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28Oct

Salesforce Maps Winter 25

The Salesforce Maps Winter 25 release will be available in production environments between October 29 – 31. Auto-Enablement of the new Maps experience in October To enhance your experience in Salesforce Maps on desktop, the new features currently available in all environments will be auto-enabled in the Winter ’25 release. The Enhanced User Experience setting in the admin configuration settings will remain and can be manually disabled until the Spring ‘25 release. Get Release Ready-Salesforce Maps Winter 25 To ensure a smooth transition, please take the following actions prior to the production release. What This Change Brings 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 28, 2024in Data

18Oct

Recent advancements in AI

Recent advancements in AI have been propelled by large language models (LLMs) containing billions to trillions of parameters. Parameters—variables used to train and fine-tune machine learning models—have played a key role in the development of generative AI. As the number of parameters grows, models like ChatGPT can generate human-like content that was unimaginable just a few years ago. Parameters are sometimes referred to as “features” or “feature counts.” While it’s tempting to equate the power of AI models with their parameter count, similar to how we think of horsepower in cars, more parameters aren’t always better. An increase in parameters can lead to additional computational overhead and even problems like overfitting. There are various ways to increase the number of parameters in AI models, but not all approaches yield the same improvements. For example, Google’s Switch Transformers scaled to trillions of parameters, but some of their smaller models outperformed them in certain use cases. Thus, other metrics should be considered when evaluating AI models. The exact relationship between parameter count and intelligence is still debated. John Blankenbaker, principal data scientist at SSA & Company, notes that larger models tend to replicate their training data more accurately, but the belief that more parameters inherently lead to greater intelligence is often wishful thinking. He points out that while these models may sound knowledgeable, they don’t actually possess true understanding. One challenge is the misunderstanding of what a parameter is. It’s not a word, feature, or unit of data but rather a component within the model‘s computation. Each parameter adjusts how the model processes inputs, much like turning a knob in a complex machine. In contrast to parameters in simpler models like linear regression, which have a clear interpretation, parameters in LLMs are opaque and offer no insight on their own. Christine Livingston, managing director at Protiviti, explains that parameters act as weights that allow flexibility in the model. However, more parameters can lead to overfitting, where the model performs well on training data but struggles with new information. Adnan Masood, chief AI architect at UST, highlights that parameters influence precision, accuracy, and data management needs. However, due to the size of LLMs, it’s impractical to focus on individual parameters. Instead, developers assess models based on their intended purpose, performance metrics, and ethical considerations. Understanding the data sources and pre-processing steps becomes critical in evaluating the model’s transparency. It’s important to differentiate between parameters, tokens, and words. A parameter is not a word; rather, it’s a value learned during training. Tokens are fragments of words, and LLMs are trained on these tokens, which are transformed into embeddings used by the model. The number of parameters influences a model’s complexity and capacity to learn. More parameters often lead to better performance, but they also increase computational demands. Larger models can be harder to train and operate, leading to slower response times and higher costs. In some cases, smaller models are preferred for domain-specific tasks because they generalize better and are easier to fine-tune. Transformer-based models like GPT-4 dwarf previous generations in parameter count. However, for edge-based applications where resources are limited, smaller models are preferred as they are more adaptable and efficient. Fine-tuning large models for specific domains remains a challenge, often requiring extensive oversight to avoid problems like overfitting. There is also growing recognition that parameter count alone is not the best way to measure a model’s performance. Alternatives like Stanford’s HELM and benchmarks such as GLUE and SuperGLUE assess models across multiple factors, including fairness, efficiency, and bias. Three trends are shaping how we think about parameters. First, AI developers are improving model performance without necessarily increasing parameters. A study of 231 models between 2012 and 2023 found that the computational power required for LLMs has halved every eight months, outpacing Moore’s Law. Second, new neural network approaches like Kolmogorov-Arnold Networks (KANs) show promise, achieving comparable results to traditional models with far fewer parameters. Lastly, agentic AI frameworks like Salesforce’s Agentforce offer a new architecture where domain-specific AI agents can outperform larger general-purpose models. As AI continues to evolve, it’s clear that while parameter count is an important consideration, it’s just one of many factors in evaluating a model’s overall capabilities. To stay on the cutting edge of artificial intelligence, contact Tectonic today. 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 18, 2024in Data, Generative AI, Google, Salesforce

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 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

July 15, 2023in Salesforce

01Jun

Just When You Thought We Were GPT’d Out, Here Comes Slack and Generative AI

Since its public introduction in 2014, Slack has transformed from its original concept, a searchable log of all conversation and knowledge, into a comprehensive productivity platform that has reshaped how work and co-working is conducted. Get ready! Here comes Slack and Generative AI! In a recent release, Salesforce Slack unveiled a next-generation platform. A platform designed to facilitate seamless automation and integration for users of all technical levels, regardless of coding proficiency. This platform simplifies the utilization of data within Slack, offering enhanced automation and intelligence, allowing for the creation of no-code workflows, custom integrations, and the incorporation of generative AI. Steve Wood, Slack’s SVP of Product and Platform, highlights the significance of placing automation and generative AI tools directly into users’ hands as a pivotal step in Slack’s journey to redefine not only how people work but also how machines and humans interact in the future. Wood delves into the unique features of the new Slack platform, emphasizing its modular architecture grounded in building blocks like functions, triggers, and workflows. These components are remixable, reusable, and seamlessly integrate with the data flow within Slack. The platform enables developers to create tailored solutions, such as integrating with Salesforce, fostering more efficient collaboration, and automating workflows across various business functions. The introduction of generative AI, like Slack GPT, further enhances the platform’s capabilities. Slack GPT can use Einstein GPT to gain actionable data from Salesforce Customer 360 and Data Cloud. Wood underscores the potential of this combination to revolutionize work interactions by simplifying automation into reusable building blocks, accessible to both humans and machines. He emphasizes the transformative power of pairing data with AI and automation, anticipating a significant shift in how technology is leveraged in the workplace. Slack and GPT Wood also explains the recent Slack GPT news, detailing its native integration into the Slack user experience. Slack GPT brings generative AI directly into the platform, allowing users to summarize conversations, catch up on missed messages, and edit content effortlessly. The integration of Einstein GPT into Slack expands the conversational interface to Customer 360, providing real-time customer insights directly in Slack. This can be used to automatically generate case summaries based on data from Service Cloud AND Slack. As AI evolves over time, Wood shares his excitement about observing how people utilize Slack GPT in real-world scenarios. The focus remains on empowering platform users through native generative AI and leveraging data and behaviors to enhance the product continuously. Historical Content Wood emphasizes the historical context stored within Slack, highlighting the collective past as a valuable resource for future decision-making. Integrating AI technologies into this rich dataset within Slack presents a substantial opportunity for improving workflows and tools. Regarding the integration of Slack with Salesforce Customer 360, Wood stresses the importance of having relevant information easily accessible in one place. Slack serves as the hub where work occurs, and by incorporating generative AI, the platform aims to enhance transparency, alignment, and effectiveness in decision-making. Drawing in and analyzing the data from Slack as well as the other Salesforce platforms provides vital customer information. In reflection on the rapid adoption of this technology, Wood acknowledges the unique challenges presented by the unknown behavior of generative AI. Stability, accuracy, and safety are top concerns, with ethical and responsible development practices crucial for building trust. The future, as Wood sees it, hinges on maintaining a commitment to ethical development, ensuring customers feel confident in trusting the transformative capabilities of generative AI in the workplace and the Slack platform. 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

June 1, 2023in Salesforce Data Cloud, Salesforce Hospitality Travel and Transportation, Salesforce Manufacturing Automotive Energy, Salesforce Slack

04May

What is Health Cloud?

Salesforce Health Cloud, a specialized platform designed for healthcare organizations, aims to streamline operations and provide personalized care. By leveraging cloud technology, mobile capabilities, and social integration, Health Care Cloud enhances productivity, reduces costs, and offers a comprehensive view of patient data. Salesforce keeps electronic personal health information (ephi) safe and secure. Since its inception, Salesforce Health Cloud has evolved significantly to meet the unique and challenging needs of the healthcare sector. The introduction of Customer 360 for Health marks a milestone in fostering empathetic care and promoting collaboration among healthcare teams. What is Salesforce Health Cloud? At its core, Health Cloud facilitates seamless collaboration among healthcare teams to ensure optimal patient care. With robust security measures in place, patient data remains secure and accessible only to authorized personnel. Salesforce Health provides care teams with convenient access to both clinical and non-clinical patient information, including health conditions, medications, appointment history, communication preferences, and data from Electronic Health Records (EHRs). Categorized under Salesforce Industry Clouds, Health Cloud combines CRM capabilities with specialized features tailored for healthcare providers, payers, medical device companies, and pharmaceutical manufacturers. Its goal is to deliver personalized, connected care across the entire, sometimes disjointed healthcare ecosystem. Why Use Salesforce Health Cloud? Salesforce Health offers benefits to various stakeholders within the healthcare industry, including providers, payers, manufacturers, and research organizations. In a challenging healthcare landscape, Health Cloud addresses concerns such as managing complex patient data and enhancing patient experiences effectively. Success stories from organizations like New England Biolabs and John Muir Health highlight the impact of Health Salesforce in streamlining operations, improving customer satisfaction, and enhancing care coordination. Despite its transformative potential, widespread adoption and success require rebuilding patient trust, delivering empathetic care, and prioritizing data security. Tools like Health Cloud play a crucial role in achieving these goals. The Health Cloud Data Model Central to Salesforce Health is the flexible Health Cloud Data Model, built atop the Salesforce Platform. This model organizes diverse healthcare data sources, leveraging standard objects like Accounts and Contacts alongside custom objects tailored for healthcare applications. The data model revolves around organizing data around the patient, providing care teams with a holistic view for informed decision-making and personalized care. It supports interoperability and adheres to healthcare data standards like HL7 and FHIR, enabling seamless integration with EHRs and other healthcare applications. Key Features of Salesforce Health Salesforce Health Cloud offers a suite of robust features, including: Patient 360: Provides a unified view of the patient, consolidating demographics, clinical data, and interactions for informed decision-making and personalized care. Care Plans: Enables care teams to create, track, and manage personalized care plans, incorporating goals, tasks, and milestones to monitor patient progress. Care Coordination: Facilitates collaboration among care teams, supporting secure messaging and information sharing across providers. Health Timeline: Offers an interactive visualization of a patient’s medical history, including encounters, lab results, and medications, aiding in understanding and decision-making. Einstein Analytics for Healthcare: Utilizes AI to deliver advanced insights and predictions, empowering organizations to enhance patient outcomes and operational efficiency. Patient 360 for Health In addition to existing features, Salesforce introduces new innovations under Patient 360 for Health, powered by AI. These updates include: Advanced Therapy Management: Streamlines treatment processes for pharmaceutical companies, offering full visibility into patient treatment. Salesforce Genie for Healthcare: Integrates real-time clinical and non-clinical data to create comprehensive patient profiles. AI-Supported Health Timeline: Enhances navigation and accessibility of patient records and journeys through AI. Home Health: Automates intake and scheduling for in-home treatment, optimizing patient preferences. Data Cloud for Healthcare: Connects clinical and non-clinical patient data from multiple sources into a real-time patient profile. MuleSoft Direct for Health Cloud: Provides out-of-the-box connectors to access EHR data, facilitating smoother integration. Tableau Accelerators for Health Cloud: Offers ready-to-use dashboards for various use cases, empowering teams with data-driven insights. Integration With MuleSoft Salesforce Health seamlessly integrates with MuleSoft, enabling organizations to connect with existing EHRs, billing systems, and healthcare applications. MuleSoft’s Anypoint Platform provides pre-built connectors and APIs to simplify integration and unlock the full potential of healthcare data. Salesforce Health Cloud is not only a powerful platform for healthcare organizations but also a catalyst for positive change in patient care and operational efficiency. By leveraging HC’s capabilities, healthcare organizations can deliver exceptional patient experiences, streamline processes, and ensure data security and compliance. With continuous integration and innovation within the Salesforce ecosystem, Health Cloud remains positioned to drive transformative change in the healthcare industry, addressing challenges and paving the way for a brighter future in healthcare. Considering Salesforce Health Cloud for your practice? 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

May 4, 2023in Data, Mulesoft, Salesforce, Salesforce Health Cloud, Technology

17Aug

Salesforce Automation

In today’s fast-paced business landscape, efficiency reigns supreme, and the optimization of processes is paramount for success. Salesforce automation tools stand as indispensable allies in this pursuit, empowering businesses to automate repetitive tasks, eliminate errors, and liberate valuable time for employees to focus on mission-critical endeavors. From sales to marketing and customer service, Salesforce offers a comprehensive suite of automation tools designed to enhance efficiency, productivity, and ultimately, profitability. This insight serves as a guide to the value and various components of Salesforce automation. At the core of Salesforce’s effectiveness lies its sophisticated suite of automation tools, reshaping how organizations manage and streamline their sales operations. For administrators entrusted with overseeing the Salesforce environment, mastering these automation tools transcends mere skill; it’s a strategic necessity. Salesforce Automation Tools empower administrators to craft and deploy intricate workflows, facilitating seamless coordination of tasks and processes within the CRM platform. From lead generation to deal closure, these tools offer a comprehensive array of features that can metamorphose manual, time-consuming tasks into streamlined, automated processes. As businesses aspire for agility and efficiency in their operations, administrators assume the role of architects, leveraging Salesforce automation tools to erect a robust foundation for success. This deep dive into Salesforce Automation Tools aims to arm administrators with the knowledge and insights required to navigate the platform’s intricacies adeptly. We will dive into the fundamental components of automation, including workflow rules, process builder, and flow builder, unraveling their functionalities and showcasing how they can be tailored to suit specific business requirements. Through real-world examples and practical guidance, this exploration seeks to empower administrators to unleash the full potential of Salesforce Automation Tools, transforming them into proficient conductors of the CRM symphony. As we embark on this journey, administrators will cultivate a comprehensive understanding of Salesforce’s automation capabilities, enabling them to optimize processes, enhance productivity, and elevate the overall user experience. Whether you’re a seasoned Salesforce administrator or a newcomer to the platform, this deep dive promises invaluable insights and hands-on expertise to navigate the dynamic realm of Salesforce automation with confidence and proficiency. What is Salesforce Automation? Salesforce automation represents a sophisticated sales management solution that automates pivotal sales components such as lead management, sales forecasting, and team performance management. Leveraging Software-as-a-Service (SaaS) products enables the automation of repetitive and redundant tasks and processes. Workflow rules enable organizations to design and enforce business processes systematically. For example, as leads progress through the sales pipeline, workflow rules can automatically assign tasks to sales representatives, update opportunity stages, and notify relevant stakeholders. By establishing these rules, administrators lay the groundwork for a more streamlined and error-resistant workflow, allowing teams to focus on high-impact activities while the system handles routine, rule-based tasks. Understanding the foundations of Salesforce automation through workflow rules is crucial for administrators seeking to optimize their CRM environment. As businesses evolve, the ability to adapt and scale automation becomes paramount. This foundational knowledge not only empowers administrators to create efficient workflows but also sets the stage for exploring more advanced automation tools within the Salesforce ecosystem, ensuring a robust and responsive foundation for the dynamic world of CRM. Centralized Data Storage and Enhanced Lead Tracking Centralizing customer-related data stands as one of the most significant advantages of Salesforce automation. This consolidation facilitates streamlined lead tracking, performance monitoring, and revenue prediction. By automating non-revenue-generating tasks, which can consume up to two-thirds of a sales representative’s time, sales teams can redirect their efforts towards high-impact, revenue-generating activities, thereby fostering overall business growth. Automated Sales Processes: Boosting Productivity The automation of repetitive sales processes emerges as a direct response to research indicating the imperative to enhance sales productivity. Through automation, sales representatives can leverage their time more effectively, focusing on tasks that directly contribute to revenue generation and organizational success. Understanding the Crucial Role of Salesforce Automation The importance of Salesforce automation cannot be overstated in the realm of sales management. By offering centralized data storage, streamlined lead tracking, and enhanced performance monitoring, Salesforce automation revolutionizes the sales landscape. Discover – Controllers in Salesforce: What It Is, Types and Features. Key Benefits of Salesforce Automation The benefits of Salesforce automation are manifold. Firstly, it facilitates the consolidation of customer-related data, enabling efficient lead tracking, reminder setting, and performance monitoring. Additionally, automation saves time and minimizes errors, allowing sales representatives to concentrate on revenue-generating activities such as deal closure and client relationship building. Moreover, Salesforce automation ensures a personalized and consistent customer experience, empowering sales reps to tailor interactions based on customer preferences and behaviors. Furthermore, Salesforce automation fosters efficient collaboration and communication within sales teams, providing a centralized platform for accessing essential information and insights. Additionally, it offers valuable analytics and insights to optimize sales strategies, analyzing customer behavior, sales performance, and market trends to drive revenue growth. Components of Salesforce Automation Lead Management: Involves collecting, tracking, and analyzing customer data and interactions to streamline the sales pipeline and convert leads into customers. Sales Forecasting: Enables organizations to make educated decisions and prepare for future development by forecasting sales revenue based on market analysis and demands. Team Performance Management: Involves monitoring sales team performance, identifying areas for improvement, and providing feedback and coaching to enhance team performance. Email and Social Media Marketing: Automates marketing platforms to communicate better with consumers and prospects, customizing campaigns based on customer preferences and behaviors. Workflow and Approval Processes: Ensures that sales processes follow established procedures and workflows, reducing manual errors and enhancing organizational performance. Data and Analytics: Tracks key performance indicators (KPIs) and provides actionable insights to inform decision-making and drive revenue growth. Streamlining Appointment Scheduling: Assists in scheduling sales appointments efficiently, mitigating the risk of double-booking meetings. Prioritizing Leads: Analyzes leads based on various metrics to maximize sales and productivity. Salesforce automation has revolutionized sales operations, fostering efficiency, effectiveness, and customer-centricity. As technology continues to evolve, Salesforce automation remains a critical tool for businesses striving to stay ahead of the competition and achieve their sales goals. In the dynamic world of Salesforce, administrators

August 17, 2022in Data, Marketing Automation, Salesforce, Salesforce Implementation Services, Technology

18Feb

Salesforce Integrations Explained

Introducing Salesforce Integration – Fundamental Concepts Before diving deep into more the complex aspects, let’s explore the basics of Salesforce Integrations, encompassing three key areas: integration types, integration capabilities, and integration patterns. When we talk about integration, it means to create a connection between a specific Salesforce instance and another database, third party product, or system. The connection can be inbound, outbound, or bi-directional, and you may be connecting to another database, another Salesforce instance, or another cloud-based data source. What is Integration? Salesforce Integration involves bringing together two or more systems to streamline distinct processes, enabling the efficient management of information across various business processes that span multiple systems. Salesforce Integration is a process of connecting two or more applications. This provides both a sharing of data between systems and end user improved efficiency. Enterprise systems use many applications, many or most of which are not designed to work with one another out of the box. How many integrations does Salesforce have? Salesforce has over 3,000 integrations available on its AppExchange marketplace alone. Apart from those, you can use: low-code and no-code integrations like Coupler.io or Zapier for data automation. Why is Integration Important with Salesforce? In our digital era, enhancing efficiency and customer experience is crucial for competitiveness and user adoption. Integration ensures that systems work seamlessly together by fostering a scalable and faster collaborative environment. How do you make Salesforce even better? Integrate it with the apps you already use. From productivity to marketing to collaboration and beyond, now you can connect your Salesforce to the other tools you need to run your business. MuleSoft is Salesforce’s integration and automation technology and offers connectivity solutions for all of your apps. What is an API? API, or Application Programming Interface, facilitates communication between two applications. It enables the smooth exchange of data, ensuring processes occur without interruptions. Different API types will be covered in the ‘Salesforce Integration Capabilities’ section. Types of Salesforce Integration Architectures Three integration architectures come with both their benefits and drawbacks: Salesforce Integration Capabilities Consider the following aspects for efficient Salesforce integration: Understanding integration involves recognizing its fundamental concepts, including types, architectures, and capabilities. 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

February 18, 2022in Data, Enterprise, Mulesoft, Salesforce, Technology

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