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FDMM

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The Federal Government
Data Maturity Model
Analytics
Capability
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Summary reports
Descriptive analytics
Data use is uncoordinated
and ad-hoc. Quality issues
limit usefulness
Data use is by request
Quality programs are
nascent
Data managed in silos.
documentation sparse;
standards not regularly
applied
Data managed in silos;
some documentation exists;
standards not regularly
applied
No dedicated personnel
performing data duties
Some siloed data teams;
no clear career path for data
personnel
Data is stored in siloed
systems; data are frequently
copied to facilitate use
Data are stored in siloed
systems; some data can be
programmatically accessed
Loose affiliations of
technical staff
Bureau-level collaboration,
data ownership and
stewardship
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Data
Culture
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Low Capability
Federal Government Data Maturity Model:
The following document details the six lanes of the Federal Gov­
ernment Data Maturity Model, including each of the five milestones
within the lanes. The six lanes are: Analytics Capability, Data
Culture, Data Management, Data Personnel, Data Systems and
Technology, and Data Governance. The power of the model is in
its simplicity, therefore, this document provides more context and
detail around the lanes and milestones so that concepts are well
defined, allowing for a common language and understanding to
be established among practitioners.
Purpose
The purpose of this model is threefold.
First, this model helps agencies with a high-level assessment of
current capabilities and supporting processes. The framework al­
lows agencies to easily understand their "current state" and helps
users conceptualize where they want their organization to be in the
long term. It then provides some practical steps for getting there.
Second, this model helps with strategic communication between
agency data professionals and agency leadership. It can also
serve to communicate to the broader agency about the strategic
direction of data improvement initiatives.
Diagnostic analytics
Predictive analytics
Some data and analytics
are routine and have quality
programs supporting key
assets
High demand for data across
agency. Drives decision
making
Data managed across the
agency; documentation is
unifonn; some standards
are applied
Data are managed with
cross-functional applications
in mind; documentation is
unifonn; standards regular1y
agJ>lied
Cross.functional prescriptive
analytics
lnfar.egancy
communities d I
shar8 analy9as,
practicas
Data are managed
considering agency-wide
needs; documentation is
unifonn; standards are
unifonnly applied
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Data professionals
integrated with subject
matter experts
Multidisciplinary teams
solving agency mission and
operational challenges
Some common data
systems; some data can be
programmatically accessed
Some common data
systems; key data can be
programmatically accessed;
some common tools exist
Cont common data
systems; key data can be
programmatically acca11ad;
common tools ara in use
across
Agency�evel organization
accountable for data
governance
Multi-agency advancement
of data ownership and
stewardship
Agency level collaboration,
data ownership and
stewardship
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Professional development
path established for data
personnel
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High Capability
Finally, the model provides a common language and framework
to help promulgate common solutions and best practices across
federal agencies toward advancing data-driven decision making.
This document contains helpful guidance within each of the lanes
that address numerous organizational dynamics when creating
organizational change.
How to Use this Model
It is important to note that the purpose of this model is not to provide
a rigid or prescriptive "one size fits all" approach to improving data
capabilities within federal agencies. Nor does it maintain that all
agencies need to reach or complete all milestones within all lanes to
achieve optimal data capabilities. It simply provides a framework of
organized ideas and suggestions to help agencies consider what
works best for them as they carve out a path to success.
Outcome Measure
The top lane of the model, ':A-nalytics Capability'' should be consid­
ered an outcome measure for capability. This lane provides a contin­
uum of demonstrated capability from the simplest summary reporting
to the most complex prescriptive analytics requiring a vast amount
of data and supporting processes. The other five lanes all help to
support and enable greater capability, and are essential to achieving
lasting change across an organization.
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