Case Study: The Weather Channel®


The people at The Weather Channel® (TWC) have a word for rain – in fact they have more than a dozen words for rain – drizzle, mist, sprinkle, etc. They’re in the weather business so that’s a good thing. It’s not a good thing to have a lot of different terms – in this case, 15 specific different terms — for the same thing. Lots of terms for the same thing does not help  when you’re trying to find everything having to do with a weather situation, like storm preparation or cleanup. And it’s especially not a good thing when your job consists of logging, storing, and retrieving video and still photos about weather. With a Library that included nearly 18,000 media assets totaling hundreds of hours of weather-related video, a consistent, dependable way to log and describe those assets was vital.

In the past, a producer putting together a video about storm damage could ask for a clip of an 18-wheeler overturned by a tornado. To be thorough, because the clips of his “18-wheeler” had been logged in as a “semi” or “tractor trailer,” the TWC staff may have had to perform multiple searches to find exactly the one the producer requested.


Since taking a long time to find the requested videos or hiring more staff simply weren’t options, TWC staff decided to purchase Data Harmony’s MAI (Machine Aided Indexer) to solve their logging/finding of video problem – the hardest part of their jobs.  Using MAI,

a TWC employee logged in a clip with an overall title like “Greensburg Kansas Tornado” and a subtitle of “cleanup” or “aftermath.” But it’s the synopsis field that really separated the clip from all the other tornado cleanup clips. A description of what the TWC staffer saw on the video is entered in natural language – “large pile of debris,” “closeup of chainsaw,” “cutting fallen branches,” etc.

Then the MAI Lookup button was clicked bringing up a list of suggested keywords that were selected using rules like “near” or “with,” as in, if “chainsaw” appears near “fallen branches,” M.A.I. returned “storm cleanup” in the list of keywords. As for the 18-wheeler problem, now when that term was entered, the 18-wheeler was either accepted or the user was redirected to use “semi” or “tractor trailer” or “big truck.”


Since implementing MAI, the search now takes just a few minutes and the results are exactly what was asked for. Findability is increased, saving staff time and minimizing frustration.

Joyce Jefferson, Manager Library Services, recalled,

“In order to identify all possible information that would satisfy a request, an average of seven searches was conducted for each search request. MAI has improved the accuracy of information retrieval and eliminated duplicated efforts, thus improving productivity by processing more material in the same amount of time.”