The peterjamesthomas.com Data and Analytics Dictionary is an active document and I will continue to issue revised versions of it periodically. A larger update is in the works, but for now here are a dozen new definitions:
- Binary
- Business Analyst
- Chief Analytics Officer (CAO)
- Data
- Data Analyst
- Data Business Analyst
- Data Marketplace
- Data Steward
- Digital
- End User Computing (EUC)
- Information
- Web Analytics
As previously stated, ideas for what to include next would be more than welcome (any suggestions used will also be acknowledged).
From: peterjamesthomas.com, home of The Data and Analytics Dictionary
Some of these may be useful/relevant –
(1) Curation
(2) Explainable AI
(3) Canonical Data Model
(4) Wrangling
(5) Federation
Thanks – if you have some text to associate with each, happy to include [edited] versions of these and add you to the acknowledgements.
Peter
An initial attempt at 3 of these, feel free to edit –
Data Curation: A collection of processes, tools and techniques to manage and maintain data across its lifecycle, from the time data is mastered through to integration, provision and consumption of data with a continuous focus on improving the value and usability of data incrementally over time. Data Curation has increased relevance with the emergence of Data Lakes which typically ingest all/most data from the various sources, but data will be curated over time as and when use cases are identified and the characteristics of the underlying data are discovered.
Explainable AI: An emerging theme within the Artificial Intelligence (AI) domain that focuses on building artificial intelligence systems that have the ability to explain the characteristics and rationale that underpin their results/recommendations. Focus is on improving transparency associated with the AI systems/models and thereby increasing business confidence in the models to supporting enhanced adoption within the organisation.
Data Wrangling: Data wrangling is the process of cleansing, mapping and transforming raw data into a format suitable for exploration and analytics. Data Wrangling has emerged due to an increased need for self-service capability to enable analysts and business end users to explore and exploit data quickly. Data Wrangling tools complement ETL tools within the Data landscape with ETL solutions supporting enterprise wide data integration and transformation requirements and Data Wrangling tools enabling business focused exploratory and iterative use cases.