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14Jan

On Premise Gen AI

In 2025, enterprises transitioning generative AI (GenAI) into production after years of experimentation are increasingly considering on-premises deployment as a cost-effective alternative to the cloud. Since OpenAI ignited the AI revolution in late 2022, organizations have tested large language models powering GenAI services on platforms like AWS, Microsoft Azure, and Google Cloud. These experiments demonstrated GenAI’s potential to enhance business operations while exposing the substantial costs of cloud usage. To avoid difficult conversations with CFOs about escalating cloud expenses, CIOs are exploring on-premises AI as a financially viable solution. Advances in software from startups and packaged infrastructure from vendors such as HPE and Dell are making private data centers an attractive option for managing costs. A survey conducted by Menlo Ventures in late 2024 found that 47% of U.S. enterprises with at least 50 employees were developing GenAI solutions in-house. Similarly, Informa TechTarget’s Enterprise Strategy Group reported a rise in enterprises considering on-premises and public cloud equally for new applications—from 37% in 2024 to 45% in 2025. This shift is reflected in hardware sales. HPE reported a 16% revenue increase in AI systems, reaching $1.5 billion in Q4 2024. During the same period, Dell recorded a record $3.6 billion in AI server orders, with its sales pipeline expanding by over 50% across various customer segments. “Customers are seeking diverse AI-capable server solutions,” noted David Schmidt, senior director of Dell’s PowerEdge server line. While heavily regulated industries have traditionally relied on on-premises systems to ensure data privacy and security, broader adoption is now driven by the need for cost control. Fortune 2000 companies are leading this trend, opting for private infrastructure over the cloud due to more predictable expenses. “It’s not unusual to see cloud bills exceeding 0,000 or even million per month,” said John Annand, an analyst at Info-Tech Research Group. Global manufacturing giant Jabil primarily uses AWS for GenAI development but emphasizes ongoing cost management. “Does moving to the cloud provide a cost advantage? Sometimes it doesn’t,” said CIO May Yap. Jabil employs a continuous cloud financial optimization process to maximize efficiency. On-Premises AI: Technology and Trends Enterprises now have alternatives to cloud infrastructure, including as-a-service solutions like Dell APEX and HPE GreenLake, which offer flexible pay-per-use pricing for AI servers, storage, and networking tailored for private data centers or colocation facilities. “The high cost of cloud drives organizations to seek more predictable expenses,” said Tiffany Osias, vice president of global colocation services at Equinix. Walmart exemplifies in-house AI development, creating tools like a document summarization app for its benefits help desk and an AI assistant for corporate employees. Startups are also enabling enterprises to build AI applications with turnkey solutions. “About 80% of GenAI requirements can now be addressed with push-button solutions from startups,” said Tim Tully, partner at Menlo Ventures. Companies like Ragie (RAG-as-a-service) and Lamatic.ai (GenAI platform-as-a-service) are driving this innovation. Others, like Squid AI, integrate custom AI agents with existing enterprise infrastructure. Open-source frameworks like LangChain further empower on-premises development, offering tools for creating chatbots, virtual assistants, and intelligent search systems. Its extension, LangGraph, adds functionality for building multi-agent workflows. As enterprises develop AI applications internally, consulting services will play a pivotal role. “Companies offering guidance on effective AI tool usage and aligning them with business outcomes will thrive,” Annand said. This evolution in AI deployment highlights the growing importance of balancing technological innovation with financial sustainability. 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

January 14, 2025in Data, Enterprise, Generative AI, Google, Technology0 Comments

Autonomy, Architecture, and Action

Redefining AI Agents: Autonomy, Architecture, and Action AI agents are reshaping how technology interacts with us and executes tasks. Their mission? To reason, plan, and act independently—following instructions, making autonomous decisions, and completing actions, often without user involvement. These agents adapt to new information, adjust in real time, and pursue their objectives autonomously. This evolution in agentic AI is revolutionizing how goals are accomplished, ushering in a future of semi-autonomous technology. At their foundation, AI agents rely on one or more large language models (LLMs). However, designing agents is far more intricate than building chatbots or generative assistants. While traditional AI applications often depend on user-driven inputs—such as prompt engineering or active supervision—agents operate autonomously. Core Principles of Agentic AI Architectures To enable autonomous functionality, agentic AI systems must incorporate: Essential Infrastructure for AI Agents Building and deploying agentic AI systems requires robust software infrastructure that supports: Agent Development Made Easier with Langflow and Astra DB Langflow simplifies the development of agentic applications with its visual IDE. It integrates with Astra DB, which combines vector and graph capabilities for ultra-low latency data access. This synergy accelerates development by enabling: Transforming Autonomy into Action Agentic AI is fundamentally changing how tasks are executed by empowering systems to act autonomously. By leveraging platforms like Astra DB and Langflow, organizations can simplify agent design and deploy scalable, effective AI applications. Start building the next generation of AI-powered autonomy 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 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

January 11, 2025in Agentic AI, Data, Enterprise, Technology0 Comments

04Dec

How to Connect Multiple Data Sources in Power BI Desktop

In today’s data-driven world, the ability to analyze diverse data sources can set a business apart. With Power BI Desktop, a Microsoft tool, analysts can seamlessly integrate data from various platforms and transform raw information into actionable insights. For instance, you could combine Excel-based sales figures with financial data from SQL Server and customer information from Salesforce into an interactive report. Mastering these techniques can be easier through structured learning, such as Microsoft Power BI courses, which offer practical insights into leveraging this powerful tool. This guide will help you connect, combine, and visualize multiple data sources in Power BI Desktop to make smarter, data-driven decisions. Why Combine Multiple Data Sources? Organizations often face the challenge of managing data stored across disparate systems. Financial records may reside in SQL Server, sales data in Excel, and customer information in cloud platforms like Salesforce. Insights from these datasets are often hidden unless they are integrated. Using Power BI Desktop, you can load multiple data sources into a unified model, providing a comprehensive view that enables better analysis and decision-making. Getting Started with Power BI Desktop Before integrating datasets, ensure you have Power BI Desktop installed. The tool is available for download from the official Power BI website. Once installed, launch Power BI Desktop to begin connecting your data sources. Step-by-Step Guide 1. Connecting Your First Data Source Follow these steps to connect to your first data source: At this stage, you can use Power Query Editor to clean and transform the data as needed. 2. Adding Additional Data Sources Enhance your report by adding more data sources: For example, you could link an Excel file for sales data, a SQL Server database for product details, and Azure for supplementary information, all within a single report. 3. Combining Data from Multiple Sources To merge data from different sources into a cohesive model: This process creates a unified data model that allows for cross-tabulation and advanced visualizations. 4. Using Power Query Editor for Data Transformation Before combining datasets, you may need to clean and transform the data. Use Power Query Editor to: Access Power Query Editor by selecting Transform Data on the Home tab. 5. Creating Visualizations with Combined Data With your unified data model, you can create compelling visualizations: 6. Refreshing Data Connections Power BI Desktop enables you to refresh data connections effortlessly, ensuring your reports stay updated: Best Practices for Connecting Multiple Data Sources Conclusion Integrating multiple data sources in Power BI Desktop empowers businesses to uncover deep insights and make informed decisions. By following these steps, you can connect, aggregate, and visualize diverse datasets with ease. To further enhance your expertise, explore free resources or consider professional courses to master the versatility of Power BI Desktop—a vital tool for data professionals and business analysts. 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

December 4, 2024in Data, Google, Mulesoft, Salesforce0 Comments

21Nov

Collaborative Business Intelligence

Collaborative BI combines BI tools with collaboration platforms, enabling users to connect data insights directly within their existing workflows. This integration enhances decision-making by reducing misunderstandings and fostering teamwork through real-time or asynchronous discussions about data. In traditional BI, data analysis was handled by data scientists and statisticians who translated insights for business users. However, the rise of self-service BI tools has democratized data access, allowing users of varying technical skills to create and share visualizations. Collaborative BI takes this a step further by embedding BI functions into collaboration platforms like Slack and Microsoft Teams. This setup allows users to ask questions, clarify context, and share reports within the same applications they already use, enhancing data-driven decisions across the organization. One real-life time saver in my experience is being able as a marketer to dig in to our BI and generate lists myself, without depending upon a team of data scientists. Benefits of Collaborative BI Leading Collaborative BI Platforms Several vendors offer collaborative BI solutions, each with unique integrations for communication and data sharing: Collaborative BI bridges data analysis with organizational collaboration, creating an agile environment for informed decision-making and effective knowledge sharing across all levels. 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

November 21, 2024in Data, Salesforce

16Nov

AI Agent Rivalry

Microsoft and Salesforce’s AI Agent Rivalry Heats Up The battle for dominance in the AI agent space has escalated, with Salesforce CEO Marc Benioff intensifying his criticism of Microsoft’s AI solutions. Following remarks at Dreamforce 2024, Benioff took to X (formerly Twitter) to call out Microsoft for what he called “rebranding Copilot as ‘agents’ in panic mode.” The AI Agent rivalry winner may be determined not by flashy features but by delivering tangible, transformative outcomes for businesses navigating the complexities of AI adoption. AI Agent Rivalry. Benioff didn’t hold back, labeling Microsoft’s Copilot as “a flop”, citing issues like data leaks, inaccuracies, and requiring customers to build their own large language models (LLMs). In contrast, he touted Salesforce’s Agentforce as a solution that autonomously drives sales, service, marketing, analytics, and commerce without the complications he attributes to Microsoft’s offerings. Microsoft’s Copilot: A New UI for AI Microsoft recently unveiled new autonomous agent capabilities for Copilot Studio and Dynamics 365, positioning these agents as tools to enhance productivity across teams and functions. CEO Satya Nadella described Copilot as “the UI for AI” and emphasized its flexibility, allowing businesses to create, manage, and integrate agents seamlessly. Despite the fanfare, Benioff dismissed Copilot’s updates, likening it to “Clippy 2.0” and claiming it fails to deliver accuracy or transformational impact. Salesforce Expands Agentforce with Strategic Partnerships At Dreamforce 2024, Salesforce unveiled its Agentforce Partner Network, a global ecosystem featuring collaborators like AWS, Google Cloud, IBM, and Workday. The move aims to bolster the capabilities of Agentforce, Salesforce’s AI-driven platform that delivers tailored, autonomous business solutions. Agentforce allows businesses to deploy customizable agents without complex coding. With features like the Agent Builder, users can craft workflows and instructions in natural language, making the platform accessible to both technical and non-technical teams. Flexibility and Customization: Salesforce vs. Microsoft Both Salesforce and Microsoft emphasize AI’s transformative potential, but their approaches differ: Generative AI vs. Predictive AI Salesforce has doubled down on generative AI, with Einstein GPT producing personalized content using CRM data while also providing predictive analytics to forecast customer behavior and sales outcomes. Microsoft, on the other hand, combines generative and predictive AI across its ecosystem. Copilot not only generates content but also performs autonomous decision-making in Dynamics 365 and Azure, positioning itself as a comprehensive enterprise solution. The Rise of Multi-Agent AI Systems The competition between Microsoft and Salesforce reflects a broader trend in AI-driven automation. Companies like OpenAI are experimenting with frameworks like Swarm, which simplifies the creation of interconnected AI agents for tasks such as lead generation and marketing campaign development. Similarly, startups like DevRev are introducing conversational AI builders to design custom agents, offering enterprises up to 95% task accuracy without the need for coding. What Lies Ahead in the AI Agent Landscape? As Salesforce and Microsoft push the boundaries of AI integration, businesses are evaluating these tools for their flexibility, customization, and impact on operations. While Salesforce leads in CRM-focused AI, Microsoft’s integrated approach appeals to enterprises seeking cross-functional AI solutions. In the end, the winner may be determined not by flashy features but by delivering tangible, transformative outcomes for businesses navigating the complexities of AI adoption. AI Agent Rivalry. 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

November 16, 2024in Agentforce Platform, Data, Enterprise, Generative AI, Google, Salesforce

12Nov

AI Won’t Hurt Salesforce

Marc Benioff Dismisses AI Threats, Sets Sights on a Billion AI Agents in One Year Salesforce CEO Marc Benioff has no doubts about the transformative potential of AI for enterprise software, particularly Salesforce itself. At the core of his vision are AI agents—autonomous software bots designed to handle routine tasks, freeing up human workers to focus on more strategic priorities. “What if your workforce had no limits? That’s a question we couldn’t even ask over the past 25 years of Salesforce—or the 45 years I’ve been in software,” Benioff said during an appearance on TechCrunch’s Equity podcast. The Billion-Agent Goal Benioff revealed that Salesforce’s recently launched Agentforce platform is already being adopted by “hundreds of customers” and aims to deploy a billion AI agents within a year. These agents are designed to handle tasks across industries—from enhancing customer experiences at retail brands like Gucci to assisting patients with follow-ups in healthcare. To illustrate, Benioff shared his experience with Disney’s virtual Private Tour Guides. “The AI agent analyzed park flow, ride history, and preferences, then guided me to attractions I hadn’t visited before,” he explained. Competition with Microsoft and the AI Landscape While Benioff is bullish on AI, he hasn’t hesitated to criticize competitors—particularly Microsoft. When Microsoft unveiled its new autonomous agents for Dynamics 365 in October, Benioff dismissed them as uninspired. “Copilot is the new Clippy,” he quipped, referencing Microsoft’s infamous virtual assistant from the 1990s. Benioff also cited Gartner research highlighting data security issues and administrative flaws in Microsoft’s AI tools, adding, “Copilot has disappointed so many customers. It’s not transforming companies.” However, industry skeptics argue that the real challenge to Salesforce isn’t Microsoft but the wave of AI-powered startups disrupting traditional enterprise software. With tools like OpenAI’s ChatGPT and Klarna’s in-house AI assistant “Kiki,” companies are starting to explore GenAI solutions that can replace legacy platforms like Salesforce altogether. For example, Klarna recently announced it was moving away from Salesforce and Workday, favoring GenAI tools that enable seamless, conversational interfaces and faster data access. Why Salesforce Is Positioned to Win Despite the noise, Benioff remains confident that Salesforce’s extensive data infrastructure gives it a significant edge. “We manage 230 petabytes of customer data with robust security and sharing models. That’s what allows AI to thrive in our ecosystem,” he said. While companies may question how other platforms like OpenAI handle data, Salesforce offers an integrated approach, reducing the need for complex data migrations to other clouds, such as Microsoft Azure. Salesforce’s Own Use of AI Benioff also highlighted Salesforce’s internal adoption of Agentforce, using AI agents in its customer service operations, sales processes, and help centers. “If you’re authenticated on help.salesforce.com, you’re already interacting with our agent,” he noted. AI Startups: Threat or Opportunity? As for concerns about AI startups overtaking Salesforce, Benioff sees them as acquisition opportunities rather than existential threats. “We’ve made over 60 acquisitions, many of them startups,” he said. He pointed to Agentforce itself, which was built using technology from Airkit.ai, a startup founded by a former Salesforce employee. Salesforce Ventures initially invested in Airkit.ai before acquiring and integrating it into its platform. The Path Forward Benioff is resolute in his belief that AI won’t hurt Salesforce—instead, it will revolutionize how businesses operate. While skeptics warn of a seismic shift in enterprise software, Benioff’s strategy is clear: lean into AI, leverage data, and stay agile through innovation and acquisitions. “We’re just getting started,” he concluded, reiterating his vision for a future where AI agents expand the possibilities of work and customer experience like never before. 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

November 12, 2024in Agentforce Platform, AI Tools, Data, Enterprise, Laws, Salesforce, Technology

14Oct

Battle of Copilots

Salesforce is directly challenging Microsoft in the growing battle of AI copilots, which are designed to enhance customer experience (CX) across key business functions like sales and support. In this competitive landscape, Salesforce is taking on not only Microsoft but also major AI rivals such as Google Gemini, OpenAI GPT, and IBM watsonx. At the heart of this strategy is Salesforce Agentforce, a platform that leverages autonomous decision-making to meet enterprise demands for data and AI abstraction. Salesforce Dreamforce Highlights One of the most significant takeaways from last month’s Dreamforce conference in San Francisco was the unveiling of autonomous agents, bringing advanced GenAI capabilities to the app development process. CEO Marc Benioff and other Salesforce executives made it clear that Salesforce is positioning itself to compete with Microsoft’s Copilot, rebranding and advancing its own AI assistant, previously known as Einstein AI. Microsoft’s stronghold, however, lies in Copilot’s seamless integration with widely used products like Teams, Outlook, PowerPoint, and Word. Furthermore, Microsoft has established itself as a developer’s favorite, especially with GitHub Copilot and the Azure portfolio, which are integral to app modernization in many enterprises. “Salesforce faces an uphill battle in capturing market share from these established players,” says Charlotte Dunlap, Research Director at GlobalData. “Salesforce’s best chance lies in highlighting the autonomous capabilities of Agentforce—enabling businesses to automate more processes, moving beyond basic chatbot functions, and delivering a personalized customer experience.” This emphasis on autonomy is vital, given that many enterprises are still grappling with the complexities of emerging GenAI technologies. Dunlap points out that DevOps teams are struggling to find third-party expertise that understands how GenAI fits within existing IT systems, particularly around security and governance concerns. Salesforce’s focus on automation, combined with the integration prowess of MuleSoft, positions it as a key player in making GenAI tools more accessible and intuitive for businesses. Elevating AI Abstraction and Automation Salesforce has increasingly focused on the idea of abstracting data and AI, exemplified by its Data Cloud and low-level UI capabilities. Now, with models like the Atlas Reasoning Engine, Salesforce is looking to push beyond traditional AI assistants. These tools are designed to automate complex, previously human-dependent tasks, spanning functions like sales, service, and marketing. Simplifying the Developer Experience The true measure of Salesforce’s success in its GenAI strategy will emerge in the coming months. The company is well aware that its ability to simplify the developer experience is critical. Enterprises are looking for more than just AI innovation—they want thought leadership that can help secure budget and executive support for AI initiatives. Many companies report ongoing struggles in gaining that internal buy-in, further underscoring the importance of strong, strategic partnerships with technology providers like Salesforce. In its pursuit to rival Microsoft Copilot, Salesforce’s future hinges on how effectively it can build on its track record of simplifying the developer experience while promoting the unique autonomous qualities of Agentforce. 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

October 14, 2024in AI Tools, Data, Enterprise, Google, Mulesoft, Salesforce, Salesforce Copilots, Salesforce Optimization, Technology

12Oct

SingleStore Acquires BryteFlow

SingleStore Acquires BryteFlow, Paving the Way for Real-Time Analytics and Next-Gen AI Use Cases SingleStore, the world’s only database designed to transact, analyze, and search petabytes of data in milliseconds, has announced its acquisition of BryteFlow, a leading data integration platform. This move enhances SingleStore’s capabilities to ingest data from diverse sources—including SAP, Oracle, and Salesforce—while empowering users to operationalize data from their CRM and ERP systems. With the acquisition, SingleStore will integrate BryteFlow’s data integration technology into its core offering, launching a new experience called SingleConnect. This addition will complement SingleStore’s existing functionalities, enabling users to gain deeper insights from their data, accelerate real-time analytics, and support emerging generative AI (GenAI) use cases. “This acquisition marks a pivotal step in our mission to deliver unparalleled speed, scale, and simplicity,” said Raj Verma, CEO of SingleStore. “Customer demands are evolving rapidly due to shifts in big data storage formats and advancements in generative AI. We believe that data is the foundation of all intelligence, and SingleConnect comes at a perfect time to address this need.” BryteFlow’s platform provides scalable change data capture (CDC) capabilities across multiple data sources, ensuring data integrity between source and target. It integrates seamlessly with major cloud platforms like AWS, Microsoft Azure, and Google Cloud, making it a powerful tool for cloud-based data warehouses and data lakes. Its no-code interface allows for easy and accessible data integration, ensuring that existing BryteFlow customers will experience uninterrupted service and ongoing support. “By combining BryteFlow’s real-time data integration expertise with SingleStore’s capabilities, we aim to help global organizations extract maximum value from their data and scale modern applications,” said Pradnya Bhandary, CEO of BryteFlow. “With SingleConnect, developers will find it easier and faster to access enterprise data sources, tackle complex workloads, and deliver exceptional experiences to their customers.” 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

October 12, 2024in Data, Enterprise, Generative AI, Google, Salesforce, Technology

08Oct

Veeam Latest Acquisition

Veeam continues its acquisition strategy with the purchase of Alcion, bolstering its capabilities in AI and as-a-service offerings. This acquisition follows Veeam’s investment in Microsoft 365 backup-as-a-service provider Alcion last year, and brings in a team of AI and security specialists. Analysts and Veeam executives see this move as a key step in expanding Veeam’s as-a-service offerings. Earlier this year, the company launched Veeam Data Cloud, a backup-as-a-service solution for Microsoft 365 and Azure workloads. “After years of resisting, Veeam has fully embraced the as-a-service model,” said Christophe Bertrand, an analyst at TheCube Research. Veeam Latest Acquisition The acquisition, which closed in mid-September, marks the second time Veeam has purchased a company founded by Niraj Tolia and Vaibhav Kamra. In 2020, Veeam acquired Kasten, their Kubernetes backup provider. A year ago, Veeam led a million funding round for Alcion, which has since developed AI-driven data protection solutions. Veeam has been active in acquisitions, joining a broader trend in the data protection market. Recently, Commvault acquired Clumio, Cohesity merged with Veritas, and Veeam itself bought Cirrus from CT4, which later became part of the Veeam Data Cloud. Earlier this year, Veeam also acquired Coveware, an incident response vendor. “Veeam hasn’t traditionally been an acquisition-heavy company, but that has changed in recent years,” said Rick Vanover, Veeam’s VP of product strategy. “I expect this trend to continue.” Alcion’s Role at Veeam This acquisition strengthens Veeam’s expertise in the fast-growing as-a-service market. Alcion’s team of fewer than 50 employees, including founders Niraj Tolia and Vaibhav Kamra, joins Veeam, with Tolia stepping in as Veeam’s new CTO. Tolia will lead product strategy and engineering for Veeam Data Cloud, succeeding Danny Allan, who recently became CTO at cybersecurity company Snyk. Alcion, which has hundreds of customers, will offer those customers the opportunity to transition to Veeam Data Cloud. However, Veeam has not finalized the future of Alcion’s product or established a timeline for its integration. “This acquisition brings incredible talent and thought leadership to Veeam, especially from Niraj and the Alcion team,” said Brandt Urban, Veeam’s senior VP of worldwide cloud sales. “Their expertise will help us rapidly enhance Veeam Data Cloud, adding more capabilities and expanding workload coverage.” Analysts, like Bertrand, expect Veeam to broaden its data protection offerings for additional SaaS platforms beyond Microsoft 365, looking toward collaboration and DevOps tools as potential areas for growth. AI and Security at the Forefront Alcion’s AI-powered features allow administrators to optimize backups, detect malware, and respond proactively to threats. According to Krista Case, an analyst at The Futurum Group, Alcion uses AI strategically to adapt backup schedules based on data modification patterns, trigger backups when potential threats are identified, and recommend the best recovery points. “When practitioners talk about cyber resilience, they’re focused on minimizing data loss and downtime—Alcion’s AI capabilities directly address these concerns,” said Case. Veeam has also been integrating AI into its existing products, offering inline malware detection and an Intelligent Diagnostics service. A forthcoming Copilot feature for Microsoft 365 backups will further enhance AI-driven data protection. Veeam Latest Acquisition “AI is a real asset when applied thoughtfully—it’s not just hype,” said Bertrand, adding that users are more interested in AI’s ability to drive outcomes, like detecting threats that could otherwise go unnoticed. Veeam executives echoed the importance of delivering clear, tangible AI benefits. “We keep user outcomes front and center because, otherwise, AI becomes an expensive experiment,” Vanover said. 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

October 8, 2024in Data

08Oct

Data Cloud Model

While many Salesforce products like Sales Cloud, Service Cloud, Education Cloud, Health Cloud, and various industry clouds are built on a common ‘core’ platform that shares the same datastore (an Oracle relational database), Salesforce Data Cloud operates on (Data Cloud Model) a distinct architecture and technology stack. This insight explores the data models and related concepts that underpin the platform. Data Cloud Architecture and Storage Starting with the storage layer, Salesforce Data Cloud integrates multiple services: This combination enables Data Cloud to provide a petabyte-scale data store, overcoming the scalability and performance constraints of traditional relational databases. The physical architecture of Data Cloud is represented as a set of data objects, which form the foundation for how data is ingested, harmonized, and activated within the platform. Key Data Cloud Objects Data Source A Data Source is the initial data layer in Data Cloud, representing an external platform or system from which your data originates. Data Sources can include: Data Stream A Data Stream is an entity extracted from a Data Source, such as ‘Orders’ from Commerce Cloud, ‘Contacts’ from Sales Cloud, or ‘Subscribers’ from Marketing Cloud. Once a Data Source is connected to Data Cloud, Data Streams provide access to these entities and require assignment to a category: Profile, Engagement, or Other. A single Data Source can contain multiple Data Streams. Data Source Object (DSO) Data Streams are ingested into Data Source Objects (DSOs), which serve as temporary staging stores containing data in its raw, native format (e.g., CSV files). Minor transformations can be applied to fields during data ingestion. Data Lake Object (DLO) The next step in the data flow is the Data Lake Object (DLO). DLOs are typed, schema-based, materialized views that reside in the data lake (Amazon S3) and are generally stored as Apache Parquet files. This format, along with Apache Iceberg’s abstraction layer, supports efficient data storage and retrieval. DLOs are the first objects available for inspection and preparation of data, including field mapping and additional transformations. Data Model Object (DMO) Unlike DSOs and DLOs, which use a physical data store, Data Model Objects (DMOs) provide a virtual, non-materialized view into the data lake. Queries against a DMO return results based on the current data snapshot in DLOs, without storing them. DMOs can aggregate attributes from different Data Streams, Calculated Insights, and other sources. DMOs inherit their category from the first DLO mapped to them, and only DLOs with the same category can be mapped to the DMO. Like Salesforce objects, DMOs offer a canonical data model with pre-defined attributes (standard objects), and you can also create custom DMOs (custom objects). DMOs can have one-to-one or many-to-one relationships with other DMOs. Currently, there are 89 standard DMOs in Data Cloud, and this number is growing to support more use cases. DMOs are organized into various subject areas, including: Data Spaces Data Spaces offer logical partitions, allowing data to be segregated between different brands, regions, or departments, without the need for multiple Data Cloud instances. They also align with a Software Development Lifecycle (SDLC), enabling you to stage and test Data Objects in separate environments without affecting production data. Data Sources, Data Streams, and DLOs can be shared across Data Spaces, while DMOs and other platform features are isolated based on user permissions. Final Thoughts With the drop in storage costs and advancements in technology, companies now have vast datasets at their disposal. Every customer interaction, whether it’s a purchase, an email open, or a web page view, can be captured and stored. When properly organized, this data allows businesses to understand customers better, predict their needs, personalize interactions, and much more. However, one of Salesforce’s challenges is that its original platform, built over 20 years ago, was designed for a different era and struggles with handling voluminous, “big” data. Salesforce Data Cloud addresses these limitations with a modern architecture that overcomes the constraints of relational databases. Data Cloud is poised to become the backbone of the Salesforce platform, supporting the data needs of customers and products for the next two decades. If you’re interested in learning how to implement and integrate Salesforce Data Cloud in your organization, following best practices, reach out 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 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

October 8, 2024in Data, Google, Salesforce, Salesforce Data Cloud, Snowflake, Technology, Unified Knowledge

05Oct

AI Era Demands an Intelligent Data Infrastructure

Why the AI Era Demands an Intelligent Data Infrastructure NetApp aims to bridge the gap between cloud-based AI models and on-premises data management systems, addressing a major challenge many organizations face as they explore emerging AI capabilities, such as generative AI. AI Era Demands an Intelligent Data Infrastructure. While the most advanced AI tools often reside in hyperscale clouds, the key data of most organizations remains on-premises. As companies move from AI experimentation to scaling their initiatives, they face the complex task of closing the divide between their data and AI. At first glance, the solution seems simple: Organizations can integrate their proprietary data with cloud-based AI services to enhance large language models (LLMs) and other AI tools, leveraging their industry-specific expertise to generate unique insights and drive value. In fact, a recent Enterprise Strategy Group (ESG) survey found that 84% of respondents believed incorporating enterprise data was critical to supporting their generative AI efforts. However, achieving this at scale is far more difficult than it appears. Many organizations are reluctant to send their valuable data to public clouds due to concerns about security, such as exposing sensitive intellectual property or personal information. Additionally, managing the process of transferring large datasets, creating data copies, updating models with fresh data, and keeping track of everything over time adds cost and complexity. Beyond these logistical issues, many organizations struggle with a fundamental challenge: poor data management. Rapid data growth, fragmentation, and a lack of understanding of their own data forces business users and data scientists to spend excessive time “data wrangling”—the process of identifying, gathering, and preparing data for AI models. This inefficiency delays training and deploying models, pushing back the point of inference, which is the key to unlocking value from AI. Increased latency also raises the risk that inferencing is based on outdated data, leading to repeated cycles and further inefficiency. This challenge has made data management a critical obstacle in AI adoption. ESG research identified poor data quality as the top challenge organizations face when implementing AI. Inadequate data is rapidly becoming a roadblock that hampers AI initiatives across industries. To address these challenges, NetApp has shifted its focus toward building an “intelligent data infrastructure” for the AI era. At the company’s Insight 2024 conference, NetApp outlined a strategy to support AI success by bridging the gap between cloud-based AI models and on-premises data environments. The company’s vision includes simplifying, automating, and mitigating risks in the data management workflow needed to scale AI in the enterprise. NetApp’s approach includes innovations like a global metadata namespace and enhancements to its OnTap software, which enable exploration, classification, and management of data across the NetApp ecosystem. These capabilities integrate directly into AI data pipelines, enabling scalable searches and retrieval-augmented generation inferencing. Additionally, NetApp’s new “disaggregated” storage architecture will support more cost-effective scaling for compute-heavy AI tasks such as model training. NetApp’s strategy also extends to the public cloud, where it already has strong partnerships with all three hyperscalers, offering NetApp services as first-party options in their clouds. The company is expanding this by developing additional cloud-native capabilities for AI, such as integrating Azure NetApp Files with Microsoft Azure AI services, FSx for NetApp OnTap with Amazon Bedrock, and Google Cloud NetApp Volumes with Google Vertex AI and BigQuery. These integrations will allow organizations to securely enrich public cloud-based AI models with their on-premises data, creating a scalable and secure environment for AI development. While some aspects of NetApp’s strategy will be delivered over the next year, many cloud integrations will be available sooner. NetApp is also building a partner ecosystem, including hardware partners like Lenovo and Nvidia, software ISVs, and service providers like Domino Data Labs, in addition to its cloud alliances. With this vision for intelligent data infrastructure, NetApp is positioning itself as a key enabler of AI-driven innovation, and it will be intriguing to watch how this strategy unfolds in the coming months. 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

October 5, 2024in AI Tools, Data, Enterprise, Generative AI, Google

04Oct

Databricks Tools

Databricks recently introduced Databricks Apps, a toolkit designed to simplify AI and data application development. By integrating native development platforms and offering automatic provisioning of serverless compute, the toolkit enables customers to more easily develop and deploy applications. Databricks Apps builds on the existing capabilities of Mosaic AI, which allows users to integrate large language models (LLMs) with their enterprise’s proprietary data. However, the ability to develop interactive AI applications, such as generative AI chatbots, was previously missing. Databricks Apps addresses this gap, allowing developers to build and deploy custom applications entirely within the secure Databricks environment. According to Donald Farmer, founder and principal of TreeHive Strategy, Databricks Apps removes obstacles like the need to set up separate infrastructure for development and deployment, making the process easier and more efficient. The new features allow companies to go beyond implementing AI/ML models and create differentiated applications that leverage their unique data sets. Kevin Petrie, an analyst at BARC U.S., highlighted the significance of Databricks Apps in helping companies develop custom AI applications, which are essential for maintaining a competitive edge. Databricks, founded in 2013, was one of the pioneers of the data lakehouse storage format, and over the last two years, it has expanded its platform to focus on AI and machine learning (ML) capabilities. The company’s $1.3 billion acquisition of MosaicML in June 2023 was a key milestone in building its AI environment. Databricks has since launched DBRX, its own large language model, and introduced further functionalities through product development. Databricks Apps, now available in public preview on AWS and Azure, advances these AI development capabilities, simplifying the process of building applications within a single platform. Developers can use frameworks like Dash, Flask, Gradio, Shiny, and Streamlit, or opt for integrated development environments (IDEs) like Visual Studio Code or PyCharm. The toolkit also provides prebuilt Python templates to accelerate development. Additionally, applications can be deployed and managed directly in Databricks, eliminating the need for external infrastructures. Databricks Apps includes security features such as access control and data lineage through the Unity Catalog. Farmer noted that the support for popular developer frameworks and the automatic provisioning of serverless compute could significantly impact the AI development landscape by reducing the complexity of deploying data architectures. While competitors like AWS, Google Cloud, Microsoft, and Snowflake have also made AI a key focus, Farmer pointed out that Databricks’ integration of AI tools into a unified platform sets it apart. Databricks Apps further enhances this competitive advantage. Despite the added capabilities of Databricks Apps, Petrie cautioned that developing generative AI applications still requires a level of expertise in data, AI, and the business domain. While Databricks aims to make AI more accessible, users will still need substantial knowledge to effectively leverage these tools. Databricks’ vice president of product management, Shanku Niyogi, explained that the new features in Databricks Apps were driven by customer feedback. As enterprise interest in AI grows, customers sought easier ways to develop and deploy internal data applications in a secure environment. Looking ahead, Databricks plans to continue investing in simplifying AI application development, with a focus on enhancing Mosaic AI and expanding its collaborative AI partner ecosystem. Farmer suggested that the company should focus on supporting nontechnical users and emerging AI technologies like multimodal models, which will become increasingly important in the coming years. The introduction of Databricks Apps marks a significant step forward in Databricks’ AI and machine learning strategy, offering users a more streamlined approach to building and deploying AI applications. 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

October 4, 2024in AI Tools, Data, Enterprise, Generative AI, Google, Snowflake

03Oct

Scale and AI Influence Shape Partner Ecosystems

Hyperscalers’ Scale and AI Influence Shape Partner Ecosystems Despite their seemingly saturated networks, the largest cloud vendors continue to dominate as top ecosystems for service providers, according to a recent survey. Hyperscalers are playing a critical role in partner alliances, a trend that has only intensified in recent years. A study released by Tercera, an investment firm specializing in IT services, highlights the dominance of cloud giants AWS, Google Cloud, and Microsoft Azure in the partner ecosystem landscape. More than 50% of the 250 technology service providers surveyed by Tercera identified one of these three vendors as their primary partner. This data comes from Tercera’s third annual report on the Top 30 Partner Ecosystems. The report emphasizes the “gravitational pull” of these hyperscalers, attracting partners despite their already vast networks. Each of the major cloud vendors maintains relationships with thousands of software and services partners. “The hyperscalers continue to defy the law of large numbers when you look at how many partners are in their ecosystems,” said Michelle Swan, CMO at Tercera. The Shift in Channel Alliances The emergence of cloud vendors as top partners for service providers has been evident since at least 2021. That year, a survey by Accenture of 1,150 channel companies found that AWS, Google, and Microsoft accounted for the majority of revenue for these partners. This represents a significant shift in channel economics, where traditionally large hardware companies occupied the top spots in partner alliances. AI’s Role in Partner Ecosystem Growth The rise of generative AI (GenAI) is reshaping alliance strategies, as service providers increasingly align themselves with hyperscalers and their AI technology partners. For instance, AWS channel partners interested in GenAI are likely to work with Anthropic, following Amazon’s $4 billion investment in the AI company. Meanwhile, Microsoft partners tend to collaborate with OpenAI, as Microsoft has committed up to $13 billion in investments to expand their partnership. “They have their own solar systems,” Swan remarked, referencing AWS, Google, Microsoft, and the AI startups within their ecosystems. Tiers of Partner Ecosystems Tercera categorizes its top 30 ecosystems into three tiers. The first tier, known as “market anchors,” includes AWS, Google, Microsoft, and large independent software vendors (ISVs) such as Salesforce and ServiceNow. The second tier, “market movers,” features publicly traded vendors with evolving partner ecosystems. The third tier, “market challengers,” is made up of privately held vendors with a partner-centric focus, such as Anthropic and OpenAI. Generative AI Ecosystem Survey A 2024 generative AI survey conducted by TechTarget and its Enterprise Strategy Group supports the idea that the leading cloud vendors play a central role in AI ecosystems. In a poll of 610 GenAI decision-makers and users, Microsoft topped the list of ecosystems supporting GenAI initiatives, with 54% of respondents citing it as the best ecosystem. Microsoft’s partner, OpenAI, followed with 35%. Google and AWS ranked third and fourth, with 30% and 24% of the responses, respectively. The survey covered a wide range of industries, including business services and IT, further reinforcing the dominant role hyperscalers play in shaping AI and partner ecosystems. 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

October 3, 2024in Data, Enterprise, Generative AI, Google, Hyperforce, Salesforce, Technology

02Oct

Ingest Salesforce Data to Microsoft Fabric

I’m using Dataflow Gen 2 in Microsoft Fabric to ingest data from Salesforce via the Salesforce Objects connector, which is authenticated through an Organizational Account (OAuth 2.0). However, unlike Azure Synapse’s SalesforceV2 type, this connector doesn’t offer fields to input a client ID, client secret, or environment URL. Here are the key concerns: 1. Reauthentication Requirement Will reauthentication be required regularly (e.g., after access tokens expire), and how often will that occur? What factors contribute to the frequency of reauthentication? With OAuth 2.0, the system typically provides an access token (short-lived, often around 1 hour) and a refresh token, which can last longer. Reauthentication is necessary when both expire. While Dataflow Gen 2 does not allow manual token management, it should handle refreshing access tokens automatically. The reauthentication frequency depends largely on: 2. Cons of Using an Organizational Account What are the potential downsides of using an Organizational Account for this connection, particularly in a production setting where automation and stability are critical? Potential drawbacks: To mitigate these risks, I recommend using a service account (rather than individual accounts) to centralize and secure access. 3. Workaround for Client Credentials Flow Is it possible to implement a client credentials flow (i.e., providing a client ID, client secret, and environment URL) to prevent frequent reauthentication, similar to Azure Synapse or Data Factory? If not, what options are available for maintaining a stable, long-term data connection from Salesforce? Currently, there doesn’t appear to be support for client credentials flow in Dataflow Gen 2. You may want to reach out to Microsoft support for confirmation. As an alternative, you could explore: 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

October 2, 2024in Data, Salesforce, Salesforce Implementation Services, Salesforce User Questions

20Sep

Collaborative Business Intelligence

Collaborative Business Intelligence: Connecting Data and Teams In today’s data-driven world, the ability to interact with business intelligence (BI) tools is essential for making informed decisions. Collaborative business intelligence (BI), also known as social BI, allows users to engage with their organization’s data and communicate with data experts through the same platforms where they already collaborate. While self-service BI empowers users to generate insights, understanding the data’s context is critical to avoid misunderstandings that can derail decision-making. Collaborative BI integrates BI tools with collaboration platforms to bridge the gap between data analysis and communication, reducing the risks of misinterpretation. Traditional Business Intelligence Traditional BI involves the use of technology to analyze data and present insights clearly. Before BI platforms became widespread, data scientists and statisticians handled data analysis, making it challenging for non-technical professionals to digest the insights. BI evolved to automate visualizations, such as charts and dashboards, making data more accessible to business users. Previously, BI reports were typically available only to high-level executives. However, modern self-service BI tools democratize access, enabling more users—regardless of technical expertise—to create reports and visualize data, fostering better decision-making across the organization. The Emergence of Collaborative BI Collaborative BI is a growing trend, combining BI applications with collaboration tools. This approach allows users to work together synchronously or asynchronously within a shared platform, making it easier to discuss data reports in real time or leave comments for others to review. Whether it’s through Slack, Microsoft Teams, or social media apps, users can receive and discuss BI insights within their usual communication channels. This seamless integration of BI and collaboration tools offers a competitive edge, simplifying the process of sharing knowledge and clarifying data without switching between applications. Key Benefits of Collaborative Business Intelligence Leading Collaborative BI Platforms Here’s a look at some of the top collaborative BI platforms driving innovation in the market: Conclusion Collaborative BI empowers organizations by improving decision-making, democratizing data access, optimizing data quality, and ensuring data security. By integrating BI tools with collaboration platforms, businesses can streamline their operations, foster a culture of data-driven decision-making, and enhance overall efficiency. Choosing the right platform is key to maximizing the benefits of collaborative BI. 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

September 20, 2024in Data, Google, Salesforce, Technology

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