Buyer’s Guide to Processing Automation: Getting More with Advanced Features 

Two weeks ago, we introduced this series with a focus on functionality that distinguishes “Okay” automation from “Awesome” automation. 

This week, we wanted to go one step further, focusing on advanced features that can help teams get more from their automation and administration of data processing environments.  

This series is inspired by the increased market activity we’ve seen in 2021. As we stated last week, growing volumes, pressure to support new use cases, importance of data quality to analytics, and the hyper-tight labor market all make automation an essential and high-return investment.  

Rampiva has been supporting clients in this space for 5 years. Our clients collectively process over 500 TB each year, and we know that because we stay with them through the years to make sure we’re delivering day in and day out. 

Based on this experience, we wanted to share our thoughts on how to evaluate your different options. Again, let’s assume that every tool delivers the basics: workflow templates, a job scheduling queue, and centralized reporting.  

How do more advanced features drive productivity?

  • Security Policies: There are a lot of risks that come with processing data, from mistakes that waste resources, to access of sensitive information and the potential for handling data protected by privacy regulations in an inappropriate manner.

Okay automation tools will provide basic role-based access controls – maybe to limit a user’s ability to change settings or manage the environment. 

Really awesome tools go deeper, with controls that manage access to specific Cases, Workflows, Resource Pools and more. This enables technical enforcement of policy, eliminates risks, and allows teams to better align resources with incremental projects. 

For example, a Rampiva client wanted to set up a “self-service” eDiscovery model. This required that all of their different clients be able to log into the same Rampiva Scheduler environment, but not be able to see jobs scheduled by users from other clients. Similarly, different user groups could not see, access, or change Workflows that had been created for other clients. Finally, some clients paid a premium for priority SLAs, meaning that jobs created by certain users were given priority access to processing resources.  

Coordinating across these requirements required the ability to configure granular access controls policies. 

The more you can control, the more agile and dynamic your automation can become. 

  • In-Queue and In-Case Logic: There’s a big – and important – difference between automating the first data processing Workflow and the 10th data processing Workflow. Many teams will start with a standard workflow, but may need to make incremental changes based on unique criteria set by the Analyst or even details determined in the Case itself. 

A simple example might be adjusting the time-zone for a new Evidence Store being processed into a Case with standard Workflow. Using Rampiva Parameters, teams can design Workflows that prompt the Analyst to enter this information when starting the Job. It will execute without having to make any changes to the standard automated Workflow, all while ensuring that the Analyst cannot accidentally change other options or steps in the Workflow. 

A more complex example might use scripts inside the Nuix Case itself to trigger actions based on a fact or pattern identified earlier in the Workflow. A search operation could identify items that are encrypted, triggering an External Commands operation to point a password decryption tool at the subset and run all unique words and alphanumeric patterns as password options. 

This sort of interactivity helps teams automate “the last mile” of their Workflows. Which is really important, because a team that use okay automation software to tackle routine activity may see limited adoption, could miss ROI targets, and will not be as productive as teams that went all-in on awesome. 

  • Review Connectors: The goal of most data processing projects is to get material to subject matter experts to review. Most okay automation platforms will do this for one platform. Realistically, you might need to support several review platforms. 

Automating the push to Relativity doesn’t do you much good for clients who want to review in Discover – and, vice versa. Discover connectors don’t help if you’re trying to do first-pass review in Nuix Investigate.  

Awesome automation aligns with the user needs. 

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