Salesforce Data Cloud - gettectonic.com - Page 5
Salesforce Data Cloud
08Sep

Salesforce Data Cloud Evolution

Data Cloud stands as the fastest-growing organically built product in Salesforce’s history, signifying a significant milestone in solving the enduring data problem within Customer Relationship Management (CRM). Salesforce Data Cloud Evolution since its beginnings is an interesting story. With an average of 928 systems per company, identity resolution becomes challenging, especially when managing more than one system. Salesforce’s expansion into AI-powered CRM emphasizes the synergy between AI and data, recognizing that AI’s optimal functionality requires robust data support. Data Cloud acts as the foundation accelerating connectivity across different ‘clouds’ within the Salesforce platform. While it’s available for purchase, even Salesforce customers without licensed Data Cloud still benefit from its foundational advantages, with increased strength when utilized as a personalization and data unification platform. The history of Data Cloud reflects its evolution through various iterations, from Customer 360 Audiences to Salesforce Genie, ultimately settling as Data Cloud in 2023. This journey marked significant developments, expanding from a marketer’s tool to catering for sales, service, and diverse use cases across the Salesforce platform. Data harmonization with Data Cloud simplifies the complex process, requiring fewer efforts compared to traditional methods. It comes pre-wired to Salesforce objects, reducing the need for extensive data modeling and integration steps. The technical capability map showcases a comprehensive integration of various technologies, making Data Cloud versatile and adaptable. Data Cloud’s differentiators include being pre-wired to Salesforce objects, industry-specific data models, prompt engineering capabilities, and the inclusion of the Einstein Trust Layer, addressing concerns related to generative AI adoption. Looking ahead, Data Cloud continues to evolve with constant innovation and features in Salesforce’s major releases. The introduction of Data Cloud for Industries, starting with Health Cloud, signifies ongoing enhancements to cater to industry-specific needs. Closing the skills gap is crucial for effective Data Cloud implementation, requiring a blend of developer skills, data management expertise, business analyst skills, and proficiency in prompt engineering. Salesforce envisions Data Cloud, combined with CRM and AI, as the next generation of customer relationship management, emphasizing the importance of sound data and skillful implementation. Data Cloud represents the ‘Holy Grail of CRM,’ offering a solution to the long-standing data challenges in CRM. However, its success as an investment depends on the organization’s readiness to demonstrate return on investment (ROI) through solid use cases, ensuring unified customer profiles and reaping the rewards of this transformative technology. FAQ When did Salesforce introduce data cloud? Customer 360 Audiences: Salesforce’s initial CDP offering, launched in 2020. Salesforce CDP: The name changed in 2021 to align with how the blooming CDP market was referring to this technology. Does Salesforce data cloud compete with Snowflake? They offer distinct capabilities and cater to diverse business needs. Salesforce Data Cloud specializes in data enrichment, personalization, and real-time updates, while Snowflake boasts scalable data warehousing and powerful analytics capabilities. What is the data cloud in Salesforce? Deeply integrated into the Einstein 1 Platform, Data Cloud makes all your data natively available across all Salesforce applications — Sales Cloud, Service Cloud, Marketing Cloud, Commerce Cloud, Tableau, and MuleSoft — to power automation and business processes and inform AI. Is Salesforce Genie now data cloud? Announced at Dreamforce ’22, Salesforce Genie was declared the greatest Salesforce innovation in the company’s history. Now known as Data Cloud, it ingests and stores real-time data streams at massive scale, and combines it with Salesforce data. This paves the way for highly personalized customer experiences Like1 Related Posts 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 Salesforce Government Cloud: Ensuring Compliance and Security Salesforce Government Cloud public sector solutions offer dedicated instances known as Government Cloud Plus and Government Cloud Plus – Defense. Read more

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September 8, 2023in Salesforce Data Cloud, Salesforce Einstein
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 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 26, 2023in Einstein 1 Platform, Generative AI, Salesforce Data Cloud, Salesforce Einstein
Data Quality A Challenge and A Priority
15Aug

Data Quality A Challenge and A Priority

Data accuracy — and confidence in data accuracy — is a key component of trusted data. This makes Data Quality A Challenge and A Priority. Departments closest to the data, like data and analytics teams, have the highest confidence in their data accuracy. Confidence among line-of-business leaders is lower, revealing an opportunity to instill data confidence across marketing, sales, and service teams. 57% of data and analytics leaders have complete confidence in their data’s accuracy. Overall, there is room for improvement. Increased tracking of critical metrics, such as data quality, data utilization, data management and costs, data services delivery, and the ROI of data initiatives, could be one giant leap forward in making such an improvement. Surging Data Overwhelms Users — And Poses an Opportunity Business leaders’ second biggest data challenge, dealing with overwhelming volumes of data, shows no signs of abating. Over two-thirds of analytics and IT leaders expect data volumes to increase 22% on average over the next year. They expect similar growth rates across a variety of sources including third-party data and device data. For data leaders, increasing and diverse data sources require more effort to standardize data. This is likely to worsen a major challenge for analytics and IT leaders: lack of data harmonization (i.e., standardizing data from different sources). Overcoming this challenge presents an opportunity for differentiation. Almost two-thirds (65%) of customers say they expect companies to adapt experiences to match their changing needs, yet 80% of business leaders say personalization is difficult to scale. For companies looking to serve more tailored experiences, mature data management capabilities are a key competitive advantage. 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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August 15, 2023in Salesforce Data Cloud, Salesforce Einstein
How Data Cloud Vector Databases Work
27Jul

How Data Cloud Vector Databases Work

How Data Cloud Vector Databases Work 1. Ingest Unstructured Data in Data Cloud With the help of a new, unstructured data pipeline, relevant unstructured data for case deflection, such as product manuals or upgrade eligibility knowledge articles, can be ingested in Data Cloud and stored as unstructured data model objects. 2. Chunk and Transform Data for Use in AI In Data Cloud, teams will then be able to select the data that they want to use in processes like search, chunking this data into small segments before converting it into embeddings – numeric representations of data optimized for use in AI algorithms.  This is done through the Einstein Trust Layer, which securely calls a special type of LLM called an “embedding model” to create the embeddings. It is then indexed for use in search across the Einstein 1 platform alongside structured data. How Data Cloud Vector Databases Work. 3. Store Embeddings in Data Cloud Vector Database In addition to supporting chunking and indexing of data, Data Cloud now natively supports storage of embeddings – a concept called “vector storage”. This frees up time for teams to innovate with AI instead of managing and securing an integration to an external vector database. 4. Analyze and Act on Unstructured Data Use familiar platform tools like Flow, Apex, and Tableau to use unstructured data, such as clustering customer feedback by semantic similarity and creating automations that alert teams when sentiment changes significantly. 5. Deploy AI Search in Einstein Copilot to Deflect Cases With relevant data, such as knowledge articles, securely embedded and stored in Data Cloud’s vector database, this data can also be activated for use in Einstein AI Search within Einstein Copilot. When a customer visits a self-service portal and asks for details on how to return a product, for example, the Einstein Copilot performs semantic search by converting the user query into an embedding, after which it compares that query to the embedded data in Data Cloud, retrieving the most semantically relevant information for use in its answer while citing the sources it pulled from. The end result is AI-powered search capable of understanding the intent behind a question and retrieving not just article links but exact passages that best answer the question, all of which are summarized through a customer’s preferred LLM into a concise, actionable answer – boosting customer satisfaction while deflecting cases. 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 27, 2023in Einstein 1 Platform, Salesforce Data Cloud
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
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 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 How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more Capture Initial Traffic Source With Google Analytics To ensure the proper sequencing of Tags, modify the Tag sequencing in the Google Analytics preview Tag settings. The custom Read more Snowflake and Salesforce with Embed Snowflake has deepened its partnership with investor Salesforce by introducing two tools that seamlessly connect their cloud-native systems. Snowflake and Read more

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July 11, 2023in Salesforce Data Cloud
Salesforce Data Cloud
22Jun

Salesforce Data Cloud Terminology

The Data Cloud remains one of Salesforce’s most enigmatic products, often touted for its seemingly ‘magical’ capabilities. Recently, Salesforce made waves by announcing complimentary Data Cloud licenses (albeit with certain restrictions), prompting numerous organizations to explore this platform’s potential. Salesforce Data Cloud Terminology. When diving into any significant facet of the Salesforce ecosystem, navigating a learning curve is par for the course. Familiarizing oneself with the terminology and its practical implications is a crucial starting point to feeling confident with the technology. Introducing Your Guide to Salesforce Safety Net 3 Within this guide, we explain essential terminology to grasp data modeling concepts and elucidate how data traverses through various stages within the Data Cloud, culminating in the activation of refined segments. Understanding these foundational concepts in data sourcing is pivotal when working with the Data Cloud. Given the diverse origins of streamed data, akin to Marketing Cloud data extensions, a grasp of these terms proves invaluable. Primary Key: A distinguishing field within a dataset, such as the Salesforce record ID. Foreign Key: Facilitates linking data across distinct tables or sources; for instance, correlating an OrderID between customer records and order details datasets from an eCommerce platform. To satiate the voracious appetite of the Data Cloud, ingestion serves as the conduit for feeding it with data. Various methods, including SDKs, Connectors, and the Ingestion API, facilitate this process. SDKs: Accelerate integration setup, with examples like the Interactions SDK and Engagement Mobile SDK from Salesforce. Connectors: Pre-built integrations simplifying connections between Salesforce products and Data Cloud. Ingestion API: Enables developers to construct integrations from scratch for data sources not covered by SDKs or connectors. Datasets from disparate sources enter the Data Cloud as data streams, with their frequency of updates dictated by operational needs and API capabilities. Real-time data streams: Immediate data updates. Batched data streams: Data updates occur at predetermined intervals, such as hourly or daily. Visualize the Salesforce data model, where objects relate to one another; these objects collaboratively manage ingested data within the Data Cloud. Data Source object: Initial repository for ingested data in its raw format. Data Lake object: Facilitates data mapping to other sources and applies transformations. Data Model object: Resembles Salesforce objects structurally, facilitating relational data management without storing data internally. The mapping canvas provides a visual interface for aligning disparate data points, crucial for rendering ingested data usable through mappings from data source to data lake objects. During this process, primary keys and match/reconciliation rules are specified. Data Cloud’s strength lies in resolving discrepancies to compile comprehensive records, essential for maintaining unified profiles across platforms without merging records. Building upon traditional Salesforce duplicate and matching rules, Data Cloud offers deterministic and probabilistic matching, catering to various data representation nuances. Similar to Salesforce deduplication concepts, reconciliation rules determine the preferred value for fields, aiding in mass deduplication. Ranking data sources according to reliability helps prioritize trustworthy data over less accurate sources within the Data Cloud. Identity resolution culminates in unified profiles, representing the ‘golden record’ of individuals, adaptable to evolving data streams. Comparable to Salesforce roll-up fields, calculated insights derive new data points from existing ones, enriching data analysis capabilities. Streaming insights offer real-time or near-real-time analysis, suited for smaller datasets requiring swift insights. Activation transpires when perfected segments are dispatched to destinations for personalized interactions, spanning Marketing Cloud, advertising platforms, and other repositories. Data actions trigger alerts or events based on streaming insights and engagement data, fostering automation and integration across Salesforce platforms. In Summary Mastering the Data Cloud entails navigating its terminologies and understanding how data evolves through its lifecycle, culminating in the activation of refined segments for personalized interactions. 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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June 22, 2023in Salesforce Data Cloud
Salesforce Data Cloud
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. 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 15, 2023in Mulesoft, Salesforce Data Cloud, Salesforce Einstein, Salesforce Implementation Services
Einstein GPT Links to Flow and Data Cloud
07May

Einstein GPT Links to Flow and Data Cloud

Salesforce Harnesses AI and Data Integration to Drive Autonomous, Intelligent Enterprise-Einstein GPT Links to Flow and Data Cloud Salesforce is forging ahead with innovative advancements, seamlessly integrating Einstein GPT and Data Cloud into Flow to propel the autonomous, intelligent enterprise. This article explores how these technologies collaborate to enhance customer experiences and streamline business operations. Einstein GPT Links to Flow and Data Cloud for Intelligent Enterprise Einstein GPT, Salesforce’s groundbreaking generative AI CRM technology, merges proprietary AI models with cutting-edge generative AI from diverse partners, alongside real-time data from the Salesforce Data Cloud. With Einstein GPT, users can leverage this data within their Salesforce CRM to generate adaptive content using natural-language prompts, responding dynamically to evolving customer information and preferences. The Data Cloud consolidates a company’s customer data from various channels into a unified, real-time customer profile. By empowering Flow with the Data Cloud, customers can automate intricate workflows triggered by real-time changes. Flow paired with Einstein GPT offers a conversational interface for creating and adjusting automation, significantly simplifying the process and reducing barriers for non-technical users. Einstein GPT Across Sales, Service, Marketing, and Development Salesforce introduces Einstein GPT across Sales, Service, Marketing, and Development to automate tasks such as composing emails, scheduling meetings, generating knowledge articles from case notes, and crafting personalized content across multiple platforms. Developers benefit from enhanced productivity with AI-driven code generation and assistance using Salesforce Research’s language model. By combining Einstein AI models with ChatGPT or similar models, customers can use natural-language prompts on CRM data to trigger powerful automation and generate personalized content efficiently. Generative AI Fund Salesforce Ventures launches a $250 million Generative AI Fund to invest in promising startups, bolster the startup ecosystem, and advance responsible and trusted generative AI technologies. Real-World Applications Flow, empowered by the Data Cloud, enables businesses to personalize every interaction. For instance, marketers optimize retail experiences with real-time data-driven automation for in-store discounts, while financial services automate fraud detection by flagging suspicious transactions. Manufacturing companies enhance efficiency by monitoring machine performance and automating maintenance requests based on real-time data. Einstein GPT Links to Flow and Data Cloud Real-time automation transforms energy solution commissioning. Salesforce automation streamlines energy production snapshots and system registration for incentives, enhancing operational efficiency and ensuring customer satisfaction. Salesforce is leading the charge in revolutionizing AI-driven content and real-time data integration to automate workflows and deliver personalized customer experiences. The integration of Einstein GPT and Data Cloud into Flow simplifies automation creation and fosters accessibility for all users. With the launch of Einstein GPT for diverse business functions and the Generative AI Fund, Salesforce demonstrates its commitment to responsible, trusted, and innovative AI solutions. Across industries, Salesforce is paving the way for a future powered by autonomous, intelligent enterprises. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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May 7, 2023in Generative AI, Salesforce Data Cloud, Salesforce Implementation Services
Salesforce Data Cloud
12Apr

Salesforce Data Cloud vs Salesforce CDP

Salesforce Genie, hailed as the most significant innovation in Salesforce’s history, has morphed into Salesforce Data Cloud. Operating on a grand scale, Data Cloud seamlessly processes and stores real-time data streams, integrating them with Salesforce data to unlock highly personalized customer experiences. Salesforce Data Cloud vs Salesforce CDP – which one is for me? You might wonder if this aligns with what Data Cloud (formerly Salesforce CDP) accomplishes—unifying versions of individuals across applications and providing customer experiences based on diverse data sources. To clarify, while Data Cloud shares similar goals and benefits with CDP, it represents an evolution beyond the technology of the former Salesforce CDP. In the words of Eric Stahl, EVP Marketing at Salesforce, “With [Data Cloud], we moved the real-time data capabilities into the [Salesforce] platform so we can ingest, manage and activate data from anywhere. It’s also nested with Einstein for AI and Flow for automation.” Data Cloud vs. Salesforce CDP: Key Differences Data Cloud inherits the capabilities of Salesforce CDP but extends its benefits across the entire “Customer 360,” covering Salesforce’s product portfolio. Here are key differences: Data Cloud, the successor to Salesforce CDP, extends beyond traditional CDP definitions. With a focus on diverse use cases beyond marketing and a zero-data copy architecture, Data Cloud stands as one of Salesforce’s most promising products. While Data Cloud shares purposes and benefits with CDPs, it represents a new era in Salesforce’s commitment to customer data unification, activation, and insight generation. Salesforce CDP remains available and operational, providing users with distinct options tailored to their specific needs. If it is time to explore the power of Salesforce Data Cloud to your sales and marketing efforts, contact Tectonic today. Like2 Related Posts 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 How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more Marketing Cloud Account Engagement and Salesforce Campaigns The interplay between Account Engagement and Salesforce Campaigns often sparks confusion and frustration among users. In this insight, we’ll demystify Read more Consent Management Analytics and Data Quality Understanding Data Analytics Consent and Consent Management Why Consent Management is Crucial Consent Management Analytics and Data Quality. With laws Read more

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April 12, 2023in Salesforce Data Cloud, Salesforce Marketing Automation
data cloud and marketing cloud personalization
30Mar

Data Cloud and Marketing Cloud Personalization

Choosing the correct Customer Data Platform (CDP) for your organization is crucial for adapting to challenges and capitalizing on opportunities in the evolving marketing technology landscape. While AI, behavioral patterns, and infrastructure play pivotal roles in this decision-making process, it’s essential to understand the landscape. However, the same factors, including AI, behavioral habits, and infrastructure, can influence this decision. Data Cloud and Marketing Cloud Personalization together capture and utilize customer data. Selecting the right tools makes it easier to know and cater to your prospects and customers. Without them, you are firing into the darkness. You must understand the necessary infrastructure for a marketing technology team to meet challenges and leverage new opportunities. It integrates four essential AdTech (Advertising Technology) principles applicable to MarTech in the evolving landscape. The external market poses challenges, notably the discontinuation of third-party cookies by major browsers like Google. This shift impacts prospecting and underscores the significance of first-party data. The rise of AI, exemplified by technologies like ChatGPT and integrated into platforms like Salesforce’s Einstein, further complicates the landscape. The AI era raises concerns about data usage and collection, employment risks, and the ethical consideratins. Organizations rush to incorporate AI, with Salesforce introducing Einstein GPT shortly after the emergence of ChatGPT. In this dynamic environment, organizations grapple with managing diverse data sources, implementing AI/ML, and ensuring privacy. AdTech principles become imperative in MarTech for effective targeting, personalization, and measurement. The focus shifts to the role of a Customer Data Platform (CDP) within the MarTech stack. Distinguishing between Data Management Platforms (DMPs), CDPs, Data Warehouses, and Data Lakes sets the stage. The article explores three CDP types: Enterprise, Event-Based, and Real-Time Personalization. The significance of a Customer Data Platform (CDP) like Salesforce’s Data Cloud cannot be stressed enough. Bear in mind there are differences between DMPs, CDPs, Data Warehouses, and Data Lakes, each with their own use cases. And for your situation a DMP, Data Warehouse, or Data Lake might be required. Salesforce’s CDP platform undergoes scrutiny, aligning its features with AdTech principles. Read more about Tectonic’s thoughts on Data Cloud here. The CDP’s contribution to targeting, personalization, and both deterministic and probabilistic measurement is detailed. Salesforce’s Data Cloud and Marketing Cloud Personalization (Interaction Studio) emerge as solutions catering to distinct needs. In conclusion we must underscore the criticality of choosing the right CDP for organizational resilience, superior customer experiences, and addressing privacy concerns. A robust infrastructure facilitates efficient data management, collaboration, and scalability, empowering organizations to make informed decisions with AI/ML and business intelligence. data cloud and marketing cloud personalization Like1 Related Posts 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 Data Studio Data Studio Overview Salesforce Data Studio is Salesforce’s premier solution for audience discovery, data acquisition, and data provisioning, offering access Read more How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more Integration of Salesforce Sales Cloud to Google Analytics 360 Announced In November 2017, Google unveiled a groundbreaking partnership with Salesforce, outlining their commitment to develop innovative integrations between Google Analytics Read more

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March 30, 2023in Salesforce Data Cloud, Salesforce Marketing Cloud
Salesforce Data Cloud
27Feb

Salesforce Data Cloud

Revolutionizing Customer Engagement with the First Real-Time Platform – Salesforce Data Cloud In today’s business environment, customer expectations are soaring, with 71% anticipating personalized interactions at every touchpoint. However, the deluge of data, diverse devices, and evolving experiences poses a challenge for companies to maintain relevance. The typical business juggles an average of 1,061 form completions resulting in fragmented customer profiles and a sense of anonymity. The experience rather than giving personalized value. Leading to a sense of disconnection. Imagine a solution that consolidates all customer data, spanning channels and systems, into a unified source of truth. What if this real-time data could transform customer interactions? Empowering you to make them feel not just recognized but truly understood? Enter Salesforce Data Cloud, the inaugural real-time platform designed for customer magic. Data Cloud seamlessly integrates real-time data into Customer 360, unlocking the potential for enchanting customer experiences. Equipped with built-in connectors, Data Cloud aggregates data from diverse sources—Salesforce apps, mobile, web, connected devices, legacy systems via MuleSoft, and historical data from proprietary lakes—all in real time. However, the true power lies in distilling this vast pool of data into a cohesive customer view. Data Cloud achieves this by harmonizing and storing customer data at an extensive scale, crafting a dynamic, real-time customer graph—a singular source of truth. Real-Time Visibility The Data Cloud process involves connecting data streams, harmonizing the data into a real-time customer graph, and making it available across the Customer 360, facilitating engagement and creating magical experiences—all unfolding in real time. This real-time customer graph continually evolves as it absorbs more customer data. Activating this data across the entire Customer 360 empowers companies to create personalized, magical experiences. The real-time data from Data Cloud enhances automation with Flow and augments intelligence with Einstein, all under the reliable umbrella of Hyperforce infrastructure. The result? An automated, intelligent, and real-time Customer 360, delivering cost savings, time efficiency, and revenue growth. Data Cloud’s impact spans across the Customer 360 ecosystem: In essence, Data Cloud empowers businesses across industries to deliver real-time, intelligent, and automated experiences, fostering loyalty, cost efficiency, and increased ROI—a truly magical transformation! 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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February 27, 2023in Salesforce Data Cloud, Salesforce Implementation Services, Salesforce Managed Services
Salesforce Data Cloud
25Jan

Data Cloud Salesforce

What is the data cloud in Salesforce? A hyperscale data platform built into Salesforce. Activate all your customer data across Salesforce applications with Data Cloud. Empower teams to engage customers, at every touchpoint, with relevant insights and contextual data in the flow of work. What is the benefit of data cloud? With Data Cloud, your company can feel confident in your customer information, and know that you’re taking the right steps to engage with your customer base across channels. The team at Tectonic can help you bring value to your wealth of customer data when you leverage Data Cloud. Data Cloud enables you to build a comprehensive, 360-degree view of your customers across all products, services, and interactions, ensuring all of your employees can quickly access and easily act on centralized, real-time information about their customers. Natively integrated into the Einstein1 Platform, companies can power automation, activation, analytics, and action across the world’s #1 AI CRM. Data has always been the foundation of CRM. But the connection between enterprise data and CRM is broken. Companies are collecting more data and signals about their customers than ever before, yet that data sits in siloes and is disconnected from the customer experience. And while most companies have invested in centralizing their data, it’s trapped. Over time, data strategies and customer strategies have diverged. Data Cloud isn’t just about bringing data together. It’s about unlocking your data in service of the customer. Deeply integrated into the Einstein1 Platform, Data Cloud makes all your data natively available across all Salesforce applications — Sales Cloud, Service Cloud, Marketing Cloud, Commerce Cloud, Tableau, and MuleSoft — to power automation and business processes and inform AI. Like Related Posts Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. 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 How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more Advantages of a Cloud Managed Service Provider Considering outsourcing your IT management to a cloud managed service provider? Here are several benefits of opting for a cloud Read more

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January 25, 2023in Salesforce Data Cloud, Salesforce Managed Services
Snowflake Database
23Jan

Snowflake Database

What is Snowflake Database? Snowflake Database serves as the repository for an organization’s structured and semi-structured data sets, facilitating processing and analysis. It offers automated management of various aspects of data storage, including organization, structure, metadata, file size, compression, and statistics. Snowflake: The Global Data Cloud Platform Snowflake stands as a unified global platform powering the Data Cloud, connecting businesses worldwide across diverse data types, scales, and workloads, fostering seamless data collaboration. Understanding Snowflake Database Snowflake, a relational database hosted in the cloud, serves as a data warehousing solution. Leveraging infrastructure from Google Cloud Platform, Azure, and AWS, it combines traditional database features with innovative functionalities. Snowflake: More Than Just a Data Warehouse Snowflake’s Data Cloud encompasses a pure cloud-based SQL data warehouse, uniquely engineered to handle all data and analytics aspects. It offers high performance, concurrency, simplicity, and affordability unmatched by other data warehousing solutions. Snowflake Database’s Role in ETL Processes Snowflake streamlines data loading, transformation, and storage, eliminating the need for additional ETL tools. Its unique features, scalability, and security have led many organizations worldwide to adopt it as their primary Data Warehousing solution. Snowflake’s Integration with SQL and Python Built on a new SQL database engine, Snowflake’s data warehouse architecture is tailored for the cloud. Moreover, Snowflake provides first-class Python APIs for managing core resources, enabling seamless integration without SQL queries. Challenges and Advantages of Snowflake Despite its advantages such as scalability, performance tuning, and data security, Snowflake faces challenges like higher costs and limited support for unstructured data. Snowflake’s Position in Comparison to Other Databases Snowflake offers faster, easier-to-use, and more flexible data storage and analytic solutions compared to traditional offerings. It is not built on existing database technology or big data software platforms like Hadoop. Ownership and Integration Snowflake operates on major public clouds like AWS, Azure, and GCP, offering pre-warmed virtual machines to support rapid compute. Salesforce had a stake in Snowflake but sold its holdings, making Snowflake an independent entity. Snowflake vs. Salesforce: Choosing the Right Solution Snowflake is preferable for businesses requiring a versatile data platform, whereas Salesforce Data Cloud suits organizations already using Salesforce products due to its seamless integration. Some companies utilize both platforms for diverse needs. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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January 23, 2023in Salesforce, Salesforce Data Cloud, Salesforce Development Services, Salesforce Implementation Services, Snowflake

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