# Data Governance Maturity Assessment: Where Does Your Organization Stand? > Source: https://netray.co/tools/data-governance-maturity-assessment > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Free Tools ## Short answer Score your organization across data policy, ownership, quality controls, and compliance to see your data governance maturity level and next steps. ## Category erp-operations ## Tool **Kind:** assessment **Questions:** #### Do you have a documented data governance policy that is actively followed? **Options:** ##### Yes, documented, communicated, and actively enforced **Score:** 10 ##### Documented but inconsistently followed **Score:** 6 ##### Informal practices, nothing documented **Score:** 3 ##### No policy of any kind exists **Score:** 0 #### Is there a designated data governance owner or committee with real authority? **Options:** ##### Yes, a cross-functional committee with executive backing **Score:** 10 ##### A single owner, but limited authority to enforce decisions **Score:** 6 ##### Informal ownership by IT alone **Score:** 3 ##### No ownership structure exists **Score:** 0 #### How are data quality issues currently detected and resolved? **Options:** ##### Automated monitoring with defined remediation workflows **Score:** 10 ##### Manual periodic audits catch most issues **Score:** 6 ##### Issues surface reactively when someone complains **Score:** 3 ##### No systematic detection exists **Score:** 0 #### How well is data access controlled based on role and sensitivity? **Options:** ##### Fine-grained, role-based access with regular audits **Score:** 10 ##### Basic role-based access, not regularly audited **Score:** 6 ##### Broad access with minimal restriction **Score:** 3 ##### Effectively no access control beyond system login **Score:** 0 #### Do you have a business glossary with agreed-upon definitions for key terms? **Options:** ##### Yes, maintained and used organization-wide **Score:** 10 ##### Exists for some domains, not comprehensive **Score:** 6 ##### Attempted once, now outdated **Score:** 3 ##### No shared glossary exists **Score:** 0 #### How do you handle regulatory and compliance requirements around data (ITAR, CMMC, GDPR, or similar)? **Options:** ##### Formal compliance program with regular audits **Score:** 10 ##### Aware of requirements, partially implemented controls **Score:** 6 ##### Reactive compliance, addressed only when required **Score:** 3 ##### Not currently a structured focus area **Score:** 0 #### Can your organization currently produce reliable data lineage for a critical report or model? **Options:** ##### Yes, automated lineage tracking exists **Score:** 10 ##### Possible manually, with significant effort **Score:** 6 ##### Only for a handful of critical systems **Score:** 3 ##### No, tracing lineage would require significant investigation **Score:** 0 #### How does data governance factor into your current or planned AI initiatives? **Options:** ##### AI governance (model risk, data lineage, audit trails) is fully integrated **Score:** 10 ##### Some AI-specific governance considerations exist **Score:** 6 ##### AI initiatives proceed with minimal governance oversight **Score:** 3 ##### No connection between data governance and AI work exists **Score:** 0 **Bands:** #### Ad hoc: governance is effectively informal **Min:** 0 **Max:** 24 **Verdict:** Data governance at your organization currently relies on individual diligence rather than systematic process. This is a common starting point, but it creates real risk for compliance exposure and for any AI initiative that will inherit whatever data quality and access control gaps exist today. **Recommendations:** - Establish a named data governance owner, even part-time, as the first structural step. - Start a business glossary for your 20-30 most contested terms rather than attempting full coverage. - Document access control for your most sensitive data domains first (financial, PII, export-controlled). - Delay significant AI initiatives on ungoverned data until basic ownership and access control exist. #### Developing: foundational pieces exist but are inconsistent **Min:** 25 **Max:** 49 **Verdict:** You have started building governance discipline in pockets, but it is not yet systematic or organization-wide. This is a workable foundation to build on, provided the inconsistency gets addressed deliberately rather than left to expand as more data and systems come online. **Recommendations:** - Formalize the informal governance practices that are working into documented, repeatable policy. - Expand access control auditing beyond the systems currently covered. - Invest in automated data quality monitoring rather than continuing to rely on manual periodic checks. - Build AI-specific governance considerations (lineage, audit trails) into any AI initiative currently underway. #### Managed: governance is systematic in most areas **Min:** 50 **Max:** 74 **Verdict:** Your organization has real governance infrastructure: policy, ownership, and quality controls exist and function in most areas. The gaps that remain are worth closing deliberately, particularly around AI-specific governance, which is a newer discipline most organizations at this maturity level have not fully integrated yet. **Recommendations:** - Close remaining gaps in automated lineage tracking, which pays off disproportionately for AI initiatives. - Extend compliance program rigor to cover any newly regulated data categories or jurisdictions. - Build a formal AI governance framework (model risk, data lineage for training data, audit trails) if not already present. - Benchmark against industry peers to identify which remaining gap carries the highest risk. #### Optimized: governance is a genuine competitive capability **Min:** 75 **Max:** 100 **Verdict:** Data governance at your organization is mature, systematic, and increasingly a source of competitive advantage rather than just risk mitigation. This maturity level is what lets an organization move faster on AI initiatives than peers still fixing foundational data problems, because the foundation is already solid. **Recommendations:** - Use your governance maturity as a differentiator when pursuing regulated contracts or partnerships. - Continue extending automated governance tooling to keep pace as data volume and AI initiatives scale. - Mentor or benchmark other business units or acquired entities toward the same maturity level. - Periodically reassess as new data sources, regulations, and AI capabilities emerge; maturity requires maintenance. ## Content **Intro:** This free data governance maturity assessment scores your organization across policy, ownership, data quality controls, access management, and AI readiness, built for CIOs and data governance leads who need an honest baseline before investing further in governance tooling or process. Answer eight questions about your current practices and get a maturity band with specific next steps for your level. The organizations that struggle most with AI adoption are rarely the ones with the weakest technology, they are the ones whose data governance maturity never caught up to the ambitions of what they wanted to build on top of that data. **Sections:** #### Why governance maturity, not tooling, is the real gap **Body:** Most organizations that score low on this assessment have already purchased some governance tooling, a catalog, an access management system, a data quality platform, and still find themselves at ad hoc maturity, because tooling amplifies existing discipline rather than creating it. A catalog without a governance committee that enforces ownership assignment sits unused. An access control system without a periodically audited policy drifts toward overly broad permissions within a year. Maturity is fundamentally an organizational discipline question, and the tooling question should come after that discipline is established, not before. **Bullets:** - Governance tooling amplifies existing organizational discipline; it rarely creates discipline that did not exist. - A governance committee with real enforcement authority matters more than which specific tool you buy. - Access control policy without periodic audit drifts toward over-permissioning within roughly a year. - Assess organizational readiness honestly before the next governance tooling purchase, not after. #### The compliance dimension that changes urgency for regulated manufacturers **Body:** For aerospace and defense manufacturers subject to ITAR and CMMC, and for any organization handling GDPR-relevant personal data, governance maturity is not purely an efficiency question, it directly determines audit and compliance risk exposure. An organization that scores low on the compliance-related questions in this assessment while operating under these frameworks carries real regulatory risk that should reprioritize governance investment above where general maturity alone would suggest, since the cost of a compliance failure dwarfs the cost of the governance program that would have prevented it. **Bullets:** - ITAR and CMMC compliance failures carry contract and legal risk far beyond typical data quality costs. - Regulated manufacturers should treat compliance-related governance gaps as higher priority than general maturity gaps. - An audit is the worst time to discover you cannot produce required data lineage or access documentation. - Build compliance requirements into governance policy explicitly, not as a generic best-practice afterthought. #### Why AI governance is the newest and most commonly missing piece **Body:** Traditional data governance frameworks were built around structured databases and reporting, and most organizations' governance maturity, even where it is otherwise solid, has a gap specifically around AI: which data trained a model, whether that training data included anything it should not have, how model outputs are audited, and what happens when an AI agent takes an action based on data it should not have had access to. This is a genuinely new governance domain, not a simple extension of existing data governance, and it is the piece most organizations at 'managed' maturity have not yet built, which is exactly the gap that turns an AI pilot into a governance incident. **Bullets:** - AI governance (training data lineage, output audit trails, access boundaries for agents) is a distinct discipline from traditional data governance. - Even mature governance organizations commonly have a gap specifically around AI-specific controls. - An AI agent acting on data outside its intended access scope is a governance failure, not a technical bug. - Build AI governance into any initiative from the start; retrofitting it after an incident is reactive and costly. #### How Netray builds governance maturity alongside AI deployment **Body:** Netray works with manufacturers to close governance gaps as part of deploying DataRay and ERPray, because we have found that AI governance specifically, audit trails, access boundaries, and training data lineage, is the piece most clients' existing governance programs have not yet addressed. We build governance maturity assessment into the earliest phase of any AI engagement rather than treating it as a separate initiative, since the two are inseparable in practice: an AI system deployed on top of weak governance inherits every gap underneath it. Engagements start with this assessment applied against your actual policies and systems, not a generic checklist. **Cta:** Get a governance maturity roadmap that closes the specific gaps blocking your AI initiatives fastest. **Faq:** #### Where should we start if our assessment score is low across the board? **Answer:** Start with ownership: assign a named data governance owner or committee with real authority, even if only part-time initially. Every other improvement, policy documentation, access control auditing, quality monitoring, depends on someone being accountable for driving it. Without that ownership structure, governance initiatives tend to stall regardless of how much budget or tooling gets thrown at them. #### Is AI governance really different from regular data governance? **Answer:** Yes, meaningfully. Traditional data governance focuses on structured data quality, access, and lineage for reporting and analytics. AI governance adds new questions: what data trained a model and whether that was appropriate, what an AI agent is allowed to see and do autonomously, how model outputs get audited for accuracy and bias, and how to maintain an audit trail for AI-driven decisions. Organizations frequently have solid traditional governance and a real gap specifically in this newer AI-specific dimension. #### How urgent is closing our governance gap if we operate under ITAR or CMMC? **Answer:** More urgent than general maturity alone would suggest. Compliance failures under these frameworks carry contract and legal consequences that dwarf typical data quality costs, and an audit is the worst possible moment to discover you cannot produce required lineage or access documentation. If your assessment scored low specifically on the compliance question while operating under these frameworks, prioritize that gap above other governance improvements. #### Can we improve governance maturity while an AI project is already underway? **Answer:** Yes, and it is common to do both simultaneously rather than sequentially, provided you are deliberate about it. Build governance controls, access boundaries, audit logging, data lineage documentation, into the AI project itself as it develops rather than treating governance as a parallel track that might catch up later. Retrofitting governance onto a live AI system already in production is considerably harder than building it in from the start. #### How often should we reassess governance maturity? **Answer:** Annually at minimum, and additionally whenever a significant new data source, regulation, or AI initiative comes online, since each of these can shift your risk profile meaningfully. Governance maturity is not a one-time achievement; organizations that assess once and consider the work done frequently find their practical maturity has quietly regressed a year or two later as new systems and initiatives outpaced the original governance framework. ## Search terms data governance maturity assessment, data governance maturity model, enterprise data governance checklist, data governance readiness, data policy maturity, data quality governance assessment, data governance for AI, governance framework scorecard --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json