Data Cloud Archives - gettectonic.com - Page 17
How Good is Our Data
28Oct

How Data Cloud and Salesforce Success Depend on Data Quality

Optimizing AI’s Impact on Your Business: The Crucial Role of Data Quality in Salesforce In the ever-evolving digital landscape, the convergence of data quality and artificial intelligence (AI) is a linchpin for organizational success. Success depends on data quality within the Salesforce ecosystem. The synergy between Einstein, an advanced AI system, and Data Cloud underscores the pivotal role of high-quality, comprehensive, and real-time data. Thereby unleashing the full potential of AI-driven insights and interactions with customers and prospects. Let’s explore how data quality profoundly influences these two emerging features. This insight will be shedding light on the repercussions of poor data quality and how Einstein and Data Cloud can elevate your organization to greater levels of sales success. Understanding Data Value Depends on Data Quality: Quality data extends beyond merely addressing duplicate records or inaccurate phone numbers It isn’t just about ensuring the area code field doesn’t contain zip codes. It is more than aligning contacts to accounts. It encompasses factors such as completeness, accuracy, and timeliness in your CRM: Consequences of Bad Data: Poor-quality data leads to inefficiencies and wasted time. Oftentimes causing flawed decision-making and strains on organizational resources. More critically, these poor business decisions often lead to tangible financial losses. Transforming bad data into quality data is imperative. Quality is key for relying on it to enhance company performance, requiring ongoing strategies rather than a one-stop solution. The Financial Impact of Accurate Data: Accurate data holds immense value. With data volumes projected to exceed 180 zettabytes by 2025, organizations must harness the power of their data. Proactive handling of data quality not only ensures higher data quality but also mitigates the financial impact of poor data quality. The sooner a plan is implemented to enhance and sustain data quality, the fewer negative repercussions organizations face in leveraging their data for growth. Your next decision is based on your last data. Is it going to help you or hurt you? Salesforce Einstein and the GIGO Principle: Salesforce Einstein, positioned as Artificial Intelligence for everyone, underscores trust as a core value. The system’s ability to create relevant and timely content and interactions is contingent on the quality of the data it operates on. Similar to the historical concept of “Garbage In, Garbage Out” (GIGO), AI results are only as reliable and valuable as the completeness and accuracy of the input data. No surprise, right? Introduction to Salesforce Data Cloud: Enter Salesforce Data Cloud, a platform allowing the organization and segmentation of customer data from any source. This open, extensible platform enables data enrichment from various sources, creating an optimal customer record. This enriched record empowers Sales, Service, and Marketing teams to perform intelligently and swiftly, ultimately driving enhanced results for the company. The WIIFM Factor: Amidst discussions about AI and Data Cloud, addressing the “What’s in it for me?” (WIIFM) question is crucial for organization adoption. Individual organizations must evaluate the reliability and accuracy of their data and determine forward-looking strategies for maintaining quality data, regardless of the source. The common theme remains: for data to yield valuable insights, it must be complete, timely, relevant, and accurate. Ultimately, success depends on data quality. Like3 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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October 28, 2023in Artificial Intelligence, Data, Salesforce, Salesforce Data Cloud, Salesforce Einstein, Salesforce Financial Cloud, Salesforce Managed Services, Salesforce Sales Cloud
Retrieval Augmented Generation in Artificial Intelligence
26Oct

RAG – Retrieval Augmented Generation in Artificial Intelligence

Salesforce has introduced advanced capabilities for unstructured data in Data Cloud and Einstein Copilot Search. By leveraging semantic search and prompts in Einstein Copilot, Large Language Models (LLMs) now generate more accurate, up-to-date, and transparent responses, ensuring the security of company data through the Einstein Trust Layer. Retrieval Augmented Generation in Artificial Intelligence has taken Salesforce’s Einstein and Data Cloud to new heights. These features are supported by the AI framework called Retrieval Augmented Generation (RAG), allowing companies to enhance trust and relevance in generative AI using both structured and unstructured proprietary data. RAG Defined: RAG assists companies in retrieving and utilizing their data, regardless of its location, to achieve superior AI outcomes. The RAG pattern coordinates queries and responses between a search engine and an LLM, specifically working on unstructured data such as emails, call transcripts, and knowledge articles. How RAG Works: Salesforce’s Implementation of RAG: RAG begins with Salesforce Data Cloud, expanding to support storage of unstructured data like PDFs and emails. A new unstructured data pipeline enables teams to select and utilize unstructured data across the Einstein 1 Platform. The Data Cloud Vector Database combines structured and unstructured data, facilitating efficient processing. RAG in Action with Einstein Copilot Search: RAG for Enterprise Use: RAG aids in processing internal documents securely. Its four-step process involves ingestion, natural language query, augmentation, and response generation. RAG prevents arbitrary answers, known as “hallucinations,” and ensures relevant, accurate responses. Applications of RAG: RAG offers a pragmatic and effective approach to using LLMs in the enterprise, combining internal or external knowledge bases to create a range of assistants that enhance employee and customer interactions. Retrieval-augmented generation (RAG) is an AI technique for improving the quality of LLM-generated responses by including trusted sources of knowledge, outside of the original training set, to improve the accuracy of the LLM’s output. Implementing RAG in an LLM-based question answering system has benefits: 1) assurance that an LLM has access to the most current, reliable facts, 2) reduce hallucinations rates, and 3) provide source attribution to increase user trust in the output. Retrieval Augmented Generation in Artificial Intelligence Content updated July 2024. Like2 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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October 26, 2023in Einstein 1 Platform, Generative AI, Salesforce, Salesforce Data Cloud
Salesforce Life Sciences Cloud
24Oct

Salesforce Life Sciences Cloud

Salesforce has unveiled Life Sciences Cloud, a secure and trusted platform tailored for pharmaceutical (pharma) and medical technology (medtech) organizations. This innovative solution aims to expedite drug and device development, streamline patient enlistment and retention throughout the clinical trial journey, and harness AI capabilities to deliver personalized customer experiences. The significance of this announcement lies in the life sciences industry’s urgent need for accurate and accessible data to advance research and development efforts and enhance clinical trials. Despite this need, the industry has been slow to adopt digital tools, with a staggering 88% of healthcare and life sciences organizations yet to achieve their digital transformation objectives. Amit Khanna, SVP & GM of Health and Life Sciences at Salesforce, emphasized the necessity for integrated, compliant, and data-driven solutions in the life sciences industry. He highlighted Salesforce’s commitment to enhancing stakeholder engagement across the R&D and commercialization spectrum by leveraging data, AI, and CRM capabilities. The Salesforce solution encompasses: Commercial Operations, available now, provides insights into the commercial lifecycle, including contract compliance, pricing, and inventory management. AI-powered bots offer timely alerts to field representatives and forecasting insights to optimize sales strategies. Clinical Operations offers tools to set up and execute efficient trials, including Data Cloud for Health, Chain of Custody Management, and Participant Management features, aiming to enhance patient recruitment, safety, and engagement. Pharma CRM facilitates personalized engagement with stakeholders, managing interactions and digital content while ensuring compliance with regulations. Features like Healthcare Professional (HCP) Engagement and Einstein for Life Sciences enhance engagement and automate tasks for streamlined operations. Customer testimonials, such as from SI-BONE, highlight the tangible benefits of digitizing processes and improving efficiency with Salesforce solutions. Availability details for various features are provided, with some features already generally available and others set to roll out in the coming months and years. To learn more about Salesforce’s offerings for healthcare and life sciences, access industry insights, and explore the potential of CRM and AI in this sector, interested parties are encouraged to dig into the available resources or contact Tectonic today. Additionally, it’s noted that sales automation functionality for pharma/biotech customers will be available from mid-2025 onward. Learn about Salesforce for healthcare and life sciences  Learn more about Salesforce Life Sciences 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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October 24, 2023in Life Sciences Cloud, Salesforce Einstein, Salesforce Health and Life Sciences Solutions, Salesforce Implementation Services
Einstein 1 is Coming
09Oct

Einstein 1 is Coming

What Does the New Einstein 1 Data Cloud Mean for Your Organization? Einstein 1 is Coming One of the major announcements at Dreamforce was the exciting intro that Einstein 1 is Coming. The Einstein 1 Data Cloud is now natively integrated with the Einstein 1 Platform. This integration allows users to connect any data, create unified customer profiles, and enhance every customer experience with AI, automation, and analytics. This is a giant step for Salesforce-kind. It can revolutionize the ways businesses engage with their customers. While this announcement is exciting, what does it mean for organizations at different stages of their Salesforce journey? In this insight, we explore the announcement details, considerations for using the Einstein 1 Data Cloud in your company, and how Tectonic can assist in navigating this new offering. What’s New with the Platform? The integration of Salesforce Data Cloud and Einstein AI into the Einstein 1 Platform marks a significant enhancement. The platform integration enables companies to securely connect any data, build AI-powered apps with low code, and deliver superior CRM experiences. It unifies data across the enterprise by mapping it to Salesforce’s underlying metadata framework, regardless of how the data is structured in disparate systems. Regardless of how complex it is. What is Einstein 1 Data Cloud? The Key to Unified Data Salesforce Einstein 1 Data Cloud unifies customer data, enterprise content, telemetry data, Slack conversations, and other structured and unstructured data to create a single view of the customer. This integration unlocks otherwise siloed data and scales operations in new ways: Salesforce has announced that Enterprise Edition and above customers can use Data Cloud at no additional cost. However, organizations should consider their position on the Salesforce maturity curve before implementation. Data Cloud’s capabilities, while extensive, might not fully optimize data for organizations further along in their Salesforce journey without a thorough trial. What is the Einstein Conversational Assistant? An AI-Powered Shift Einstein now includes a generative AI-powered conversational assistant featuring Einstein Copilot and Einstein Copilot Studio. These tools operate within the Einstein Trust Layer, a secure AI architecture native to the Einstein 1 Platform that ensures data privacy and security. Why Should Organizations Consider Einstein 1? Customer data is often fragmented and siloed across disparate systems, preventing a unified view necessary for informed business decision-making. Data unification is essential for data-driven decision making and fully getting the full ROI of AI. AI is a major trend in technology, but effective AI requires comprehensive, aligned data. Without a unified data foundation, AI’s potential is limited. Einstein 1 with Data Cloud provides the solution by consolidating data, enabling the training of AI models to make optimal decisions and recommendations. How Can Tectonic Help You Transition? Tectonic brings extensive Salesforce expertise and industry-specific experience in sectors heavily reliant on data, such as healthcare, financial services, and travel and tourism. These industries face strict data regulations and often have siloed data in legacy systems. Einstein 1 helps organizations achieve a 360-degree view of their customers by unifying data. Tectonic can assist in maximizing AI on the Salesforce platform by building a robust data foundation and providing a roadmap for future scalability. While both Einstein 1 and AI Cloud are Salesforce terms that promise AI-driven capabilities, there are differences to consider. Einstein 1 Platform is a comprehensive suite that includes Data Cloud, AI tools, and automation capabilities. In contrast, AI Cloud is more of an overarching term that might encompass Einstein 1 as part of its suite, focusing on the broader application of AI across Salesforce’s entire range of products and services. Understanding these distinctions is critical in identifying which solution aligns with your organizational 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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October 9, 2023in Einstein 1 Platform, Mulesoft, Salesforce, Salesforce Data Cloud, Salesforce Einstein
Data Cloud Free Licenses
15Sep

Data Cloud Free Licenses

Salesforce Announces Data Cloud Free Licenses at Dreamforce 2023 At Dreamforce 2023, Salesforce announced that free Data Cloud licenses are now included for all Enterprise Edition or above customers to help them familiarize themselves with new capabilities and develop use case ideas. Starting September 19th, 2023, Enterprise Edition and above customers can get started with Data Cloud Provisioning at no cost by signing up under Your Account. Data Cloud Provisioning includes: Unlimited Plus Edition customers will get access to 2,500,000 Data Service credits. Two Tableau Creator licenses are a separate line item and can be quoted by your Salesforce Account Executive. Salesforce has been focusing on large data and AI tools for several years, acquiring Tableau, accelerating their Einstein AI tools, and significantly extending the Data Cloud product. Data Cloud allows you to easily harmonize data, analyze it in Tableau, and make it actionable across marketing, sales, and service. What Can I Do with Data Cloud? Data Cloud enables customers to start with one of three use cases: Across these use cases, customers can ingest data from multiple sources, unify data with identity resolution, calculate insights, visualize data in Tableau (with the provisioning of the Tableau Cloud – Creator for Data Cloud SKU), and view consolidated data on the contact record. Differences Between Data Cloud and Data Cloud Provisioning Functionality: Data Cloud Provisioning includes all the features of the existing Data Cloud offerings, except Segmentation and Activation. Credits for Segmentation and Activation can be purchased as add-ons through Marketing Cloud account teams. Capacity: Both include 1 TB of data storage, 1 Data Cloud admin, 100 internal Data Cloud identity users, 1,000 Data Cloud PSL, and 5 integration users. Entitlement: Data Cloud Provisioning entitlement is the same for all Enterprise Edition and above customers. Additional Information Sandbox Availability: Data Cloud is not available in Sandbox orgs; it can only be provisioned to an existing production org. Professional Edition Access: Data Cloud Provisioning is not available to Professional Edition customers. Existing Data Cloud or CDP Customers: Those with an existing Data Cloud or CDP tenant cannot sign up for Data Cloud Provisioning. Unlimited Edition Plus Bundle Customers: Data Cloud Provisioning is not available, as the bundle includes a Data Cloud tenant. Edition Information: Check your Salesforce org’s edition in Setup > Company Information > Organization Edition. Government Cloud: Data Cloud Provisioning is not available. Non-Profit Customers: Data Cloud Provisioning is available. Industry Cloud Customers: Industry Cloud customers with Enterprise Edition and above are eligible. ISV Partners: Data Cloud Provisioning is not accessible via Your Account in ISV Enterprise Edition orgs. ISV Partners need to create a support case with the Partner Ops team to request provisioning. Existing Tableau Customers: Tableau Cloud – Creator for Data Cloud is intended to provision a new Tableau tenant (aka site). Multiple Instances: Only one Data Cloud Provisioning instance is allowed per account/tenant. Access to Tableau Cloud – Creator for Data Cloud: To get access, you must have or include on the same quote any of the following: 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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September 15, 2023in Salesforce Data Cloud
Crucial Role of Data and Integration in AI at Dreamforce
13Sep

The Crucial Role of Data and Integration in AI at Dreamforce

Understanding The Crucial Role of Data and Integration in AI at Dreamforce At this year’s Dreamforce, AI is the star of the show, but two essential supporting actors are data and integration. Enterprises are increasingly recognizing the importance of unifying their diverse data sources for effective analysis and swift action, and the race to harness AI makes this integration even more critical. Integration is key not only for merging data but also for automating end-to-end processes, enabling organizations to move faster and deliver better outcomes to customers. Crucial Role of Data and Integration in AI at Dreamforce. It’s no surprise that MuleSoft, acquired by Salesforce five years ago, is now a major contributor to Salesforce’s growth. Brian Millham, President and COO at Salesforce, highlighted this during the company’s recent Q2 earnings call: “In Q2, nearly half of our greater than $1 million deals included MuleSoft. As customers integrate data from all sources to drive efficiency, growth, and insights, MuleSoft has become mission-critical and was included in half of our top 10 deals.” Breaking Down Silos Param Kahlon, EVP and General Manager for Automation and Integration at Salesforce, recently discussed the investments customers are making in data and integration. He emphasized the importance of breaking down operational silos: “We are in the business of breaking silos across systems to ensure that data can travel seamlessly through multiple systems and people for processes like order-to-cash or procure-to-pay. Our technology connects these dots.” The surge in AI interest has increased the urgency to act, as Kahlon explained: “Creating data repositories for AI algorithms requires real-time data across silos, driving significant demand for our integration solutions.” Consolidating Data Enterprises have long struggled with data consolidation due to monolithic application stacks with separate data stores. This has been a challenge even within Salesforce’s own products. Last year, Salesforce introduced a Customer Data Platform (CDP) called Data Cloud, which includes a real-time data layer named Genie. Kahlon elaborated on its significance: “Data Cloud’s strength lies in its understanding and storage of Salesforce metadata. This native integration allows for real-time actions within Salesforce, enhancing the ability to aggregate, reason over, and act on data.” For example, when a customer contacts a bank, Data Cloud can compile their ATM usage, website interactions, and recent support cases, providing the agent with a comprehensive view to better assist the customer. Leveraging Metadata for AI Salesforce’s metadata layer, which has been fine-tuned over two decades, gives it a distinct advantage. Kahlon noted: “This metadata-based architecture allows us to create meaningful AI algorithms that are natively consumed within Salesforce, enabling visualization and action based on real-time data.” This is crucial for training the underlying Large Language Model (LLM) accurately, ensuring generated content is contextually grounded and trustworthy. Kahlon emphasized: “The trust layer is essential. We need to ensure no hallucination or toxicity in the LLM’s responses, and that communications align with our company’s values.” Real-Time Data and API Management Data Cloud’s ability to connect to other data sources like Snowflake without duplicating data is a significant benefit. Kahlon commented: “Duplicating data is not desirable. Customers need real-time access to the actual source of truth.” On the integration front, APIs have simplified connecting applications and data sources. However, managing API sprawl is crucial. Kahlon explained: “Standardizing API use and publishing them in a centralized portal is essential for reusability and consistency. Low-code platforms and connectors are becoming increasingly relevant, enabling business users to access data without relying on IT.” Automation and AI The demand for automation is growing, and low-code tools are vital. Instead of integration experts being overwhelmed, organizations should establish Centers for Excellence to focus on creating reusable connectors and automations. Kahlon added: “Companies need low-code tools to involve more business users in the transformation journey without slowing down due to legacy applications.” In the future, AI may further ease the workload on integration specialists. MuleSoft recently introduced an API Experience Hub to make APIs discoverable, and AI might eventually help monitor execution logs and manage APIs more effectively. Kahlon concluded: “AI could help developers find and use APIs efficiently, enhancing security and governance while simplifying access to data across the organization.” 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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September 13, 2023in Mulesoft, Salesforce, Salesforce Einstein, Snowflake
SaaS Data Protection from Own
07Sep

Reporting With Own

In any Salesforce organization, vast amounts of data are generated constantly from sales activities, customer interactions, marketing campaigns, and more. Summarizing and digesting this information quickly is crucial, especially when presenting the big picture to leadership. This is where Salesforce reports come into play. The Salesforce Reports feature enables organizations to analyze, visualize, and summarize data in real time. By pulling data from across your Salesforce environment, reports help consolidate information into easily digestible formats, such as charts, tables, and graphs. Salesforce reports are essential for: How Historical Data Can Improve Reporting in Salesforce While real-time reports are valuable, incorporating historical data can significantly enhance reporting by offering deeper insights into your organization’s long-term performance. Here’s how: Challenges of Reporting with Historical Data in Salesforce While incorporating historical data is smart, Salesforce’s native reporting capabilities impose certain limitations: Don’t Let Salesforce Reporting Limitations Hold You Back With Own Discover, customers can effortlessly generate time-series datasets from any objects and fields over any time period in just a few clicks. These datasets can be accessed using standard query and reporting tools without requiring a data warehouse or the need to enrich existing data warehouses, overcoming Salesforce’s native limitations. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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September 7, 2023in Enterprise
Einstein in Salesforce
26Aug

Einstein in Salesforce

Salesforce AI and CRM Evolution Salesforce has long been a leader in customer relationship management (CRM) by pioneering cloud technologies. Recently, the platform has significantly advanced with the integration of generative artificial intelligence (AI) and AI-powered features, thanks to its Einstein technology. Einstein in Salesforce is like a super smart computer overseeing and analyzing the data in your CRM. This guide explores Salesforce’s AI strategy, exploring its specific products and features to help business teams understand and benefit from this technology. Exploring Salesforce’s Advanced AI Features Einstein, Salesforce’s AI technology, powers various advanced features within the platform. This guide will cover these capabilities, provide real-life adoption examples, and discuss their benefits. Additionally, it offers best practices, solutions, and services to facilitate your Einstein implementation. Salesforce’s Comprehensive CRM Solution Salesforce remains a number one in the CRM software world, offering robust solutions for managing relationships across various departments. Specific clouds within Salesforce enable teams to handle marketing, sales, customer service, e-commerce, and more. The platform focuses on customer experience and provides robust data analytics to support decision-making. Enhancements Through Generative AI Salesforce’s generative AI has rapidly enhanced the platform’s automation, workflow management, data analytics, and assistive capabilities for customer management. A prime example is Salesforce Copilot, which aids internal users with outreach and analysis tasks while improving the external user experience. What is Salesforce Einstein? Salesforce Einstein is the first comprehensive AI for CRM, integrating AI technologies to enhance the Customer Success Platform and bring AI to users everywhere. It is seamlessly integrated into many Salesforce products, offering generative AI built specifically for CRM. Key Features of Salesforce Einstein Comprehensive AI Capabilities of Salesforce Einstein Einstein extends its capabilities across the Salesforce CRM platform under the Customer 360 umbrella, enhancing intelligence and providing personalized customer experiences. Key Benefits of Salesforce Einstein Salesforce Einstein helps close deals faster, personalize customer service, understand customer behaviors, target audience segments better, and create personalized shopping experiences. It ensures data protection and privacy through the Einstein Trust Layer, maintaining strong data governance controls. Responsible AI Principles Salesforce is committed to responsible AI principles, ensuring Einstein is trustworthy and safe for every organization. Organizations can select from various principles to ensure ethical AI use in their operations. Implementation of Salesforce Einstein Salesforce Einstein is a powerful AI solution transforming how businesses interact with customers. By leveraging machine learning and data analysis, it personalizes experiences, predicts customer behavior, and automates tasks, boosting sales, enhancing service, and driving growth. As AI evolves, its impact on CRM will continue to grow, making it an indispensable tool for businesses aiming to stay competitive in today’s data-driven landscape. Top 4 Benefits of Salesforce Einstein in an Organization Einstein Essentials Salesforce Einstein and GPT (Generative Pretrained Transformer) technologies represent significant advancements in AI, particularly in CRM and natural language processing. Here’s a brief overview of their relevance and potential intersection: Data Handling and Ethics in Salesforce Salesforce manages a vast amount of customer data, and the ethical handling of this data is crucial. Key considerations include data privacy, secure storage, access controls, compliance with regulations like GDPR and CCPA, and the ethical use of AI and machine learning. It’s important to maintain transparency, avoid biases, and ensure AI models are making ethical decisions. Newest Einstein Features for 2024 In the rapidly evolving ecosystem of Salesforce, AI offers a suite of tools to spark innovation, streamline operations, and provide richer business insights. Explore these potentials and let Einstein AI reshape your work in 2024. Content updated June 2024. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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August 26, 2023in Einstein 1 Platform, Generative AI, Salesforce Data Cloud, Salesforce Einstein
Salesforce Lightning and Hyperforce
19Aug

Difference Between Salesforce Lightning and Hyperforce

Salesforce Lightning Platform and Hyperforce: An Overview The Lightning Platform forms the core infrastructure of Salesforce, while Hyperforce is a new infrastructure model provided by the CRM platform. Salesforce Lightning Salesforce Lightning elevates customization with its modern and flexible framework. It offers a powerful drag-and-drop interface called the Lightning App Builder, enabling users to design custom homepages, record pages, and dashboards effortlessly. Salesforce Hyperforce Introduced at Dreamforce in December 2020 and recently made generally available, Hyperforce is an architectural enhancement to Salesforce’s platform designed for the public cloud. It offers several benefits, including scalability, global reach, and increased control over data residency. As part of the Salesforce Customer 360 platform, Hyperforce brings enhanced flexibility, scalability, and performance to the cloud. Key Benefits of Hyperforce: Hyperforce Migration Migrating to Hyperforce does not require additional costs. Once migrated, organizations will not experience significant changes, and non-Hyperforce services will continue to run. This code-based offering enhances speed, reliability, and global performance. Reasons to Migrate to Hyperforce: Hyperforce and Public Cloud Platforms Hyperforce can run on public cloud platforms like AWS, GCP, and Azure, representing a shift from traditional Salesforce-hosted data centers. The Data Cloud and Marketing Cloud are also built on Hyperforce, offering businesses a powerful platform for customer interactions and relationship management. Hyperforce vs. Shield Hyperforce provides volume-level encryption by default for data at rest, while Shield Platform Encryption operates in parallel, offering additional encryption features. Hyperforce Migration: Mandatory Upgrade Migrating to Hyperforce is not optional, as Salesforce has declared this architecture as its future. The migration process will occur over time, and organizations will need to prepare for it. If migration timing is not suitable, businesses should contact their Salesforce account agent. Data Residency and Migration Issues: While Hyperforce is expanding, more countries need to support data residency to have a truly international impact. Some migration issues may include Einstein loading problems, bot disruptions, and glitches with the Smartcomm managed package. Basic troubleshooting can resolve minor issues, but technical considerations like manual endpoint management may be time-consuming and costly. Hyperforce Assistant The Hyperforce Assistant provides a step-by-step guide to upgrade from Salesforce-managed physical data centers to Hyperforce, ensuring admins feel confident about the migration process. By adopting Hyperforce, businesses can enhance their customer data management with innovative solutions, ensuring robust security, scalability, and efficiency in their operations. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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August 19, 2023in Hyperforce
Salesforce Einstein and Einstein Automate
09Aug

Einstein Trust

Generative AI, Salesforce, and the Commitment to Trust The excitement surrounding generative AI is palpable as it unlocks new dimensions of creativity for individuals and promises significant productivity gains for businesses. Engaging with generative AI can be a great experience, whether creating superhero versions of your pets with Midjourney or crafting pirate-themed poems using ChatGPT. According to Salesforce research, employees anticipate saving an average of 5 hours per week through the adoption of generative AI, translating to a substantial monthly time gain for full-time workers. Whether designing content for sales and marketing or creating a cute version of a beloved story, generative AI is a tool that helps users create content faster. However, amidst the enthusiasm, questions arise, including concerns about the security and privacy of data. Users ponder how to leverage generative AI tools while safeguarding their own and their customers’ data. Questions also revolve around the transparency of data collection practices by different generative AI providers and ensuring that personal or company data is not inadvertently used to train AI models. Additionally, there’s a need for assurance regarding the accuracy, impartiality, and reliability of AI-generated responses. Salesforce has been at the forefront of addressing these concerns, having embraced artificial intelligence for nearly a decade. The Einstein platform, introduced in 2016, marked Salesforce’s foray into predictive AI, followed by investments in large language models (LLMs) in 2018. The company has diligently worked on generative AI solutions to enhance data utilization and productivity for their customers. The Einstein Trust Layer is designed with private, zero-retention architecture. Emphasizing the value of Trust, Salesforce aims to deliver not just technological capabilities but also a responsible, accountable, transparent, empowering, and inclusive approach. The Einstein Trust Layer represents a pivotal development in ensuring the security of generative AI within Salesforce’s offerings. The Einstein Trust Layer is designed to enhance the security of generative AI by seamlessly integrating data and privacy controls into the end-user experience. These controls, forming gateways and retrieval mechanisms, enable the delivery of AI securely grounded in customer and company data, mitigating potential security risks. The Trust Layer incorporates features such as secure data retrieval, dynamic grounding, data masking, zero data retention, toxic language detection, and an audit trail, all aimed at protecting data and ensuring the appropriateness and accuracy of AI-generated content. Salesforce proactively provided the ability for any admin to control how prompt inputs and outputs are generated, including reassurance over data privacy and reducing toxicity. This innovative approach allows customers to leverage the benefits of generative AI without compromising data security and privacy controls. The Trust Layer acts as a safeguard, facilitating secure access to various LLMs, both within and outside Salesforce, for diverse business use cases, including sales emails, work summaries, and service replies in contact centers. Through these measures, Salesforce underscores its commitment to building the most secure generative AI in the industry. Generating content within Salesforce can be achieved through three methods: CRM Solutions: Einstein Copilot Studio: Einstein LLM Generations API: An overarching feature of these AI capabilities is that every Language Model (LLM) generation is meticulously crafted through the Trust Layer, ensuring reliability and security. At Tectonic, we look forward to helping you embrace and utilize generative AI with Einstein save time. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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August 9, 2023in Artificial Intelligence, Data, Generative AI, Salesforce, Salesforce Einstein
The Evolution of Salesforce Data Cloud
21Jul

The Evolution of Salesforce Data Cloud

The Evolution of Salesforce Data Cloud Salesforce’s journey to Data Cloud started with its acquisition of Krux in 2016, which was later rebranded as Salesforce DMP. This transformation gained momentum in 2019 when Salesforce introduced its customer data platform (CDP), incorporating Salesforce DMP. Subsequent acquisitions of Datorama, MuleSoft, Tableau, and Evergage (now Interaction Studio) enriched Salesforce CDP’s functionality, creating today’s robust Data Cloud. Understanding Customer Data Platforms (CDPs) A customer data platform (CDP) aggregates customer data from multiple channels to create a unified customer profile, enabling deeper insights and real-time personalization. A CDP serves as a centralized customer data repository, merging isolated databases from marketing, service, and ecommerce to enable easy access to customer insights. Salesforce’s “State of Marketing” report highlights the impact of CDPs, noting that 78% of high-performing businesses use CDPs, compared to 58% of underperformers. This analysis explores the evolution of CDPs and their role in transforming customer relationship management (CRM) and the broader tech ecosystem, turning customer data into real-time interactions. Key Functions of a Customer Data Platform (CDP) CDPs perform four main functions: data collection, data harmonization, data activation, and data insights. Origins of Customer Data Platforms (CDPs) CDPs evolved as the latest advancement in customer data management, driven by the need for a unified marketing data repository. Unlike earlier tools that were often limited to specific channels, CDPs enable real-time data synchronization and cross-platform engagement. Advances in AI, automation, and machine learning have made this level of segmentation and personalization attainable. The Future of Customer Data Platforms (CDPs) The next generation of CDPs, like Salesforce’s Data Cloud, supports real-time engagement across all organizational functions—sales, service, marketing, and commerce. Data Cloud continuously harmonizes and updates customer data, integrating seamlessly with Salesforce products to process over 100 billion records daily. With Data Cloud, organizations gain: Benefits of a Customer Data Platform (CDP) CDPs provide comprehensive insights into customer interactions, supporting personalization and cross-selling. Beyond segmentation, they serve as user-friendly platforms for audience analysis and data segmentation, simplifying day-to-day data management. Data Cloud allows organizations to transform customer data into personalized, seamless experiences across every customer touchpoint. Leading brands like Ford and L’Oréal utilize Data Cloud to deliver connected, real-time interactions that enhance customer engagement. The Need for Customer Data Platforms (CDPs) CDPs address critical data management challenges by unifying disjointed data sources, resolving customer identities, and enabling seamless segmentation. These capabilities empower companies to maximize the potential of their customer data. CDP vs. CRM CDPs are an evolution of traditional CRM, focusing on real-time, highly personalized interactions. While CRMs store known customer data, CDPs like Data Cloud enable real-time engagement, making it the world’s first real-time CRM by powering Salesforce’s Customer 360. Selecting the Right CDP When choosing a CDP, the focus often falls into two areas: insights and engagement. An insights-oriented CDP prioritizes data integration and management, while an engagement-focused CDP leverages data for real-time personalization. Data Cloud combines both, integrating real-time CDP capabilities to deliver unmatched insights and engagement across digital platforms. Content updated October 2024. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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July 21, 2023in Data, Mulesoft, Salesforce, Salesforce Data Cloud
Salesforce BI
15Jul

Salesforce Analytics Acronyms

Here is a helpful list of analytics acronyms you may encounter when discussing Salesforce. Salesforce Analytics Acronyms Acronym Meaning Definition AWS Amazon Web Services AWS offers “reliable, scalable, and inexpensive cloud computing services…the world’s most comprehensive and broadly adopted cloud, offering over 200 fully featured services from data centers globally.” (source)Multiple Salesforce products use AWS; however, it’s prominent in the analytics arena, where organizations rely on AWS to:”Extract, prepare, and analyze Salesforce.com data using Amazon AppFlow, AWS Glue DataBrew, and Amazon Athena.” (source) BYOAI Bring Your Own AI ‘Bring your own AI (model)’ is Salesforce’s way to meet their customers where they’re at. With Data Cloud, Salesforce not only supports Einstein (their own LLM), but also other ones like Sagemaker, Vertex AI, etc. BYOM Bring Your Own Model ‘Bring your own AI (model)’ is Salesforce’s way to meet their customers where they’re at. With Data Cloud, Salesforce not only supports Einstein (their own LLM), but also other ones like Sagemaker, Vertex AI, etc. CRMA CRM Analytics This is the analytics/business intelligence (BI) solution on the Salesforce platform, integrating Salesforce data with external data and provides the ability to analyze large datasets while performing predictive analytics. (Previously known as Wave/Einstein Analytics/Tableau CRM). EA Einstein Analytics The former name for CRM Analytics (also formerly Tableau CRM). You may still come across EA in outdated documentation. GPT Generative Pre-trained Transformer GPT is a technology concept, although it’s often mistaken for a product name (with the rise of the wildly popular ChatGPT). LLM Large Language Model An AI model that has been trained using lots of data. Salesforce has their own LLM that outperformed expectations during testing. Salesforce’s LLM leverages Data Cloud, or can be plugged into a different data lake. Organizations can also opt to their own LLM alongside their Salesforce data. NBA Einstein Next Best Action An AI-driven Salesforce product that makes recommendations to users when they are working with records in Salesforce. It appears within a Lightning component that’s typically placed on the record page sidebar. The user can choose to Accept or Reject the recommendation. NLP Natural Language Processing “Artificial intelligence that focuses on how computers can understand, interpret, and generate human language. NLP allows customers to interact with systems using normal human language rather than complex commands.” (source) SAQL Structured Query Language “Use SAQL to access data in Tableau CRM dataset. Tableau CRM uses SAQL behind the scenes in lenses, dashboards, and explorer to gather data for visualizations.” (source) TCRM Tableau CRM Tableau CRM is a self-service data visualization and business intelligence (BI) platform that integrates Salesforce data with external data. Formerly known as Einstein Analytics, and now CRM Analytics, this product boasts some of the most powerful data analysis capabilities in the market. XMD Extended Metadata “Enables you to customize the formatting of dataset fields and their values in Analytics dashboards and lenses.” (source) 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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July 15, 2023in Salesforce
data cloud and marketing cloud personalization
11Jul

What is the Difference in a Data Lake and a Data Warehouse

Is a Data Lake Necessary? Difference in a Data Lake and a Data Warehouse? Do I need both? Both Data Lakes and Data Warehouses play crucial roles in the data processing and reporting infrastructure. They are complementary approaches rather than substitutes. Relevance of Data Lakes: Data lakes are losing popularity compared to their previous standing. Advanced storage solutions like data warehouses are progressively taking their place. Can Data Lakes Replace Data Warehouses? Data lakes do not directly replace data warehouses; they serve as supplementary technologies catering to different use cases with some overlap. Organizations typically have both a data lake and a data warehouse. Distinguishing Between Data Lakes and Data Warehouses: Data lakes and data warehouses serve as storage systems for big data, utilized by data scientists, data engineers, and business analysts. Despite some similarities, their differences are more significant than their commonalities, and understanding these distinctions is vital for aspiring data professionals. Data Lake vs. Data Warehouse: Key Differences: Data lakes aggregate structured and unstructured data from multiple sources, resembling real lakes with diverse inflows. Data warehouses, on the other hand, are repositories for pre-structured data intended for specific queries and analyses. Exploring Data Lakes: A data lake is a storage repository designed to capture and store large amounts of raw data, whether structured, semi-structured, or unstructured. This data, once in the lake, can be utilized for machine learning or AI algorithms and later transferred to a data warehouse. Data Lake Examples: Data lakes find applications in various sectors, such as marketing, education, and transportation, addressing business problems by collecting and analyzing data from diverse sources. Understanding Data Warehouses: A data warehouse is a centralized repository and information system designed for business intelligence. It processes and organizes data into categories called data marts, allowing for structured data storage from multiple sources. Data Warehouse Examples: Data warehouses support structured systems and technology for diverse industries, including finance, banking, and food and beverage, facilitating secure and accurate report generation. Data Warehouses compared to Data Lakes: Data warehouses contain processed and sanitized structured data, focusing on business intelligence, while data lakes store vast pools of unstructured, raw data, providing flexibility for future analysis. Key Differences Between Warehouses and Lakes: Intended purpose, audience, data structure, access and update cost, access model, and storage and computing are crucial factors distinguishing data warehouses and data lakes. Choosing Between Data Warehouse and Data Lake: The decision depends on organizational needs, value extracted from data analysis, and infrastructure costs. Organizations may opt for agility with a data lake, a data warehouse for larger data quantities, or a combination for maximum flexibility. A data lake stores raw, unstructured data indefinitely, providing cost-effective storage, while a data warehouse contains cleaned, processed, and structured data, optimized for strategic analysis based on predefined business needs. Data Warehouse, Data Lake, and Data Hub Differences: Data warehouses and data lakes primarily support analytic workloads, whereas data hubs focus on data integration, sharing, and governance, serving different purposes in the data landscape. Salesforce Data Cloud is a powerful data warehouse solution that allows companies to effectively manage and analyze their data. It provides users with the ability to stream input data from Salesforce and other sources, making it a comprehensive platform for data integration. Content updated February 2024. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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July 11, 2023in Data, Salesforce, Salesforce Data Cloud, Technology

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