File analytics

File analytics is the analysis of file metadata to answer questions about a storage estate. Typical questions are how much capacity each project consumes, what share has not been read in three years, where duplicates sit, and which owners account for the most growth.

Key takeaways

File analytics answers questions about a storage estate from file metadata.
Metadata analytics and file analytics work from attributes, while content analytics reads what files say.
A data footprint measures the total capacity data occupies, copies included.

Why does file analytics matter?

Because a list of files is not a decision. File analytics turns the inventory into answers: which projects grow fastest, which file types use the most space, how much is cold or duplicated, and which teams hold what. Those answers are what budget, cleanup, and capacity decisions are made from.

What is metadata analytics?

Metadata analytics is the analysis of file metadata, such as size, owner, age, and location, to answer questions about a data estate without opening any file. It is another name for file analytics. Typical results are capacity by project, the share of data not read in years, and the owners behind the most growth.

What is the difference between content analytics and file analytics?

Content analytics reads what is inside files, such as the text of a document, to classify them. File analytics reads only file metadata, such as name, path, owner, and dates, and leaves the contents untouched. File analytics can cover a whole data estate quickly because it never opens a file, and it narrows the set of files that any later content review needs to look at.

What is the difference between file analytics and business intelligence?

Business intelligence, or BI, analyzes business data such as sales, finance, and operations records, usually from databases and data warehouses, to answer questions about how the business performs. File analytics analyzes the files themselves, such as their size, age, owner, type, and growth, to answer questions about the data estate: what it holds, what it costs, and what can move or go. The two meet when file metadata is loaded into a data warehouse or BI tool, so storage cost can sit on the same dashboard as the projects and departments that create it.

How do you measure a data footprint?

A data footprint is the total capacity an organization’s data occupies, every copy included, across every storage system and tier. It is measured by indexing file metadata across all systems and adding up size by system, project, owner, and age. The breakdown is the useful part, because it shows which share is active, which share is cold, and which share is duplicated.