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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

Blog, Salesforce CPQ, Salesforce Revenue Cloud

Salesforce CPQ for Medical Manufacturers

March 19, 2025 Ajay Achuthan

Medical equipment manufacturing sales is a tooth and nail business. What we often hear from our clients is the need to be flexible when it comes to pricing. That translates to the ability to support different pricing strategies for different buying groups. Recently, we delivered a transformative pricing project with a global medical equipment manufacturer. There were three key components to this transformation project: Agility Transparency Workforce enablement The overall challenge was simplifying the processes involved in delivering quotes to clients. The legacy process required coordination across multiple teams and systems. Getting a simple quote to the client required coordinations between three different teams using multiple systems. Our objective was to consolidate these disparate processes under one roof: Salesforce CPQ. The aim was to not just streamline operations but to empower Sales and Marketing with self-service pricing capabilities. Given that medical manufacturers operate in a highly regulated market, it can take years—from initial concept—to putting out a saleable product. Therefore, go-to-market pricing strategies are often kept simple using a combination of cost- and reference-based pricing. After the product is launched, the next big game is the ‘buying group’. The top three buying groups in the industry are group purchasing organizations (GPOs), aggregation groups (Agg Groups) and integrated delivery networks (IDNs—often also referred to as health systems). The size and volume of business these groups deal with often varies geographically. For instance GPOs are large, powerful groups in the US, but not so in Canada. Here is an example of how a hospital is aligned to an IDN, GPO and Agg Group: Salesforce’ CPQ offers a wide array of pricing-related functionalities out of the box. Examples include product and pricing rules, product bundling, and so on. At N28, the art and science of configuring Salesforce CPQ for simple quoting has been done and dusted many times over for various clients. The complexity in this instance was due to the intricate web of buying groups and their overlapping pricing structures. A single hospital was accessing pricing from the IDN it belonged to and also from all the GPOs and aggregation groups it was associated with. The N28 team implemented a tailored Salesforce CPQ system that enabled multiple tiers of pricing access for a single SKU. This includes: Tiered pricing for influential buying groups Bundled packages for value-based pricing, and… Support for distributor/reseller models. Here is a custom pricing access view component we developed to simplify the complexity of the underlying business model. N28 equips medical manufacturers with the tools to master the complexities of pricing strategies. Our expertise in Salesforce CPQ implementation ensures that clients can seamlessly adapt to the diverse demands of various buying groups while optimizing internal processes. The result? Enhanced efficiency and profitability in an ever-evolving and competitive market landscape. Get In Touch