Table 1

Detailed scoring criteria per feature

Feature tree						Score
Data ingestion and integration	→	Data consolidation	→	Connectivity to N data sources		5
			→	Data extraction, transformation and loading (ETL) and ETL support		5
			→	Data modelling		5
	→	Data propagation	→	Data flow orchestration, enterprise application integration, exchange of messages and transactions		5
			→	Enterprise data replication, transfer large amounts of data between databases		5
			→	Versioning and file management		5
	→	Data virtualisation	→	Data access		5
	→	Data federation	→	Enterprise information integration		5
\|					Total	40
Data preparation and cleaning	→	Parsing and standardisation	→	Tagging data with keywords, descriptions or categories		5
			→	(Data scrubbing)/cleansing/handling blank values/reformatting values/threshold checking		5
			→	Data enhancement/enrichment/curation		5
			→	Natural language processing		5
			→	Address validation/geocoding		5
			→	Master data management		5
			→	Data masking		5
	→	Identity resolution, linkage, merging and consolidation	→	Data deduping		5
			→	Machine learning/training a statistical model		5
			→	Data aggregation		5
			→	Data binning		5
			→	Grouping similar data/clustering		5
			→	Outlier detection and removal		5
	→	Master reference data management	→	‘Hub’ infrastructure to source and distribute master/reference data		5
			→	Master data versioning based on data history and timelines		5
			→	Workflow integrations to steward and publish the master/reference data		5
			→	Graph data stores to define relationships for creating a flexible knowledge graph		5
			→	Accessible API (Application Programming Interface) for real-time access to shared reference data		5
\|					Total	90
Data profiling, exploration/ pattern detection	→	Relationship discovery	→	Cross-table redundancy analysis		5
			→	Performing data quality assessment, risk of performing joins on the data		5
			→	Identifying distributions, key candidates, foreign-key candidates, functional dependencies, embedded value dependencies and performing intertable analysis.		5
	→	Content discovery	→	Data pattern discovery		5
			→	Domain analysis		5
			→	Discovering metadata and assessing its accuracy		5
	→	Structure discovery	→	Column value frequency analysis and statistics, collecting descriptive statistics like min, max, count and sum.		5
			→	Table structure analysis, collecting data types, length and recurring patterns.		5
			→	Drill-through analysis		5
\|					Total	45
Data monitoring	→	Monitoring and alerting	→	Time series data identified and collection by metric name and key/value pairs		5
			→	Flexible query language to leverage this dimensionality		5
			→	Graphing and dashboarding support		5
\|					Total	15
Data use	→	Metadata management	→	Concept identification and naming		5
			→	Data categorisation		5
			→	Lineage		5
			→	Relationship with other metadata		5
			→	Comments and remarks		5
			→	Data statistics (profiles)		5
			→	Knowledge graph		5
	→	Privacy and security	→	Data anonymisation		5
			→	Role based access control		5
			→	Secure environment setup and deployment		5
			→	Container-based deployment		5
	→	Data mining	→	Interactive data visualisation		5
			→	Visual programming and analysis		5
			→	Visual illustrations and training documentation		5
			→	Sample data/generate fake data		5
			→	Add-ons and extension functionality		5