By categorizing and analyzing all of the scenes that make it into a show, and comparing them to the scenes that are left out, trends could be identified that would enable Production Companies to avoid shooting similarly wasteful scenes in the future.
Good CNBC article about spending practices at Netflix and its competitor: “Netflix plays a different game. Its barometer for success is based on how much it spent on a show rather than hoping every show is a blowout hit…”
What do the Avengers talk about. An interesting use case for Data science and creative writing. As analytic tools become more prevalent, it is important that our understanding of their uses grows with it.
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.
I’m a huge fan of Rob Long’s podcast Martini Shot. But I couldn’t disagree more with his most recent one: Two Businesses. On it Long argues, humorously and rather unconvincingly, that Amazon should sell its studio to CBS because running a successful online retailer and grocery store (Whole Foods), is nothing like running a television […]
Consolidating the cut delivery and notes turnaround into ‘blocks’ can reduce wasted editor time and increase the quality of attention given to review cuts.
By delaying the interview recording until the very end of post production, when the story producers (and network executives) know which questions to ask, the show’s production could eliminate unnecessary expenses and improve creative consistency.
Compared to other businesses, unscripted production feels like it operates with such little data. The primary tool of our trade, the NLE, is grossly behind in this area and I’d like to see Adobe or Avid rise to the challenge.