Data Warehouse Archives - gettectonic.com

14May

Zero Copy from Salesforce

When was the last time you relocated? Remember the hassle of packing up all your stuff, loading it onto a truck, and unpacking everything at your new digs, hoping it all survived the journey? Now, imagine if your furniture and possessions could simply teleport to your new location in perfect condition. While this may not be possible (yet) in the physical world, the concept of zero copy integration offers a similar seamless experience for handling customer data. Zero Copy from Salesforce. Zero copy integration, also known as zero ETL (extract-transform-load), allows data to be shared among multiple data stores without the need for actual movement or duplication. This is particularly advantageous for companies utilizing cloud data warehouses like Snowflake or Google BigQuery. Some companies hesitate to adopt a customer data platform (CDP) due to concerns about duplicating data. However, with zero copy integration, users can leverage the benefits of a CDP—such as data harmonization, identity management, built-in analytics, and activation—without the drawbacks of physical data movement. Some functions related to data have advanced with astonishing speed: generation, dissemination, storage, even security (and yes, that one still has many challenges). But there are others still mired in the stone age—call it the 80s—of the digital era. For example, think copies. How many business professionals still make multiple copies of sensitive data, just as it’s been done for the last four decades? What does this endless duplication do for regulatory risk, security threats and privacy liabilities? This is a symptom of a larger problem: Far from the data-centric universe we all envisioned, we’re beholden instead to the apps used to create, store, and distribute data. There’s an app for everything, but also a database for every app, leading to dozens of data silos undergoing hundreds of integrations, increasing fragmentation, complexity, and costs. The basic idea, according to Dan DeMers, Cinchy’s CEO, is that the framework aims to remove application data silos by using access-based data collaboration versus standard API-base data integration that involves copying data and branding it with complex app-specific coding. This would be done by access controls set in the data layer. It would also involve: What is Zero Copy Integration? Zero copy integration enables access to data stored in multiple databases simultaneously without the need to move, copy, or reformat the data. This approach not only facilitates faster and easier access to data but also reduces costs and minimizes the risk of errors associated with data movement or transformation. Comparing Traditional Methods with Zero Copy Integration: Traditional Methods: Zero Copy Integration: How It Works: From CDP to Data Warehouse In this scenario, data is accessed from the CDP and shared with the data warehouse, a process known as data sharing. The steps typically involve: How It Works: From Data Warehouse to CDP Conversely, in this scenario, data is accessed from the data warehouse and integrated into the CDP, a process referred to as data federation. The steps typically involve: Real-World Application: How Buyers Edge Utilizes Zero Copy Technology Buyers Edge, a leading procurement optimization company in the food service industry, leverages zero copy technology to access purchase data stored in a data warehouse while building a unified customer profile in a CDP. This allows them to provide better customer insights to their sales and marketing teams. By eliminating the need for data movement, duplication, or reformatting, zero copy technology streamlines data access, harmonizes data for insights and analytics, and provides a real-time holistic view of customers. As data volumes continue to grow exponentially, technologies like zero copy integration will play a crucial role in simplifying data management and facilitating seamless access to valuable insights. Whether it’s optimizing business processes or enhancing customer experiences, zero copy integration empowers organizations to navigate the data landscape with agility and efficiency. Be a zero copy hero and unlock the full potential of your data management strategy. “In today’s digital landscape, companies struggle with islands of data spread across various systems. With this global ecosystem of partners, companies can access all of their data, no matter where it resides, and unlock the power of all of that data within Salesforce — creating more personalized customer interactions and establishing a foundation for trusted AI, in less time and at lower cost.” Brian Millham, President and Chief Operating Officer at Salesforce The Zero Copy Partner Network features initial partners Amazon Web Services (AWS), Databricks, Google Cloud, and Snowflake, and adds Microsoft, all committed to zero copy integrations with Salesforce that give customers a secure and cost-effective way to connect and take action on all of their data New zero copy support for data warehouses and data lakehouses built on open table formats, like Apache Iceberg, with Salesforce Data Cloud, removes the need to copy or move data, so customers can unlock their data, powering Customer 360 experiences with AI, automation, and analytics The network includes Salesforce ISV partners, building new Zero Copy Data Kits to bring valuable new datasets to Salesforce Data Cloud, and Salesforce SI partners including Accenture, Cognizant, Deloitte Digital, and PwC, helping customers with zero copy Salesforce Data Cloud implementations. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

May 14, 2024in Salesforce, Salesforce Data Cloud, Snowflake

21Apr

Harnessing Zero Copy Integration

Streamlining Data Management: Harnessing Zero Copy Integration for Efficient Customer Insights Zero copy integration offers a revolutionary approach to accessing and utilizing customer data without the need for burdensome data transfers. By seamlessly linking disparate databases, this innovative technique eliminates the complexities associated with moving, copying, or reformatting data. Let’s explore how zero copy integration can optimize data management processes and enhance the efficiency of customer data platforms (CDPs) alongside traditional data warehouses. Understanding Zero Copy Integration: Zero copy integration, also known as zero ETL (extract-transform-load), facilitates the sharing of data across multiple databases without physically relocating it. This method enables real-time access to data, significantly reducing costs and mitigating the risks of errors inherent in data movement processes. Unlike traditional data replication methods, zero copy integration ensures that data remains in its original location, eliminating the need for redundant storage and synchronization efforts. Good technology standards can actually bring order to the chaos and allow products from different companies to work together, fostering healthy competition and enhancing productivity. That is precisely what Zero-Copy Integration does. Zero-Copy Integration is a framework that eliminates the need for data copies and integration, making it easier to work with applications, analytics, artificial intelligence, and machine learning. Canada’s Digital Governance Council recently announced that Zero-Copy Integration has been published for open access by the public, setting a new standard for data collaboration. Moreover, Zero-Copy Integration prioritizes data-centricity and active metadata over complex code and solution modularity over monolithic design. It also facilitates data governance through data products and federated stewardship, eliminating the need for centralized teams. In addition to boosting the development and deployment of new technologies, Zero-Copy Integration also addresses the growing number of data privacy regulations. By putting data at the center of the process, Zero-Copy Integration ensures greater collaboration and control, positively affecting industries like healthcare, research, banking, and public services. Benefits of Zero-Data Integration Proponents of zero-copy integration and dataware say the framework will lower data storage costs, improve performance of IT teams, improve privacy and security of data, and drive innovation in systems for public health, social research, open banking and sustainability through innovations in: Comparing Traditional vs. Zero Copy Integration: Aspect Traditional Zero Copy Replication Requires copying data to target Data remains in original location Updates Data accuracy dependent on synchronization Real-time access to data Cost Involves data movement expenses No additional data movement costs Regulatory Compliance Complex governance due to data duplication Simplified compliance with source data only Errors Data movement introduces potential errors Minimized risk of errors with no data movement Maintenance Increases complexity with copying and synchronization Streamlined management with no data relocation Implementing Zero Copy Integration: The implementation of zero copy integration varies depending on the platform and direction of data access. Let’s explore how it works in scenarios involving Salesforce Data Cloud and Snowflake data warehouse: Case Study: Buyers Edge’s Zero Copy Success Story: Buyers Edge, a procurement optimization company, leveraged zero copy integration to unify customer profiles in a CDP while accessing purchase data from their data warehouse. By seamlessly integrating Salesforce Data Cloud with their warehouse, Buyers Edge enhanced predictive modeling capabilities, resulting in tailored sales and marketing strategies. Zero copy integration revolutionizes data management practices by enabling seamless data access and eliminating the need for data duplication or relocation. As businesses navigate the evolving data landscape, leveraging zero copy integration can streamline operations, enhance insights, and empower organizations to unlock the full potential of their data assets. MILESTONES / TIMELINE Second half 2023 – Internationalization February 21, 2023 – Information Session (watch above ☝ or via YouTube) February 15, 2023 – ‘Zero-copy’ applications published by DCA February 08, 2023 – Published by Digital Governance Council (Press release) December 15, 2022 – Approved by Standards Council of Canada June-Nov 2022 – Public consultation period Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

April 21, 2024in Salesforce, Salesforce Data Cloud, Salesforce Einstein, Snowflake

08Nov

MC Personalization Tips and Tricks

Salesforce Marketing Cloud Personalization, formerly Interaction Studio, offers incredible power for personalization. MC Personalization Tips and Tricks below will help you level up your game. Einstein Recipes: Enhancements and Challenges Multiple Dimensional Variations for Products in Einstein Recipes Einstein Recipes offer powerful and flexible tools for creating recommendations. However, the fourth step, Variations, falls short compared to other options. Currently, you can configure only a single Dimensional Variation. While multiple Item Types are available, once you select one, you cannot limit recommended products to specific numbers per category or brand. This limitation hinders control over product recommendations, especially for e-commerce sites with diverse catalogs. Unlike Dimensional Variations, multiple Boosters or Exclusions of the same type can be configured differently, which would be a valuable feature to add for Variations. Department Variation for Products in Einstein Recipes Einstein Recipes allow Dimensional Variations at the Category level, but only for primary categories. There is no option for Department (master category) level, which is limiting for e-commerce sites with broad category trees, such as: Recommendations with Category Variation set can still be dominated by similar products due to similar primary categories. Two solutions could address this: Price Reduction Ingredient in Einstein Recipes Triggered Campaigns in Journey Builder can target various events, including Catalog Triggers. Some triggers, like Product Expiring Soon, are available for Web with Einstein Recipes Ingredients. However, there is no Ingredient for the common e-commerce use case of Price Reduction. Marketing Cloud Personalization (Interaction Studio) has the required price and listPrice attributes for Triggered Campaigns. A workaround involves calculating price reductions externally and passing this information to a Related Catalog Object. More efficient solutions would be: Rating Count in Recipe’s Rating Exclusion Marketing Cloud Personalization offers Exclusions/Inclusions on Recipes to fine-tune recommendations. One option is to exclude/include items based on their rating, with an optional zero rating capture. It would be beneficial to include an option to filter based on rating count, allowing for: Currently, such filters can only be applied on the server side in the Template, which can limit recommendations. Having this feature at the recipe level would be more powerful. Abandoned Cart Retention Setting Marketing Cloud Personalization captures cart information for Einstein Recipes recommendations. However, cart content remains indefinitely unless managed proactively. A workaround involves a Web Campaign that checks cart age and pushes a clear cart action if necessary. A better solution would be a configurable option in MCP settings to automatically remove old cart data. Catalog Enhancements Full MCP Category Hierarchy Support for ETL Marketing Cloud Personalization can create a hierarchical tree of categories with automatic summing of views and revenue. However, this is currently possible only under specific conditions, such as having one Category per product and using a Sitemap format. This limitation is problematic, as ETL is often a better way to manage it. The Category ETL already provides detailed information using department and parentCategoryId attributes, but this data does not replicate the drill-down hierarchy in the Catalog UI or pass data from the bottom Category up. Ensuring feature parity between Sitemap and ETL would be beneficial. Segmentation Enhancements MCP Action Name Management Marketing Cloud Personalization captures actions from multiple sources but does not allow managing created actions. An option to view and remove unnecessary actions would improve user experience by reducing the number of options in the segmentation/targeting picklists. An even better solution would be to merge existing actions, preserving behavioral data after refactoring action names. MCP Hourly-Based Segmentation Rules Currently, segmentation rules in Marketing Cloud Personalization are based on days, limiting on-site campaign targeting. For example, to display an infobar for abandoned cart users, the current segmentation can only show users who have not performed a Cart Action today. Hourly-based segmentation rules would allow more precise targeting, showing users who have not performed a Cart Action in the last hour. Adding a picklist to choose between day or hour-based rules would enhance segmentation capabilities. Full MCP Catalog Export Marketing Cloud Personalization supports manual catalog export but only with limited data. The current export file lacks complete catalog data (e.g., promotable and archived attributes), making it unsuitable for ETL sources. An option to export the full catalog data, matching the ETL schema and including hidden items, would greatly benefit debugging and batch-modifying items for subsequent ETL import. Full MCP Catalog Metadata Visibility Marketing Cloud Personalization supports viewing custom attribute metadata in the Catalog but is limited to ETL updates. Extending this to built-in attributes and including origin and lastUpdated values for all sources (Sitemap, Mobile App, Manual update, API) would simplify debugging Catalog metadata issues, reducing admin/developer work and support tickets. ETL Enhancements External Email Campaign ETL Experience Name & ID External Email Campaign ETL allows passing behavioral data but is limited to Campaign ID and Campaign Name. To fully leverage this data in segmentation, it should also support Email ID and Email Name. Adding Experience ID and Experience Name fields to the ETL would enable targeted personalization, allowing segmentation on entire campaigns or specific emails within campaigns. External Email Campaign ETL Send Segmentation External Email Campaign ETL passes Send, Click, and Open data but does not support segmentation based on Send events. Enabling segmentation rules for Send events would unlock use cases like targeting Web or Push campaigns to users who received an email campaign but did not open it, fully leveraging cross-channel and real-time personalization. External Email Campaign ETL Unsubscription Event Type External Email Campaign ETL passes Send, Click, and Open data but cannot pass unsubscriptions. Including the Unsubscribe event would enable targeted campaigns like surveys about unsubscription reasons, win-back campaigns, or replacing email subscription prompts with other channel recommendations. By addressing these enhancements and challenges, Salesforce Marketing Cloud Personalization (Interaction Studio) can further improve its capabilities and provide more precise, effective, and user-friendly tools for personalized marketing. Reporting Enhancements: Direct Attribution at the MCP Campaign Level Current Reporting in Marketing Cloud Personalization (MCP) Marketing Cloud Personalization (Interaction Studio) offers various reports based on Activity, Results, and Visits. However, it

November 8, 2023in Salesforce Development Services, Salesforce Financial Services Solutions, Salesforce for Nonprofits, Salesforce Health and Life Sciences Solutions, Salesforce Hospitality Travel and Transportation, Salesforce Implementation Services, Salesforce Marketing Cloud, Salesforce Personalization

26Sep

Case Study: Large Restaurant Entity-Salesforce Sales/Service/Experience Clouds

An American chain store of bakery-cafe fast food restaurants with over 2,000 locations, all of which operate in 48 states, the District of Columbia and Canada. The restaurant offers a sit-down restaurants (some with drive-throughs) where customers can enjoy a variety of freshly made sandwiches on your choice of bread, accompanied by soup or salad. Salesforce Case Study: Transforming a Large Restaurant Entity by leveraging Salesforce. Sales Cloud Service Cloud Experience Cloud Implementation PROBLEM SOLUTION RESULTS . Like2 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

September 26, 2023in Data, Salesforce, Salesforce Experience Cloud, Salesforce Implementation Services, Salesforce Sales Cloud, Salesforce Service Cloud, Salesforce Success Stories, Technology

data cloud and marketing cloud personalization

11Jul

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

Is a Data Lake Necessary? Difference in a Data Lake and a Data Warehouse? Do I need both? Both Data Lakes and Data Warehouses play crucial roles in the data processing and reporting infrastructure. They are complementary approaches rather than substitutes. Relevance of Data Lakes: Data lakes are losing popularity compared to their previous standing. Advanced storage solutions like data warehouses are progressively taking their place. Can Data Lakes Replace Data Warehouses? Data lakes do not directly replace data warehouses; they serve as supplementary technologies catering to different use cases with some overlap. Organizations typically have both a data lake and a data warehouse. Distinguishing Between Data Lakes and Data Warehouses: Data lakes and data warehouses serve as storage systems for big data, utilized by data scientists, data engineers, and business analysts. Despite some similarities, their differences are more significant than their commonalities, and understanding these distinctions is vital for aspiring data professionals. Data Lake vs. Data Warehouse: Key Differences: Data lakes aggregate structured and unstructured data from multiple sources, resembling real lakes with diverse inflows. Data warehouses, on the other hand, are repositories for pre-structured data intended for specific queries and analyses. Exploring Data Lakes: A data lake is a storage repository designed to capture and store large amounts of raw data, whether structured, semi-structured, or unstructured. This data, once in the lake, can be utilized for machine learning or AI algorithms and later transferred to a data warehouse. Data Lake Examples: Data lakes find applications in various sectors, such as marketing, education, and transportation, addressing business problems by collecting and analyzing data from diverse sources. Understanding Data Warehouses: A data warehouse is a centralized repository and information system designed for business intelligence. It processes and organizes data into categories called data marts, allowing for structured data storage from multiple sources. Data Warehouse Examples: Data warehouses support structured systems and technology for diverse industries, including finance, banking, and food and beverage, facilitating secure and accurate report generation. Data Warehouses compared to Data Lakes: Data warehouses contain processed and sanitized structured data, focusing on business intelligence, while data lakes store vast pools of unstructured, raw data, providing flexibility for future analysis. Key Differences Between Warehouses and Lakes: Intended purpose, audience, data structure, access and update cost, access model, and storage and computing are crucial factors distinguishing data warehouses and data lakes. Choosing Between Data Warehouse and Data Lake: The decision depends on organizational needs, value extracted from data analysis, and infrastructure costs. Organizations may opt for agility with a data lake, a data warehouse for larger data quantities, or a combination for maximum flexibility. A data lake stores raw, unstructured data indefinitely, providing cost-effective storage, while a data warehouse contains cleaned, processed, and structured data, optimized for strategic analysis based on predefined business needs. Data Warehouse, Data Lake, and Data Hub Differences: Data warehouses and data lakes primarily support analytic workloads, whereas data hubs focus on data integration, sharing, and governance, serving different purposes in the data landscape. Salesforce Data Cloud is a powerful data warehouse solution that allows companies to effectively manage and analyze their data. It provides users with the ability to stream input data from Salesforce and other sources, making it a comprehensive platform for data integration. Content updated February 2024. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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

06Jul

Leverage AI and Machine Learning in Your Data Warehouse

5 Reasons to Leverage AI and Machine Learning in Your Data Warehouse Incorporating AI and machine learning (ML) into a data warehouse transforms it into a powerful tool for decision-making and insight generation across the entire organization. Here are five key benefits of integrating AI and ML into your data warehouse: 1. Improved Efficiency AI and ML streamline data warehouse operations by automating time-consuming tasks like data validation and cleansing. These technologies can manage repetitive processes, such as extraction, transformation, and loading (ETL), freeing data teams to focus on higher-priority tasks that drive business value. AI and ML ensure that inconsistencies are addressed automatically, which boosts overall operational efficiency. 2. Faster Performance ML can monitor query performance in real time, identifying bottlenecks and optimizing processes to increase speed and accuracy. Automating data ingestion and delivery enables users to act on insights faster, making real-time decision-making possible. Faster data processing leads to more timely and effective business strategies. 3. Increased Accessibility for All Users AI and ML enhance data quality and simplify data queries, making insights accessible even to non-technical users. By allowing natural language inputs and generating easy-to-understand visualizations, these technologies empower employees at all skill levels to interact with data. When everyone in the organization works from the same data foundation, decision-making becomes more aligned and consistent. 4. More Accurate Forecasting ML’s predictive capabilities allow data warehouses to anticipate trends and proactively solve problems before they arise. Predictive models and anomaly detection help prevent downtime, improve customer demand forecasting, and enhance overall accuracy. The more these algorithms are used, the more refined and effective they become, improving insights and forecasts over time. 5. Reduced Data Storage Costs AI and ML analyze data usage to optimize storage solutions, identifying and eliminating redundant data to free up space. These technologies can also optimize data architecture, making the warehouse more efficient and reducing operational costs. As an organization scales, AI and ML help manage growing data volumes without increasing expenses, ensuring cost-effective data storage and processing. By integrating AI and ML into a data warehouse, organizations can enhance speed, efficiency, and accuracy, driving better decision-making and improving business outcomes. 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 Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

July 6, 2023in Data

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 Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

May 30, 2023in Salesforce

26May

What Makes Snowflake So Popular?

Why opt for Snowflake over its competitors? What Makes Snowflake So Popular? One of Snowflake’s standout features is its multi-cloud capability, being accessible on major cloud platforms like Azure, AWS, and GCP. This is particularly advantageous for companies operating in multi-cloud environments, as they can seamlessly query Snowflake data from any of these platforms. Snowflake distinguishes itself as a true self-managed service, eliminating the need for users to handle hardware selection, installation, configuration, or management. Moreover, there is minimal software involvement, with ongoing maintenance, management, upgrades, and tuning efficiently managed by Snowflake. If your scaling requirements are primarily related to data warehousing, Snowflake’s data-centric scalability makes it a preferred choice. On the other hand, AWS might be more suitable for general infrastructure scalability across diverse cloud infrastructure components. Organizations opt for Snowflake’s cloud-built data warehouse to achieve significant benefits. These include reducing query times from hours to seconds, providing universal access to all business users, handling structured and semi-structured data swiftly, and doing so more cost-effectively compared to other data analytics platforms. Snowflake’s storage and compute separation enables seamless sharing of live data across business units, eliminating the need for data marts or maintaining multiple data copies. The flexibility to scale virtual warehouses based on specific needs, without concerns about underlying hardware, sets Snowflake apart. Additionally, its pricing model and central data repository contribute to enhanced flexibility, scalability, and cost-effectiveness. Furthermore, Snowflake allows users to share data with partners and customers, irrespective of region or cloud, fostering collaboration on a global scale. If you would like learn more about Snowflake cloud-build data warehouse, contact Tectonic today. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

May 26, 2023in Salesforce Development Services, Snowflake

15May

Salesforce Data Cloud Explained

Salesforce Data Cloud, previously recognized as Salesforce CDP/Genie, made its debut at Dreamforce 2022, hailed by Salesforce as one of the most significant innovations in the company’s history. A hyperscale data platform built into Salesforce. Activate all your customer data across Salesforce applications with Data Cloud. Data Cloud facilitates the intake and storage of real-time data streams on a massive scale, empowering automated tasks that result in highly personalized experiences. Data can be sourced from diverse Salesforce data outlets, including Mulesoft, Marketing Cloud, and others, along with customers’ proprietary applications and data sources. Subsequently, it can dynamically respond to this real-time data by automating actions across Salesforce CRM, Marketing Cloud, Commerce, and more, inclusive of automating actions through Salesforce Flow. What is the Salesforce data cloud? Data Cloud is the fastest growing organically built product in Salesforce’s history (i.e. Salesforce built it themselves, not via acquisitions). Data Cloud could be described as the ‘Holy Grail of CRM’, meaning that the data problem that’s existed since the infancy of CRM is now finally solvable. Data Cloud is the foundation that speeds up the connectivity between different ‘clouds’ across the platform. However, Data Cloud is also a product that can be purchased. While not all Salesforce customers have licensed Data Cloud, being at the foundation means they are still taking advantage of Data Cloud to a degree – but this all becomes even stronger with Data Cloud as a personalization and data unification platform. What is the history of Data Cloud? Salesforce has gone through several iterations with naming its CDP product: Customer 360 Audiences → Salesforce CDP → Marketing Cloud Customer Data Platform → Salesforce Genie → Salesforce Data Cloud. In some instances, changes were made because the name just didn’t stick – but what’s more important to note, is that some of the name changes were to indicate the significant developments that happened to the product. Salesforce Data Cloud Differentiators Data Cloud, in itself, is impressive. While many organizations would consider it expensive, if you were to flip the argument on its head, by buying your own data warehouse, building the star schema, and paying for ongoing compute storage, you’d be looking to spend 5 to 10 times more than what Salesforce is charging for Data Cloud. Plus, data harmonization works best when your CRM data is front and center. There are other key differentiators that helps Data Cloud to stand out from the crowd: Is data cloud a data lakehouse? That means that Data Cloud is now not just a really good CDP, it’s now a data lake which will be used in sales and service use cases. But it also means that we can start to fundamentally move some of our higher-scale consumer products like Marketing and Commerce onto the platform. Is Snowflake a data Lakehouse? Snowflake offers customers the ability to ingest data to a managed repository, in what’s commonly referred to as a data warehouse architecture, but also gives customers the ability to read and write data in cloud object storage, functioning as a data lake query engine. What is the benefit of Salesforce data cloud? Data Cloud empowers Salesforce Sales Cloud with AI capabilities and automation that quickly closes deals and boosts productivity across every channel. It drives customer data from all the touchpoints and unifies it separately in individual customer profiles. Salesforce Data Cloud is a powerful data warehouse solution that allows companies to effectively manage and analyze their data. What is the difference between Salesforce CDP and data lake? Talking abut Salesforce CDP is a little bit like a history lesson. While a CDP provides a unified, structured view of customer data, a data lake, on the other hand, is more of a raw, unstructured storage repository that holds a vast amount of data (more than just customer data) in its native format until it’s needed. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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

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 Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

March 30, 2023in Data, Enterprise, Google, Salesforce, Salesforce Data Cloud, Salesforce Marketing Cloud, Technology

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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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

16Sep

Case Study: Transforming player development and marketing automation

Tribal gaming and resort property needed real ability to see a 360 degree view of the business, the guests, and the facility.

September 16, 2022in Data, Marketing Automation, Salesforce, Salesforce Development Services, Salesforce Hospitality Travel and Transportation, Salesforce Implementation Services, Salesforce Marketing Cloud, Salesforce Service Cloud, Salesforce Success Stories, Technology

09Sep

Salesforce Customer 360

Salesforce Customer 360 is a family of Salesforce products and services that help businesses manage and connect with their customers across all touchpoints. With Customer 360, businesses can access a comprehensive view of their customers, including all their interactions and data, in one unified platform. This centralized data model allows businesses to deliver personalized and connected experiences to their customers across all channels and platforms. Customer 360 fully integrates marketing, sales, commerce, service, IT, and analytics into one unified platform, enabling businesses to streamline processes, improve decision-making, automate workflows, and enhance communication and collaboration. With the help of Salesforce solutions partners like Tectonic, businesses can customize Customer 360 to fit their specific needs and business model. Customer 360 is a framework that consolidates all existing customer insights from various tables and models within your data warehouse. In addition, Customer 360 provides real-time insights and metrics through customizable dashboards and reports, helping businesses track key performance indicators and make data-driven decisions. Salesforce Customer 360 can help businesses drive innovation, increase efficiency, and deliver a better customer experience, enabling them to digitally transform their operations. 85% of customers expect consistent interactions across all departments. With Salesforce’s Customer 360, important customer data from every step of their journey is captured and stored in one place. Adding more than one app to your Customer 360 solution has measurable benefits: Salesforce and Tectonic will kickstart your business’s digital transformation in 2023. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

September 9, 2022in Data, Google, Salesforce, Salesforce Hospitality Travel and Transportation, Salesforce Implementation Services, Salesforce Manufacturing Automotive Energy

10Mar

Understanding Salesforce Integration

What is Salesforce Integration? Salesforce Integration is the process of connecting two or more systems to streamline workflows and enhance data consistency across platforms. Consider situations where vital information is stored in one system but also needed in another. By integrating these systems, you ensure seamless data flow, improve efficiency, and enable smooth business processes. Why is Integration Important? In today’s digital landscape, businesses must continuously enhance efficiency and customer experience to stay competitive. Operating in isolation is no longer an option. Effective system integration ensures faster, scalable, and more reliable operations. What is an API? An API (Application Programming Interface) enables different applications to communicate with each other. For instance, when you use a mobile app, it connects to the internet, retrieves data from a server, and displays it in a readable format. The right API ensures this process runs smoothly and efficiently. Different types of APIs will be discussed later in the Salesforce Integration Capabilities section. Types of Salesforce Integration Architectures Each integration architecture has advantages and drawbacks. Here are the three main types: 1. Point-to-Point Integration This is a one-to-one integration model where each system has a direct connection to another. For example, a sales application sends order details separately to a billing system, a shipping application, and a tracking system. However, this approach is costly to maintain and lacks scalability, as adding new integrations requires extensive modifications. 2. Hub-and-Spoke Integration With this model, a central hub facilitates communication between systems. Instead of creating multiple direct integrations, each system only connects to the hub. This setup simplifies management and scalability compared to point-to-point integration. 3. Enterprise Service Bus (ESB) Integration An evolution of the hub-and-spoke model, ESB uses an integration engine to connect various applications. ESB provides: Each system connects through an adapter, making it easy to scale integrations as business needs evolve. Salesforce Integration Capabilities Understanding APIs and integration capabilities is crucial. Here are key Salesforce integration tools: 1. REST API Best for web or mobile applications, REST API operates using: It uses JSON or XML and functions synchronously, meaning it waits for a response before proceeding. 2. SOAP API SOAP API is suited for back-end system integrations requiring structured payloads. It uses XML and supports asynchronous communication, meaning it can process requests without waiting for immediate responses. 3. Bulk API Designed for handling large data volumes, Bulk API efficiently processes up to 100 million records within a 24-hour period. It is asynchronous, making it ideal for initial data migrations and batch processing. 4. Streaming API Built on the publish/subscribe model, Streaming API supports near real-time data updates. It includes: This API is essential for event-driven architectures. 5. Outbound Messages This declarative option sends messages to external systems when triggered by workflow rules or approval processes. It is asynchronous but requires acknowledgment from the receiving system. 6. Web Service Callouts Salesforce can initiate outbound requests to external systems for data validation or process execution. These callouts require Apex coding and can be synchronous or asynchronous. 7. Salesforce Connect Salesforce Connect enables real-time data access from external systems without storing the data in Salesforce. This “data virtualization” reduces storage costs and ensures up-to-date information is available when needed. 8. Heroku Connect Heroku Connect synchronizes data between Salesforce and Postgres databases, making it ideal for high-volume applications where not all data needs to reside in Salesforce. Salesforce Integration Patterns Integration patterns define how systems interact. Consider: 1. Remote Call-In External systems retrieve, update, or delete Salesforce data (e.g., an order management system updating Salesforce records). 2. Request and Reply Salesforce calls an external system and waits for a response before proceeding (e.g., address validation services). 3. Fire and Forget Salesforce sends a request but does not wait for a response (e.g., outbound messages and platform events). 4. Batch Data Synchronization Data is periodically synchronized between Salesforce and external systems in bulk (e.g., nightly updates to a data warehouse). 5. UI Update Based on Data Changes Salesforce UI updates dynamically when backend data changes (e.g., real-time case status updates for support agents). 6. Data Virtualization Salesforce displays external data in real time without storing it, reducing storage costs and improving efficiency (e.g., Salesforce Connect). Conclusion Salesforce integration streamlines business processes, enhances efficiency, and improves data consistency. Understanding integration architectures, capabilities, and patterns helps businesses select the right approach for their needs. By leveraging Salesforce’s integration tools, organizations can achieve seamless connectivity across their technology ecosystem. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

March 10, 2022in Data, Enterprise, Salesforce, Salesforce Implementation Services, Technology

10Jun

What is BI in Salesforce?

Salesforce BI helps to create fast, digestible reports to help you make informed decisions at the right time. Salesforce Einstein is a leading business intelligence software solution that will help streamline your operations. Read on in this insight to learn how Salesforce BI capabilities including Tableau rank in the Gartner Magic Quadrant. Make the right decision every time using analytics that go beyond business intelligence software. See why Gartner named Salesforce (Tableau) a Leader in the Gartner® Magic Quadrant™ for Analytics and Business Intelligence Platforms for the 11th consecutive year. Data and analytics leaders must use analytics and BI platforms to support the needs of IT, analysts, consumers and data scientists. While integration with cloud ecosystems and business applications is a key selection requirement, buyers also need platforms to support openness and interoperability. Analytics and business intelligence (ABI) platforms enable less technical users, including business people, to model, analyze, explore, share and manage data, and collaborate and share findings, enabled by IT and augmented by artificial intelligence (AI). For several years, the Magic Quadrant for Analytic and Business Intelligence Platforms has emphasized visual self-service for end users augmented by AI to deliver automated insights. While this remains a significant use case, the ABI platform market will increasingly need to focus on the needs of the analytic content consumer and business decision makers. To achieve this, automated insights must be relevant in context of a user’s goals, actions and workflow. Many platforms are adding capabilities for users to easily compose low-code or no-code automation workflows and applications. This blend of capabilities is helping to expand the vision for analytics beyond simply delivering datasets and presenting dashboards. Today’s ABI platforms can deliver enriched contextualized insights, refocus attention on decision-making processes and ultimately take actions that will deliver business value. In addition to the increasing consumer design focus trend, we see other key market trends, including the need for improved governance of analytic content creation and dissemination, and the demand for a headless, open architecture. For example, a headless ABI platform would decouple the metrics store from the front-end presentation layer, enabling more interoperability with competitive products. ABI platform functionality includes the following 12 critical capabilities, which have been updated to reflect areas of market change, differentiation and customer demand: Gartner added three new critical capabilities as part of our metrics store evaluation criteria this year: ABI platforms have always been about measurement. For decades, the slicing and dicing of measures by their dimensional attributes was synonymous with the act of performing business intelligence. However, over the last decade, the focus on metrics and measurement was overshadowed by data visualization. As data visualization became the most conspicuous capability, some business executives began to conflate ABI platforms with data visualization — as if ABI platforms are glorified chart wizards. This misconception minimizes much of the work performed and the business value delivered by ABI platforms. Establishing metrics stores as a critical capability to execute makes it clear that defining and communicating performance measures throughout an organization is one of the key purposes of an ABI platform. Analytics collaboration is a combination of many features (such as Slack/Teams integration, action frameworks) that collectively improve an organization’s ability to make decisions with consensus. Data science integration reflects the increasing likelihood that a business analyst may want to use data science to test certain hypotheses, and that data scientists will need to leverage features such as data prep and data visualization. In addition, Gartner is changing “catalogs” to “analytic catalogs” to emphasize a set of requirements that are not being met by ABI platform vendors today. Most large enterprises have thousands of reports built across multiple ABI platforms, but consumers in these organizations have no easy way to access these reports. The name change to analytic catalogs reflects the need for ABI platform vendors to deliver analytic content with the consumer in mind. Three critical capabilities were removed from our evaluation criteria: security, natural language generation (NLG; rolled into data storytelling) and cloud analytics (which will no longer be considered a platform capability, but instead a go-to-market strategy covered in the Magic Quadrant). And one of the security sub-criteria, about the granularity of authorization (e.g., row-based security) has been moved to the enterprise reporting capability. Salesforce (Tableau) Tableau, a Salesforce company, is a Leader in this Magic Quadrant. Its products are mainly focused on visual-based exploration that enables business users to access, prepare, analyze and present findings in their data. CRM Analytics, formerly Tableau CRM, provides augmented analytics capabilities for analysts and citizen data scientists. Tableau has global operations and serves clients of all sizes. In 2022, Tableau reinforced its augmented consumer vision to provide contextualized insights with deeper integration with Salesforce Data Cloud. IT also improved decision intelligence by bringing domain-aware insights into action with Revenue Intelligence and other Salesforce-native apps. The extensible design and x-platform integrations (Salesforce Flow, MuleSoft, UiPath and Looker) further enable composable analytics to bring insights into workflow with agility. Strengths Cautions Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

June 10, 2021in Artificial Intelligence, Data, Enterprise, Google, Mulesoft, Salesforce, Salesforce Data Cloud, Salesforce Einstein, Salesforce Flow, Salesforce Managed Services, Technology

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