manufacturing data

Salesforce Data Cloud Series Part 3: What the Informatica Acquisition Means for Enterprises That Count on Data Trust and Compliance

In regulated sectors like Medtech, Life Sciences, Manufacturing and IoT, innovation doesn’t begin with data—it begins with trusted data. These industries already invest heavily in ensuring data confidence, compliance, and actionable insights, all while navigating complex regulatory requirements. Salesforce’s $8 billion acquisition of Informatica becomes particularly significant within this landscape. More than just a tech merger, this move signals enhanced data governance for Salesforce, accelerating enterprise data trust and compliance across industry standard regulations like GDPR, HIPAA, and DSAR. In this final post of our Salesforce Data Cloud series, we explore what this acquisition could unlock for organizations managing complex, regulated data environments, and for the future of trusted, scalable enterprise data. In case you missed previous installments in this series, here’s Part 1: The Foundation for AI-Ready Data, where we covered the essential building blocks of Salesforce Data Cloud and Part 2: AI-Ready Data in Action, where we explored critical use cases for AI-ready data across Medtech, High-Tech & IoT, and Manufacturing. What Informatica Brings to This Acquisition: Scalable Trust and Intelligent Governance Informatica has long been recognized as a leader in enterprise-grade data management. According to Gartner’s 2024 Magic Quadrant for Data Integration Tools, Informatica was positioned as a Leader for the 19th consecutive year, ranked highest for its ability to execute and furthest for completeness of vision. Before we explore the potential significance of this acquisition, it’s worth looking at what Informatica already delivers. Known for its depth in data management, Informatica brings a mature set of capabilities that help enterprises across highly regulated industries govern, clean, and protect data at scale. Data Governance and Transparency Informatica provides tools for data cataloging, metadata management, and lineage tracking that show how data moves through the enterprise. This visibility supports audit readiness, simplifies compliance reporting, and improves oversight across increasingly complex data estates. Data Quality and Profiling With automated profiling, cleansing, and anomaly detection, Informatica ensures that data used in AI, analytics, and operations is consistent and reliable from the start. That means fewer delays, less manual cleanup, and stronger confidence in downstream decisions. Automated Privacy and Compliance Management Informatica streamlines regulatory compliance with built-in tools for data masking, anonymization, and consent tracking. It also supports DSAR (Data Subject Access Requests) compliance, required under privacy laws like GDPR and CCPA, helping teams respond quickly to individual data rights requests without tying up valuable resources. These enterprise-grade Master Data Management capabilities make Informatica a key enabler of data trust and governance in industries requiring accuracy, transparency, and compliance. Building on Salesforce’s Existing Strengths A Unified AI-Data Platform Salesforce already offers a robust suite of tools—Einstein AI, Data Cloud, MuleSoft, and Tableau—that help enterprises unify data, extract insights, and operationalize intelligence across the business. With Informatica, these capabilities are reinforced by deeper governance and quality controls that support more reliable, scalable data strategies. Data Cloud: Informatica’s Master Data Management (MDM) creates “golden records” by resolving duplicate profiles and standardizing key attributes. This enables a single, trusted view of customers and stakeholders—critical for personalization, reporting, and compliance. MuleSoft: With cleaner, governed data flowing through APIs, integrations are more dependable. This reduces failure points between systems and improves the consistency of data powering downstream workflows. Tableau: In addition to visualization, users can access metadata such as data lineage, quality scores, and compliance status. That context improves confidence in analytics and supports audit-readiness. Together, these capabilities strengthen Salesforce’s role as the backbone of enterprise data strategy—supporting more accurate reporting, faster automation, and smarter AI-driven outcomes. The Opportunity: Real-World Impact of Improved Trust & Governance The integration of Informatica’s capabilities into the Salesforce ecosystem could help enterprises build governed, high-confidence data workflows with greater scale and precision. Here’s a glimpse into what that could look like across regulated industries. MedTech & Life Sciences Clinical trials, patient records, EHR systems, and connected devices generate massive volumes of sensitive data, often trapped in disconnected systems. Informatica’s Master Data Management (MDM) creates unified “golden records” (consolidated, accurate customer or patient profiles) that resolve duplicates and enable cleaner, audit-ready datasets. Paired with Data Cloud’s real-time activation, organizations could spend 20% less time chasing orders, respond faster to care delivery needs, and automate HIPAA compliant workflows. The result: faster clinical decisions, fewer data risks, and greater patient trust. Manufacturing & IoT Siloed ERP data, supply chain systems, and production line sensors often lead to costly inefficiencies and blind spots. With Informatica’s data integration and quality layers feeding governed data into Salesforce and Einstein AI, predictive agents can proactively surface issues—whether it’s a delayed component delivery or a machinery failure. Early pilots show up to 30% efficiency gains in sales and production planning with significant cost savings from predictive maintenance powered by cleaner, more consistent data. Compliance at Scale From GDPR to HIPAA, compliance requires continuous and verifiable control over how sensitive data is accessed, processed, and stored. Informatica’s privacy tools automate consent tracking, data masking, and DSAR fulfillment, ensuring AI systems only act on data that meets privacy policies. For example, if consent is missing or inconsistent, the system can automatically pause related processes—minimizing compliance risks and reducing manual audit efforts. The combination of these platforms and strategic implementation could empower enterprises to move beyond data firefighting towards faster, more confident decisions on trusted, compliant data. CLAIRE + Agentforce: Context-Aware AI That Operates With Confidence Informatica’s CLAIRE engine brings deep, metadata-driven intelligence to enterprise data. When combined with Salesforce’s Agentforce platform, it powers a new generation of AI agents—ones that don’t just access data, but understand the context, rules, and relationships that govern it. Imagine asking, “Why is revenue different in Salesforce vs. Tableau?” Instead of raising a ticket for manual investigation, an AI agent powered by Claire GPT could trace the data lineage, flag the inconsistency, and suggest next steps with complete transparency. Or consider an AI agent detecting an anomaly in an IoT device, checking regulatory impact, verifying service policies, scheduling a technician, and notifying the customer. Every action is logged to meet compliance requirements, and decisions

Salesforce Data Cloud Series Part 2: AI-Ready Data in Action

No matter how advanced your AI models get, the true power of enterprise AI boils down to the quality and structure of data that fuels it. Without a clean, unified foundation, even the most ambitious AI strategies can fall short, leading to fragmented insights, unreliable automation, and limited scalability. In our previous post, Salesforce Data Cloud Series Part 1 — The Foundation for AI-Ready Data, we covered the various steps your enterprise data takes to become AI-ready—from ingestion and harmonization to identity resolution and real-time activation. But, what can this AI-ready data unlock for your enterprise? In Part 2 of this 3-part series, we move from architecture to impact—highlighting real-world use cases of Salesforce Data Cloud’s AI capabilities across Medtech, Manufacturing, High-Tech and IoT. Figure 1: Unified, comprehensive customer profiles are a core outcome of Salesforce Data Cloud’s Identity Resolution step, post data ingestion and harmonization. (Source: Salesforce.com) AI-Ready Data in MedTech – Enhancing Patient Outcomes and Device Management The Challenge In MedTech, enterprise data is everywhere—scattered across clinical trials, patient records, and connected devices. Without a unified view, your teams lack real-time insights, and are often left waiting or relying on guesswork to answer critical questions like: “How are our devices performing in the field?”, “Are we pricing effectively?”, “How can we accelerate deal cycles?”, and “Where are our biggest compliance gaps?” This data fragmentation undermines efforts to drive patient outcomes, operational efficiency, and regulatory compliance. Gaining a clean, unified view of your data is essential to ensure better device performance and tangible business impact. How Salesforce Data Cloud Unifies MedTech Data to Power Enterprise Outcomes Salesforce Data Cloud transforms MedTech’s fragmented data into actionable insights in real-time by: Ingesting data from clinical trials, patient records, and connected devices into a unified platform. Standardizing and cleaning data to resolve inconsistencies across systems. Resolving patient identities across scattered sources to create accurate, consolidated patient profiles. Segmenting data by geography, usage trends, provider, or clinical pathways to enable targeted actions. Leveraging AI models to identify patterns in device usage and patient response—helping teams anticipate maintenance, spot at-risk patients, and support care proactively. In practice, this means your teams have consistent, unified access to real-time insights that enable proactive device management and patient outcomes. What Enterprise Outcomes Can AI-Ready Data Drive for MedTech? AI-ready data delivers tangible, enterprise-level benefits, including: Better patient outcomes with personalized care and proactive device management that cut downtime and boost treatment effectiveness. Greater operational efficiency through automated insights, streamlined pricing, and reduced manual work and errors. Stronger regulatory confidence with accurate, harmonized data, full audit trails, and real-time quality monitoring. For example, fragmented data can cause MedTech teams to spend up to 20% of their time on administrative tasks like order tracking. By integrating disparate systems and leveraging AI-ready data, MedTech organizations can significantly reduce these inefficiencies—cutting operating costs by up to 30% and enabling teams to focus on strategic priorities. AI-ready data is critical for MedTech companies aiming to simplify operations, improve patient outcomes, and drive growth, while remaining compliant. Wondering what AI-ready data can do for your MedTech enterprise? Get in touch to explore our offerings. AI-Ready Data in Manufacturing – Maximizing Operational Efficiency and Forecast Accuracy The Challenge In manufacturing, enterprise data is scattered across ERP systems, production lines, inventory, and quality control. Without a unified view, your teams lack real-time visibility and are often forced to rely on manual workarounds or incomplete data to answer questions like: “Where are we running short or overproducing?”, “Are we catching downtime risks before they escalate?”, and “How reliable is our forecast?” This data fragmentation undermines your efforts to prevent downtime, optimize planning, and maintain consistent production quality. Gaining a clean, unified view of your data is essential to streamline operations, improve forecast accuracy, and deliver tangible business impact. How Salesforce Data Cloud Unifies Manufacturing Data to make it AI-Ready Salesforce Data Cloud transforms manufacturing’s fragmented data into actionable insights in real time by: Ingesting data from ERP systems, production lines, and quality control into a centralized platform—giving operations teams a single source of truth. Segregating data by manufacturing unit or region to allow local teams control while ensuring centralized visibility and governance. Powering real-time dashboards and automating alerts for inventory shortages or production anomalies—so teams can respond faster and more effectively. Leveraging advanced forecasting models to predict equipment failures and demand fluctuations—helping teams minimize downtime and optimize resource planning. In practice, this means your teams have consistent, unified access to real-time insights that keep production efficient, planning accurate, and operations resilient. What Enterprise Outcomes Can AI-Ready Data Drive for Manufacturing? AI-ready data delivers tangible, enterprise-level benefits, including: Greater operational efficiency through real-time visibility and automated alerts that reduce downtime and keep production flowing. Lower costs from proactive maintenance and accurate forecasting that minimize production waste and expensive equipment failures. Improved customer experience through reliable supply chains and production schedules that support consistent delivery. For example, fragmented data can cause inefficiencies in sales planning and forecasting—limiting visibility and delaying decision-making. By integrating disconnected systems and leveraging AI-ready data, manufacturing organizations can boost sales planning efficiency by up to 30% and improve forecast accuracy across the board. AI-ready data is essential for manufacturers looking to reduce downtime, lower costs through proactive maintenance, and improve forecast accuracy and customer experience. Wondering what AI-ready data can do for your Manufacturing enterprise? Get in touch to explore our offerings. AI-Ready Data in High-Tech & IoT – Driving Real-Time Insights and Scalable Innovation The Challenge In High-Tech and IoT, enterprise data flows in from everywhere—devices, sensors, apps, and user interactions—often scattered across disconnected systems. Without a unified view, your teams lack real-time visibility and are left relying on fragmented signals to answer key questions like: “Which features are actually being used—and which aren’t?”, “Where and when are our devices at risk of failure?”, “How can we tailor support based on individual user behavior?” This data fragmentation slows innovation, limits personalization, and makes it harder for your teams to respond to users