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Exploring Google Vertex AI
21Feb

Vertex AI

Exploring Google Vertex AI Conversation — Dialogflow CX with Generative AI, Data Stores, and Generators Vertex AI Conversation, built on Dialogflow and Vertex AI, introduces generative conversational features that utilize large language models (LLMs) for natural language understanding, crafting responses, and managing conversation flow. These advancements streamline agent design and enhance the quality of interactions. With Vertex AI Conversation, you can employ a state machine approach to develop sophisticated, generative AI-powered agents for dynamic conversation design and automation. In this insight, we’ll delve into the cutting-edge Dialogflow CX Generative AI technology, focusing on Data Stores and Generators. Data Stores: The Library of Information for Conversations Imagine Data Stores as an extensive library. When a question is asked, the virtual assistant acts as a librarian, locating relevant information. Dialogflow CX’s Data Store feature makes it easy to create conversations around stored information from various sources: For data preparation guidance, visit Google’s official documentation. Generators: LLM-Enhanced Dynamic Responses Dialogflow CX also enables Generators to use an LLM directly in Dialogflow CX without webhooks. Generators can perform tasks like summarization, parameter extraction, and data manipulation. Sourced from Vertex AI, they create real-time responses based on your prompts. For example, a Generator can be customized to summarize lengthy answers—an invaluable feature for simplifying conversations in chat or voice applications. You can find common Generator configurations in Google Cloud Platform (GCP) documentation. Creating a Chat Application with Vertex AI To start building, go to the Search and Conversation page in Google Cloud, agree to the terms, activate the API, and select “Chat.” Setting Up Your Agent After naming your agent and configuring data sources, like a Cloud Storage bucket with PDF documents, you’ll see your new chat app under Search & Conversation | Apps. Navigate to Dialogflow CX, where you can use your data store by setting up parameters for the agent and configuring responses. Once your agent is ready, you can test it in the Agent simulator. Adding a Generator for Summarization Using the Generator feature, you can further refine responses. Set parameters to target the Generator’s summarization feature, and link it to a specific page for summarized responses. This improves chat flow, providing concise answers for faster interactions. Integrating with Discord If you want to deploy your agent on platforms like Discord, follow Google’s integration guide for Dialogflow and adjust your code as needed. With the integration, responses will include hyperlinks for easy reference. Conclusion Vertex AI Conversation, with Dialogflow CX, enables powerful, human-like chat experiences by combining LLMs, Data Stores, and Generators. Ready to build your own dynamic conversational experiences? Now is the perfect time to experiment with this technology and see where it can take you. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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February 21, 2024in Data, Generative AI, Google, Technology
crm analytics
26Jan

Build Better Tableau Dashboards

The effort made to build better Tableau dashboards pays tenfold in there readability and usability. “Dashboard design is not about making dashboards ‘pretty. It’s making them functional and helping the user to get the information they need as efficiently as possible.” ALEXANDER WALECZEK, ANALYTICS PRACTICE LEAD AND TABLEAU AMBASSADOR Effective communication with your audience involves considering their needs from start to finish. The key lies in posing the right questions. To convey information to your readers in an engaging manner, it is crucial to grasp fundamental aspects, such as: Possibly, when tailoring content for a time-pressed salesperson with only 15 seconds to spare for crucial performance indicators, it is imperative to present the most vital information in a glance. Additionally, ensuring that the dashboard is mobile-friendly and loads swiftly becomes essential. On the other hand, if your target audience consists of a team set to review quarterly dashboards over an extended period, offering more detailed views of the data might be advisable. Build Better Tableau Dashboards for Your Audience Take into account the expertise level of your audience. Gain a deeper understanding of their skill set by inquiring about their priorities and data consumption habits. This insight is crucial for determining the most effective way to present data, guiding key design decisions. For instance, a novice may require more action-oriented labels for filters or parameters compared to an advanced user. Here are four effective methods to assess the dashboard and data proficiency of your audience: Adjust Your Narrative Adjust your narrative accordingly. Tailoring your dashboards to suit the intended audience enhances their impact. Below are three visualizations depicting the distribution of tornadoes in the United States for the first nine months of the year. The distinction lies in the level of visual information employed to convey the narrative. There might e an extremely minimal presentation, progressing in complexity towards the right. None of these approaches is inherently superior to the others. The minimal visualization on the left might be ideal for audiences well-versed in the subject matter, appreciating simplicity and the elimination of redundancy. On the other hand, for newcomers or individuals viewing the visualization just once, the explicitness of the visualization on the right could be more effective. Determining what constitutes clutter versus essential information is where collaboration with colleagues becomes crucial. Crafting persuasive dashboards involves making a lasting impact on partnership. By closely collaborating with line-of-business stakeholders, you can secure the buy-in and engagement needed to tailor the dashboard to their specific requirements and expectations. This collaborative approach forms the essence of dashboard persuasion. A Work in Progress Demonstrate your process and embrace iterative refinement. Establishing a culture of analytics should be accompanied by a culture of supportive and frequent critique. Creating multiple versions of your work and actively seeking feedback throughout the process will contribute to a superior final product. Avoid isolation and stagnation; share your progress with others, use the feedback to refine your work, and repeat the process until you achieve a satisfactory result. Much like the formation of a diamond requiring extraordinary heat, pressure, and time, the outcome is worth the effort. Encouraging critiques is essential for cultivating a culture of constructive feedback. Trust among colleagues is important, arguably it enables mutual respect and trust in each other’s feedback. Developing a thick skin is also necessary, focusing on designing dashboards that cater to users and clients’ needs rather than personal preferences. Similar to writers who must “kill their darlings,” designers must prioritize the overall effectiveness of the dashboard, making honest assessments and adjustments when needed. “It also helps to have a public place—on a real or virtual wall—for sharing work. Making work public creates constant opportunities for feedback and improvements.” Tableau Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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January 26, 2024in Salesforce, Salesforce Tableau
19Sep

Audience Builder Marketing Cloud

Marketing Cloud Audience Builder dynamically generates targeted audiences from contacts stored in your account based on attribute and behavioral values. These audiences can be used to target or exclude contacts from your marketing activities. In today’s world, where a staggering 347.3 billion emails are sent globally every day, email inboxes have become increasingly cluttered. In your specific niche, you’re not the only one trying to reach your target audience; numerous others are vying for their attention. With consumers having a multitude of options, marketers bear the responsibility of positioning themselves in a way that makes it impossible for potential customers to overlook them. Achieving this requires embracing customer-centricity, which involves deeply engaging with different buyer personas by segmenting your contact list based on various parameters such as age, gender, location, interests, preferences, past purchases, browsing history, and position in the sales funnel. However, manually managing this segmentation, especially with a large contact list, can be overwhelming. This is where a dependable tool like Salesforce Marketing Cloud’s Audience Builder proves invaluable. The SFMC Audience Builder empowers marketers to create granular segmentation frameworks based on demographic and behavioral data, making the execution of targeted campaigns effortless. It dynamically generates targeted audiences by utilizing contacts in your account and leveraging behavioral values and stored attributes as guiding parameters. In this overview, we aim to provide a comprehensive understanding of SFMC’s Audience Builder. Key Entities and Terminologies: Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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September 19, 2023in Data, Salesforce, Salesforce Marketing Cloud
vector index
25Aug

Choosing the Right Vector Index

Finding the Needle in the Digital Haystack: Choosing the Right Vector Index Imagine searching for a needle in a vast digital haystack of millions of data points. In AI and machine learning, selecting the right vector index is like equipping yourself with a magnet—it transforms your search into a faster, more precise process. Whether you’re building a recommendation system, chatbot, or Retrieval-Augmented Generation (RAG) application, the vector index you choose significantly impacts your system’s performance. So how do you pick the right one? Let’s break it down. What Is Similarity Search? At its core, similarity search is about finding items most similar to a query item based on a defined metric. These items are often represented as high-dimensional vectors, capturing data like text embeddings, images, or user preferences. This process enables applications to deliver relevant results efficiently and effectively. What Is a Vector Index? A vector index is a specialized organizational system for high-dimensional data. Much like a library catalog helps locate books among thousands, a vector index enables algorithms to retrieve relevant information from vast datasets quickly. Different techniques offer varying trade-offs between speed, memory usage, and accuracy. Popular Vector Indexing Techniques 1. Flat Index The Flat Index is the simplest method, storing vectors without alteration, like keeping all your files in one folder. 2. Inverted File Index (IVF) The IVF improves search speed by clustering vectors, reducing the number of comparisons. 3. Product Quantization (PQ) PQ compresses high-dimensional vectors, reducing memory requirements and speeding up calculations. 4. Hierarchical Navigable Small World Graphs (HNSW) HNSW offers a graph-based approach that excels in balancing speed and accuracy. Composite Indexing Techniques Blending techniques can help balance speed, memory efficiency, and accuracy: Conclusion Choosing the right vector index depends on your specific needs—speed, memory efficiency, or accuracy. By understanding the trade-offs of each indexing technique and fine-tuning their parameters, you can optimize the performance of your AI and machine learning models. Whether you’re working with small, precise datasets or massive, high-dimensional ones, the right vector index is your key to efficient, accurate searches. 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 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 We Are All Cloud Users My old company and several others are concerned about security, and feel more secure with being able to walk down Read more

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August 25, 2023in Data
Google Analytics 4 UTM Codes
31May

Google Analytics 4 UTM Codes

How to Generate UTM Codes Generating Google Analytics 4 UTM Codes can be done in several ways. A common method is using Google’s Campaign URL Builder, where you enter your original URL and add the desired UTM parameters. Alternatively, tools like Measureschool’s UTM Tool in Google Sheets or other free online tools can also be used to create UTM codes. Viewing UTM Data in Google Analytics 4 In Google Analytics 4 (GA4), UTM data is found in the standard Acquisition reports. Specifically, you can view this data in the Acquisition Overview, User Acquisition: First User Default Channel Grouping, and Traffic Acquisition reports. Additionally, the Explore section in GA4 allows for the creation of custom reports using UTM data. Best Practices for UTM Tagging When using UTM tags, consider the following best practices: Google Analytics 4 UTM Codes allows you to better track your customer journeys and measure ROI on your advertising and search engine optimization efforts. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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May 31, 2023in Google Analytics
tectonic logo
14Nov

AI Large Language Models

What Exactly Constitutes a Large Language Model? Picture having an exceptionally intelligent digital assistant that extensively combs through text, encompassing books, articles, websites, and various written content up to the year 2021. Yet, unlike a library that houses entire books, this digital assistant processes patterns from the textual data it undergoes. This digital assistant, akin to a large language model (LLM), represents an advanced computer model tailored to comprehend and generate text with humanlike qualities. Its training involves exposure to vast amounts of text data, allowing it to discern patterns, language structures, and relationships between words and sentences. How Do These Large Language Models Operate? Fundamentally, large language models, exemplified by GPT-3, undertake predictions on a token-by-token basis, sequentially building a coherent sequence. Given a request, they strive to predict the subsequent token, utilizing their acquired knowledge of patterns during training. These models showcase remarkable pattern recognition, generating contextually relevant content across diverse topics. The “large” aspect of these models refers to their extensive size and complexity, necessitating substantial computational resources like powerful servers equipped with multiple processors and ample memory. This capability enables the model to manage and process vast datasets, enhancing its proficiency in comprehending and generating high-quality text. While the sizes of LLMs may vary, they typically house billions of parameters—variables learned during the training process, embodying the knowledge extracted from the data. The greater the number of parameters, the more adept the model becomes at capturing intricate patterns. For instance, GPT-3 boasts around 175 billion parameters, marking a significant advancement in language processing capabilities, while GPT-4 is purported to exceed 1 trillion parameters. While these numerical feats are impressive, the challenges associated with these mammoth models include resource-intensive training, environmental implications, potential biases, and more. Large language models serve as virtual assistants with profound knowledge, aiding in a spectrum of language-related tasks. They contribute to writing, offer information, provide creative suggestions, and engage in conversations, aiming to make human-technology interactions more natural. However, users should be cognizant of their limitations and regard them as tools rather than infallible sources of truth. What Constitutes the Training of Large Language Models? Training a large language model is analogous to instructing a robot in comprehending and utilizing human language. The process involves: Fine-Tuning: A Closer Look Fine-tuning involves further training a pre-trained model on a more specific and compact dataset than the original. It is akin to training a robot proficient in various cuisines to specialize in Italian dishes using a dedicated cookbook. The significance of fine-tuning lies in: Versioning and Progression Large language models evolve through versions, with changes in size, training data, or parameters. Each iteration aims to address weaknesses, handle a broader task spectrum, or minimize biases and errors. The progression is simplified as follows: In essence, large language model versions emulate successive editions of a book series, each release striving for refinement, expansiveness, and captivating capabilities. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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November 14, 2022in Data, Salesforce, Salesforce Einstein, Technology
snowpark container services
08Feb

Snowpark Container Services

Snowpark Container Services is a fully managed solution designed to simplify deploying, managing, and scaling containerized applications within the Snowflake ecosystem. Key Features Benefits of Snowpark Container Services in Native Apps Integrating Snowpark Container Services into Snowflake Native Apps enables developers to create solutions for various use cases, including: Developers benefit from Snowflake’s Native Apps Framework safeguards, which ensure secure and efficient operations. Development Workflow for Snowpark Container Services Here’s a quick guide to the development cycle for building and deploying containerized services within Snowflake. 1. Develop Code Locally 2. Build and Push an Image 3. Provision Resources 4. Deploy and Test Services Getting Started To build containerized applications with Snowpark Container Services: For a deep dive into architecture and workflows, explore Snowflake’s guides and tutorials on Snowpark Container Services and Native Apps. The Snowflake Native Applications Framework allows developers to package Snowflake features into standalone applications that can be installed across multiple accounts. This framework now supports advanced features for managing resource provisioning and deploying container services. Below is an overview of how to integrate container services into a Native App. Key Components 1. Manifest File The manifest file defines the configuration and setup properties for the application, including: Example Manifest File: Specifying Container Images yaml Copy code manifest_version: 1 version: name: V1 label: “Version One” comment: “The first version of my amazing Snowflake Native App” artifacts: readme: readme.md setup_script: scripts/setup.sql container_services: images: – /provider_db/provider_schema/provider_repo/server:prod – /provider_db/provider_schema/provider_repo/web:1.0 Here: Setting a Web UI as the Landing Page For apps with a web UI, specify a default_web_endpoint field to make the UI the landing page. yaml Copy code default_web_endpoint: service: ux_schema.ux_service endpoint: ui Here, service corresponds to the name defined in the CREATE SERVICE command, and endpoint refers to the service’s endpoint in the specification file. Using Streamlit as a Landing Page Alternatively, you can specify a default_streamlit field to use Streamlit as the landing page. Note: default_web_endpoint and default_streamlit are mutually exclusive—you can only use one. 2. Setup Script The setup script executes during the application installation and upgrade processes. It contains SQL statements for creating: Resource Provisioning for Snowpark Container Services The setup script also includes logic for provisioning resources like compute pools. However, these provisioning procedures are executed post-installation to ensure the consumer has granted the necessary privileges. Managing Compute Pools and Privileges Consumer-Controlled Resource Provisioning A foundational principle of the Snowflake Native Apps Framework is that consumers maintain full control over the resources the application accesses. This includes compute resources. Privileges Required To enable an application to provision resources, specific privileges must be requested in the manifest file: Example Privileges in the Manifest File yaml Copy code privileges: – CREATE COMPUTE POOL description: “Enable application to create its own compute pool(s)” – BIND SERVICE ENDPOINT description: “Enables application to expose service endpoints” Note: These privileges are not granted by default. The installation user may need to request them from another user with the appropriate permissions within the organization. By wrapping container services into a Snowflake Native App, developers can: This approach ensures that both providers and consumers retain control over their resources and data, while maximizing the capabilities of Snowflake Native Apps. Managing Privileges and Deploying Services in Snowflake Native Apps Once a Snowflake Native App is installed, consumers can grant the necessary privileges through various supported mechanisms. Here’s an overview of how to configure privileges, deploy services, and manage app components effectively. Granting Privileges 1. Snowsight UI Configuration Snowflake Native Apps with Snowpark Container Services provide a custom Snowsight configuration UI. When privileges are granted via this UI, it triggers the app’s grant_callback procedure specified in the manifest file. This procedure receives a list of privileges as input, making it ideal for initiating resource provisioning, such as creating compute pools. For containerized apps, this is the recommended approach. Example Manifest Configuration: yaml Copy code manifest_version: 1 configuration: log_level: debug trace_level: always grant_callback: setup.grant_callback 2. Streamlit Integration Using Snowflake’s Permissions SDK, developers can create Streamlit-based pop-ups to request privileges. After privileges are granted through the pop-up, resource provisioning can begin. Specify Streamlit as the landing page in the manifest using the default_streamlit field. This configuration allows Streamlit to invoke the necessary resource provisioning procedures. 3. SQL Worksheet Consumers preferring SQL-based interaction can grant privileges and execute resource provisioning procedures directly through the SQL worksheet. Example Grant Callback Procedure: sql Copy code CREATE OR REPLACE PROCEDURE setup.grant_callback(privileges ARRAY) RETURNS STRING AS $$ BEGIN CREATE COMPUTE POOL IF NOT EXISTS backend_compute_pool MIN_NODES = 1 MAX_NODES = 1 INSTANCE_FAMILY = CPU_X64_XS; CALL services.start_backend(‘backend_compute_pool’); RETURN ‘Callback successful’; END; $$; GRANT USAGE ON PROCEDURE setup.grant_callback(ARRAY) TO APPLICATION ROLE app_public; This procedure creates a compute pool and then calls another procedure to set up services within the pool. Deploying Services Once compute pools are created, the application can deploy one or more services. Scenario 1: The App Has All Required Resources If the app has everything it needs, it can directly execute CREATE SERVICE commands in the grant_callback procedure to set up its services. For services requiring warehouses, the app can request the CREATE WAREHOUSE privilege in the manifest and create warehouses as needed. Scenario 2: The App Needs Additional Consumer Resources If the app requires additional objects (e.g., external access integrations or secrets) from the consumer, references can be used. A reference includes a register_callback procedure that binds the required resources. This procedure can also trigger service creation after the reference is bound. Example Register Callback Procedure: sql Copy code CREATE OR REPLACE PROCEDURE v1.register_single_callback(ref_name STRING, operation STRING, ref_or_alias STRING) RETURNS STRING LANGUAGE SQL AS $$ BEGIN CASE (operation) WHEN ‘ADD’ THEN SELECT SYSTEM$SET_REFERENCE(:ref_name, :ref_or_alias); CASE (:ref_name) WHEN ‘external_access_backend_reference’ THEN CALL services.start_service_with_egress(); WHEN ‘secret_reference’ THEN CALL services.start_service_using_secret(); END CASE; WHEN ‘REMOVE’, ‘CLEAR’ THEN SELECT SYSTEM$REMOVE_REFERENCE(:ref_name); ELSE RETURN ‘Unknown operation: ‘ || operation; END CASE; RETURN ‘Operation ‘ || operation || ‘ succeeds.’; END; $$; This procedure: Packaging Service Specifications Service creation requires specification files, which must be included in the app package (typically alongside the manifest.yml). These files define the service

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February 8, 2022in Data, Snowflake
Salesforce Platform Manager
01May

Salesforce Platform Manager Explained

A Salesforce Platform Manager serves as the human interface between the business and the Salesforce platform, taking on the responsibility of overseeing the entire management of the platform. This role involves leading project teams, collecting requirements, designing solutions, and implementing configurations on Salesforce.com. In contrast, Product Managers primarily concentrate on expanding their user base, financial metrics, and ensuring customer satisfaction with the product. Platform Managers, on the other hand, have a more internal focus, being oriented towards technical aspects and architecture. They are not customer-facing and have a greater emphasis on technical and architectural considerations. Not to say they don’t need good people and communication skills, but they interact as much with the platform as with team members. The role of a Platform Manager encompasses being the primary administrator of the software, orchestrating its setup, and overseeing ongoing maintenance. They are tasked with establishing the initial parameters for the website and managing all constituent data in a pubic sector instance. For Platform Product Managers, the primary goal is to deliver outcomes that contribute to business scalability and achievement of goals. Effective management with different teams is crucial for success, and collaboration, communication, and recognition of other teams play a key role in their responsibilities. Platform Services Managers are responsible for initiating and maintaining the operation of application processes. They ensure the continuous running of processes, which is essential for users to access the system and run reports. The process management framework involves three key process utilities: sapmon, sapmgr, and sap. If your core Salesforce team is missing a solid Salesforce Platform Manager, reach out to Tectonic today for assistance. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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May 1, 2018in Data, Enterprise, Salesforce

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