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Clear Set Of Processes

Abstract

  • Define standardized processes for intake, data access, privacy, stewardship, and governance.
  • Use a 3-tier data classification model (L2: default, L1: strategic, L0: PII).
  • Form a Steering Committee to review projects, policies, and escalations.
  • Enable self-service via data catalogs and automated access workflows.
  • Ensure compliance with CCPA/GDPR through structured PII request handling.
  • Year 1 cost: $150K–$300K, with scalable, low-friction governance.

Objective

To define a set of standardized, scalable, and secure processes that enable the Data Office to:

  • Prioritize the right data initiatives
  • Ensure consistent governance and privacy compliance
  • Align with executive and business goals
  • Enable transparent, secure, and auditable data usage
  • Empower self-service analytics while protecting sensitive information

Key Benefits

  • Improved alignment between business and data strategy
  • Clear decision-making frameworks for project intake and prioritization
  • Stronger compliance with CCPA/GDPR and internal policies
  • Enhanced trust in data through stewardship, traceability, and classification
  • Accelerated time-to-insight while maintaining governance discipline

Proposed Core Data Office Processes

New Data Initiative / Project Intake Process

Purpose: Allow business units to propose new data use cases (e.g., analytics, dashboards, AI models)

Steps:

  • Submission via a centralized portal (template includes: business impact, data domains needed, urgency)
  • Review by a Data Governance Steering Committee (meets monthly)
  • Scoring by criteria (ROI, risk, compliance, technical complexity)
  • Prioritization and assignment to Data Office squads

Tools: Notion, Jira, or ServiceNow

Template: Standard intake form with weighted scoring

Frequency: Continuous intake with monthly review

Data Governance Steering Committee Formation

Purpose: Ensure governance policies reflect cross-functional priorities

Structure:

Chair: Chief Data Officer

Voting Members: 1 per business function (Marketing, R&D, Finance, Ops, Legal)

Non-voting Advisors: Data Architects, InfoSec, Legal, Compliance

Elections / Terms:

  • Members selected by business unit heads (1-year renewable terms)
  • Meets monthly, emergency sessions ad hoc

Responsibilities:

  • Policy creation (retention, classification, access)
  • Escalation authority for Level 0/1 data access
  • Project prioritization and investment alignment

Data Classification & Access Control Process

Purpose: Enforce a tiered access model based on sensitivity and business criticality

Three-Layer Data Access
Three-Layer Data Access System
Level Description Access Rules
Level 2 Default data (non-sensitive, widely used KPIs) Free access (open to company employees)
Level 1 Strategic internal data (e.g., pricing, sales forecasts) Requires BU + CDO approval
Level 0 PII, HR, compliance-bound data Requires InfoSec + Legal + CDO approval

Implementation:

  • Data tagged with level in catalog (Atlan or Purview)
  • Policies enforced via IAM, SSO, data masking tools (e.g., Snowflake, Power BI)
  • Quarterly review of access lists

Data Extraction / Right to Erasure (CCPA/GDPR) Process

Purpose: Ensure compliance with privacy regulations for deletion or extraction of personal data

Steps:

  • Request intake via privacy portal (authenticated user)
  • Lookup across governed datasets (automated via OneTrust + Ataccama / Snowflake tags)
  • Approval routing to InfoSec, Legal
  • Data redaction or deletion executed via data engineering team
  • Audit trail stored in secure repository

Timeline SLA: 30 days (regulatory requirement)

Tools: OneTrust, Ataccama, legal DSR module, Snowflake tagging, Glue job triggers

Self-Service Data Access Workflow

Purpose: Empower analysts and citizen data users with governed access to certified data sets

Steps:

  • User browses catalog (Informatica, Collibra, Sales Force, etc...) and requests access to Level 1/2 datasets
  • Approvals via automated workflow (manager + CDO delegate for Level 1)
  • Access granted via group-based role assignment in Snowflake / Power BI

Features:

  • Audit logs of all approvals and access
  • Revocation after 90 days unless renewed

Data Stewardship & Quality Reporting

Purpose: Maintain trust and ownership of datasets

Process:

  • Every dataset is assigned a data owner + steward
  • Data quality metrics (freshness, null %, row counts) monitored via Soda.io or Great Expectations
  • Monthly report to Governance Committee

Tools: dbt tests, data quality dashboards, incident workflow via Jira

Execution Plan & Timeline (12 Months)

Phase Duration Key Deliverables
Phase 1 0–2 months Define data levels, draft policies, form Steering Committee
Phase 2 2–4 months Launch project intake workflow, access request templates
Phase 3 4–6 months Implement classification in catalog; tag existing datasets
Phase 4 6–9 months Pilot privacy/erasure requests; roll out dashboards
Phase 5 9–12 months Conduct audits, adjust based on feedback, publish KPIs

Ideal Templates

  1. Project Intake Template
    • Business impact, data domains needed, urgency, expected ROI, data sensitivity level
  2. Access Request Form
    • Dataset, Level, Justification, Duration, Manager approval
  3. Stewardship Dashboard
    • Data health, ownership, incident log, SLA adherence
  4. PII Request Tracker
    • ID, dataset match, owner notified, actions taken, time to resolution

Estimated Cost of Process Implementation

Component Cost
Process Design & Consulting $50K–$100K
Workflow Automation Tools (ServiceNow, Notion, custom apps) $30K–$60K
Legal/Compliance Tooling (privacy portal, DSR) $40K–$80K
Staff Time (Training, Workshops, Audits) $50K–$100K
Total $150K–$300K (Year 1)

Data Office Core Processes

sequenceDiagram
    autonumber
    participant BU as Business Units
    participant DO as Data Office
    participant GC as Governance Council
    participant IS as InfoSec / Legal

    BU->>DO: Submit new business data request
    DO->>DO: Prioritize request and align with data strategy
    DO->>DO: Allocate resources & assign data stewards

    DO->>DO: Extract and ingest data (from sources)
    DO->>DO: Clean, transform and normalize data
    DO->>DO: Enrich and catalog datasets

    alt Level 2 (Open Data)
        DO->>BU: Publish to company-wide self-service portal
    else Level 1 (Strategic Data)
        DO->>GC: Submit gate review for strategic approval
        GC->>DO: Approve with restrictions
        DO->>BU: Share restricted access dataset
    else Level 0 (Sensitive/PII)
        DO->>IS: Submit request for legal and InfoSec review
        IS->>DO: Validate CCPA/GDPR compliance
        DO->>BU: Release dataset under access controls
    end

    BU->>BU: Consume data in analytics/products
    DO->>GC: Report usage metrics and update data classification
    IS->>DO: Monitor for audit and compliance

Data Office Processes + Tools + Timeline

flowchart TD
    %% Timeline Stages
    subgraph Phase_1["Phase 1: Setup (0–2 months)"]
        P1["Define Data Levels (0/1/2)"]
        P2[Form Governance Committee]
    end

    subgraph Phase_2["Phase 2: Intake & Catalog (2–4 months)"]
        P3[ServiceNow: Project Intake Form]
        P4[Governance Scoring Workflow]
        P5[Assign Steward & Tag Dataset]
        P6[Microsoft Purview: Register Metadata]
    end

    subgraph Phase_3["Phase 3: Access Controls (4–6 months)"]
        P7[Snowflake IAM: Implement Role-Based Access]
        P8[Power BI: Configure Data Sharing Rules]
        P9[Apigee: Secure API Access to Level 2]
    end

    subgraph Phase_4["Phase 4: Privacy & Compliance (6–9 months)"]
        P10[ServiceNow: PII Request Portal]
        P11[Automated Matching via Purview Classifications]
        P12[Glue Jobs: PII Redaction / Deletion]
    end

    subgraph Phase_5["Phase 5: Reporting & Automation (9–12 months)"]
        P13[Soda.io / Dynatrace: Monitor Data Health]
        P14[Jira: DQ Incident Routing to Stewards]
        P15[Monthly Report to Governance Committee]
    end

    %% Dependencies & Flow
    P1 --> P3
    P2 --> P4
    P3 --> P4
    P4 --> P5
    P5 --> P6

    P6 --> P7
    P7 --> P8
    P8 --> P9

    P1 --> P10
    P10 --> P11
    P11 --> P12

    P6 --> P13
    P13 --> P14
    P14 --> P15

Notes

  • Each Phase represents a major milestone in the rollout of a mature, tool-integrated Data Office.
  • Technologies are integrated as real steps:
    • OneTrust for intake and PII workflows
    • Informatica, Collibra, SalesForce for metadata, classification, and cataloging
    • Snowflake / Tableau / Apigee for access enforcement
    • Soda.io / Dynatrace / Jira for monitoring and incident response

Risks & Remedies

Risks: - Overly bureaucratic approval flows (especially for Level 0/1 data) - Fragmented process definitions between teams - Inconsistent audit trails for data usage or compliance

Remedies: - Introduce a tiered, SLA-based data request system (e.g., Level 2 self-service, Level 1 reviewed in <48 hrs) - Document and publish standardized processes in a living handbook - Automate logs, approvals, and exception tracking for governance-sensitive data