Jignesh Rathod - N28tech

AI, Blog, Enterprise Data News, Informatica Acquisition, Salesforce Data Cloud

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

August 7, 2025 Jignesh Rathod

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

AI, Blog, Enterprise Data Management, Salesforce Data Cloud

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

June 25, 2025 Jignesh Rathod

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

Salesforce Data Cloud Series Part 1: The Foundation of AI-Ready Data Header Image

AI, Blog, Salesforce Data Cloud

Salesforce Data Cloud Series Part 1: The Foundation for AI-Ready Data

June 11, 2025 Jignesh Rathod

Imagine trying to run a high-performance car on low-quality fuel—no matter how advanced your engine, it won’t perform at its best. The same is true for AI. It’s only as powerful as the data you build it on. And the reality is stark. According to Gartner Research, only 4% of organizations report their data is prepared for AI. This gap highlights that while companies are eager to deploy AI, their enterprise data often isn’t prepared to fuel it. Fragmented across CRMs, ERPs, and third-party tools, these data silos pose a critical challenge for enterprise data management, often delaying your data-to-AI pipeline and limiting strategic decisions. Fortunately, Salesforce Data Cloud is designed to solve exactly that by unifying, cleaning, and activating your data across sources, unlocking its full potential across your enterprise. In this Part 1 of a 3-part series, we begin with the enterprise data architecture—the building blocks that power your Salesforce Data Cloud. To truly make sense of the outcomes it can unlock for your enterprise, you’ll first need to understand the foundations it’s built on. The Journey from Raw Data to AI-Ready Insights From initial capture to real-time insights, enterprise data undergoes a rigorous journey to become reliable and ready to drive strategic outcomes. That journey begins with Ingestion. Salesforce Data Cloud’s architectural journey: from raw data ingestion and harmonization through unification and insight generation, preparing data for AI applications. (Source: Salesforce Developer Documentation) 1. Ingestion: Capturing Data in its Raw Form Whether it’s coming from your Sales Cloud, ERP, IoT devices, or external files, every piece of data passes through a Data Source Object (DSO). The DSO is like a diligent front desk, logging each entry exactly as it arrives. It preserves every detail before any processing or transformation begins, ensuring nothing gets lost or distorted in this first step. Once ingested, your data then moves into Data Lakes Objects (DLOs) —the structured back office. DLOs store enterprise data in efficient, industry-standard formats, making it easy to query, analyze, and enrich. After this initial structuring, your data is now ready for the next crucial step —Data Harmonization. 2. Data Harmonization: Making Data Speak the Same Language Similar information within your data might be stored or labeled differently depending on the systems they originate from. A customer field, for example, could be labeled a “Contact” in your CRM, a “Patient” in healthcare records, or a “Guest” within a booking platform. This critical step of standardizing and unifying disparate data labels and formats is called Data Harmonization. This is where your data enters Data Model Objects (DMOs). They act like a translation office for your data, unifying different labels by mapping them to a single, common object —such as “Individual” in the above example. This ensures each entity is treated as one cohesive profile no matter the data source it came from. Beyond translation, DMOs also apply standardization rules. They align date formats, normalize units like weight and accuracy, and structure names and fields consistently. This is a crucial step in ensuring your teams and AI models speak a common language, and work from a shared playbook of truth. 3. Identity Resolution: One Truth for Every Customer The third phase, Identity resolution, is where Salesforce Data Cloud’s capabilities truly come to the forefront. This is the stage where records that represent the same person, like “John Smith” in your CRM, “J. Smith” in your email list, and “Johnny S.” are recognized and merged into a singular, accurate profile. It deploys several matching techniques to achieve this: Fuzzy Matching finds close matches that aren’t exact (e.g., “Jon Smith” vs. “John Smith”) Exact Matching finds matching through precise identifiers (e.g., the same customer ID) Normalized Matching finds matches after standardization (e.g., phone numbers ‘(123)-456-7890’ and ‘123-456-7890’ would standardize to ‘1234567890’, detecting that these belong to the same person despite different formatting rules). Here’s a real-world example: A hospital sees a patient in both its CRM and EHR data, with minor differences in their name or contact information. Identity resolution assigns rules like “most recent update” to select the latest record as the most accurate, while “source priority” rules choose data from more reliable systems to create a singular, trusted profile for every user. 4. Dynamic Segmentation: Turning Data into Meaningful Groups Dynamic Segmentation in action: Leveraging a unified view of customer data (as seen in the dashboard) to target specific segments for tailored outreach and enhanced personalization. With this unified data at your fingertips, you now have a complete, accurate view of every customer, partner, or asset across your organization. You could group individuals or entities by their behaviors, demographics, or any attribute that matters to your business. This process, known as dynamic segmentation, allows you to tailor your outreach, spot emerging trends, and target specific customer needs with precision. For example, a manufacturer might segment customers by industry, purchase history, or service needs, enabling sales teams to craft personalized campaigns that resonate with specific customer pain-points and challenges. Or, a healthcare provider could group patients by risk factors, ensuring timely interventions and better care. The possibilities are as diverse as your data—and with every segment, you unlock new opportunities to engage, support, and build lasting customer relationships. 5. Data Actions: Automating Insights into Action Segmentation is just the beginning. The real magic of your Salesforce Data Cloud starts to unfold when your unified data starts to drive action automatically through Data Actions. This feature enables you to set up sophisticated cloud workflow automations that respond to real-time changes within your data. For instance, if a high-value customer shows signs of inactivity, a Data Action can trigger precise Salesforce automation, like a re-engagement campaign or alert a sales rep to reach out to the customer. Similarly, a sudden spike in support requests from a particular customer segment could escalate the issue to your service team automatically. This level of automation turns insights into action, so your teams are always a step ahead. Data Actions make your data work for you—reducing