automating data processing in Nuix: 

To complete the series, we want to explore the different ways our clients have measured the real impact of data automation on their Nuix environment. 

The first big lesson for us is that the number one pressure point for data processing teams is staffing. There just are not enough people to keep up with the compounding demand, so we see technical experts stuck running routine jobs while backlogs build up. 

More computer resources won’t solve that problem. 

Training new hires is time consuming and expensive.  

And, we risk burnout and turnover. 

So, one of the first places we look for ROI is increasing the productivity of the team. This can be measured in two ways.  

The first, “Staff Hours in Case” focuses on the amount of human time required to stage, run, and quality check the results of data processing jobs. On average, our clients estimate that it takes between 30 to 90 minutes of staff time to run a job through to export. Most of the variation comes from the complexity of the workflow, because every step in a manual workflow needs to be checked to make sure it was executed correctly.  

In Rampiva, that same job will take less than 5 minutes of staff time to run in our job queue, Scheduler. This is independent of the complexity of the workflow, and, because automated operations are executed exactly the same way every time, the burden of checking the outcome goes down also. 

The second way to measure the team’s productivity is calculating the average number of Nuix Sessions run per day. A Session is a contiguous series operations executed while Nuix Workers are checked out of the Management Server pool. It is a little dependent on the specific client’s unique processing strategy – some will have a single “Load through Export” workflow in Rampiva that is executed in a single Session. Others will have Rampiva workflows that get data ready for manual examination in Nuix Workstation, then a different Rampiva Workflow for export and load into a review platform. Of course, there’s also always a scenario where a team will work an entire project manually, from start to finish. 

By calculating average Nuix Sessions per day, we can capture this variation and show that the team is delivering more Sessions per day. In fact, when we look at our benchmarking across more than 10,000 manual and automated Nuix Cases, the average team will increase average number of Sessions executed per day by 8-10X. 

With these two metrics – Staff Hours per Case and Average Nuix Sessions per Day – teams can compare an investment in Rampiva Automate against the cost of adding new staff to support growing caseloads. In our experience, Rampiva Automate can add 3 or more years of runway before you have to make that next hire.  


On the subject of staff retention, there are two other metrics that we encourage clients to track closely. They’re less related to ROI and more making sure your team isn’t overworked. 

The first metric is the number of manual Sessions that are executed during Off Shift hours. In manual data processing environments, it is common for team members to stay late, or VPN into the lab in the middle of the night, just so they can start the next data processing operation. With Rampiva’s 24-hour job queue, this activity should disappear, which hopefully means your team is maintaining a better work-life balance. 

The second metric is the share of Sessions executed by each user, particularly when those sessions ae executed manually. Of course, we expect this to vary based on role and responsibility, but it is helpful to know whether specific team members are shouldering more of that activity. This can be a sign of over-work… or, if a team member continues to do most of their work manually, there might be an opportunity to train them on the new process. 

Next week, we’ll tackle ways to measure the productivity of your computer resources. 

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