You are living dangerously when you get to 80% disk usage. This is because batch jobs write intermediate data to local non-HDFS disk (map-reduce writes a lot of data to local disk, tez less so) and that temp data can approach or exceed 20% of available disk (depends of course on the jobs you are running). Also, if you are on physical servers (vs cloud) you need the lead time to provision, rack, stack etc to scale out and add new data nodes, and you likely will continue to ingest new data during this lead time.
It is a good practice to set it at 70% and have a plan in place when it reaches that. (If you are ingesting large volumes on a scheduled basis, you may want to go lower).
Another good practice is to compress data that you rarely process, using non-splittable codecs (you can decompress on the rare times you need the data) and possible other data that is still processed using splittable codecs. Automating compression is desirable. Compression is a bit of an involved topic. This is a useful first reference: http://www.dummies.com/programming/big-data/hadoop/compressing-data-in-hadoop/
I would compress or delete data in the cluster you are referencing, and add more data nodes ASAP.