The Federal Government Data Maturity Model

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Analytics Capability Summary reports Descriptive analytics

Data Culture

Data use is uncoordinated and ad-hoc. Quality issues

limit usefulness

Data use is by request Quality programs are

nascent

Data Management

Data managed in silos . documentation sparse; standards not regularly

applied

Data managed in silos; some documentation exists;

standards not regularly applied

Data Personnel

No dedicated personnel performing data duties

Some siloed data teams; no clear career path for data

personnel

Systems/ Technology

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

Data Governance

Loose affiliations of technical staff

Bureau-level collaboration, data ownership and

stewardship

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 High demand for data across

programs supporting key agency. Drives decision making assets

Data managed across the Data are managed with cross-functional applications agency; documentation is in mind; documentation is unifonn; some standards

are applied unifonn; standards regular1y agJ>lied

Professional development Data professionals path established for data integrated with subject

personnel matter experts

Some common data Some common data systems; key data can be systems; some data can be programmatically accessed programmatically accessed;

some common tools exist

Agency level collaboration, Agency�evel organization data ownership and accountable for data

stewardship governance

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

Multidisciplinary teams solving agency mission and

operational challenges

Cont common data systems; key data can be

programmatically acca11ad; common tools ara in use

across

Multi-agency advancement of data ownership and

stewardship

High Capability

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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.