Management Pipeline

NLE Analytics: 3 User Scenarios

NLE Analytics: 3 User Scenarios. Our NLE’s are missing an entire dimension of tools. They are woefully inadequate when it comes to helping us make informed business decisions.

Longtime readers know that I believe that our NLE’s are missing an entire dimension of tools. They are woefully inadequate when it comes to helping us make informed business decisions. A new class of tools I call NLE Analytics are required to enable Producers, at the production management level, to make informed decisions. It is my hope that the three user scenarios below prompt Product Managers at Adobe (Premiere), Apple (FCPX), Avid (Media Composer), and Blackmagic Design (Resolve) to take this need seriously, and consider adding these tools into future versions of their software.

Reel One from Stars Wars The Last Jedi. SO much inaccessible data!

Evaluating Music Libraries:

Rene is the Post Supervisor of an unscripted show that was just renewed for its fourth season. Last season her editors complained that the show’s music library was beginning to feel stale. It is Rene’s responsibility to negotiate with the music vendors, but she doesn’t know which are providing value and which are resting on their laurels. Is she paying one music vendor more than another, but using their library less?

The current process is to ask the editors what they think; which is really asking them what they feel; subject to all of the cognitive biases that lead us into bad decision making. The other option available to Rene is to load the previous season’s music cue sheets into Excel and go through numerous contortions to arrive at a general quantitative number like: “music company X provided 32 of 100 cues used in episode 303.” However, this doesn’t provide a full picture. Perhaps a company that has a smaller number of cues are actually being used for longer duration? Or perhaps one company provided a lot of cues early in the season, but dropped off later, demonstrating a lack of library depth.

And yet all of this information is so close at hand. The final sequences of each episode can provide all of this data and more. The right implementation could create a music usage dashboard providing Rene with a real time snapshot of what music her editors are currently using. If music company X is under represented, Rene would be empowered to reach out mid season and let her under performing music vendors know that their royalty checks are going to be a lot lighter if they don’t step up.

Specialty Footage:

Robert is the Production Supervisor of a second season show. His camera department is always asking for the additional resources (i.e. money) required to set up complicated car cameras, time lapses, drone, and b-roll sliding shots. The additional manpower and camera gear adds up to several thousands of dollars each week of shooting. Yet when Robert watched the last season on TV it feels like most of this specialty footage was never used.

The Post Supervisor can tell Robert how much storage space the specialty footage takes up, but this number is a poor indicator of the true cost of loading and organizing and maintaining this footage. Maybe the Post Supervisor can provide Robert with something like, “we used 2 minutes of car camera footage in our 44 minute episode 303,” but there is no easy way of providing Robert with an exact usage for the entire season. Or definitely no way to broke down it done by camera type.

And yet all of this information is so close at hand. The final sequences of each episode can provide all of this data and more. The right implementation could create a camera usage dashboard. In addition to specialty footage, the dashboard could provide up to the minute information about which cameraman’s footage is being used, perhaps indicating crew troubles.

Anomalous footage:

Outliers are things that stand out from the crowd. In statistics they indicate data that needs attention for a variety of reasons. In NLE Analytics, a shot could be considered an outlier if surrounded by a majority of footage from a different date/time. For example; if a scene is made up of footage from April 1st, but one shot is from May 15th, this should prompt the producer to ask, “why?” This anomalous shot could indicate a pickup shot due to camera issues. But perhaps it was due to creative discovery during the editing process? Or maybe it was a pickup interview because someone forgot to ask the right questions during the sit down?

Ep 12 days after
Wouldn’t it be great if your NLE could generate information this easily?

Whatever the reason, having a tool that easily identifies anomalous footage and brings it to the producer’s attention would be a valuable start to rethinking the entire production process from pre-production through delivery. I’ve explored how this could be applied to interviews in my post about: What the Bravo docu-soap can learn from Netflix part 1 & part 2.


When he founded Digital Domain, James Cameron said that he wanted to create a place, “where technology would not just serve, but actually inform the creative process” (emphasis mine). The idea of NLE Analytics is to create a tool set that informs the creative process by pointing out inefficiencies and diverting money to places that will enhance the creative vision.

Today’s non-linear editing systems are huge repositories of data. But this enormously valuable information is completely inaccessible to us. With the right tools Producers can make informed business decisions and join the creative conversation. The entire production process could be reconsidered; a necessity in a world of shrinking budgets and increased deliverables.

I have two hopes. Firstly, that Product Managers at the big 4 developers take this idea seriously and consider implementing these tools into future versions of their software. Secondly, that Producers take these ideas seriously and let the big developer know that these are tools they want to use, and that future business will go to the ones who make it happen.


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