The document outlines the evolution of an organization's data and analytics capabilities from initial siloed and inconsistent usage to a mature and world-class "data-driven" state. Key stages included establishing data stewardship, developing a data value council, implementing a metadata catalogue and defining data quality, and building out an enhanced "Big Data Fabric" platform to support predictive and prescriptive analytics as well as artificial intelligence applications. The organization ultimately reached a world-class level where data drives business value through insights and automation, and the data team coaches others to become "Data Citizens."

1. • Siloed analytical capability
• No metadata catalogue
• Inconsistent tool usage
• Suboptimal platforms
• No view of data value
• Data Stewardship introduced
• Data Value Council set up
• Opportunities road-mapped
• Metadata catalogue in place
• Data quality defined
• Enhanced data landscape
• Enhanced analytical tools
• Big Data Fabric started
• Data team career model in
place
• Data Stewardship mature
• Data Value Council mature
• Benefit realisation monitored
• Metadata catalogue crowd-
sourced
• Data quality implemented
• Data landscape mature
• Analytical tools mature
• Big Data Fabric maturing
• Predictive analytics common
• Some prescriptive analytics
• Experiments with Artificial
Intelligence
• Big Data Fabric implemented
• Data team mature
• Data is an enterprise asset -
the foundation of the
collective intelligence
• Data drives business value
through insight and innovation
• Data-driven decision making is
embedded in the culture
• Data drives enhanced business
capabilities and outcomes
across the enterprise
• Many successful analytics
implementations
• Metadata catalogue
automatically populated
• Big Data Fabric mature
• Prescriptive analytics common
• Some Artificial Intelligence
implementations
• Data team enabling others
• Other organisations see this as
an exemplar of world class Big
Data Fabric capability
• Staff publish materials about
“how to” become a data-
driven enterprise
• Data team coaches others to
become Data Citizens
• Metadata catalogue is used to
drive analytics initiatives
• Data quality is continually
improved
• Big Data Fabric complete,
logged and monitored
• Data lineage is known and
tracked
• Data lifecycle is managed
• Descriptive, Predictive &
Prescriptive Analytics mature
• Artificial Intelligence advisors
used throughout the
organisation
• A large proportion of analytical
tasks are automated
Nascent
Governed
Mature
World
Class
Data-Driven