Role of Data Governance in Embedded Analytics 

Data governance for embedded analytics is the set of policies, controls, and processes that make in-app dashboards secure, compliant, and trustworthy. It covers identity and access management, row-level security, data privacy, data quality and lineage, and monitoring. The goal is to ensure the right user sees the right data—reliably and safely—every time. 

Why governance matters in embedded analytics ? 

Embedding analytics inside your product raises the bar. Users expect answers in context, with no security slips. Good governance prevents data leaks, boosts trust, and reduces time spent on manual fixes. It also shortens sales cycles by proving compliance and accelerates adoption because users can safely self-serve. 

Data governance controls for embedded analytics: the core building blocks 

1. Identity & access (SSO + fine-grained authorization) 

• What it is: Centralized authentication (SAML/OIDC) and role-based or attribute-based authorization. 

• Why it matters: One login, consistent policies across your app and analytics. 

• What good looks like: SSO with just-in-time provisioning; SCIM for de-provisioning; least-privilege roles 

2. Row-level security (RLS) and object security 

• What it is: Filter rules that ensure users only see their rows (e.g., by tenant, region, customer account). 

• Why it matters: Prevents cross-tenant data exposure. 

• What good looks like: Centralized RLS policy per tenant; object security for dashboards, metrics, and datasets. 

3. Data privacy (PII/PHI) and masking 

• What it is: Controls that protect sensitive fields, with masking and tokenization where needed. 

• Why it matters: Reduces risk and meets regulatory requirements. 

• What good looks like: Field-level policies; masked views for support and demo users. 

4. Data quality, catalog, and lineage 

• What it is: Shared definitions, owners, upstream sources, freshness SLAs. 

• Why it matters: Users trust numbers when definitions are consistent and traceable. 

• What good looks like: A catalog with business terms; lineage from app KPIs back to the warehouse; freshness alerts. 

5. Encryption, auditing, and monitoring 

• What it is: Encryption at rest and in transit; audit logs for access and changes; performance and anomaly monitoring. 

• Why it matters: Proves control to auditors and speeds incident response. 

• What good looks like: End-to-end TLS; KMS-managed keys; central audit trail with alerting. 

6. Compliance alignment 

• What it is: Mapping controls to frameworks (HIPAA, SOC 2, ISO 27001, GDPR). 

• Why it matters: Reduces sales friction and compliance costs. 

• What good looks like: A control matrix that ties each requirement to owners, evidence, and cadence. 

Where each control should live (architecture at a glance) 

Control	Application Layer	BI/Embedded Layer	Storage/Compute Layer
SSO and MFA	Intitiate & Enforce	Propagate Identity
Authorization (RBAC/ABAC)	Business Roles	Dashboard/Object Permissions
Row Level Security		Policy Enforcement	Policy Storage  (Views/Tables)
Data Masking		Mask at semantic layer	Masked views/functions
Catalog & Lineage	Secrets hygiene	TLS	At-Rest (KMS)
Auditing	App access logs	BI access/change logs	Query & data logs

Implementation Checklist  

S.No.	Item	Description
1	Define identities & roles	Map business personas to roles and attributes.
2	Set up SSO & SCIM	Centralize login; automate user lifecycle.
3	Design RLS policies	Draft tenant/region/account rules; store once, reuse across assets.
4	Harden the semantic layer	Central metrics, masking rules, and object permissions.
5	Establish a data catalog	Owners, definitions, data contracts, and freshness SLAs.
6	Enable auditing & alerting	Log access and changes; alert on anomalies and failures.
7	Compliance mapping	Align controls to HIPAA/SOC 2/ISO/GDPR with evidence collection.
8	Pilot & iterate	Run a limited rollout; validate UX, performance, and control gaps.

Key KPIs to track  

S.No.	KPI	What it Means ?	What good looks like ?
1	Security incidents  (data access)	Any cross-tenant or unauthorized data exposure in embedded views	Zero cross-tenant exposures.
2	Time-to-provision	Time from user created in IdP to correct access in app + embedded analytics	< 15 Mins with SCIM
3	Data Freshness SLA	% of dashboard views served within dataset’s promised cadence	≥ 99% of dashboards on time
4	Definition Conflicts	Conflicting numbers/definitions for the same KPI across assets	Downward trend month-over-month
5	Audit coverage	% of sensitive actions & queries that are logged and retained	100% access and changes logged
6	Support tickets on “wrong data”	User-reported data mismatch/issues tied to governance	Steady decline after catalog launch

Conclusion  

Strong data governance for embedded analytics keeps every in-app insight secure, compliant, and reliable. Start with SSO, centralize RLS and masking, certify metrics with a catalog, and log everything. Prove it with a control matrix and KPIs. You’ll unlock faster deals, higher adoption, and safer self-service. 

Glossary of Key Terms  

S.No.	Terms	Definition
1	Authentication	Verifying a user is who they claim (e.g., via SSO/MFA).
2	Authorization (RBAC/ABAC)	Deciding what an authenticated user can access—by role (RBAC) or attributes like tenant/region (ABAC).
3	SSO (Single Sign-On)	One login that your app and embedded analytics both trust and use.
4	SCIM(System for Cross-domain Identity Management)	Standard to auto-provision/de-provision users and roles across systems.
5	JWT (JSON Web Token)	Signed token your app passes to analytics to carry user identity/attributes.
6	Least Privilege	Granting only the minimum access needed to do the job.
7	Multi-Tenant	One platform serving many customers with strict isolation between them.
8	Row-Level Security (RLS)	Filters that ensure users see only the rows they’re allowed (by tenant, region, account, etc.).
9	Object-Level Security	Permissions on dashboards, datasets, tiles, and metrics themselves.
10	Data Masking	Hiding sensitive values (e.g., partially obscured IDs or emails).
11	Data Privacy (PII/PHI)	Protecting personal/health data per policy and law.
12	Encryption  (At Rest / In Transit)	Protecting stored data and network traffic (e.g., disk encryption, TLS).
13	Audit Log	Tamper-evident trail of who accessed what, when, and what changed.
14	Compliance	Meeting external standards/regulations (HIPAA, SOC 2, ISO 27001, GDPR).
15	Data Catalog / Business Glossary	Central index of datasets, owners, definitions, and SLAs.
16	Data Lineage	Trace from a metric back to source tables and transformations.
17	Data Quality	How correct, complete, consistent, and timely your data is.
18	Freshness	How up-to-date the data is versus the promised update cadence (the SLA).
19	SLA (Service Level Agreement)	A measurable promise (e.g., “updates hourly by :10” or uptime targets).
20	Semantic Layer	Central, governed logic for metrics, joins, masking, and RLS that all dashboards share.

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