Salesforce Data 360
Data & Analytics data ai Gen AI Generative AI Informatica Customer 360° Data Management Salesforce
Blog Post by PDI Marketing Team
Blog Post by PDI Marketing Team

Published: January 9, 2026

Introduction 

Your exec team wants AI-powered growth now. But here’s what usually happens inside IT:
  • The data cloud project starts with excitement.
  • Then reality hits: siloed systems, inconsistent identities, unclear consent, and brittle pipelines.
  • And “Customer 360” becomes a dashboard—rather than a system that triggers real outcomes.
If that sounds familiar, you’re not alone. Gartner predicts that by 2027, half of business decisions will be augmented or automated by AI agents—raising the bar for trustworthy, well-governed data foundations. (Gartner)

This is where Salesforce Data 360 (Data Cloud) and Informatica can deliver measurable results—when deployed as a unified data platform approach: connect data where it lives, unify it into usable profiles, apply data governance at scale, and activate insights across systems.

Salesforce describes Data 360 as a cloud-native, metadata-driven data platform designed to unify siloed data for analytics and AI. (Salesforce)

This blog shows you how to prove ROI with a practical framework, a 90-day runbook, and KPIs your CFO will accept.
 
What is data management (and why it’s the ROI multiplier)?

Before the tech stack, anchor the definition.

IBM defines data management as the practice of collecting, processing, and using data securely and efficiently for better business outcomes. (IBM)

DAMA adds an enterprise lens: data management spans plans, policies, programs, and practices that protect and enhance the value of data assets across their lifecycle. (DAMA International®)

That’s the point: ROI doesn’t come from “having a platform.” It comes from managing data as an asset—so it can be activated reliably across teams, clouds, and use cases.

The story: how Customer 360 ROI breaks (and how to fix it)

Most Customer 360 programs under-deliver for four predictable reasons:
  1. Data is stale (batch ETL, delays, duplication)
  2. Identity is messy (duplicates, false merges, inconsistent keys)
  3. Governance is inconsistent (policies don’t follow data into activation)
  4. Activation is manual (handoffs, spreadsheets, one-off segments)
Salesforce’s positioning for Data 360 is to “turn existing data into action instantly, without moving it,” enabling connected profiles across marketing, sales, service, and commerce. (Salesforce)

The fix is a structured path: connect → harmonize → govern → activate → measure.
The story_ how Customer 360 ROI breaks (and how to fix it) - visual selection-1

What Salesforce Data 360 provides (mapped to measurable ROI)

1) Connect: Zero Copy integration + broad connectivity

Salesforce describes Zero Copy Data Federation as access to data in external platforms like Snowflake, BigQuery, Databricks, and Amazon Redshift from Data Cloud—without creating copies. (Salesforce)

Salesforce also highlights enterprise interoperability across Salesforce and external data lakes/warehouses as a core architectural theme. (Salesforce Architects)

Informatica’s role: establish repeatable connectivity and onboarding patterns across AWS/Azure and hybrid sources, while enforcing consistent definitions and quality checks as data enters the activation layer. (Informatica)

2) Harmonize: unified data + identity resolution into a Customer 360

Salesforce explains that identity resolution consolidates data from different sources using matching and reconciliation rules to create unified profiles that include contact point values from all sources. (Salesforce)

Trailhead notes unified profiles are mutable and update as source data or identity rules change. (Trailhead)

Informatica’s role: improve match outcomes with standardization, deduplication, and survivorship patterns—so the “unified data” is accurate enough to drive targeting and automation at scale.

3) Govern: policy-based data governance that survives activation

Salesforce’s Data 360 Governance emphasizes automated tagging/classification of unified data and metadata to drive policy-based governance across use cases. (Salesforce)

Salesforce also announced Data Cloud Governance GA with “native, consistent policy enforcement” for the AI era. (Salesforce)

Salesforce documentation describes how tagging and classification links policies to tags so enforcement scales as data grows. (Salesforce)

Informatica’s role: broaden governance beyond one platform with catalog + governance workflows and data quality context (e.g., Informatica Cloud Data Governance and Catalog combines governance, catalog, and quality capabilities). (Informatica)

4) Activate: segments + triggered flows + data sharing back out

Salesforce positions Data 360 for real-time activation—connected profiles and real-time activation across channels. (Salesforce)

Its “Zero Copy and Sharing” materials explicitly pair federation (data-in) with data sharing back to external platforms. (Salesforce)

Informatica’s role: maintain trust and operability as activation scales (quality thresholds, lineage awareness, change impact analysis, stewardship workflows).
What Salesforce Data 360 provides (mapped to measurable ROI) - visual selection (2)
Structured data vs unstructured data (and why it changes your ROI plan)

If your 2026 roadmap includes agents, search, or personalization, you can’t ignore unstructured content.

Salesforce notes that unstructured data can include emails, social posts, web pages, and multimedia like images/audio/video, while structured data uses defined schemas and is ingested via Data Lake Objects (DLOs) and Data Streams. (Salesforce)

Practical takeaway for IT leaders:
  • Start ROI with structured data (profiles, transactions, consent, service events) because KPIs are easier to measure.
  • Add unstructured data when you have clear use cases (sentiment, case summaries, knowledge grounding) and governance controls ready.
Examples of metadata (why “metadata-driven” matters for governance)

Salesforce Architects describes Data 360 as metadata-driven, which is a big deal for governance and scale. (Salesforce Architects)

Microsoft’s Cloud Adoption Framework breaks metadata into:
  • Business metadata (ownership, usage, origination, business terms/definitions)
  • Technical metadata (schema, formats/protocols, encryption/decryption keys) (Microsoft Learn)
Why this matters for ROI: good metadata reduces time-to-troubleshoot, improves audit readiness, and makes policy enforcement practical—not manual.

The ROI model: 4 levers you can measure in dollars and hours
The ROI model_ 4 levers you can measure in dollars and hours - visual selection (1)

Lever 1 — Cost avoidance (reduce duplication + pipeline sprawl)
When Zero Copy is appropriate, you can reduce replicated datasets and retire redundant pipelines. (Salesforce)

Measure: pipelines retired, TB duplication avoided, compute/runtime reductions.

Lever 2 — Speed-to-activation (shorten time-to-value)
Data 360’s “unify and act” framing is explicitly about accelerating activation from unified data. (Trailhead)

Measure: time from data request → segment/flow live.

Lever 3 — Revenue lift (better targeting + better service decisions)
Unified profiles + segments enable more precise activation across channels. (Salesforce)

Measure: conversion lift, churn reduction, cost-to-serve improvements tied to specific segments/workflows.

Lever 4 — Risk reduction (governance coverage and policy enforcement)
Data Cloud Governance GA emphasizes governing data at scale for the AI era. (Salesforce)

Measure: % sensitive fields tagged/classified, policy violations prevented, audit cycle time.

Mini runbook: a 90-day plan to prove ROI (without boiling the ocean)

Phase 1 (Weeks 1–2): Define the ROI thesis + baseline
  • Pick 2 use cases with measurable impact (e.g., churn prevention + service deflection)
  • Baseline: time-to-activate, data freshness, identity quality, governance coverage
Phase 2 (Weeks 3–6): Connect the minimum viable dataset footprint
  • Use Zero Copy integration for governed systems of record (Snowflake/Databricks/BigQuery/Redshift) where latency and compute are acceptable (Salesforce)
  • Ingest only what must be curated for performance, modeling, or regulatory needs
Informatica’s role: implement repeatable ingestion/federation patterns, data quality gates, and catalog visibility to prevent “one-off pipelines” from multiplying. (Informatica)

Phase 3 (Weeks 7–9): Harmonize identity into unified profiles
  • Implement identity resolution rules (matching + reconciliation) (Salesforce)
  • Validate with sampling to monitor false merges
  • Create your first unified profiles and one segment
Phase 4 (Weeks 10–13): Apply governance, activate, and measure
  • Apply policy-based governance via tagging/classification (Salesforce)
  • Launch one activation path (segment → workflow / segment → downstream destination)
  • Review KPIs weekly and refine identity + transformation logic
Mini runbook_ a 90-day plan to prove ROI (without boiling the ocean) - visual selection-1
The KPI set: 5 metrics that make ROI undeniable

  1. Time-to-activate: dataset → segment/flow live
  2. Data duplication avoided: TB avoided + pipelines retired
  3. Identity resolution quality: match rate + sampled false merge rate (Salesforce)
  4. Governance coverage: % sensitive fields tagged/classified + policy enforcement coverage (Salesforce)
  5. Outcome KPI: conversion lift / churn reduction / service cost reduction tied to the activated segment
Common pitfalls (and how to avoid them)

  • Starting with “all data” instead of “one measurable outcome.”
  • Celebrating match rate without tracking false merges. (Sampling is your friend.)
  • Treating data governance as documentation. Governance must be enforceable via policy controls. (Salesforce)
  • Ignoring metadata. Without business + technical metadata, audits and impact analysis become slow and expensive. (Microsoft Learn)
  • Not designing the “data sharing back out” path early. Value multiplies when segments and insights can be reused across platforms. (Salesforce)
Analyst insights (proof points that align with this approach)

  • Gartner: Gartner’s 2025 D&A predictions highlight AI agents augmenting/automating decisions and governance risks if data practices don’t keep up.
    What it means: governance and activation aren’t “nice to have”—they’re prerequisites for safe scale. (Gartner)
  • McKinsey: McKinsey’s State of AI notes many orgs still haven’t embedded AI deeply enough into workflows to realize material enterprise impact.
    What it means: the fastest returns come from operationalizing trusted data into repeatable processes (segments + workflows), not endless pilots. (McKinsey & Company)
  • IDC: IDC’s FutureScape framing emphasizes unstructured data, AI agents, and unified governance as AI adoption grows.
    What it means: structured + unstructured data strategies need a shared governance foundation. (IDC)
  • Forrester: Forrester highlights continued enterprise interest and maturation in CDPs, reinforcing that the “long game” is making unified customer data operational.
    What it means: success shifts from “owning a platform” to proving identity quality, governance, and activation outcomes. (Forrester)
If you want a 90-day ROI plan tailored to your environment—Salesforce Data 360 (data cloud) connected to Snowflake/Databricks/BigQuery across AWS/Azure—request a working session. We’ll help you baseline KPIs, choose Zero Copy vs ingestion, and design a governance-first activation path.

Request a demo/working session today: Click Here
Image of Blog Post by PDI Marketing Team

Blog Post by PDI Marketing Team

Pacific Data Integrators Offers Unique Data Solutions Leveraging AI/ML, Large Language Models (Open AI: GPT-4, Meta: Llama2, Databricks: Dolly), Cloud, Data Management and Analytics Technologies, Helping Leading Organizations Solve Their Critical Business Challenges, Drive Data Driven Insights, Improve Decision-Making, and Achieve Business Objectives.



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