2. Data classification
As data gets housed in data lakes and other increasingly connected ways, another challenge is classification. Who is allowed to look at particular data? From government security classifications to confidential HR information, data shouldn’t be accessible to everyone. Data must be properly classified, and those categories and the limits they entail must be maintained and live on as companies integrate and harness data in new ways.
3. Stability
A lot of data is transient. If you’re taking data from sensors, for example, you need to understand how often you’ll refresh the data based on sensor readings. This is an issue of data stability, as constantly changing data may lead to different results.
Data is also aging. For example, imagine you had a specific process for raising a job requisition for a new employee for nine years, but you revised the process last year. If you use all 10 years’ worth of data to train a model and then ask how to open a job requisition, most of the time, you will get a wrong answer because most of the data is outdated.
