Data Access

11Oct

Transforming Business with Agentforce Agents

Agents are autonomous software systems that assist users by reasoning, planning, and taking actions based on user input or environmental conditions. These digital assistants combine expert knowledge with access to relevant data to achieve specific tasks or goals, resembling intelligent digital assistants in their function and scope. Agents are poised to become integral to everyday life, reshaping how businesses operate and engage with customers. For instance, a service agent can act as a company’s most knowledgeable support representative, available around the clock. A marketing agent, similar to a self-driving car, can respond to real-time business conditions by proactively adjusting pricing or launching campaigns. This article explores the AI advancements driving the rise of agents and how these systems are transforming businesses while revolutionizing software development. Powered by Large Language Models (LLMs) Agents are powered by large language models (LLMs), which provide two key capabilities: However, LLMs alone have limitations, including: A New Software Paradigm Agents bridge the gap between the language capabilities of LLMs and the practical needs of businesses, introducing a new approach to software. Instead of building full-fledged applications, companies can develop granular building blocks of functionality, referred to as actions, which can be orchestrated by agents. In Salesforce, these actions (such as “Locate Order” or “Change Order Address”) are grouped under functional areas known as topics (e.g., “Order Management”). An agent operates by: The Ultimate Application Composition Platform This new paradigm allows agents to handle requests that weren’t anticipated in advance, solving problems on the fly by composing actions in novel ways. For example, Salesforce’s Agentforce agents can access a wide range of actions across Sales Cloud, Service Cloud, Marketing Cloud, Commerce Cloud, and more. Developers can further extend the agents’ capabilities with custom actions built via code, APIs, or prompt templates. Agents gain essential capabilities through actions, including: Levels of Autonomy Agents can operate at varying levels of autonomy: Regardless of their autonomy, establishing guardrails is essential for maintaining reliability, security, and business practices. Agentforce uses a multi-layered approach to enforce these guardrails, including the Einstein Trust Layer, which secures LLM interactions, and Agentforce Analytics, which provides insights into performance and reliability. Ready-to-Use Agents for Sales and Service Salesforce has introduced prebuilt agents, including: Creating and Customizing Agents with Agentforce Salesforce’s Agentforce empowers users to create, customize, and deploy agents that combine AI, data, and actions. Key components of this platform include: Conclusion: Transforming Business with Agentforce Agents Agents are becoming pervasive, changing how businesses operate by enabling systems to reason, take action, and provide personalized experiences. Agentforce Agents lead this transformation by being: As businesses evolve, Agentforce Agents are at the forefront of creating a more efficient, intelligent future. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

October 11, 2024in Data, Enterprise, Salesforce

10Oct

Managing Salesforce Roles and Profiles

How to Set Up Salesforce Roles Salesforce is a powerful platform used by many organizations to streamline operations across departments, from sales and marketing to customer support. With so many users working within the platform, it’s essential to manage access to data securely. Salesforce achieves this through its system of roles and profiles, which define who can see and do what in the system. Salesforce Roles vs. Profiles Roles determine what data users can view. They establish a hierarchy of access, with higher roles having visibility into records owned by those below them. This helps ensure that only authorized users can access sensitive information. Profiles, on the other hand, define what actions users can take within Salesforce, such as creating or editing records. Profiles are assigned when user accounts are created and can be customized to suit specific job functions. Role Hierarchy in Salesforce The role hierarchy in Salesforce is designed to control data access, not mirror the organizational chart. Higher roles can generally view data from roles below them, but access can be restricted based on business needs. Types of Salesforce Profiles Salesforce comes with several standard profiles, and administrators can create custom profiles as needed. Some key profiles include: In addition to profiles, permission sets allow admins to grant users specific permissions without changing their profile. Managing Salesforce Roles and Profiles Setting up Salesforce roles and profiles is crucial for maintaining data security and ensuring users have the correct access for their responsibilities. By efficiently configuring roles and profiles, businesses can tailor the platform to their needs, enhancing both user experience and operational security. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

October 10, 2024in Salesforce

07Oct

Coveo and Salesforce Data Cloud

Coveo Integrates with Salesforce Data Cloud for Enhanced Data Access Coveo has officially integrated with Salesforce Data Cloud, providing companies with improved access to a broad range of data across the Salesforce ecosystem. This integration enhances the capabilities of Salesforce’s Service Cloud, Commerce Cloud, and Experience Cloud by offering secure access to a wide array of enterprise data sources and engagement data from Coveo-powered search experiences. By leveraging Coveo’s advanced indexing and security features, the integration enables enterprises to seamlessly inject external content into Salesforce experiences through Data Cloud. It also delivers comprehensive analytics across both platforms, allowing businesses to track interactions and touchpoints more effectively. “Coveo has worked closely with Salesforce on a global scale for over a decade,” said Laurent Simoneau, president and CTO of Coveo. “The integration of Coveo with Salesforce creates a powerful synergy, particularly beneficial for large enterprises with complex content ecosystems. Coveo’s robust indexing and relevance capabilities, combined with Salesforce Data Cloud, provide secure access to external data sources, boosting performance and delivering added value across all Salesforce Clouds. This integration sets a new standard for unifying enterprise data and elevating the customer experience.” Kishan Chetan, executive vice president and general manager of Service Cloud at Salesforce, emphasized the impact on service interactions: “Providing precise answers during service interactions is crucial for reducing service costs and improving customer satisfaction, especially for large enterprises managing vast amounts of content and diverse customer bases. Coveo’s ability to connect to extensive content sources and enhance relevance is a key enabler for enterprises with complex search needs in Service Cloud.” Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

October 7, 2024in Salesforce Data Cloud

03Oct

Supercharged Sales and Service Excellence

When sales and service teams are seamlessly aligned, organizations can transform every service interaction into a powerful opportunity to drive revenue and build customer loyalty. By delivering contextual, personalized offers at the right time, businesses can turn each customer touchpoint into a chance to solve problems, create value, and enhance engagement. With the help of real-time data and AI, we move beyond treating customer interactions as isolated events. Instead, we focus on understanding the unique needs and preferences of each customer, whether they’re reaching out for support, visiting a store, or engaging online. Every touchpoint becomes an opportunity to deliver tailored solutions, strengthen loyalty, and increase revenue. These examples are just a glimpse of the solutions we help our clients deploy. With our deep expertise in AI implementation and industry-specific insights, Tectonic is ideally positioned to guide you through activating AI agents that build on your successes. Our extensive experience across diverse sectors enables us to identify the most impactful opportunities for AI, ensuring tailored solutions that align with your business strategy. Tectonic’s proven approach ensures AI agents are deployed efficiently and effectively, driving ROI and delivering measurable results. Agentforce: Open, Extensible, and Transformative Agentforce is designed to foster smooth interactions between agents created on the platform and external agents and applications. With integration into Salesforce Data Cloud, it enables real-time data access for better, faster decision-making. Tectonic can help facilitate this interconnectivity across various channels and platforms, ensuring seamless operations. In a society that prioritizes digital, artificial intelligence is changing sales and service tactics. Adopting AI agents is crucial for business leaders to keep ahead of the competition and develop more effective and efficient methods of customer and employee support. AI agents are not just upgrades—they are transformative forces that redefine what’s possible. They empower organizations to move from envisioning possibilities to realizing a future of enhanced sales performance, streamlined operations, and unmatched customer satisfaction, all powered by AI. Salesforce’s Agentforce: Scaling Your Workforce with Autonomous AI Agents Agentforce provides fully customizable autonomous AI agents that can integrate with enterprise data and take action across sales, service, marketing, and commerce. The Agent Builder allows users to create agents that automate any business process, in any industry, using existing Salesforce tools such as workflows, Apex code, and prompt templates. Leading companies like The Adecco Group, BACA Systems, OpenTable, Saks, and Wiley are already leveraging Agentforce Service Agents to deliver faster, more personalized customer responses while improving operational efficiency. Salesforce (NYSE: CRM), the world’s #1 AI CRM, has announced the general availability of Agentforce. This new platform layer enables businesses to create and deploy AI agents that autonomously take action across any business function. Unlike traditional chatbots and copilots, Agentforce agents possess advanced reasoning abilities to make decisions and take actions such as resolving customer issues, qualifying sales leads, and optimizing marketing efforts. These agents operate independently—triggered by changes in data, business rules, pre-built automations, or external signals from APIs. Agentforce includes out-of-the-box agents that can be easily customized and deployed with low-code or no-code tools, working around the clock across any channel. The first of these, the Agentforce Service Agent, outperforms traditional chatbots by handling a wide range of tasks—from simple to complex—using pre-built topics and actions for customer support. These agents are fully customizable to serve various industries, such as retail with order management or financial services with billing and payment support. “Agentforce is redefining business possibilities, ushering in an era of AI-driven abundance and limitless workforces that enhance every employee’s capabilities, strengthen customer relationships, and fuel unprecedented growth,” said Marc Benioff, Chair and CEO of Salesforce. “Built on Salesforce’s trusted, fully customizable platform—integrating enterprise data, metadata, AI models, workflows, security, and applications—Agentforce is everything AI was meant to be.” The Power of Agentforce With Agentforce, there’s no need for DIY AI. Unlike other platforms that require complex integrations and custom builds, Agentforce is natively built into the Salesforce Platform. Customers can instantly convert their existing Flows, prompt templates, Apex code, and APIs into agent actions, seamlessly connecting to enterprise data and security models. With the added power of native tools like Data Cloud, Slack, and MuleSoft, Agentforce offers a streamlined and efficient approach to scaling your workforce with AI. The new Agent Builder empowers Salesforce admins and developers to easily create AI agents with natural language instructions and guardrails, enabling even more customization and control. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

October 3, 2024in Agentforce Platform, Data, Enterprise, Mulesoft, Salesforce, Salesforce Data Cloud

01Oct

Data Governance in Salesforce

Use Case Highlights Introduction Salesforce is a powerful platform for building strong customer relationships. However, to maximize the value of your data, a strategic approach is essential. Unmanaged data can lead to inconsistencies, security vulnerabilities, and compliance issues, making it difficult to rely on Salesforce data for strategic decision-making. This data governance insight looks into data governance for Salesforce and highlights top tools that integrate seamlessly with the platform. While these tools are not exclusively built for Salesforce, they offer powerful features that enhance the accuracy, security, and regulatory compliance of your data. Implementing a data governance strategy with the right tools unlocks the hidden potential of your Salesforce data. Imagine making decisions based on reliable information, streamlining workflows, and maximizing data value—all possible with clean, trustworthy data at your fingertips. Disclaimer: The specific third-party security solutions mentioned in this insight are for informational purposes only and do not constitute endorsements by Tectonic. 6 Data Governance Tools for Salesforce Users Understanding the Importance of Data Governance in Salesforce Salesforce, a cornerstone for managing customer relationships, thrives on a rich data repository. However, unmanaged data growth can lead to inconsistencies, security vulnerabilities, and compliance hurdles, hindering the data’s potential to drive strategic decisions. Data governance goes beyond data management. It establishes principles and practices to ensure your Salesforce data is: Why is data governance crucial for Salesforce? Data governance transforms data from a potential liability into a strategic asset, driving informed decisions, fostering trust, and empowering your organization to achieve its goals. Key Challenges in Salesforce Data Governance Implementing robust data governance within Salesforce presents challenges: Evaluating Data Governance Tools for Salesforce Choosing the right data governance tool for Salesforce requires careful evaluation: Comparing Top Data Governance Solutions for Salesforce When comparing top data governance solutions, assess factors like features, pricing, scalability, and customer reviews. A comparison table can offer a quick overview of key features and differences between solutions. Product Key Features Pricing Collibra Centralized platform, business glossary management, data lineage tracking, access controls Starting at $170,000/year Dataedo Collaborative data catalog, data documentation, quality rules, improved data literacy Starting at $18,000/year Alation Data discovery and intelligence platform, searchable data catalog, business terms & data lineage Starting at $60,000/year OneTrust Data Governance AI-driven discovery & classification, integrated catalog, governance policy management Contact for pricing Talend Data Fabric Data quality checks, automated metadata management, data inventory & Trust Score, stewardship workflows Starting at $12,000/year Informatica Axon AI-powered discovery & quality assessment, stewardship communication, curated marketplaces, common dictionary Contact for pricing Data Governance in Salesforce This table provides a snapshot of key features and pricing, helping organizations make informed decisions based on their needs and budget. Reach out to Tectonic if you need assistance. Tectonic’s Remarks This insight has provided the knowledge to harness the true potential of your Salesforce data. Data governance is no longer a distant concept but a powerful practice that every organization must embrace. Success lies in selecting the right data governance solution. Consider your specific needs—whether it’s robust data lineage tracking offered by tools like Collibra and Talend, user-friendly interfaces from Dataedo and Alation, or cost-effectiveness. Several tools explored in this insight also offer strong security features to safeguard your customer data. Tools like Informatica Axon can assist with data discovery and quality assessment, empowering data stewards. By embracing data governance and leveraging the right tools, you can transform Salesforce data from a raw resource into a strategic asset. This newfound data clarity fuels informed decision-making, fosters customer trust, and drives your organization toward achieving its goals. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

October 1, 2024in Salesforce

28Sep

Salesforce Data Cloud and Zero Copy

As organizations across industries gather increasing amounts of data from diverse sources, they face the challenge of making that data actionable and deriving real-time insights. With Salesforce Data Cloud and zero copy architecture, organizations can streamline access to data and build dynamic, real-time dashboards that drive value while embedding contextual insights into everyday workflows. A session during Dreamforce 2024 with Joanna McNurlen, Principal Solution Engineer for Data Cloud at Salesforce, discussed how zero copy architecture facilitates the creation of dashboards and workflows that provide near-instant insights, enabling quick decision-making to enhance operational efficiency and competitive advantage. What is zero copy architecture?Traditionally, organizations had to replicate data from one system to another, such as copying CRM data into a data warehouse for analysis. This approach introduces latency, increases storage costs, and often results in inconsistencies between systems. Zero copy architecture eliminates the need for replication and provides a single source of truth for your data. It allows different systems to access data in its original location without duplication across platforms. Instead of using traditional extract, transform, and load (ETL) processes, systems like Salesforce Data Cloud can connect directly with external databases, such as Google Cloud BigQuery, Snowflake, Databricks, or Amazon Redshift, for real-time data access. Zero copy can also facilitate data sharing from within Salesforce to other systems. As Salesforce expands its zero copy partner network, opportunities to easily connect data from various sources will continue to grow. How does zero copy work?Zero copy employs virtual tables that act as blueprints for the data structure, enabling queries to be executed as if the data were local. Changes made in the data warehouse are instantly visible across all connected systems, ensuring users always work with the latest information. While developing dashboards, users can connect directly to the zero copy objects within Data Cloud to create visualizations and reports on top of them. Why is zero copy beneficial?Zero copy allows organizations to analyze data as it is generated, enabling faster responses, smarter decision-making, and enhanced customer experiences. This architecture reduces reliance on data transformation workflows and synchronizations within both Tableau and CRM Analytics, where organizations have historically encountered bottlenecks due to runtimes and platform limits. Various teams can benefit from the following capabilities: Unlocking real-time insights in Salesforce using zero copy architectureZero copy architecture and real-time data are transforming how organizations operate. By eliminating data duplication and providing real-time insights, the use of zero copy in Salesforce Data Cloud empowers organizations to work more efficiently, make informed decisions, and enhance customer experiences. Now is the perfect time to explore how Salesforce Data Cloud and zero copy can elevate your operations. Tectonic, a trusted Salesforce partner, can help you unlock the potential of your data and create new opportunities with the Salesforce platform. Connect with us today to get started. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

September 28, 2024in Data, Google, Salesforce, Salesforce Data Cloud, Snowflake

28Sep

Data Data Data

With the unprecedented surge in data—expected to grow by 22% this year according to the Salesforce State of Data and Analytics 2023 report—and the rapid evolution of AI, maintaining data quality and security has become more critical than ever. The AI and Information Management 2024 Report highlights that 52% of organizations encountered data quality issues during AI implementation, while 71% expressed serious concerns about data privacy and security in AI deployments. The cornerstone of success in any data-driven initiative is strong data governance, ensuring data quality, trust, and privacy. The report also emphasized that effective data governance maximizes the value of data assets. Beyond compliance and quality control, it democratizes data access across an organization, making it essential for long-term success. This insight explores common challenges undermining Salesforce data quality and outlines how robust data governance can lead to high-quality, reliable data. Challenges to Maintaining Quality Salesforce Data Challenge 1: Data Silos Create Fragmented Customer Information Use Case: In the Consumer Packaged Goods (CPG) sector, nearly half of companies struggle to collect and integrate the massive amount of data required for AI adoption. Data fragmentation across supply chains, product categories, and SKUs creates silos, leading to inconsistent customer information. For instance, sales and marketing teams might work with different data sets, leading to disjointed customer interactions and ineffective campaigns. This results in missed sales opportunities, inefficient resource use, and poor customer experiences, ultimately weakening strategic decision-making and competitiveness. Solution: Implementing an information lifecycle management system can address data fragmentation. It provides a unified view for managing content across repositories, helping companies maintain a holistic customer view. Challenge 2: Poor Data Quality Erodes Trust Use Case: The Office of the National Coordinator for Health Information Technology found that at least 7 out of 100 medical records are mismatched or duplicated. A healthcare organization using Salesforce without proper data governance may face incomplete or inaccurate patient information, resulting in incorrect treatments and inefficient care, which damages patient trust and increases legal risks. Solution: You leverages AI to eliminate duplicate records, ensuring clean and accurate data. It classifies content based on metadata and assigns governance policies to prevent duplication, ensuring high-quality data across systems. Additionally, Backup for Salesforce tools detect suspicious anomalies or potential ransomware activity, proactively preserving data integrity. Challenge 3: Data Growth Increases Cyber Risks Use Case: As 65% of e-commerce organizations aim to become fully data-driven by 2026, increasing data volumes introduce greater risks. Without robust protection strategies, companies face potential data loss and operational disruptions. Solution: Tectonic’s solution offers automatic daily backups and on-demand restores, safeguarding data integrity and minimizing downtime in the event of cyberattacks or data breaches. Building a Strong Data Governance Foundation To overcome these challenges, organizations must adopt a comprehensive approach to data governance. Here are seven essential steps to establish effective Salesforce data governance: Champion Salesforce Data Governance with Tectonic Effective Salesforce data governance requires collaboration across all levels of the organization. With over two decades of experience, Tectonic empowers organizations to enhance their digital operations, mitigate risks, and achieve compliance in a dynamic data landscape. By establishing a strong data governance framework, your organization can confidently navigate the challenges of data growth, security, and quality. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

September 28, 2024in Salesforce

27Sep

Salesforce Einstein Copilot Security

Salesforce Einstein Copilot Security: How It Works and Key Risks to Mitigate for a Safe Rollout With the official rollout of Salesforce Einstein Copilot, this conversational AI assistant is set to transform how sales, marketing, and customer service teams interact with both customers and internal documentation. Einstein Copilot understands natural language queries, streamlining daily tasks such as answering questions, generating insights, and performing actions across Salesforce to boost productivity. Salesforce Einstein Copilot Security However, alongside the productivity gains, it’s essential to address potential risks and ensure a secure implementation. This Tectonic insight covers: Einstein Copilot Use Cases Einstein Copilot enables users to: All of these actions can be performed with simple, natural language prompts, improving efficiency and outcomes. How Einstein Copilot Works Here’s a simplified breakdown of how Einstein Copilot processes prompts: The Einstein Trust Layer Salesforce has built the Einstein Trust Layer to ensure customer data is secure. Customer data processed by Einstein Copilot is encrypted, and no data is retained on the backend. Sensitive data, such as PII (Personally Identifiable Information), PCI (Payment Card Information), and PHI (Protected Health Information), is masked to ensure privacy. Additionally, the Trust Layer reduces biased, toxic, and unethical outputs by leveraging toxic language detection. Importantly, Salesforce guarantees that customer data will not be used to train the AI models behind Einstein Copilot or be shared with third parties. The Shared Responsibility Model Salesforce’s security approach is based on a shared responsibility model: This collaborative model ensures a higher level of security and trust between Salesforce and its customers. Best Practices for Securing Einstein Copilot Rollout Prepare Your Salesforce Org for Einstein Copilot To ensure a smooth rollout, it’s critical to assess your Salesforce security posture and ready your data. Tools like Salesforce Shield can help organizations by: By following these steps, you can utilize the power of Einstein Copilot while ensuring the security and integrity of your data. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

September 27, 2024in Data, Enterprise, Salesforce, Salesforce Data Cloud, Salesforce Einstein

22Sep

How to Achieve AI Democratization

AI democratization empowers non-experts by placing AI tools in the hands of everyday users, enabling them to harness the technology’s potential without requiring specialized technical skills. Today, IT leaders are increasingly focused on expanding AI’s benefits across the enterprise. The growing number of AI-based tools is making this more achievable. In some respects, democratization extends the concept of low- and no-code development—allowing non-developers to create software—into the realm of AI. However, it’s also about ensuring data is accessible and fostering data literacy throughout the organization. This doesn’t mean every employee needs to write machine learning scripts. Instead, it means business professionals should understand AI’s potential, identify relevant use cases, and apply insights to drive business outcomes. Achieving AI democratization is feasible, thanks to decentralized governance models and the emergence of AI-focused services. However, as with any new technology, democratization brings both benefits and challenges. How to Achieve AI Democratization AI is no longer reserved for experts. Tools like Google Colab and Microsoft’s Azure OpenAI Service have simplified AI development, enabling more employees to participate by writing and sharing code for various projects. To maximize the impact, enterprises must train business users on the basics of AI and how it can enhance their daily work. According to Arpit Mehra, Practice Director at Everest Group, decentralized governance models can help organizations build strategies for data and technology learning. Key strategies include: Arun Chandrasekaran, VP and Analyst at Gartner, also advises companies to focus on intelligent applications in areas such as customer engagement and talent acquisition, which can provide specialized training. Benefits and Challenges of AI Democratization AI democratization can significantly expand an organization’s capabilities. By placing AI in the hands of more employees, businesses reduce barriers to adoption, cut costs, and create highly accurate AI models. “Making AI more accessible broadens the scope of what businesses can achieve,” said Michael Shehab, PwC U.S. Technology and Innovation Leader. AI democratization also helps companies address IT talent shortages by upskilling employees and enabling them to integrate AI into their workflows. This approach improves productivity, allowing businesses to more easily spot trends and patterns within large data sets. However, challenges also arise. If AI is implemented without proper oversight, the technology is susceptible to bias. Poor training could lead to decision-making based on inaccurate or skewed data. Business leaders must ensure they understand who is using AI tools and establish standards for responsible use. Without careful testing, AI applications can automate mistakes that go unnoticed but may cause significant issues. Ed Murphy, SVP and Head of Data Science at 1010data, emphasizes the importance of testing to prevent these errors. To mitigate risks, organizations should invest in upskilling and reskilling employees. A well-defined training plan will enable nontechnical teams to participate in AI adoption and deployment effectively. Mehra from Everest Group also suggests exploring MLOps technologies to simplify AI development and streamline processes. Ultimately, AI democratization will benefit businesses that recognize AI’s potential beyond a small group of experts. While the benefits are clear, organizations must remain vigilant about the risks to ensure successful AI integration and reap the rewards of their efforts. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

September 22, 2024in Salesforce

22Sep

Tableau Einstein is Here

Tableau Einstein marks a new chapter for Tableau, transforming the analytics experience by moving beyond traditional reports and dashboards to deliver insights directly within the flow of a user’s work. This new AI-powered analytics platform blends existing Tableau and Salesforce capabilities with innovative features designed to revolutionize how users engage with data. The platform is built around four key areas: autonomous insight delivery through AI, AI-assisted development of a semantic layer, real-time data access, and a marketplace for data and AI products, allowing customers to personalize their Tableau experience. Some features, like Tableau Pulse and Tableau Agent, which provide autonomous insights, are already available. Additional tools, such as Tableau Semantics and a marketplace for AI products, are expected to launch in 2025. Access to Tableau Einstein is provided through a Tableau+ subscription, though pricing details remain private. Since being acquired by Salesforce in 2019, Tableau has shifted its focus toward AI, following the trend of many analytics vendors. In February, Tableau introduced Tableau Pulse, a generative AI-powered tool that delivers insights in natural language. In July, it also rolled out Tableau Agent, an AI assistant to help users prepare and analyze data. With AI at its core, Tableau Einstein reflects deeper integration between Tableau and Salesforce. David Menninger, an analyst at Ventana Research, commented that these new capabilities represent a meaningful step toward true integration between the two platforms. Donald Farmer, founder of TreeHive Strategy, agrees, highlighting that while the robustness of Tableau Einstein’s AI capabilities compared to its competitors remains to be seen, the platform offers more than just incremental add-ons. “It’s an impressive release,” he remarked. A Paradigm Shift in Analytics A significant aspect of Tableau Einstein is its agentic nature, where AI-powered agents deliver insights autonomously, without user prompts. Traditionally, users queried data and analyzed reports to derive insights. Tableau Einstein changes this model by proactively providing insights within the workflow, eliminating the need for users to formulate specific queries. The concept of autonomous insights, represented by tools like Tableau Pulse and Agentforce for Tableau, allows businesses to build autonomous agents that deliver actionable data. This aligns with the broader trend in analytics, where the market is shifting toward agentic AI and away from dashboard reliance. Menninger noted, “The market is moving toward agentic AI and analytics, where agents, not dashboards, drive decisions. Agents can act on data rather than waiting for users to interpret it.” Farmer echoed this sentiment, stating that the integration of AI within Tableau is intuitive and seamless, offering a significantly improved analytics experience. He specifically pointed out Tableau Pulse’s elegant design and the integration of Agentforce AI, which feels deeply integrated rather than a superficial add-on. Core Features and Capabilities One of the most anticipated features of Tableau Einstein is Tableau Semantics, a semantic layer designed to enhance AI models by enabling organizations to define and structure their data consistently. Expected to be generally available by February 2025, Tableau Semantics will allow enterprises to manage metrics, data dimensions, and relationships across datasets with the help of AI. Pre-built metrics for Salesforce data will also be available, along with AI-driven tools to simplify semantic layer management. Tableau is not the first to offer a semantic layer—vendors like MicroStrategy and Looker have similar features—but the infusion of AI sets Tableau’s approach apart. According to Tableau’s chief product officer, Southard Jones, AI makes Tableau’s semantic layer more agile and user-friendly compared to older, labor-intensive systems. Real-time data integration is another key component of Tableau Einstein, made possible through Salesforce’s Data Cloud. This integration enables Tableau users to securely access and combine structured and unstructured data from hundreds of sources without manual intervention. Unstructured data, such as text and images, is critical for comprehensive AI training, and Data Cloud allows enterprises to use it alongside structured data efficiently. Additionally, Tableau Einstein will feature a marketplace launching in mid-2025, which will allow users to build a composable infrastructure. Through APIs, users will be able to personalize their Tableau environment, share AI assets, and collaborate across departments more effectively. Looking Forward As Tableau continues to build on its AI-driven platform, Menninger and Farmer agree that the vendor’s move toward agentic AI is a smart evolution. While Tableau’s current capabilities are competitive, Menninger noted that the platform doesn’t necessarily set Tableau apart from competitors like Qlik, MicroStrategy, or Microsoft Fabric. However, the tight integration with Salesforce and the focus on agentic AI may provide Tableau with a short-term advantage in the fast-changing analytics landscape. Farmer added that Tableau Einstein’s autonomous insight generation feels like a significant leap forward for the platform. “Tableau has done great work in creating an agentic experience that feels, for the first time, like the real deal,” he said. Looking ahead, Tableau’s roadmap includes a continued focus on agentic AI, with the goal of providing each user with their own personal analyst. “It’s not just about productivity,” said Jones. “It’s about changing the value of what can be delivered.” Menninger concluded that Tableau’s shift away from dashboards is a reflection of where business intelligence is headed. “Dashboards, like data warehouses, don’t solve problems on their own. What matters is what you do with the information,” he said. “Tableau’s push toward agentic analytics and collaborative decision-making is the right move for its users and the market as a whole.” Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

September 22, 2024in Data, Enterprise, Generative AI, Salesforce

21Sep

Embedded Salesforce Einstein

In a world where data is everything, businesses are constantly seeking ways to better understand their customers, streamline operations, and make smarter decisions. Enter Salesforce Einstein—a powerful AI solution embedded within the Salesforce platform that is revolutionizing how companies operate, regardless of size. By leveraging advanced analytics, automation, and machine learning, Einstein helps businesses boost efficiency, drive innovation, and deliver exceptional customer experiences. Embedded Salesforce Einstein is the answer. Here’s how Salesforce Einstein is transforming business: Imagine anticipating customer needs, market trends, or operational challenges before they happen. While it’s not magic, Salesforce Einstein’s AI-powered insights and predictions come remarkably close. By transforming vast amounts of data into actionable insights, Einstein enables businesses to anticipate future scenarios and make well-informed decisions. Industry insight: In financial services, success hinges on anticipating market shifts and client needs. Banks and investment firms leverage Einstein to analyze historical market data and client behavior, predicting which financial products will resonate next. For example, investment advisors might receive AI-driven recommendations tailored to individual clients, boosting engagement and satisfaction. Manufacturers also benefit from Einstein’s predictive maintenance tools, which analyze data from machinery to anticipate equipment failures. A car manufacturer, for instance, could use these insights to schedule maintenance during off-peak hours, minimizing downtime and preventing costly disruptions. Personalization is now a necessity. Salesforce Einstein elevates personalization by analyzing customer data to offer tailored recommendations, messages, and services. Industry insight: In e-commerce, personalized recommendations are often the key to converting browsers into loyal customers. An online bookstore using Einstein might analyze browsing history and past purchases to suggest new releases in genres the customer loves, driving repeat sales. In healthcare, Einstein’s personalization can improve patient outcomes by providing customized follow-up care. Hospitals can use Einstein to analyze patient histories and treatment data, offering reminders tailored to each patient’s needs, improving adherence to care plans and speeding recovery. Salesforce Einstein’s sales intelligence tools, such as Lead Scoring and Opportunity Insights, enable sales teams to focus on the most promising leads. This targeted approach drives higher conversion rates and more efficient sales processes. Industry insight: In real estate, Einstein helps agents manage numerous leads by scoring potential buyers based on their engagement with property listings. A buyer who repeatedly views homes in a specific area is flagged, prompting agents to prioritize their outreach, accelerating the sales process. In the automotive industry, Einstein identifies leads closer to purchasing by analyzing behaviors such as online vehicle configuration and test drive bookings. This allows sales teams to focus on high-potential buyers, closing deals faster. Automation is at the heart of Salesforce Einstein’s ability to streamline processes and boost productivity. By automating repetitive tasks like data entry and customer inquiries, Einstein frees employees to focus on strategic activities, improving overall efficiency. Industry insight: In insurance, Einstein Bots can handle routine tasks like policy inquiries and claim submissions, freeing up human agents for more complex issues. This leads to faster response times and reduced operational costs. In banking, Einstein-powered chatbots manage routine inquiries such as balance checks or transaction histories. By automating these interactions, banks reduce the workload on call centers, allowing agents to provide more personalized financial advice. Einstein Discovery democratizes data analytics, making it easier for non-technical users to explore data and uncover actionable insights. This tool identifies key business drivers and provides recommendations, making data accessible for all. Industry insight: In healthcare, predictive insights are helping providers identify patients at risk of chronic conditions like diabetes. With Einstein Discovery, healthcare providers can flag at-risk individuals early, implementing targeted care plans that improve outcomes and reduce long-term costs. For energy companies, Einstein Discovery analyzes data from sensors and weather patterns to predict equipment failures and optimize resource management. A utility company might use these insights to schedule preventive maintenance ahead of storms, reducing outages and enhancing service reliability. More Than a Tool – Embedded Salesforce Einstein Salesforce Einstein is more than just an AI tool—it’s a transformative force enabling businesses to unlock the full potential of their data. From predicting trends and personalizing customer experiences to automating tasks and democratizing insights, Einstein equips companies to make smarter decisions and enhance performance across industries. Whether in retail, healthcare, or technology, Einstein delivers the tools needed to thrive in today’s competitive landscape. Tectonic empowers organizations with Salesforce solutions that drive organizational excellence. Contact Tectonic today. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

September 21, 2024in Salesforce Einstein

20Sep

Collaborative Business Intelligence

Collaborative Business Intelligence: Connecting Data and Teams In today’s data-driven world, the ability to interact with business intelligence (BI) tools is essential for making informed decisions. Collaborative business intelligence (BI), also known as social BI, allows users to engage with their organization’s data and communicate with data experts through the same platforms where they already collaborate. While self-service BI empowers users to generate insights, understanding the data’s context is critical to avoid misunderstandings that can derail decision-making. Collaborative BI integrates BI tools with collaboration platforms to bridge the gap between data analysis and communication, reducing the risks of misinterpretation. Traditional Business Intelligence Traditional BI involves the use of technology to analyze data and present insights clearly. Before BI platforms became widespread, data scientists and statisticians handled data analysis, making it challenging for non-technical professionals to digest the insights. BI evolved to automate visualizations, such as charts and dashboards, making data more accessible to business users. Previously, BI reports were typically available only to high-level executives. However, modern self-service BI tools democratize access, enabling more users—regardless of technical expertise—to create reports and visualize data, fostering better decision-making across the organization. The Emergence of Collaborative BI Collaborative BI is a growing trend, combining BI applications with collaboration tools. This approach allows users to work together synchronously or asynchronously within a shared platform, making it easier to discuss data reports in real time or leave comments for others to review. Whether it’s through Slack, Microsoft Teams, or social media apps, users can receive and discuss BI insights within their usual communication channels. This seamless integration of BI and collaboration tools offers a competitive edge, simplifying the process of sharing knowledge and clarifying data without switching between applications. Key Benefits of Collaborative Business Intelligence Leading Collaborative BI Platforms Here’s a look at some of the top collaborative BI platforms driving innovation in the market: Conclusion Collaborative BI empowers organizations by improving decision-making, democratizing data access, optimizing data quality, and ensuring data security. By integrating BI tools with collaboration platforms, businesses can streamline their operations, foster a culture of data-driven decision-making, and enhance overall efficiency. Choosing the right platform is key to maximizing the benefits of collaborative BI. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

September 20, 2024in Data, Google, Salesforce, Technology

19Sep

Salesforce AI Agents and New Data Cloud Features

Salesforce has announced several major updates to its Data Cloud at the annual Dreamforce conference, aimed at enhancing data analysis and boosting the capabilities of its new AI agents, Agentforce. These updates include support for unstructured data in audio and video formats, 50 new data connectors, and a low-latency pipeline to ensure faster AI responses. Salesforce describes Data Cloud as a customer data platform designed to unify data from various sources, providing actionable insights for sales, service, and marketing teams. The latest features will further improve this functionality, according to Rahul Auradkar, Salesforce’s General Manager of Unified Data Services and Einstein. Key Updates 1. Unstructured Data Support: Audio and Video Formats Salesforce has introduced the ability to process unstructured audio and video data. This allows enterprises to analyze information from sources like customer calls, training sessions, product demos, and webinars. The unstructured data can enrich customer profiles, provide deeper insights into behavior, and enhance the performance of AI agents within Agentforce by improving response accuracy. IDC’s Hayley Sutherland highlighted that vectorization of this content enables retrieval-augmented generation (RAG), enhancing the use of large language models (LLMs) to generate more effective and context-aware responses. 2. 50 New Data Connectors Salesforce has expanded its connector library with 50 new options, raising the total to 200. This helps businesses integrate diverse data sources, facilitating improved personalization and decision-making. These connectors are also critical for feeding data to AI agents, enabling them to generate more accurate insights. 3. Sub-second Data Layer for Real-time Responses A new sub-second data layer has been rolled out, allowing enterprises to ingest, unify, analyze, and act on data in real time. This layer powers Einstein Personalization, enabling instant AI-driven recommendations and analytics. It’s especially important for the low-latency needs of Agentforce’s autonomous AI agents, ensuring faster, more accurate customer interactions. 4. Governance and Security Enhancements Salesforce has introduced AI tagging and classification features for unstructured data, automating labeling and organization to ensure secure access. Policy-based governance will offer granular control over data access based on tags, metadata, and user attributes. Additionally, customer-managed encryption keys ensure data security, while the newly available private connect feature allows secure data sharing between Data Cloud and public cloud environments. 5. Data Cloud One and Hybrid Search Data Cloud One, a new feature, will allow enterprises to connect multiple Salesforce accounts across different departments or regions into a single instance, simplifying data management. The hybrid search feature, which combines vector and keyword search, is designed to improve the speed and accuracy of finding industry-specific information. Other updates include Tableau Semantics, which helps organize data based on its meaning, and a new data community platform to foster collaboration among data professionals. These updates reflect Salesforce’s commitment to enhancing data-driven decision-making and delivering powerful AI capabilities for enterprises. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

September 19, 2024in Data, Enterprise

19Sep

Chatbot-less AI-ifying

AI-ify Your Product Without Adding a Chatbot: Inspiration from Top AI Use Cases Artificial intelligence doesn’t always need to look like a chatbot. Some of the most innovative implementations of AI have created intuitive user experiences (UX) without relying on traditional conversational interfaces. Here are seven standout patterns from leading companies and startups that demonstrate how AI can elevate your product in ways that feel natural and empowering for users. These are just a preview of the 24 trending AI-UX patterns featured in the “Trending AI-UX Patterns” ebook by AIverse—perfect for borrowing (or expensing to your company). Pattern 1: Linear Back-and-Forth (Classic Chat) While chat interfaces revolutionized access to AI, this pattern is just the beginning. Think of ChatGPT—its conversational simplicity opened the door to powerful LLMs for non-tech audiences. But beyond basic chat, consider integrating generative UI commands or API-based functionality into your product to transform linear data access into something seamless and engaging. Pattern 2: Non-Linear Conversations Inspired by Subform, this pattern mirrors how humans think—connecting ideas in a web, not a straight line. Non-linear exploration allows users to navigate through information like dots on a map, offering a flexible, intuitive flow. For example, imagine an AI that surfaces related ideas or actions based on user input—ideal for creative tools or brainstorming apps. Pattern 3: Context Bundling Why stop at simple text input when you can bundle context visually? Figma’s dual-tone matrix simplifies tone adjustments for text by letting users drag across a 2D grid. It eliminates the need for complex prompts while maintaining control over customization. Think of ways to integrate pre-bundled prompts directly into your UI to create an intuitive, visually driven experience. Pattern 4: Living Documents Tools like Elicit bring AI into familiar interfaces like spreadsheets by enhancing workflows without disrupting them. Elicit’s bulk data extraction uses subtle animations and transparency—highlighting “low confidence” answers for clarity. This hybrid approach integrates AI in a way that feels natural and predictable, making it a great choice for data-heavy tools or reporting systems. Pattern 5: Work With Me One of the most human-centered AI patterns comes from Granola, which uses meeting summaries based on your rough notes. Instead of overwhelming users with full transcriptions, it creates concise, actionable insights, perfectly blending human oversight with AI-powered efficiency. This pattern exemplifies the “human-in-the-loop” trend, ensuring collaboration between the user and AI. Pattern 6: Highlight and Curate Take inspiration from Lex’s “@lex” comment feature, which allows users to highlight and comment directly in the flow of their work—no app switching or disruption required. By building on familiar text-interaction patterns, this approach integrates AI subtly, offering suggestions or enhancements without breaking the user’s autonomy. Pattern 7: Invisible AI (Agentive UX) AI can work quietly in the background until needed, as demonstrated by Ford’s lane assist. This feature seamlessly takes control during critical moments (e.g., steering) and hands it back to the user effortlessly. Visual, auditory, and haptic feedback make the transition intuitive and reassuring. This “agentive” pattern is perfect for products where AI acts as a silent partner, ready to assist only when necessary. Tectonic Conclusions These patterns prove that AI can elevate your product without resorting to a chatbot. Whether through non-linear exploration, visual bundling, or seamless agentive experiences, the key is to integrate AI in a way that feels intuitive, empowering, and aligned with user needs. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

September 19, 2024in Artificial Intelligence, Data

15Sep

Cross Cloud Zero-Copy Data

Simplifying Secure Data Access Across Clouds In today’s data-driven world, secure and prompt access to information is crucial. However, with critical analytics data spread across various cloud vendors, achieving this expediency can be challenging. Cross-cloud zero-copy data sharing doesn’t have to be complex. By leveraging your Autonomous Database, you can swiftly establish secure data sharing with your Salesforce CRM Data Stream in just seconds. This guide will walk you through the straightforward process of connecting your Salesforce CRM data to your Autonomous Database using the Salesforce CRM data connector type. Requirements for Salesforce Integration To connect Salesforce CRM data with your Autonomous Database, you’ll need the following: 1. Confirm Data Stream Configuration On the Data Streams Dashboard, verify the Data Stream Name, Data Connector Type, and Data Stream Status. 2. Set Up Your Autonomous Database Create Your Credentials: sqlCopy codeBEGIN DBMS_CLOUD.CREATE_CREDENTIAL( credential_name => ‘<your credential name>’, username => ‘<your salesforce log-in id>’, password => ‘<your salesforce password>’); END; / Create Your Database Link: sqlCopy codeBEGIN DBMS_CLOUD_ADMIN.CREATE_DATABASE_LINK( db_link_name => ‘<your database link name>’, hostname => ‘<your host>.my.salesforce.com’, port => ‘19937’, service_name => ‘salesforce’, ssl_server_cert_dn => NULL, credential_name => ‘<your credential name>’, gateway_params => JSON_OBJECT( ‘db_type’ value ‘salesforce’, ‘security_token’ value ‘<your security token>’)); END; / 3. Check Connectivity Details The HETEROGENEOUS_CONNECTIVITY_INFO view provides information on credential and database link requirements for external databases. For example: sqlCopy codeSELECT database_type, required_port, sample_usage FROM heterogeneous_connectivity_info WHERE database_type = ‘salesforce’; 4. Demonstration: Connecting to Salesforce Data Follow these steps to connect to your Salesforce CRM organization using the Salesforce Data Cloud Sales synthetic data in the Account_Home Data Stream: 5. Set Up Connectivity Using DBMS_CLOUD.CREATE_CREDENTIAL, create the necessary credentials to connect to Salesforce. Then, use DBMS_CLOUD_ADMIN.CREATE_DATABASE_LINK to establish the database link. Once configured, execute the SELECT statement against the ACCOUNT data to verify successful connection. 6. Utilize Zero-Copy Data Sharing With zero-copy data access to the Salesforce CRM Data Lake ACCOUNT object, you can: Conclusion As demonstrated, secure and efficient cross-cloud zero-copy data access can be straightforward. By following these simple steps, you can bypass cumbersome ETL operations and gain immediate, secure access to your Salesforce CRM data. This approach eliminates the overhead of complex data pipelines and provides you with real-time access to critical business data. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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