Salesforce Data Archives - gettectonic.com - Page 10
Salesforce Backup and Recovery
17Aug

Salesforce Backup: A Complete Guide for IT Admins

Salesforce Backup: Safeguarding Your Data with Confidence-Salesforce Backup A Complete Guide for IT Admins In a rapidly evolving digitally-based landscape, protecting your Salesforce data is crucial to ensuring business continuity and maintaining customer trust. Salesforce Backup offers a comprehensive solution to prevent permanent data loss, seamlessly manage system upgrades, and mitigate the impact of cyberattacks, all while meeting compliance and legal requirements. Key Benefits of Salesforce Backup: Why Choose Salesforce Backup? Salesforce Backup goes beyond data protection—it promises swift data restoration, top-tier performance, and robust data resilience, setting the standard for reliability in a native Salesforce solution. How Salesforce Backup Works: For Salesforce Admins: Salesforce Backup was crafted with Salesforce Admins in mind, offering streamlined implementation and configuration within Salesforce’s trusted infrastructure. Say goodbye to complex integrations and custom coding—protect your organization’s data efficiently and securely. What’s Next for Admins? Salesforce Backup is now generally available (GA), offering powerful features, a simple user interface, and high-scale technology to empower IT leaders and admins to recover from data loss incidents with ease. Attend the Salesforce webinar on August 29 to explore Salesforce Backup’s capabilities firsthand and redefine data protection strategies for your 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 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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August 17, 2023in Salesforce
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 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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July 21, 2023in Data, Mulesoft, Salesforce, Salesforce Data Cloud
Generative AI Regulations
17Jul

Salesforce, Data Science, and Generative AI

Is Salesforce utilized in the field of data science? Salesforce data science and Generative AI Data Science-as-a-Service (DSaaS) democratizes access to machine learning through the Salesforce Data Management Platform, enabling widespread adoption of data science capabilities. Utilizing Salesforce for Data Science Empowerment: The integration of Salesforce into data science represents a transformative endeavor aimed at democratizing machine learning through Data Science-as-a-Service (DSaaS). By leveraging the Salesforce Data Management Platform, the objective is to empower individuals across various domains with the potential of data science. Democratization of Data Science: DSaaS introduces a versatile workbench that capitalizes on machine learning to refine segmentation, enhance activation strategies, and uncover deeper insights. Through robust analytics tools, users can gain profound insights into individual customer behaviors. Supported by a formidable 20-petabyte analytics environment and a real-time big data infrastructure, data-driven analytics are taken to unprecedented levels. Harnessing Modeling Resources: Data owners enjoy the flexibility to harness their data, algorithms, and models either within the Salesforce Data Management Platform or within their independent environments. Spearheading this initiative is the Salesforce Unified Intelligence Platform (UIP) team, constructing a centralized data intelligence platform aimed at enriching business insights, enhancing user experience, improving product quality, and optimizing operational efficiency, all while upholding the core value of trust embedded in the Salesforce platform. Salesforce Data Science and Generative AI Emphasizing Security and Design: Security stands as a cornerstone of the Salesforce platform, with the UIP’s evolution tracing back to a transition from a colossal Hadoop cluster to UIP in public clouds. The architectural journey prioritized data classification early on, engaging in meticulous reviews with legal and security experts to classify data intended for storage within UIP. Adopting the “zero-trust infrastructure” principle, the architecture is fortified against both internal and external threats, ensuring robust defense mechanisms against potential data breaches. Unlocking Data Science Potential through DSaaS: DSaaS serves as a catalyst in democratizing machine learning through the Salesforce Data Management Platform, spotlighting the pivotal role of data science in fostering generative AI and cultivating trustworthy AI. Data scientists play a critical role in ensuring data quality and organization to steer clear of issues such as biased or irrelevant outcomes. Navigating Data Science Challenges: Despite the transformative potential of data science, businesses encounter various challenges including managing diverse data sources, scarcity of skilled professionals, data privacy and security concerns, data cleansing complexities, and effectively communicating findings to non-technical stakeholders. Proposed Solutions: Addressing these challenges involves leveraging data integration tools, investing in the upskilling and reskilling of data professionals, implementing robust data privacy measures, employing data governance tools for data cleansing, and honing communication skills for reporting findings to non-technical stakeholders. The success of generative AI hinges on well-organized data, and data science is pivotal in achieving this. Whether utilizing AI tools built with the expertise of data scientists or building a data science team, businesses can navigate the evolving landscape of AI and data science with confidence. Content updated March 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 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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July 17, 2023in AI Tools, Data, Generative AI, Salesforce, Salesforce Einstein, Salesforce Sales 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 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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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 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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July 11, 2023in Data, Salesforce, Salesforce Data Cloud, Technology
Salesforce Service Cloud
18Jun

AI in Salesforce Service Cloud

Deliver personalized service and save time with AI built directly into your flow of service work. Utilize Salesforce’s trusted AI for customer service to create seamless conversational, predictive, and generative AI experiences for your agents and customers. Service Cloud has everything you need to scale now and drive immediate value. Salesforce launched Service Intelligence, a powerful new analytics app for Service Cloud designed to boost agent productivity, cut costs, and enhance customer satisfaction.  And now you have AI in Service Cloud. Powered by Data Cloud, Salesforce’s real-time hyperscale data engine, Service Intelligence gives users access to all of their data directly within Service Cloud, eliminating the need to toggle between screens for information. Pre-built, customizable dashboards inside Service Intelligence provide a view of essential metrics like customer satisfaction and individual and team workloads. And, with Einstein Conversation Mining, service professionals can use AI to analyze customer chat and email conversations to uncover insights — like specific challenges customers face during service interactions — assess the likelihood of complaint escalation, and proactively address the issue with the customer. To add AI in Service Cloud to your instance, contact Tectonic today. Service Intelligence, a new analytics app for Service Cloud is designed to boost productivity, cut costs, and enhance customer satisfaction.  AI is gaining prominence among service professionals, with an 88% increase in AI adoption from 2020 to 2022. This is no surprise, as 63% of service professionals say AI will help them serve customers faster. By embracing AI, service professionals can make informed decisions fast and enhance customer satisfaction, securing a competitive edge. Like Related Posts 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 Salesforce Artificial Intelligence Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust Read more CRM Cloud Salesforce What is a CRM Cloud Salesforce? Salesforce Service Cloud is a customer relationship management (CRM) platform for Salesforce clients to Read more Salesforce’s Quest for AI for the Masses The software engine, Optimus Prime (not to be confused with the Autobot leader), originated in a basement beneath a West Read more

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June 18, 2023in Salesforce Einstein, Salesforce Manufacturing Automotive Energy, Salesforce Service Cloud
Migrating from Zoho to Salesforce
03Jun

Migrating from Zoho to Salesforce

Migrating from Zoho to Salesforce is a major undertaking that can significantly enhance a business’s CRM processes as it scales. To ensure a smooth transition, careful planning, data preparation, and execution are essential for accurate and efficient data transfer. In this guide, we’ll outline a step-by-step approach to migrating from Zoho to Salesforce, covering everything from pre-migration planning to post-migration follow-up. Step 1: Pre-Migration Planning 1. Evaluate Your Business RequirementsBegin by assessing your current and future CRM needs. Identify critical functionalities in Zoho that you depend on and determine which Salesforce features will either replace or enhance them. Consider: This evaluation ensures that Salesforce is configured to align with your business processes during the migration. 2. Assign a Project TeamForming a dedicated project team is crucial for a successful migration. Include stakeholders from various departments, designate a project manager to oversee the migration, and enlist CRM specialists familiar with both Zoho and Salesforce. You might also consider hiring a certified Salesforce consultant or partner to assist with technical aspects. 3. Backup Your DataBefore starting the migration, back up your data from Zoho. Export all relevant information, including: This backup ensures you have a complete copy in case any issues arise during the migration. Step 2: Preparing Salesforce for Migration 1. Customize Salesforce to Fit Your NeedsNow is the time to tailor Salesforce to reflect your existing business processes. Review the custom fields, objects, workflows, and automations in Zoho, replicating or enhancing them in Salesforce. Salesforce’s high customization level allows you to optimize your CRM for your team’s specific needs. 2. Set Up IntegrationsIf you’ve been using third-party tools integrated with Zoho, establish the corresponding integrations in Salesforce. The Salesforce AppExchange offers a range of integrations, from email marketing platforms to accounting systems. Ensure the necessary tools are connected before migrating your data. Step 3: Data Migration 1. Map Data FieldsTo facilitate accurate data transfer between Zoho and Salesforce, map your Zoho data fields to their Salesforce counterparts. For instance, “Account Name” in Zoho should correspond to “Account” in Salesforce. Create equivalent custom fields in Salesforce if they exist in Zoho. 2. Cleanse Your DataData cleansing is vital to ensure only relevant, accurate, and up-to-date information is migrated. Remove duplicates, outdated contacts, and incorrect data from Zoho. This step streamlines the migration process and enhances the quality of your Salesforce database. 3. Choose a Migration ToolSelect an appropriate tool for migrating data between CRMs, such as: Depending on your data’s complexity, choose a tool that provides additional support, such as mapping or automation features. 4. Perform a Test MigrationConduct a test migration using a subset of your data before proceeding with the full migration. This allows you to verify that everything transfers correctly and identify any issues with data formatting, relationships between objects, or custom fields. Adjust your mapping and migration processes based on the test results. Step 4: Full Data Migration Once you’re satisfied with the test migration results, proceed with the complete data migration from Zoho to Salesforce. This process may take time, depending on your data‘s size and complexity, so plan for downtime or staggered migrations to minimize disruptions to your team’s workflow. Step 5: Post-Migration Setup and Testing 1. Validate Your DataAfter the full migration, verify that all data has been transferred accurately. Check for discrepancies between Zoho and Salesforce records, ensuring that relationships (e.g., contacts linked to accounts) remain intact. 2. Recreate Automation and WorkflowsRe-establish any automations, workflows, or triggers that were functioning in Zoho within Salesforce. Utilize Salesforce’s automation tools, like Process Builder or Flow, to replicate these processes. Adjust them as necessary to leverage Salesforce’s advanced capabilities. 3. Train Your TeamSuccessful migration extends beyond data transfer; it’s crucial to ensure your team is equipped to use Salesforce effectively. Conduct training sessions focused on new features and workflows, providing documentation or cheat sheets to facilitate adjustment. 4. Monitor System PerformancePost-migration, monitor your Salesforce instance for any performance issues or data inconsistencies. Make necessary adjustments and ensure that integrations are functioning as expected. Step 6: Post-Migration Follow-Up 1. Run Parallel OperationsConsider operating both systems in parallel for a brief period to confirm Salesforce is fully functional before phasing out Zoho. This allows your team to continue using Zoho if critical issues arise in Salesforce during the transition. 2. Optimize Your Salesforce InstanceOver time, identify opportunities to further customize and optimize Salesforce to meet your evolving needs. Regularly review workflows, automations, and reporting to fully utilize Salesforce’s advanced features. Who Should You Work With A successful migration from Zoho to Salesforce involves key stakeholders from various departments to ensure the new CRM setup aligns with company processes. Typical Migration Timelines The duration of a Zoho to Salesforce migration typically depends on your data’s complexity and the required customizations. For smaller businesses with straightforward CRM setups, the process may take 4 to 6 weeks, encompassing planning, data mapping, testing, and training. However, larger organizations with extensive custom fields, workflows, and third-party integrations may require 3 to 6 months. Allow ample time for thorough testing and validation to ensure data accuracy and system functionality before going live. User training and post-migration follow-up are also critical for a smooth transition. Conclusion Migrating from Zoho to Salesforce is a growth step in enhancing your company’s CRM capabilities and positioning it for future growth. By meticulously planning your migration, cleansing your data, and configuring Salesforce to meet your business requirements, you can facilitate a smooth transition and leverage Salesforce’s powerful features from day one. With the right preparation and support, your business will be well-equipped to scale, streamline operations, and achieve long-term success using Salesforce. If you need assistance with your migration, don’t hesitate to contact Tectonic for a quote. Content updated September 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

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June 3, 2023in Data, Mulesoft, Salesforce
Use Salesforce Data Protection Services
24May

Use Salesforce Data Protection Services

Ensuring Data Security in Salesforce: Use Salesforce Data Protection Services How is Data Protected in Salesforce? Salesforce.com employs cutting-edge Internet security technology, including Secure Socket Layer (SSL) with server authentication and data encryption, ensuring the protection of user information during access via supported web browsers. Why Use Salesforce Data Protection Services? Data Security is imperative to meet evolving privacy standards and safeguard sensitive information shared over the internet. Unauthorized access could pose significant risks to the entire organization, making data security a crucial aspect. Salesforce GDPR Certification: Yes, Salesforce is GDPR certified, as confirmed in Section 11 of the Data Processing Addendum, where Salesforce commits to providing reasonable assistance to customers. Salesforce CCPA Compliance with Customer 360: Salesforce 360 facilitates CCPA compliance by seamlessly connecting Marketing Cloud to Sales Cloud, ensuring a cohesive flow of customer data across the organization. Introduction to Salesforce Shield: Salesforce Shield comprises three security tools – Shield Platform Encryption, Event Monitoring, and Field Audit Trail – embedded into critical business applications to enhance trust, compliance, and governance. Data Protection and Privacy Regulations: Salesforce assists companies in complying with various data protection and privacy regulations, including GDPR, CCPA, PIPA, LGPD, PIPEDA, and more, offering guidance on common privacy principles. Guidance for Data Management and Privacy Compliance: Salesforce provides tools for efficient data management and privacy compliance, covering aspects such as data deletion, consent management, restriction of processing, data access and portability, and privacy center functionalities. Encryption of Data at Rest with Salesforce Shield Platform Encryption: Salesforce Shield Platform Encryption encrypts data at rest within servers, databases, search index files, and the file system, preserving functionality and ensuring secure storage. Use Salesforce Data Protection Services: To enable Data Privacy and Protection in Salesforce, users can navigate through Lightning Experience or Salesforce Classic, accessing the setup and entering “Data Protection and Privacy” in the Quick Find box. Various Data Security Settings in Salesforce: Salesforce offers multiple data security settings, including Object-Level Security through profiles, Permission Sets, Permission Set Groups, Ownership-Based Sharing Rules, Criteria-Based Sharing Rules, and Guest User Sharing Rules. Salesforce Data Ownership and Digital Chain of Custody: Salesforce Data Ownership involves maintaining a Digital Chain of Custody, ensuring meticulous tracking of data through each replication process. This guarantees data remains under control and protected at all times. Salesforce and DLP Functionality: Salesforce does not have built-in Data Loss Prevention (DLP) functionality. It relies on third-party apps like Nightfall to provide DLP capabilities, crucial for maintaining compliance with regulations such as HIPAA and PCI. 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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May 24, 2023in Salesforce, Salesforce Marketing Cloud, Salesforce Sales Cloud, Salesforce Service Cloud, Salesforce Shield
Google Analytics and Salesforce Integration
18May

Google Analytics and Salesforce Integration

Syncing Google Analytics Data to CRM Analytics To integrate Google Analytics and Salesforce Integration using the Google Analytics connector, follow these steps: Important Note: As of July 1, 2023, the main product Google Analytics has been decommissioned and replaced with the new product Google Analytics 4 (GA4). The Salesforce announcement GA4 Set to Replace Universal Analytics gives an overview on this. Creating a Connection Required Settings: Google Analytics 4 Integration To sync Google Analytics 4 data to Salesforce Data Pipelines: Connector Considerations: Google Analytics Salesforce Sales Cloud Integration User Identification Analytics provides two methods to identify users: Required Salesforce Sales Cloud Objects and Fields: Integration Steps: Testing and Viewing Imported Data: Notes: Google Data Studio and Salesforce Integration Connecting Salesforce with Google Data Studio allows for powerful visualizations that combine sales and marketing data. This integration helps in understanding which channels generate the most leads and income. Google Analytics 4 Connection Setup: Connection Details: Advanced Properties: Considerations: By following these steps, you can seamlessly integrate Google Analytics data into your CRM Analytics and Salesforce Data Pipelines, ensuring robust data analysis and informed decision-making. Decide How to Identify Your Users: Analytics offers two ways to programmatically identify your users: Client ID and User-ID. To support Data Import for Salesforce Sales Cloud, you must implement Client ID. You may optionally choose to also implement User-ID. Client ID pseudonymously identifies a browser instance and is best suited for businesses focused on lead generation and new customer acquisition. User-ID enables the analysis of groups of sessions, across devices, using a unique, persistent, and non-personally identifiable ID string representing a user. This option is best for businesses with high rates of logged-in users. How to Import CRM/ERP Data with Google Analytics 4 Using a CSV File: Transitioning to Google Analytics 4: As of March 2023, Google has automatically created GA4 properties for users unless they opt-out. Until July 1, 2023, you can continue to use and collect new data in your Universal Analytics properties. After this date, you must export your historical reports as Universal Analytics will be phased out. How Does Google Help Salesforce Marketing Cloud Users? Google Analytics provides invaluable insights into user behavior, helping Salesforce Marketing Cloud users optimize campaigns and understand customer journeys. Integration with the Google platform allows businesses to combine offline sales data with digital analytics, optimizing digital marketing strategies and improving campaign effectiveness. Additional Integration: Using datasets from Google Analytics and Google BigQuery, businesses can create interactive Tableau CRM dashboards to visualize campaign activities and performance metrics. By following these guidelines, organizations can leverage Google Analytics data effectively within their Salesforce ecosystem, enhancing decision-making and strategic planning. Content updated July 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 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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May 18, 2023in Google Analytics, Salesforce Marketing Cloud, Salesforce Sales Cloud
Salesforce Data Cloud Explained
15May

Salesforce Data Cloud Explained

Salesforce Data Cloud, previously recognized as Salesforce CDP/Genie, made its debut at Dreamforce 2022, hailed by Salesforce as one of the most significant innovations in the company’s history. A hyperscale data platform built into Salesforce. Activate all your customer data across Salesforce applications with Data Cloud. Data Cloud facilitates the intake and storage of real-time data streams on a massive scale, empowering automated tasks that result in highly personalized experiences. Data can be sourced from diverse Salesforce data outlets, including Mulesoft, Marketing Cloud, and others, along with customers’ proprietary applications and data sources. Subsequently, it can dynamically respond to this real-time data by automating actions across Salesforce CRM, Marketing Cloud, Commerce, and more, inclusive of automating actions through Salesforce Flow. What is the Salesforce data cloud? Data Cloud is the fastest growing organically built product in Salesforce’s history (i.e. Salesforce built it themselves, not via acquisitions). Data Cloud could be described as the ‘Holy Grail of CRM’, meaning that the data problem that’s existed since the infancy of CRM is now finally solvable. Data Cloud is the foundation that speeds up the connectivity between different ‘clouds’ across the platform. However, Data Cloud is also a product that can be purchased. While not all Salesforce customers have licensed Data Cloud, being at the foundation means they are still taking advantage of Data Cloud to a degree – but this all becomes even stronger with Data Cloud as a personalization and data unification platform. What is the history of Data Cloud? Salesforce has gone through several iterations with naming its CDP product: Customer 360 Audiences → Salesforce CDP → Marketing Cloud Customer Data Platform → Salesforce Genie → Salesforce Data Cloud. In some instances, changes were made because the name just didn’t stick – but what’s more important to note, is that some of the name changes were to indicate the significant developments that happened to the product. Salesforce Data Cloud Differentiators Data Cloud, in itself, is impressive. While many organizations would consider it expensive, if you were to flip the argument on its head, by buying your own data warehouse, building the star schema, and paying for ongoing compute storage, you’d be looking to spend 5 to 10 times more than what Salesforce is charging for Data Cloud. Plus, data harmonization works best when your CRM data is front and center. There are other key differentiators that helps Data Cloud to stand out from the crowd: Is data cloud a data lakehouse? That means that Data Cloud is now not just a really good CDP, it’s now a data lake which will be used in sales and service use cases. But it also means that we can start to fundamentally move some of our higher-scale consumer products like Marketing and Commerce onto the platform. Is Snowflake a data Lakehouse? Snowflake offers customers the ability to ingest data to a managed repository, in what’s commonly referred to as a data warehouse architecture, but also gives customers the ability to read and write data in cloud object storage, functioning as a data lake query engine. What is the benefit of Salesforce data cloud? Data Cloud empowers Salesforce Sales Cloud with AI capabilities and automation that quickly closes deals and boosts productivity across every channel. It drives customer data from all the touchpoints and unifies it separately in individual customer profiles. Salesforce Data Cloud is a powerful data warehouse solution that allows companies to effectively manage and analyze their data. What is the difference between Salesforce CDP and data lake? Talking abut Salesforce CDP is a little bit like a history lesson. While a CDP provides a unified, structured view of customer data, a data lake, on the other hand, is more of a raw, unstructured storage repository that holds a vast amount of data (more than just customer data) in its native format until it’s needed. 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

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May 15, 2023in Data, Generative AI, Mulesoft, Salesforce, Salesforce Data Cloud, Salesforce Einstein, Salesforce Flow, Salesforce Implementation Services, Salesforce Sales Cloud, Snowflake, Technology
09May

Eloqua Salesforce Integration

About Salesforce integration with Oracle Eloqua Synchronizes accounts, contacts, leads and opportunities in Salesforce with Oracle Eloqua. For more information, see Data imports from Salesforce to Oracle Eloqua. Uses contact data in Oracle Eloqua to update contacts and generate sales leads in Salesforce. However, the integration of Oracle Eloqua with Salesforce comes with certain limitations that can have adverse effects on businesses. In this blog post, we will explore these limitations and how Salesforce Marketing Cloud can serve as a solution. Limitations: 1. Integration Process 2. Flexibility and Customization 3. Data Synchronization 4. User Experience and Interface 5. API 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 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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May 9, 2023in Salesforce
Guide to Who is Running Flows
06May

Guide to Who is Running Flows

Understanding the Role of the Running User in Flows Guide to Who is Running Flows. The running user in a flow is the individual who initiates the flow, either the current user or the Automated Process user. This user determines what actions the flow can perform with Salesforce data when executed in user context. Grasping the significance of the running user is crucial when designing automation in Flow Builder, as it directly affects record operations and troubleshooting. Let’s dig deeper into this essential aspect. Identifying the Running User The running user is the person who triggers the flow, which can be either the current user or the Automated Process user. Recognizing the running user provides insights into flow actions and aids in diagnosing issues. For flows operating in user context, the running user’s profile and permission sets determine the flow’s object permissions and field-level access. When a flow interacts with Salesforce data, it adheres to these permissions and access levels. Flow Execution Context Flows operate within three distinct contexts: user context, system context with sharing, and system context without sharing. Each context affects how the flow interacts with Salesforce data and respects user permissions and access levels. Understanding Flow Types and Contexts Different flow types operate within specific contexts: By understanding the role of the running user, you can optimize Flow’s capabilities in your automation processes. Maximize the power of Flow with confidence, ensuring compliance with user permissions and access levels. #LetItFlow! For more automation insights, visit our Automation pages on our insights. 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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May 6, 2023in Salesforce
Salesforce Flow
05May

Einstein GPT Integration with Flow and Data Cloud Now Available

Salesforce has recently unveiled exciting new capabilities for Flow, Einstein GPT Integration with Flow and Data Cloud. Introducing Einstein GPT and Data Cloud features to its growing family of automation tools. This development empowers everyday administrators, eliminating the need for extensive engineering teams to harness large datasets for automation purposes. The groundbreaking aspect lies in the ability to achieve this in real-time, using a user-friendly interface without the need for coding. Introduction to Einstein GPT: Einstein GPT builds upon ChatGPT technology, combining public and private AI models with CRM data within Salesforce. This allows users to pose natural-language prompts directly within Salesforce CRM, receiving AI-generated content that adapts continuously to changing customer information and needs. The learning capability of Einstein GPT ensures ongoing improvement based on user input, aligning with best practices. Einstein GPT for Flow: When integrated with Salesforce Flow, Einstein GPT enables users to create and modify automations through a conversational interface, simplifying the flow creation process significantly. This fusion lowers barriers for non-technical users, enhancing the overall experience with Flow Builder and ensuring adherence to best practices. Key benefits of Einstein GPT for Flow include: Pricing details for Einstein GPT products are pending confirmation, and Salesforce will soon announce pilot program dates to broaden accessibility. Data Cloud for Flow: Introduced at Dreamforce 2022, Salesforce Data Cloud, formerly Genie, facilitates highly personalized customer experiences in real-time. Serving as a command center for customer data, Data Cloud integrates real-time data streams with Salesforce data, powering Flow with actionable insights. By combining Data Cloud with Flow, users can automate complex workflows and trigger actions based on real-time changes without the need for extensive IT involvement. This approach streamlines the process of designing, building, and testing custom integrations, reducing the burden on IT teams. Key advantages of Data Cloud for Flow include: These announcements bring forth transformative capabilities, making data utilization more accessible and streamlining the automation process within Salesforce. The combination of Einstein GPT and Data Cloud for Flow opens up possibilities for creating personalized and interconnected customer experiences across different sectors. 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

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May 5, 2023in Salesforce Data Cloud, Salesforce Einstein

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