Big Data Fabric Capability Maturity Model

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Ross Collins

A Capability Maturity Model for Big Data Fabric

Data & Analytics

• 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

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Big Data Fabric Capability Maturity Model

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

Big Data Fabric Capability Maturity Model

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