Data Lake Archives - gettectonic.com - Page 4
Account Planning With Salesforce
25Sep

CRM Analytics Limits

When using CRM Analytics, keep these limits in mind. API Call Limits These limits apply to all supported editions. API Call Limit Maximum concurrent CRM Analytics API calls per org 100 Maximum CRM Analytics API calls per user per hour 10,000 Dataset Row Storage Allocations per License In Salesforce org, your total row storage limit for all registered datasets combined depends on your license combination. Each license allocates a different number of rows. Baseline Row Allocation Allocated Rows CRM Analytics Plus 10 billion CRM Analytics Growth 100 million Sales Analytics 25 million Service Analytics 25 million Event Monitoring Analytics 50 million B2B Marketing Analytics 25 million CRM Analytics for Financial Services Cloud 25 million CRM Analytics for Health Cloud 25 million Extra Data Rows license 100 million Your total row storage limit is a combination of your active licenses. For example: Because the CRM Analytics Plus license includes the Sales Analytics and Service Analytics licenses, your total row allocation remains 10 billion. Similarly, the CRM Analytics Growth license includes the Sales Analytics and the Service Analytics licenses, so your total row allocation remains 100 million. However, if you obtain another Sales Analytics or Services Analytics license, your row limit increases by 25 million for each added license. Dataset Row Limits Each dataset supports up to 2 billion rows. If your Salesforce org has less than 2 billion allocated rows, each dataset supports up to your org’s allocated rows. Dataset Field Limits Value Limit Maximum number of fields in a dataset 5,000 (including up to 1,000 date fields) Maximum number of decimal places for each value in a numeric field in a dataset (overflow limit) 17 decimal placesWhen a value exceeds the maximum number of decimal places, it overflows. Both 100,000,000,000,000,000 and 10,000,000,000,000,000.0 overflow because they use more than 17 decimal places. A number also overflows if it’s greater (or less) than the maximum (or minimum) supported value. 36,028,797,018,963,968 overflows because its value is greater than 36,028,797,018,963,967. -36,028,797,018,963,968 overflows because it’s less than -36,028,797,018,963,967.When a number overflows, the resulting behavior in CRM Analytics is unpredictable. Sometimes CRM Analytics throws an error. Sometimes it replaces a numeric value with a null value. And sometimes mathematical calculations, such as sums or averages, return incorrect results. Occasionally, CRM Analytics handles numbers up to 19 digits without overflowing because they are within the maximum value for a 64-bit signed integer (263 – 1). But numbers of these lengths aren’t guaranteed to process.As a best practice, stick with numbers that are 17 decimal places or fewer. If numbers that would overflow are necessary, setting lower precision and scale on the dataset containing the large numbers sometimes prevents overflow. If your org hasn’t enabled the handling of numeric values, the maximum number of decimal places for each value in a numeric field in a dataset is 16. All orgs created after Spring ’17 have Null Measure Handling enabled. Maximum value for each numeric field in a dataset, including decimal places 36,028,797,018,963,967For example, if three decimal places are used, the maximum value is 36,028,797,018,963.967 Minimum value for each numeric field in a dataset, including decimal places -36,028,797,018,963,968For example, if five decimal places are used, the minimum value is -36,028,797,018,9.63968 Maximum number of characters in a field 32,000 Data Sync Limits If you extract more than 100 objects in your dataflows, contact Salesforce Customer Support before you enable data sync. Value Limit Maximum number of concurrent data sync runs 3 Maximum number of objects that can be enabled for data sync, including local and remote objects 100 Maximum amount of time each data sync job can run for local objects 24 hours Maximum amount of time each data sync job can run for remote objects 12 hours Data sync limits for each job:Marketo Connector (Beta)NetSuite ConnectorZendesk Connector Up to 100,000 rows or 500 MB per object, whichever limit is reached first Data sync limits for each job:Amazon Athena ConnectorAWS RDS Oracle ConnectorDatabricks ConnectorGoogle Analytics ConnectorGoogle Analytics Core Reporting V4 ConnectorOracle Eloqua ConnectorSAP HANA Cloud ConnectorSAP HANA Connector Up to 10 million rows or 5 GB per object, whichever limit is reached first Data sync limits for each job*:AWS RDS Aurora MySQL ConnectorAWS RDS Aurora PostgresSQL ConnectorAWS RDS MariaDB ConnectorAWS RDS MySQL ConnectorAWS RDS PostgreSQL ConnectorAWS RDS SQL Server ConnectorGoogle Cloud Spanner ConnectorMicrosoft Azure Synapse Analytics ConnectorMicrosoft Dynamics CRM ConnectorSalesforce External ConnectorSalesforce Contacts Connector for Marketing Cloud EngagementSalesforce OAuth 2.0 Connector for Marketing Cloud Engagement Up to 20 million rows or 10 GB per object, whichever limit is reached first Data sync limits for each job*:Amazon Redshift ConnectorAmazon S3 ConnectorCustomer 360 Global Profile Data Connector (Beta)Google BigQuery for Legacy SQL ConnectorGoogle BigQuery Standard SQL ConnectorHeroku Postgres ConnectorMicrosoft Azure SQL Database ConnectorSnowflake Input Connector Up to 100 million rows or 50 GB per object, whichever limit is reached first *When using these connectors, Salesforce Government Cloud org data is protected in transit with advanced encryption and can sync up to 10 million rows or 5 GB for each connected object, whichever limit is reached first. Note When using a Salesforce local input connection, CRM Analytics bulk API usage doesn’t count towards Salesforce bulk API limits. Use of the external Salesforce connection and output connection impacts your limits. The dataflow submits a separate bulk API call to extract data from each Salesforce object. The dataflow uses a batch size of 100,000–250,000, depending on whether the dataflow or the bulk API chunks the data. As a result, to extract 1 million rows from an object, the dataflow creates 4–10 batches. Recipe and Dataflow Limits Important In Winter ‘24, recipe runs over 2 minutes are counted against the limit. Previously, the recipe run counts weren’t correct. For more information, see Known Issue – Recipe runs are not counting towards the daily maximum run limit. Value Limit Maximum amount of time each recipe or dataflow can run 48 hours Maximum number of recipes 1,000 Maximum number of dataflows definitions (with data sync enabled) 100 Maximum number of dataflow and recipe runs in a rolling

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September 25, 2023in Data, Google, Google Analytics, Salesforce, Salesforce Government Cloud, Snowflake
Bring Your Own Lake With Google BigQuery
15Sep

Bring Your Own Lake With Google BigQuery

Can BigQuery Function as a Data Lake? Why you should Bring Your Own Lake With Google BigQuery. Google BigQuery serves as a fully-managed, petabyte-scale data warehouse, utilizing Google’s infrastructure’s processing power. The combination of Google Cloud Storage and BigQuery transforms Google Cloud Platform into a scalable data lake capable of storing both structured and unstructured data. Why Embrace BigQuery’s Serverless Model? In a serverless model, processing is automatically distributed across numerous machines operating in parallel. BigQuery’s serverless model allows data engineers and database administrators to concentrate less on infrastructure and more on server provisioning and deriving insights from data. Advantages of Using BigQuery as a Data Warehouse: BigQuery is a completely serverless and cost-effective cloud data warehouse designed to work across clouds, scaling seamlessly with your data. With integrated business intelligence, machine learning, and AI features, BigQuery provides a unified data platform for storing, analyzing, and sharing insights effortlessly. The Relevance of Data Lakes: Data Lakes and Data Warehouses are complementary components of data processing and reporting infrastructure, each serving distinct purposes rather than being alternatives. Data Lakes in the Evolving Landscape: Data lakes, once immensely popular, are gradually being supplanted by more advanced storage solutions like data warehouses. Data Lake Content Formats: A data lake encompasses structured data from relational databases (rows and columns), semi-structured data (CSV, logs, XML, JSON), unstructured data (emails, documents, PDFs), and binary data (images, audio, video). Building a Data Lake on GCP: Constructing a Data Lake: Introduction to Google Big Lake: BigLake serves as a storage engine, offering a unified interface for analytics and AI engines to query multiformat, multicloud, and multimodal data securely, efficiently, and in a governed manner. It aspires to create a single-copy AI lakehouse, minimizing the need for custom data infrastructure management. Data Extraction from a Data Lake: Distinguishing BigQuery as a Data Warehouse: BigQuery stands out as a serverless and cost-effective enterprise data warehouse, functioning across clouds and seamlessly scaling with data. It incorporates built-in ML/AI and BI for scalable insights. Data Lake Implementation Time: Building a fully productive data lake involves several steps, including workflow creation, security mapping, and tool and service configuration. As a result, a comprehensive data lake implementation can take several months. Acquiring a Data Lake: One option is to buy a Data Lake through a decentralized exchange (DEX) supporting the blockchain where the Data Lake resides. Connecting a crypto wallet to a DEX and utilizing a Binance account to purchase the base currency is outlined in a guide for this purpose. Like Related Posts 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 Persist Campaign Data This tag stores data in both the page referrer and URL parameters within a browser cookie. This cookie proves useful, 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 What Are UTM Parameters in Marketing Cloud What Are UTM Parameters in Marketing Cloud? UTM parameters are essential for tracking the effectiveness of your marketing messages by Read more

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September 15, 2023in Google Analytics, Salesforce Data Cloud
Salesforce BI
15Jul

Salesforce Analytics Acronyms

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

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July 11, 2023in Data, Salesforce, Salesforce Data Cloud, Technology
Cloud Analytics
30May

Cloud Analytics Explained

Understanding Cloud Analytics Cloud analytics refers to leveraging cloud computing resources to conduct data analysis more efficiently. It involves using advanced analytical tools to extract insights from vast datasets, presenting information in a user-friendly format accessible via web browsers. Core Concepts of Cloud Analytics Explained Cloud analytics shifts traditional data analytics operations, such as processing and storage, to public or private cloud environments. Similar to on-premises analytics, cloud solutions facilitate pattern identification, predictive modeling, and business intelligence (BI) insights. They leverage cloud technologies and algorithms, notably artificial intelligence (AI), including machine learning (ML) and deep learning (DL). Operational Framework of Cloud-Based Analytics Cloud analytics platforms offer capabilities to build, deploy, scale, and manage data analytics solutions in a cloud-based infrastructure. Examples include cloud enterprise data warehouses, data lakes, and on-demand BI and marketing analytics. Users can subscribe to services under flexible pricing models, alleviating concerns about scalability, performance, and maintenance. Types of Cloud Analytics Cloud-based analytics solutions vary by deployment model: Key Features and Benefits Cloud analytics offers several advantages: Applications and Use Cases Cloud analytics supports diverse applications, including: Comparing Cloud Analytics with Traditional Data Analytics Cloud analytics leverages cloud infrastructure for scalable and flexible data processing, contrasting with traditional analytics tools deployed on-premises. This shift enhances agility and accessibility while reducing operational complexities and costs. Why Cloud Analytics Matters Cloud analytics empowers organizations to harness actionable insights efficiently, driving informed decision-making and competitive advantage. It streamlines operations, fosters collaboration, and enhances data reliability and strategic planning capabilities. Adopting cloud-based analytics enables businesses to transform data into valuable intelligence, fueling innovation and growth. By leveraging cloud-based resources, organizations can achieve operational excellence, secure data-driven insights, and maintain a competitive edge in today’s dynamic business landscape. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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May 30, 2023in Salesforce

Layers of the AI Stack

The AI stack refers to the layered architecture of technologies and components that work together to build, deploy, and manage artificial intelligence (AI) systems. Each layer of the stack plays a critical role in enabling AI capabilities, from data collection to model deployment and beyond. Here’s a breakdown of the key layers of the AI stack: 1. Data Layer The foundation of any AI system is data. This layer involves collecting, storing, and managing the data required to train and operate AI models. Key Components: 2. Infrastructure Layer This layer provides the computational power and hardware needed to process data and run AI models. Key Components: 3. Framework and Tools Layer This layer includes the software frameworks and tools used to build, train, and optimize AI models. Key Components: 4. Model Layer This is the core layer where AI models are developed, trained, and fine-tuned. Key Components: 5. Application Layer This layer focuses on deploying AI models into real-world applications and integrating them with existing systems. Key Components: 6. Orchestration and Management Layer This layer ensures that AI systems are scalable, reliable, and efficient in production environments. Key Components: 7. Business Layer This layer focuses on the business value of AI, including use cases, ROI, and ethical considerations. Key Components: 8. Ecosystem Layer This layer includes the external tools, services, and communities that support AI development and deployment. Key Components: How the Layers Work Together Why the AI Stack Matters The AI stack provides a structured approach to building and deploying AI systems. By understanding and optimizing each layer, organizations can: Conclusion The AI stack is a comprehensive framework that enables organizations to harness the power of AI effectively. By mastering each layer—from data collection to business value—you can build robust, scalable, and impactful AI solutions. Whether you’re a startup or an enterprise, understanding the AI stack is key to staying competitive in the age of artificial intelligence. Content updated March 2025. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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May 27, 2023in Artificial Intelligence, Data, Deep Learning, Enterprise, Google, Salesforce, Salesforce Einstein
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 Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables 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
Customer 360 Data Model
11Apr

Customer 360 Data Model

The Customer 360 Data Model simplifies the integration of data across cloud applications by providing standardized guidelines. It enables the extension of the data model for various purposes such as creating data lakes, generating analytics, training machine-learning models, and establishing a unified view of the customer. Organized into subject areas, the Customer 360 Data Model categorizes data into major business activities like customer information, product details, and engagement data. Each subject area comprises data model objects (DMOs), which serve as views of imported data from various sources such as data streams and insights. DMOs utilize attributes, or fields, to organize data in meaningful ways. They can be either standard DMOs, based on predefined schemas, or custom DMOs created directly within an organization. To utilize data imported into Data Cloud, it must be mapped to a DMO. This mapping process involves connecting a data source to Data Cloud and creating mapping sets between objects and fields within the source and the Customer 360 Data Model. The relationships between DMOs further consolidate disparate data, facilitating comprehensive analysis and utilization. The Customer 360 Data Model includes subject areas such as Case, Engagement, Loyalty, Party, Privacy, Product, and Sales Order, each serving specific organizational needs. Additionally, it encompasses individual and contact point objects, essential for complete data streams and ensuring consistency across applications and processes. Key object types within the Customer 360 Data Model include Individual, representing individuals dealt with in the system, and Contact Point objects like Email, Phone, Address, App, OTT Service, and Social handles. These objects capture essential information about individuals and their interactions. Moreover, attributes like Party Identification and Individual ID play crucial roles in data segmentation and identity resolution within Data Cloud. Individual ID Imported data customer identifiers must be mapped to the Individual ID field to drive identity resolution behavior and to receive accurate data when creating data segments. The Individual ID object is important to ensuring successful data in Data Cloud. When importing any customer information, it’s mapped to this object and remains consistent throughout the entire product. Data Cloud has a variety of data objects including data source objects (DSO), data lake objects (DLO), and data model objects (DMO). The Data Model offers a structured framework for organizing and utilizing data effectively, enabling organizations to derive actionable insights and enhance customer experiences across various applications and business processes. Tectonic is your source for Customer 360 Data Model from Salesforce. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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April 11, 2023in Salesforce, Salesforce Development Services
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 Like2 Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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March 30, 2023in Data, Enterprise, Google, Salesforce, Salesforce Data Cloud, Salesforce Marketing Cloud, Technology
Marketing Cloud Intelligence For Data Integration
18Mar

Marketing Cloud Intelligence For Data Integration

What exactly is Salesforce Datorama, now referred to as Marketing Cloud Intelligence? It is a versatile, cloud-based marketing data platform offering a suite of solutions aimed at enhancing data integration, reporting, analysis, and optimization. Marketing Cloud Intelligence For Data Integration. However, the question arises: Does Marketing Cloud Intelligence truly deliver the cloud-based Marketing Intelligence as Salesforce touts? Let’s dive into what this platform offers and dissect its capabilities without the fluff. Understanding the Platform: Salesforce’s Marketing Cloud Intelligence, formerly known as Datorama, serves as an analytics tool meticulously designed to integrate and visualize various forms of marketing performance data. It strikes a balance, catering to both analytically inclined marketers and seasoned analysts seeking to bridge data with conventional BI tools like Tableau. Flexible SaaS with Tailored Customization: Despite its Software-as-a-Service (SaaS) nature, Datorama surprises with its flexibility. It can function autonomously, handling data storage, modeling, ETL, and visualization, or seamlessly integrate with other platforms like Azure Databricks or Looker. While it accommodates numerous data use cases, its primary focus remains on Performance Marketing. Marketing Cloud Intelligence often gets misclassified as a traditional Business Intelligence or Analytics platform, but it truly excels in data management. For those contemplating its adoption, familiarity with its functionalities through resources like “Getting to Know Marketing Cloud Intelligence” or video walkthroughs is encouraged. Transition to Marketing Cloud Intelligence: The rebranding from Datorama to Marketing Cloud Intelligence was proposed in early 2022, gaining momentum recently. Despite the name change, the platform’s features and capabilities remain intact. Origins and Evolution: Originating from an Israeli-based technology firm in 2012, Datorama swiftly gained traction under the stewardship of its founders Ran Sarig, Efi Cohen, and Katrin Ribant. In 2018, Salesforce acquired Datorama, integrating it into the Marketing Cloud suite alongside Account, Engagement, Personalization, and Data Cloud platforms. However, as of February 2, 2023, the original founders and core engineering teams have moved on, possibly signaling a shift in the platform’s trajectory. Functionalities and Capabilities: Marketing Cloud Intelligence boasts robust data onboarding and connectivity features, with a rich assortment of connectors and retrieval mechanisms supporting popular data management platforms like SAP Hana, AWS, Oracle, Vertica, and SQL Server. It excels in ingesting and managing aggregated marketing performance data, with the capacity to handle event-level data as well. Pricing and Competitors: While its pricing model revolves around data row consumption and user seats, the platform may become cost-prohibitive at higher volumes. However, recent enhancements like Data Lake offer expanded row count flexibility without escalating costs. Its primary competitors include Domo, Adverity, NinjaCat, Improvado, Looker, PowerBI, and Google Data Studio. Use Cases and Industries: Marketing Cloud Intelligence serves marketers and advertisers across various industries, including communications, media, technology, healthcare, finance, manufacturing, automotive, retail, and publishing. Its versatility lies in supporting six specific marketing data use cases, ranging from building a single source of data to producing informative dashboards. Continuous Evolution: With frequent product releases, Marketing Cloud Intelligence remains dynamic, adapting to evolving market needs and technological advancements. Its commitment to enhancing analytics, visualization, connectivity, and marketplace apps ensures its relevance in the ever-changing landscape of marketing data management. Future Outlook: As Salesforce navigates the competitive analytics space, the future of Marketing Cloud Intelligence remains intriguing. While challenges like pricing pressures persist, the platform’s integration within the Marketing Cloud ecosystem and ongoing enhancements hint at a promising trajectory. Whether it evolves into a fully integrated analytics solution or retains its standalone utility, only time will tell. But one thing is certain: Salesforce’s promotion of Marketing Cloud Intelligence will continue to shape its evolution and market positioning moving forward. Like1 Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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March 18, 2023in Google Analytics, Salesforce Communications Cloud, Salesforce Marketing Cloud
Salesforce Genie Announced
19Oct

Salesforce Genie Announced

Salesforce Genie announced this year is an innovative data platform recently unveiled at Dreamforce 2022, heralding the world’s first real-time CRM. Genie is the driving force behind Salesforce’s entire Customer 360 platform, delivering hyper-scale, real-time data capabilities. With Genie, any business can harness the power of data to create magical customer experiences, offering seamless, personalized interactions across sales, service, marketing, and commerce. It adapts effortlessly to evolving customer needs. Consider scenarios we encounter daily: the frustration of lengthy customer support calls navigating purchase history, or the challenge of locating specific items on cluttered e-commerce websites. These situations underscore the demand for real-time updates in every customer interaction, a demand that Genie aims to fulfill. In the last 12 hours alone, the volume of stored customer data worldwide has doubled, explaining the delays in customer support. However, with Salesforce Genie, businesses can make sense of their data regardless of source, system, or channel. This unified data drives unprecedented levels of personalization, akin to magic. Salesforce Genie’s Key Features: Genie is pivotal for various industries leveraging Salesforce, like banks managing vast customer records and administrative tasks. Salesforce aims to enhance data utilization without altering existing approaches. Comparison with Salesforce CDP: Genie transcends traditional Customer Data Platforms (CDPs) by: How Genie Works: Genie ingests and stores real-time data streams at scale, integrating them seamlessly with Salesforce data. It consolidates data from diverse channels, legacy systems via MuleSoft, and proprietary data lakes through connectors. Core Pillars of Salesforce Genie: Salesforce Genie’s Extensibility: Genie partners with leading data providers such as Snowflake and Amazon SageMaker, enabling seamless integration and real-time data sharing without data movement. Unified Customer 360 Use Cases: Genie unifies data across Salesforce’s Customer 360 products for various departments: In essence, Salesforce Genie revolutionizes data integration and utilization, enabling businesses to deliver unparalleled customer experiences across all touchpoints. Like1 Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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October 19, 2022in Hyperforce, Mulesoft, Salesforce, Salesforce Data Cloud, Salesforce Marketing Cloud, Salesforce Optimization, Salesforce Sales Cloud, Salesforce Slack, Snowflake
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