Prioritizing Use Cases for Immuta Deployment

Implementing Immuta successfully starts with choosing the right use cases. But organizations often struggle to identify and prioritize those use cases, especially when defining a minimum viable product (MVP). This guide provides a structured approach, with questions and examples, to help teams discover, evaluate, and prioritize use cases that align with their governance goals.

In this guide, you’ll learn

• How to define the foundational elements of governance that influence use case planning

• Questions to help identify and prioritize high-impact use cases

• Common challenges and how to avoid them

• Best practices for success

Step 1: Establish your governance foundation

Before diving into specific use cases, ensure you have a solid foundation for governance. These questions help surface existing structures, responsibilities, and tooling that support scalable data governance.

Domain inventory

What high-level domains (and sub-domains) of data exist, and who “owns” each one?

• Why it matters: Establishes clear accountability and scope up front.

• Example: Finance, HR, and Marketing domains; each has a designated Data Owner and two Domain Stewards.

Approval workflows and exception handling

Who approves access requests at the domain/sub-domain level? How are exceptions or escalations handled?

• Why it matters: Ensures you know both the normal path and who to loop in when things fall outside standard policy.

• Example: LOB Directors approve HR data; exceptions go to the CISO team.

Classification framework

Do you have a formal classification scheme (e.g., High/Medium/Low or Risk-Tier 1/2/3)? How often is it reviewed?

• Why it matters: Keeps your risk model up to date as new data sources or business priorities emerge.

• Example: Three-tier model reviewed quarterly by the Governance Council.

Policy ownership

Who writes and maintains global policies? What is the schedule for policy updates or retirement?

• Why it matters: Prevents stale or conflicting policies from eroding trust in your governance program.

• Example: Corporate Compliance writes global policies; annual review every January.

Reporting and compliance deliverables

What operational, audit, or compliance reports must be produced? At what frequency?

• Why it matters: Aligns your tooling and data pipelines to deliver required metrics on time.

• Example: Monthly access logs for Finance; quarterly GDPR compliance audit report.

Access review and recertification

Who owns periodic access recertifications, and how often do they run? How are exception cases managed?

• Why it matters: Ensures unused or inappropriate access is caught and revoked in a timely manner.

• Example: Data Stewards recertify domain access bi-annually; exceptions logged and escalated to Security.

Tools and integrations

Which platforms or tools support approval workflows, classification tagging, IAM, reporting, and recertifications?

• Why it matters: Identifies gaps where manual work may introduce risk or delay.

• Example: Workflows in ServiceNow; classifications in Collibra; IAM via Okta; reporting through Tableau.

Training and communication

How will you onboard and educate data owners, stewards, and requestors on these processes?

• Why it matters: Adoption hinges on clear guidance, training materials, and regular communications.

• Example: Quarterly governance workshops, plus a Confluence page with process flowcharts.

Regulatory and legal requirements

Are there specific external regulations (e.g., GDPR, HIPAA) that dictate any of the above elements?

• Why it matters: Ensures your governance design satisfies mandatory compliance requirements.

• Example: GDPR requires annual consent recertification for EU customer data.

Step 2: Identify and evaluate use cases

Use cases should be selected based on their value, feasibility, and alignment with business priorities. These questions will help you define each one clearly.

Problem statement

What challenge or inefficiency are you addressing?

• What's needed: Clearly define the business or operational issue the use case aims to solve. A strong problem statement identifies pain points, inefficiencies, risks, or compliance challenges the organization currently faces.

• Example: HR data needs to be shared with their respective groups. The data cannot be shared today because all lines of business are co-mingled. Sharing this data with each LOB will provide monetary value to each LOB and will reduce the burden on HR staff by 10 hrs per week.

Current workaround

How are you solving this problem today?

• What's needed: Identify current processes, tools, or manual efforts addressing the issue. Highlight gaps or limitations in current practices to clarify why change is necessary.

• Example: Today HR is running a manual report for each LOB and sending it. We receive 150 requests per week.

Business priority and impact

What value would solving this unlock?

• What's needed: Assess each use case’s alignment with your organization's strategic goals. High-priority use cases typically have significant effects on operational efficiency, revenue, compliance, or risk reduction.

• Example: This is a high-priority use case, as it meets the needs of all LOBs. Implementing this policy will eliminate these requests and allow each LOB to self-serve.

Scope

How much data and how many users are involved?

• What's needed: Quantify each use case's scale to clarify its potential impact. Consider data volume, access frequency, and the number of users or teams affected.

• Example: There are 50 HR tables in scope for this policy, with over 200 users that will start to self-serve.

Decision-makers

Who makes data access decisions, and what do they need?

• What's needed: Identify stakeholders responsible for data governance and policy decisions. Outline the information or tools they need to effectively manage data access.

• Example: Managers/Directors are already assigned their LOB number from the HR system. The policy will use those existing attributes to only show the rows for the LOB allowed.

Dependencies

What technical or organizational work is required?

• What's needed: Evaluate factors that could hinder successful implementation, including

  • Technological requirements: Determine integrations needed with data platforms, identity management systems, or metadata repositories.

  • Change management: Identify internal groups involved in executing changes, such as IT teams managing user metadata or data engineers.

  • Executive and stakeholder alignment: Ensure leadership support and clear stakeholder roles.

• Example: Integration from the HR system to the IAM to place the LOB on managers and directors. This work is prioritized in the next sprint. Change management includes communication to department heads and HR staff one week before production.

Required policies

What high-level policies need to be implemented?

• What's needed: Clearly defining these policies upfront helps set expectations and supports effective implementation. Define essential policies for the use case, such as

  • Table-level access controls

  • Column masking for sensitive data

  • Row-level filtering to restrict access based on attributes or roles

• Example: This will be a row-filtering policy and table-access policy. Managers will gain access to tables within the HR Domain. The data policy will filter rows based on the user's LOB assignment. If the user has no LOB assigned they will not see any rows.

Level of effort

What’s the timeline and effort involved?

• What's needed: Realistically evaluate resources, time, and expertise needed for each use case. This assessment informs prioritization, balancing quick wins against complex but strategic initiatives.

• Example: Integration work will take 2-4 weeks based on engineering work for the HR system and the IAM. Testing/validation will take an additional week. We target this rollout the first week in Q2.

Step 3: Plan the implementation work

Once your use case is selected, map out technical and operational details to ensure a smooth rollout.

Data sources

Where does this data live (platform, schema, table or file path)? Which system is the “source of truth”?

• Why it matters: Ensures you build policies against the correct objects and maintain a single version of record.

• Example: HR_PAYROLL table in Snowflake’s finance_db.hr schema

Classification and sensitivity

How is this data classified (High/Medium/Low or Risk-Tier 1/2/3)? Are there any sub-classifications (e.g., PII vs. financial)?

• Why it matters: Drives mask/filter rules and determines approval rigor.

• Example: Payroll data = Tier 1 (Highly Sensitive PII)

Entitlement model (auto-grant, request, deny)

Who gets automatic access? Who must submit a request? Who is explicitly denied? Is this static or attribute-based?

• Why it matters: Reduces manual overhead and ensures consistent enforcement.

• Example: Finance Analysts auto-granted (via OKTA group); Marketing must request; contractors denied.

Exception and escalation path

If someone outside the above categories needs access, who approves it and what’s the SLA?

• Why it matters: Prevents “shadow access” and provides a clear remediation path.

• Example: LOB VP approves exceptions within 48 hrs; after that, escalates to CISO.

Masking and filtering rules

Which columns or rows need masking or row-level filtering? What mask type (hash, null, redaction)? Who is exempt?

• Why it matters: Balances usability with privacy/compliance.

• Example: Mask SSN (hash) for all except Payroll Admins; filter out salary rows < $50k.

Policy implementation type

Will this be a row-filter policy, column-mask policy, table-deny policy, or a combination?

• Why it matters: Dictates the Immuta policy objects you’ll create and impacts performance.

• Example: Use one row-filter for LOB attribute and one column-mask on SSN.

Monitoring and alerting

How will you detect policy violations or anomalous access (e.g., spikes in denials)? Who receives alerts?

• Why it matters: Enables proactive governance and rapid incident response.

• Example: Set up Slack/Webhook alerts for > 5 failed access attempts in 10 min, sent to Data Ops.

Steward and support contacts

Who are the Data Steward and technical support contacts for this data set? How should issues be routed?

• Why it matters: Speeds troubleshooting and fosters accountability.

• Example: Data Steward: Jane Doe ([email protected]); Support: [email protected].

Dependencies and integrations

What upstream systems, ETL jobs, or metadata registries feed into this dataset? Are there timing or format constraints?

• Why it matters: Avoids implementation delays and ensures policy enforcement aligns with data availability.

• Example: Payroll ETL runs nightly at 2 AM; metadata tags come from Collibra catalog.

Common challenges and how to avoid them

Address these common risks to ensure successful use-case prioritization:

• Misalignment with strategic goals Confirm that each use case aligns with broader organizational objectives to gain executive support.

• Over-engineering solutions Maintain an MVP mindset initially to quickly demonstrate value and avoid prolonged deployments.

• Resource constraints Prioritize use cases delivering substantial benefits without overwhelming existing resources.

Best practices for success

• Engage stakeholders early Involve economic buyers, data stewards, and policy makers from the outset to validate and prioritize use cases effectively.

• Define clear success criteria Establish measurable outcomes to objectively assess each use case's success.

• Leverage Immuta's Customer Success team Collaborate with your Immuta Customer Success Manager to document goals, engage stakeholders, and align strategic planning sessions.