The Federal Government Data Maturity Model - NTIS

1 The Federal Government data Maturity Model C iii' '< Cl) 'i iii I s-3 rf 3 UI I 8 5.., G) ca fc 0 C el if r- ii cB c5" C i1 I S1. 3: !!!. I 0::::,::::, I 3:CC (D I 3 - ::::, (D ::::, (Q -naI -,, ::::,-I (D er -=::::S ! - C. C el 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.))

2 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.

3 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 evelorganization data ownership and accountable for data stewardship governance prescriptive analytics 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.

4 Common tools ara in use across Multi-agency advancement of data ownership and stewardship High Capability C" 0 i. en o iiiS" ::I ::I ::I a, UI C. ::I 'iQ) c. aa.)> ::I y, 8 )> 0 Q) CC ::I 6" i C. 3 C" :s:: !a- - - UI - UI 0 - ::I a' gQ) .., ::05,. C 0m- -C -t :)::I" ci;' C d - a, C g CQ en ::I" CC) (1) s.;:i. 3 - . 3 (1) ::I t C. (1) 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.

5 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.