- collection of structured and/or unstructured data - structured: e.g. tabular data (with rows and columns), JSON, XML or other similar clearly structure formats - set has clear boundaries inside a specified domain and / or when classified using algorithm - often focussed on single domain e.g. specific business, science or other domain - unstructured: e.g. collection of text documents articles, books or html pages (latter can be considered "semi" structured) - various degrees of structure(e.g. html vs. text book), set has undefined boundaries which largely depend on classification method / algorithm applied - text data body can be referred as "corpus" when set domain(s) and boundaries have been (more clearly) specified
- Consists of at least a data set and - Data set(s) which form the asset are managed actively and/or passively hence data is recognised as an asset - active management is aiming to ongoingly create additional value or increase the data set value by active data management - Active example: a data warehouse or data lake which is ongoingly maintained and enhanced and data is shared across organisation for multitude of purposes e.g. creating data products - passive management is maintaining the data asset but not (actively) recognising the value of the data asset or aiming to extract the (additional) value from the data - Passive example: a database maintained for keeping a record of physical product or service without the aim of reusing or monetizing the data - Enhanced mostly using data management techniques (not by ML or analytics) for instance, by ongoingly enhancing the existing data set with new data - Often not fully shared / made available i.e. parts are used at the time e.g. for creating products or services (see data product) - Often subject to standardised / automated ETL processes for maintaining and cleaning it
Note: Data asset can also become a (data) liability e.g. - Legacy data: old not used and/ or outdated data which is creating costs but not actively used for instance, because of regulatory requirements - Duplicate data: repeated records of the same data (yet in different quality) across organisation creating ongoing reconcilation issues and waste of storage space - "Waste" data: data which is not used, analysed and/or the collection is not known (it keeps accumulating without any use/purpose e.g. because of bad logging practices or old malfunctioning sensors)
- Created from one or more data assets - Created for specific need with limited time / space - Often a (partial) result of data asset and derived using ML, analytics or similar processing techniques - Produced and/or served for a specific customer(s), consumer(s) Example: digital / data driven map can be considered a data asset and navigation (service) is a product based on the data asset The above are indicative / subjective and exceptions are likely to apply