Data - gettectonic.com - Page 23
chatGPT open ai 01
21Sep

ChatGPT Open AI o1

OpenAI has firmly established itself as a leader in the generative AI space, with its ChatGPT being one of the most well-known applications of AI today. Powered by the GPT family of large language models (LLMs), ChatGPT’s primary models, as of September 2024, are GPT-4o and GPT-3.5. In August and September 2024, rumors surfaced about a new model from OpenAI, codenamed “Strawberry.” Speculation grew as to whether this was a successor to GPT-4o or something else entirely. The mystery was resolved on September 12, 2024, when OpenAI launched its new o1 models, including o1-preview and o1-mini. What Is OpenAI o1? The OpenAI o1 family is a series of large language models optimized for enhanced reasoning capabilities. Unlike GPT-4o, the o1 models are designed to offer a different type of user experience, focusing more on multistep reasoning and complex problem-solving. As with all OpenAI models, o1 is a transformer-based architecture that excels in tasks such as content summarization, content generation, coding, and answering questions. What sets o1 apart is its improved reasoning ability. Instead of prioritizing speed, the o1 models spend more time “thinking” about the best approach to solve a problem, making them better suited for complex queries. The o1 models use chain-of-thought prompting, reasoning step by step through a problem, and employ reinforcement learning techniques to enhance performance. Initial Launch On September 12, 2024, OpenAI introduced two versions of the o1 models: Key Capabilities of OpenAI o1 OpenAI o1 can handle a variety of tasks, but it is particularly well-suited for certain use cases due to its advanced reasoning functionality: How to Use OpenAI o1 There are several ways to access the o1 models: Limitations of OpenAI o1 As an early iteration, the o1 models have several limitations: How OpenAI o1 Enhances Safety OpenAI released a System Card alongside the o1 models, detailing the safety and risk assessments conducted during their development. This includes evaluations in areas like cybersecurity, persuasion, and model autonomy. The o1 models incorporate several key safety features: GPT-4o vs. OpenAI o1: A Comparison Here’s a side-by-side comparison of GPT-4o and OpenAI o1: Feature GPT-4o o1 Models Release Date May 13, 2024 Sept. 12, 2024 Model Variants Single Model Two: o1-preview and o1-mini Reasoning Capabilities Good Enhanced, especially in STEM fields Performance Benchmarks 13% on Math Olympiad 83% on Math Olympiad, PhD-level accuracy in STEM Multimodal Capabilities Text, images, audio, video Primarily text, with developing image capabilities Context Window 128K tokens 128K tokens Speed Fast Slower due to more reasoning processes Cost (per million tokens) Input: $5; Output: $15 o1-preview: $15 input, $60 output; o1-mini: $3 input, $12 output Availability Widely available Limited to specific users Features Includes web browsing, file uploads Lacks some features from GPT-4o, like web browsing Safety and Alignment Focus on safety Improved safety, better resistance to jailbreaking ChatGPT Open AI o1 OpenAI o1 marks a significant advancement in reasoning capabilities, setting a new standard for complex problem-solving with LLMs. With enhanced safety features and the ability to tackle intricate tasks, o1 models offer a distinct upgrade over their predecessors. 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 Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

Read More
September 21, 2024in Data, Enterprise, Generative AI
State Loan Processing Software by Salesforce
21Sep

State Loan Processing Software by Salesforce

State Loan Processing Software: A Salesforce-Powered Solution Introduction In today’s fast-paced financial environment, efficient loan management is critical for lending institutions to succeed. Traditional loan processing methods are often inefficient, prone to errors, and unable to meet the demands of modern financial services. These outdated techniques lead to delays, compliance issues, and lost revenue. The answer lies in adopting advanced loan management software that leverages technology to streamline processes and enhance customer experiences. Current Challenges Many lenders continue to rely on outdated tools like spreadsheets and manual workflows, hindering productivity and increasing the potential for human error. A study by the National Association of Federal Credit Unions found that 60% of credit unions reported inefficiencies in their loan processes, negatively impacting member satisfaction. Key challenges faced by lending institutions include: Types of Loan Management Software To address these challenges, a variety of loan management software solutions have emerged, each designed to optimize specific aspects of the lending process. Loan Management Software Description: Automates essential loan processes like origination and payment processing. Main Features: Customer Relationship Management (CRM) Software Description: Platforms like Salesforce enable lenders to efficiently manage borrower relationships. Main Features: Compliance Management Software-State Loan Processing Software by Salesforce Description: Ensures lending practices adhere to state and federal regulations. Main Features: Analytics and Reporting Tools Description: Offers data-driven insights to guide strategic decision-making. Main Features: Integrated Payment Solutions Description: Streamlines payment processing across various channels. Main Features: Final Thoughts Adopting modern loan management software brings a host of advantages, including enhanced efficiency, improved compliance, and higher customer satisfaction. Platforms like Salesforce enable lenders to revolutionize their loan processing and management, making their operations more competitive in an evolving market. For lenders seeking to transform their approach to loan management, innovative solutions like Salesforce and Tectonic offer a path to operational excellence and business growth. 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

Read More
September 21, 2024in Salesforce Financial Services Solutions, Salesforce Nonprofit Cloud, Salesforce Optimization
Training and Testing Data
21Sep

Training and Testing Data

Data plays a pivotal role in machine learning (ML) and artificial intelligence (AI). Tasks such as recognition, decision-making, and prediction rely on knowledge acquired through training. Much like a parent teaches their child to distinguish between a cat and a bird, or an executive learns to identify business risks hidden within detailed quarterly reports, ML models require structured training using high-quality, relevant data. As AI continues to reshape the modern business landscape, the significance of training data becomes increasingly crucial. What is Training Data? The two primary strengths of ML and AI lie in their ability to identify patterns in data and make informed decisions based on that data. To execute these tasks effectively, models need a reference framework. Training data provides this framework by establishing a baseline against which models can assess new data. For instance, consider the example of image recognition for distinguishing cats from birds. ML models cannot inherently differentiate between objects; they must be taught to do so. In this scenario, training data would consist of thousands of labeled images of cats and birds, highlighting relevant features—such as a cat’s fur, pointed ears, and four legs versus a bird’s feathers, absence of ears, and two feet. Training data is generally extensive and diverse. For the image recognition case, the dataset might include numerous examples of various cats and birds in different poses, lighting conditions, and settings. The data must be consistent enough to capture common traits while being varied enough to represent natural differences, such as cats of different fur colors in various postures like crouching, sitting, standing, and jumping. In business analytics, an ML model first needs to learn the operational patterns of a business by analyzing historical financial and operational data before it can identify problems or recognize opportunities. Once trained, the model can detect unusual patterns, like abnormally low sales for a specific item, or suggest new opportunities, such as a more cost-effective shipping option. After ML models are trained, tested, and validated, they can be applied to real-world data. For the cat versus bird example, a trained model could be integrated into an AI platform that uses real-time camera feeds to identify animals as they appear. How is Training Data Selected? The adage “garbage in, garbage out” resonates particularly well in the context of ML training data; the performance of ML models is directly tied to the quality of their training data. This underscores the importance of data sources, relevance, diversity, and quality for ML and AI developers. Data SourcesTraining data is seldom available off-the-shelf, although this is evolving. Sourcing raw data can be a complex task—imagine locating and obtaining thousands of images of cats and birds for the relatively straightforward model described earlier. Moreover, raw data alone is insufficient for supervised learning; it must be meticulously labeled to emphasize key features that the ML model should focus on. Proper labeling is crucial, as messy or inaccurately labeled data can provide little to no training value. In-house teams can collect and annotate data, but this process can be costly and time-consuming. Alternatively, businesses might acquire data from government databases, open datasets, or crowdsourced efforts, though these sources also necessitate careful attention to data quality criteria. In essence, training data must deliver a complete, diverse, and accurate representation for the intended use case. Data RelevanceTraining data should be timely, meaningful, and pertinent to the subject at hand. For example, a dataset containing thousands of animal images without any cat pictures would be useless for training an ML model to recognize cats. Furthermore, training data must relate directly to the model‘s intended application. For instance, business financial and operational data might be historically accurate and complete, but if it reflects outdated workflows and policies, any ML decisions based on it today would be irrelevant. Data Diversity and BiasA sufficiently diverse training dataset is essential for constructing an effective ML model. If a model’s goal is to identify cats in various poses, its training data should encompass images of cats in multiple positions. Conversely, if the dataset solely contains images of black cats, the model’s ability to identify white, calico, or gray cats may be severely limited. This issue, known as bias, can lead to incomplete or inaccurate predictions and diminish model performance. Data QualityTraining data must be of high quality. Problems such as inaccuracies, missing data, or poor resolution can significantly undermine a model’s effectiveness. For instance, a business’s training data may contain customer names, addresses, and other information. However, if any of these details are incorrect or missing, the ML model is unlikely to produce the expected results. Similarly, low-quality images of cats and birds that are distant, blurry, or poorly lit detract from their usefulness as training data. How is Training Data Utilized in AI and Machine Learning? Training data is input into an ML model, where algorithms analyze it to detect patterns. This process enables the ML model to make more accurate predictions or classifications on future, similar data. There are three primary training techniques: Where Does Reinforcement Learning Fit In? Unlike supervised and unsupervised learning, which rely on predefined training datasets, reinforcement learning adopts a trial-and-error approach, where an agent interacts with its environment. Feedback in the form of rewards or penalties guides the agent’s strategy improvement over time. Whereas supervised learning depends on labeled data and unsupervised learning identifies patterns in raw data, reinforcement learning emphasizes dynamic decision-making, prioritizing ongoing experience over static training data. This approach is particularly effective in fields like robotics, gaming, and other real-time applications. The Role of Humans in Supervised Training The supervised training process typically begins with raw data since comprehensive and appropriately pre-labeled datasets are rare. This data can be sourced from various locations or even generated in-house. Training Data vs. Testing Data Post-training, ML models undergo validation through testing, akin to how teachers assess students after lessons. Test data ensures that the model has been adequately trained and can deliver results within acceptable accuracy and performance ranges. In supervised learning,

Read More
September 21, 2024in Artificial Intelligence, Data
Agentforce and Thinking AI
21Sep

Agentforce and Thinking AI

Agentforce is how humans with AI drive customer success together, equips organizations with autonomous agents that boost scale, efficiency, and satisfaction across service, sales, marketing, commerce, and more New Agentforce Atlas Reasoning Engine autonomously analyzes data, makes decisions, and completes tasks, providing reliable and accurate results With Agentforce, any organization can build, customize, and deploy their own agents quickly and easily, with low-code tools New Agentforce Partner Network allows customers to deploy pre-built agents and use agent actions from partners like Amazon Web Services, Google, IBM, Workday, and more Customers like OpenTable, Saks, and Wiley are turning to Agentforce because it is integrated with their apps, works across customer channels, augments their employees, and scales capacity for business needs SAN FRANCISCO — September 12, 2024 – Salesforce (NYSE: CRM), the world’s #1 AI CRM, today unveiled Agentforce, a groundbreaking suite of autonomous AI agents that augment employees and handle tasks in service, sales, marketing, and commerce, driving unprecedented efficiency and customer satisfaction. Agentforce enables companies to scale their workforces on demand with a few clicks. Agentforce’s limitless digital workforce of AI agents can analyze data, make decisions, and take action on tasks like answering customer service inquiries, qualifying sales leads, and optimizing marketing campaigns. With Agentforce, any organization can easily build, customize, and deploy their own agents for any use case across any industry. The future of AI is agents, and it’s here. Our vision is bold: to empower one billion agents with Agentforce by the end of 2025. This is what AI is meant to be.” MARC BENIOFF, CHAIR, CEO & CO-FOUNDER, SALESFORCE “Agentforce represents the Third Wave of AI—advancing beyond copilots to a new era of highly accurate, low-hallucination intelligent agents that actively drive customer success. Unlike other platforms, Agentforce is a revolutionary and trusted solution that seamlessly integrates AI across every workflow, embedding itself deeply into the heart of the customer journey. This means anticipating needs, strengthening relationships, driving growth, and taking proactive action at every touchpoint,” said Marc Benioff, Chair and CEO, Salesforce. “While others require you to DIY your AI, Agentforce offers a fully tailored, enterprise-ready platform designed for immediate impact and scalability. With advanced security features, compliance with industry standards, and unmatched flexibility. Our vision is bold: to empower one billion agents with Agentforce by the end of 2025. This is what AI is meant to be.” In contrast to now-outdated copilots and chatbots that rely on human requests and struggle with complex or multi-step tasks, Agentforce offers a new level of sophistication by operating autonomously, retrieving the right data on demand, building action plans for any task, and executing these plans without requiring human intervention. Like a self-driving car, Agentforce uses real-time data to adapt to changing conditions and operates independently within an organizations’ customized guardrails, ensuring every customer interaction is informed, relevant, and valuable. And when desired, Agentforce seamlessly hands off to human employees with a summary of the interaction, an overview of the customer’s details, and recommendations for what to do next. Industry leaders like OpenTable, Saks, and Wiley are already experiencing the transformative power of Agentforce. For example, Agentforce is helping organizations like Wiley provide customers with dynamic, conversational self-service. Agentforce is configured to answer questions using Wiley’s knowledge base already built into Salesforce so it can automatically resolve account access. It also triages registration and payment issues, directing customers to the appropriate resources. With Agentforce handling routine inquiries, Wiley has seen an over 40% increase in case resolution, outperforming their old chatbot and giving their human agents more time to focus on complex cases. Why it Matters An estimated 41% of employee time is spent on repetitive, low-impact work, and 65% of desk workers believe generative AI will allow them to be more strategic, according to the Salesforce Trends in AI Report. Every company has more jobs to be done than the resources available to do them. As a result, many jobs go unaddressed or uncompleted. Agentforce provides relief to overstretched teams with its ability to scale capacity on demand so humans can focus on higher-touch, higher-value, and more strategic outcomes. The future of work is a hybrid workforce composed of humans with agents, enabling companies to compete in an ever-changing world. Supporting Customer Quotes “Piloting Agentforce has made a noticeable difference during one of our busiest periods — back-to-school season. It’s been exciting to go live with our first agent thanks to the no-code builder, and we’ve seen a more than 40% increase in case resolution, outperforming our old bot. Agentforce helps to manage routine responsibilities and free up our service teams for more complex cases.” – Kevin Quigley, Senior Manager, Continuous Improvement, Wiley “Every interaction that restaurants and diners have with our support team must be accurate, fast, and reflective of the hospitality that restaurants show their guests. Agentforce has incredible potential to help us deliver that high touch attentiveness and support while significantly freeing up our team to address more complex needs.” – George Pokorny, SVP Customer Success, OpenTable “As we advance our personalization strategy, we believe Agentforce and its AI-powered capabilities have the potential to make a real impact on our approach to customer engagement, raising the bar in luxury retail. Agentforce will improve our effectiveness across customer touchpoints, empowering our employees and augmenting their ability to deliver the elevated and more individualized shopping experiences for which Saks is known.” – Mike Hite, Chief Technology Officer, Saks Global 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

Read More
September 21, 2024in Data, Enterprise, Generative AI, Google, Salesforce, Technology
Large Action Models and AI Agents
21Sep

Large Action Models and AI Agents

The introduction of LAMs marks a significant advancement in AI, focusing on actionable intelligence. By enabling robust, dynamic interactions through function calling and structured output generation, LAMs are set to redefine the capabilities of AI agents across industries.

Read More
September 21, 2024in Data
Embedded Salesforce Einstein
21Sep

Embedded Salesforce Einstein

In a world where data is everything, businesses are constantly seeking ways to better understand their customers, streamline operations, and make smarter decisions. Enter Salesforce Einstein—a powerful AI solution embedded within the Salesforce platform that is revolutionizing how companies operate, regardless of size. By leveraging advanced analytics, automation, and machine learning, Einstein helps businesses boost efficiency, drive innovation, and deliver exceptional customer experiences. Embedded Salesforce Einstein is the answer. Here’s how Salesforce Einstein is transforming business: Imagine anticipating customer needs, market trends, or operational challenges before they happen. While it’s not magic, Salesforce Einstein’s AI-powered insights and predictions come remarkably close. By transforming vast amounts of data into actionable insights, Einstein enables businesses to anticipate future scenarios and make well-informed decisions. Industry insight: In financial services, success hinges on anticipating market shifts and client needs. Banks and investment firms leverage Einstein to analyze historical market data and client behavior, predicting which financial products will resonate next. For example, investment advisors might receive AI-driven recommendations tailored to individual clients, boosting engagement and satisfaction. Manufacturers also benefit from Einstein’s predictive maintenance tools, which analyze data from machinery to anticipate equipment failures. A car manufacturer, for instance, could use these insights to schedule maintenance during off-peak hours, minimizing downtime and preventing costly disruptions. Personalization is now a necessity. Salesforce Einstein elevates personalization by analyzing customer data to offer tailored recommendations, messages, and services. Industry insight: In e-commerce, personalized recommendations are often the key to converting browsers into loyal customers. An online bookstore using Einstein might analyze browsing history and past purchases to suggest new releases in genres the customer loves, driving repeat sales. In healthcare, Einstein’s personalization can improve patient outcomes by providing customized follow-up care. Hospitals can use Einstein to analyze patient histories and treatment data, offering reminders tailored to each patient’s needs, improving adherence to care plans and speeding recovery. Salesforce Einstein’s sales intelligence tools, such as Lead Scoring and Opportunity Insights, enable sales teams to focus on the most promising leads. This targeted approach drives higher conversion rates and more efficient sales processes. Industry insight: In real estate, Einstein helps agents manage numerous leads by scoring potential buyers based on their engagement with property listings. A buyer who repeatedly views homes in a specific area is flagged, prompting agents to prioritize their outreach, accelerating the sales process. In the automotive industry, Einstein identifies leads closer to purchasing by analyzing behaviors such as online vehicle configuration and test drive bookings. This allows sales teams to focus on high-potential buyers, closing deals faster. Automation is at the heart of Salesforce Einstein’s ability to streamline processes and boost productivity. By automating repetitive tasks like data entry and customer inquiries, Einstein frees employees to focus on strategic activities, improving overall efficiency. Industry insight: In insurance, Einstein Bots can handle routine tasks like policy inquiries and claim submissions, freeing up human agents for more complex issues. This leads to faster response times and reduced operational costs. In banking, Einstein-powered chatbots manage routine inquiries such as balance checks or transaction histories. By automating these interactions, banks reduce the workload on call centers, allowing agents to provide more personalized financial advice. Einstein Discovery democratizes data analytics, making it easier for non-technical users to explore data and uncover actionable insights. This tool identifies key business drivers and provides recommendations, making data accessible for all. Industry insight: In healthcare, predictive insights are helping providers identify patients at risk of chronic conditions like diabetes. With Einstein Discovery, healthcare providers can flag at-risk individuals early, implementing targeted care plans that improve outcomes and reduce long-term costs. For energy companies, Einstein Discovery analyzes data from sensors and weather patterns to predict equipment failures and optimize resource management. A utility company might use these insights to schedule preventive maintenance ahead of storms, reducing outages and enhancing service reliability. More Than a Tool – Embedded Salesforce Einstein Salesforce Einstein is more than just an AI tool—it’s a transformative force enabling businesses to unlock the full potential of their data. From predicting trends and personalizing customer experiences to automating tasks and democratizing insights, Einstein equips companies to make smarter decisions and enhance performance across industries. Whether in retail, healthcare, or technology, Einstein delivers the tools needed to thrive in today’s competitive landscape. Tectonic empowers organizations with Salesforce solutions that drive organizational excellence. Contact Tectonic today. 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

Read More
September 21, 2024in Salesforce Einstein
Data Governance Frameworks
21Sep

Data Governance Frameworks

Examples of Data Governance Frameworks Data governance is not a one-size-fits-all approach. Organizations must carefully choose a framework that aligns with their unique goals, structure, and culture. Data is one of an organization’s most valuable assets, and proper governance is key to unlocking its potential. Without a well-designed framework, companies risk poor data quality, privacy breaches, regulatory noncompliance, and missed insights. A data governance framework provides a structured way to manage data throughout its lifecycle, including policies, processes, and standards to ensure data is accurate, accessible, and secure. By putting clear guidelines in place, organizations can increase trust in their data and improve decision-making. Key Pillars of a Data Governance Frameworks A robust data governance framework typically rests on four key pillars: 1. Center-Out Model The center-out model places a centralized team, such as a data governance council, at the core of the governance process. This group establishes policies and oversees data management across the organization, balancing consistency with flexibility for different departments. The Data Governance Institute’s framework is an example of this model. It focuses on creating a Data Governance Office responsible for managing key governance functions such as setting data policies, assigning data stewards, and monitoring compliance. The framework provides a clear structure while allowing business units some leeway in adapting governance practices to their needs. PwC’s model also adopts a center-out approach, with an emphasis on using data governance to monetize data assets. It highlights the importance of maintaining consistency while minimizing the risk of data silos. 2. Top-Down Model In the top-down model, data governance is driven by executive leadership, ensuring alignment with strategic goals. This model provides authority for enforcing governance standards but may face challenges if business units feel disconnected from the central governance team. McKinsey’s framework exemplifies this approach, focusing on integrating data governance with broader business transformation efforts. Executive leadership plays a key role in ensuring that governance initiatives receive the necessary attention and resources. 3. Hybrid Model The hybrid model combines centralized governance with flexibility for individual business units. It establishes an enterprise-wide framework while allowing departments to adapt governance practices to their specific needs. The Eckerson Group’s Modern Data Governance Framework represents a hybrid approach. It emphasizes the importance of people and culture, alongside technology and processes, and encourages organizations to create a roadmap for governance that evolves as needs change. This model provides a balance between centralized control and decentralized flexibility. 4. Bottom-Up Model In the bottom-up model, data governance is driven by subject matter experts and data stakeholders across the organization. This approach promotes collaboration and buy-in from the people closest to the data, ensuring that governance policies are practical and effective. The DAMA-DMBOK framework, developed by the Data Management Association, is a prime example. Although flexible, it often starts as a bottom-up initiative, driven by IT departments and data experts who later gain executive support. 5. Silo-In Model The silo-in model allows individual business units or departments to create their own governance practices. While this approach addresses localized data issues, it often leads to inconsistencies and challenges when the organization needs to integrate data across the enterprise. Though not widely recommended, the silo-in approach may emerge when specific business units take the initiative to establish governance due to regulatory requirements or data management needs within their domains. However, as organizations mature, they often transition to more holistic frameworks to support cross-functional collaboration and data integration. Choosing the Right Framework Selecting the right data governance framework involves evaluating the organization’s needs, structure, and culture. Whether an organization adopts a center-out, top-down, hybrid, bottom-up, or silo-in approach, success depends on involving key stakeholders, securing executive buy-in, and committing to continuous improvement. By treating data as a critical asset and implementing a governance framework that aligns with its business strategy, an organization can ensure that its data management practices support growth, innovation, and regulatory compliance. 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 Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

Read More
September 21, 2024in Data, Enterprise, Technology
Thoughts on Workday With Illuminate
20Sep

Thoughts on Workday With Illuminate

Workday Expands AI Across HR and Finance Platforms with ‘Illuminate’ Workday is significantly enhancing its AI capabilities within its HR and finance platforms through a new set of updates called Illuminate. These updates aim to improve automation and increase productivity by embedding AI more broadly across various HR processes. From routine tasks like content generation to complex problem-solving, Workday’s AI now identifies inefficiencies in HR workflows and offers recommendations for improvement. Thoughts on Workday With Illuminate follow. A key feature of Illuminate is a series of AI agents designed to assist in areas such as succession planning. These agents can suggest internal candidates that HR teams might overlook, helping organizations identify potential leaders within their workforce. During a press briefing ahead of the Workday Rising conference, TechTarget asked if the AI agent used in succession planning could fully capture the intricacies of the employee experience and assess leadership potential. David Somers, Chief Product Officer at Workday, acknowledged the sensitivity of succession planning but emphasized that AI is used to augment—not replace—human decision-making. The agents provide recommendations, while the final hiring decisions still involve talent acquisition professionals and interview panels. Workday’s updates include tools for a wide range of tasks, from content summarization to more advanced capabilities such as detecting bottlenecks in onboarding processes and suggesting optimizations. “These AI agents will streamline common business workflows, boosting productivity and freeing up users to focus on strategic, meaningful work,” Somers explained. While AI has long been part of Workday’s offerings, generative AI is now driving rapid transformation in HR practices. Workday’s Illuminate platform combines data with contextual insights, offering features like compensation data tailored to a company’s specific information. Users can access these AI capabilities through Workday Assistant, a generative AI chatbot that integrates with Microsoft Teams and Slack. This tool will be generally available early next year, making it easier for teams to interact with Workday’s AI-powered systems. HR industry expert Josh Bersin sees Workday’s Illuminate as part of a broader trend of AI agents in the HR space, similar to SAP’s Joule. He believes Workday’s new AI agents will be a major focus for the company, though building out all the necessary Workday transactions into these tools will take time. Bersin does not foresee trust issues among Workday users regarding Illuminate, noting that the platform isn’t open to non-Workday data, which limits concerns around data security. Bersin’s own AI assistant, Galileo, is also expected to integrate with Workday’s platform in the future, further enhancing its capabilities. rativAccording to recent Gartner surveys from March and June, the majority of HR leaders are adopting AI in their organizations. Only 15% of respondents indicated they had no plans to incorporate generative AI into their HR processes, signaling widespread acceptance of AI tools like those Workday is rolling out with Illuminate. 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 Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

Read More
September 20, 2024in AI Tools, Data, Generative AI
Salesforce Underwriting Solutions
20Sep

Salesforce Underwriting Solutions

Merchant Cash Advance Solutions: Enhancing Underwriting with Salesforce In today’s fast-paced financial services industry, efficient and effective underwriting is more crucial than ever. Merchant cash advances (MCAs) have emerged as a popular alternative funding option for businesses that might not qualify for traditional loans. This insight explores how integrating Salesforce with MCA software can streamline underwriting, strengthen lender-borrower relationships, and boost overall operational efficiency. Understanding Merchant Cash Advances Merchant cash advances offer businesses upfront capital in exchange for a portion of future sales. Unlike traditional loans, MCAs are often easier to secure and come with flexible repayment options tied to daily credit card receipts. However, the unique structure of MCAs brings challenges to underwriting, due to the diversity in business models and cash flow patterns. The Role of Underwriting in MCA Underwriting is a vital step in the lending process, assessing the risk associated with providing funds to a borrower. For MCAs, underwriting involves evaluating a business’s revenue streams, creditworthiness, and overall financial health. Traditional underwriting methods can be cumbersome and slow, often causing delays in funding. Challenges in Traditional Underwriting Methods The Power of Salesforce in Streamlining Underwriting Salesforce offers powerful solutions that integrate seamlessly with MCA software, effectively addressing these challenges: Benefits of Integrating MCA Software with Salesforce Key Features to Look for in MCA Software Integrated with Salesforce When choosing an MCA solution integrated with Salesforce, consider features such as: Conclusion Integrating merchant cash advance solutions with Salesforce offers a transformative approach to streamlining underwriting processes in this niche financing sector. By automating workflows, centralizing data management, enhancing communication channels, and improving overall efficiency—all while ensuring compliance—lenders can gain a competitive edge and deliver exceptional service to their clients. If you are searching for a Merchant Cash Advance, Underwriting, or financial services solution contact Tectonic today. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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

Read More
September 20, 2024in Salesforce Development Services, Salesforce Financial Services Solutions
AI-Powered Contact Center Landscape
20Sep

AI-Powered Contact Center Landscape

Navigating the AI-Powered Contact Center Landscape: A Roadmap for Success With thousands of solutions in the contact center ecosystem, each claiming to offer “AI-powered, next-generation technology,” it’s easy to feel overwhelmed. Many of these claims are valid, as AI and machine learning are transforming contact centers and improving customer experiences. But with so many options and combinations of AI-powered solutions, how can you be sure you’re making the right decision? The answer is that it’s almost impossible without help. Trying to research and evaluate every solution on your own could take months or even years—by which time, the technology will have evolved. Plus, if you rely solely on information from manufacturers or software providers, you may only get a one-sided perspective that leads to “CCaaS FOMO” (Fear of Missing Out). A More Objective Approach to the Contact Center Journey While we can’t claim to be 100% unbiased, we take a unique approach. We start with your business, understanding your specific needs, culture, and processes before introducing solutions that fit. Not every top-rated solution is right for your business, and the roadmap below outlines how we help you navigate this complex landscape. 1. Involving Key Stakeholders The first step is ensuring you have the right people involved—those with a vested interest in the contact center‘s success. It’s helpful to break these roles into three categories: Having clear roles and expectations helps streamline the process and ensures everyone is on the same page. 2. Conducting a Contact Center Assessment This discovery phase is crucial for identifying the key drivers behind your business needs. Each contact center is different, even within the same industry. That’s why a one-size-fits-all scorecard won’t work. It’s beneficial to bring in a third-party consultant with broad industry knowledge to conduct an assessment, offering valuable insights that help create a clear vision. 3. Creating a Unique Scorecard Once you’ve completed your assessment, stakeholders can work together to establish a customized scorecard that reflects your business objectives. Whether customer service is your primary focus or you’re more telemarketing-heavy, this scorecard ensures that your solution is tailored to your specific needs. It’s also important to involve contributors and advocates in the process to gain widespread buy-in. 4. Scheduling Solution Demonstrations With a solid scorecard in hand, it’s time to identify and evaluate vendors. A contact center consultant can help streamline this process. Scoring each solution based on how well it aligns with your goals keeps the focus on substance over flash, ensuring the right solution for your business. 5. Analyzing Scorecard Data When reviewing the scorecard data, stakeholders should ask key questions: This analysis ensures that decisions are data-driven and aligned with business goals. 6. Finalizing Vendor Selection-AI-Powered Contact Center Landscape Once the data is compiled and a consensus is reached, it’s time to move forward with a contract proposal. Beyond the solution itself, discuss critical details like implementation timelines, ongoing support, and maintenance to set clear expectations and ensure accountability. Financial Modeling: Justifying the Investment Looking at your goals through a financial lens helps quantify the benefits of your contact center investment. For example, reducing average handling time by just 12 seconds across the company might result in cost-neutral savings. Similarly, reducing call abandonment by even half a percentage point can have a significant impact. These financial considerations help justify ROI and set expectations. Partnering with Tectonic: Expertise You Can Trust At Tectonic, we live and breathe contact centers. Our team of experts comes directly from this world, so we understand the challenges and opportunities. We’re here to help you navigate the complexities of the contact center ecosystem and bring clarity to your CCaaS journey. Contact us today to get started! For more resources, visit our blog or explore our AI solutions to elevate your customer experience. 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

Read More
September 20, 2024in Salesforce Service Cloud
Critical Field Service Challenges with Connected Data and AI
20Sep

Critical Field Service Challenges with Connected Data and AI

Set Up for Success: Tackling Critical Field Service Challenges with Connected Data and AI Today’s customers demand faster, more personalized service, and field service is no exception. Research shows that 74% of mobile workers report that customer expectations have risen, with 73% noting an increased demand for a personal touch. This is shaping key trends in the field service industry. Trend #1: Rising Customer Expectations Amid a Shrinking Workforce Field service teams are grappling with rising customer expectations while dealing with a declining mobile workforce. In fact, 74% of mobile workers report increasing workloads. Given that mobile workers are often the only in-person company representatives, they face intense pressure to deliver exceptional service. At the same time, fewer young people are entering skilled trades, with applications dropping nearly 50% from 2020 to 2022, while seasoned technicians are retiring. This has led to high burnout rates, with 57% of mobile workers experiencing job-related fatigue. Trend #2: Connected Data Empowers Mobile Workers Mobile workers thrive when equipped with connected data. Yet, they spend only 32% of their time interacting with customers, as much of their time is consumed by manual tasks and disjointed systems. With the right technology, mobile workers can access up-to-date customer information through a CRM mobile app, streamlining processes and enabling more personalized service. Connected data also improves sustainability, with features like route optimization and drones reducing time on the road and minimizing worker stress. Trend #3: AI is Revolutionizing Field Service AI is rapidly transforming field service operations. Today, 79% of service organizations are investing in AI, and 83% of decision-makers plan to increase their AI investments next year. AI helps mobile teams save time and cut costs by analyzing customer data to generate personalized responses and streamline processes. By automating workflows with AI, mobile workers can deliver faster, more efficient service. AI-generated summaries of asset history and service interactions help prepare workers before they arrive at a job site, enabling better service and potential upsell opportunities. What’s Next in Field Service? Technologies like generative AI, augmented reality, and mobile solutions are shaping the future of field service. Companies that embrace these innovations now will gain a competitive edge. 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

Read More
September 20, 2024in Generative AI, Salesforce Optimization, Salesforce Service Cloud
Salesforce Channel Order App
20Sep

Salesforce Channel Order App

Salesforce’s platform powers over 4.2 million apps, and Salesforce AppExchange offers more than 4,000 solutions. These numbers highlight Salesforce’s extensive ecosystem, with the Salesforce Channel Order App (COA) playing a crucial role for businesses managing complex partner relationships and order processes. This insight looks into the Salesforce Channel Order App, exploring its purpose, when and why you should use it, core features, who benefits from it, and best practices to maximize its potential. What is the Salesforce Channel Order App? The Salesforce Channel Order App is designed to streamline and automate order management across various sales channels, whether direct, through distribution partners, or a reseller network. It simplifies what would typically be a labor-intensive process by centralizing data, automating tasks, and providing real-time visibility into orders. This results in tighter control over order workflows and enhanced partner collaboration. When to Use the Salesforce Channel Order App The Salesforce Channel Order App is most effective for businesses that manage high volumes of orders from multiple channels. It’s especially useful in industries like technology, consumer goods, and manufacturing, where multi-channel sales are integral to operations. Key Use Cases: Core Features of the Salesforce Channel Order App Who Benefits from Salesforce Channel Order App? The Salesforce Channel Order App is particularly beneficial for industries where managing orders from multiple partners is crucial. Key beneficiaries include: Best Practices for Using Salesforce Channel Order App To get the most out of Salesforce Channel Order App, consider the following best practices: Final Take The Salesforce Channel Order App is an essential tool for businesses relying on channel partners to drive sales. By automating and streamlining the order management process, COA helps businesses improve efficiency, reduce errors, and ensure orders are fulfilled accurately and on time. Whether you’re a manufacturer, technology provider, or consumer goods company, adopting COA enables better order management and strengthens relationships with partners—setting your business up for long-term success. 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

Read More
September 20, 2024in Salesforce
Bye Klarna
20Sep

Bye Klarna

Fintech firm Klarna is cutting ties with two major enterprise software providers, opting to automate its services using AI, and hints that more cuts could follow. Klarna co-founder and CEO Sebastian Siemiatkowski discussed the move during a recent conference call, as reported by Seeking Alpha. The company has already stopped using Salesforce, a platform that helps businesses manage sales and marketing data, and has also removed Workday, an HR and hiring platform, from its tech stack, according to a spokesperson from Klarna. This shift towards AI-driven automation is part of a larger strategy at Klarna. “We have multiple large-scale initiatives combining AI, standardization, and simplification that will allow us to eliminate several SaaS providers,” a company spokesperson said, though they did not specify which providers or services might be next. Founded in 2005, Klarna provides payment processing for e-commerce and reports over 150 million active users worldwide. Despite posting a net loss of $241 million last year—down from nearly $1 billion in 2022—the company reported a reduced loss of $32 million for the first half of 2024. With reports suggesting that Goldman Sachs has been tapped to underwrite Klarna’s potential IPO, the company’s focus on AI could strengthen its profitability prospects. This isn’t Klarna’s first AI initiative. Earlier in 2024, the company introduced an AI-powered customer service assistant in collaboration with OpenAI, which reportedly handled 2.3 million interactions in its first month and replaced the work of 700 agents. Klarna was among the early adopters of OpenAI’s enterprise ChatGPT package, and the company claims that 90% of its employees use the tool daily for process automation. Klarna’s decision to drop Salesforce and Workday is part of a broader effort to replace third-party SaaS solutions with internally developed applications, likely built on OpenAI’s infrastructure. Siemiatkowski stated in the August call, “We are shutting down a lot of our SaaS providers as we are able to consolidate.” However, not everyone is convinced. HR technology analyst Josh Bersin questioned whether Klarna could successfully replace a robust platform like Workday. “Workday systems have decades of workflows and complex data structures, including payroll and attendance,” he told Inc.. Bersin warned that developing an in-house system could lead Klarna into a “black hole of features,” with a poor user experience as a result. Many in the tech world share Bersin’s skepticism, with some suggesting the move is more of a PR tactic as Klarna gears up for its IPO. Investors and executives voiced doubts on social media, with financial insights account BuccoCapital posting on X, “Is it actually the best use of capital to rebuild in-house? Feels like a massive distraction,” while Ryan Jones, CEO of Flighty, called the move “free marketing.” Critics also point out that Klarna has downsized its workforce significantly, reducing its headcount by 1,200 over the past year, and Siemiatkowski has hinted at further reductions, suggesting the company could benefit from cutting staff from 3,800 to 2,000 employees. Siemiatkowski remains adamant that AI will allow Klarna to maintain growth despite these cuts. Bersin also noted that many tech giants have struggled to build their own HR platforms, citing examples like Google, which recently abandoned its internally developed HR software, and Amazon, which undergoes similar cycles regularly. “Microsoft,” Bersin added, “spends money on their own products but partners with SAP for HR software.” If Klarna does succeed in developing an in-house HR platform, it would be an achievement where even some of the biggest tech companies have fallen short. 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

Read More
September 20, 2024in Salesforce
Collaborative Business Intelligence
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

Read More
September 20, 2024in Data, Google, Salesforce, Technology
Salesforce AI Agents and New Data Cloud Features
19Sep

Salesforce AI Agents and New Data Cloud Features

Salesforce has announced several major updates to its Data Cloud at the annual Dreamforce conference, aimed at enhancing data analysis and boosting the capabilities of its new AI agents, Agentforce. These updates include support for unstructured data in audio and video formats, 50 new data connectors, and a low-latency pipeline to ensure faster AI responses. Salesforce describes Data Cloud as a customer data platform designed to unify data from various sources, providing actionable insights for sales, service, and marketing teams. The latest features will further improve this functionality, according to Rahul Auradkar, Salesforce’s General Manager of Unified Data Services and Einstein. Key Updates 1. Unstructured Data Support: Audio and Video Formats Salesforce has introduced the ability to process unstructured audio and video data. This allows enterprises to analyze information from sources like customer calls, training sessions, product demos, and webinars. The unstructured data can enrich customer profiles, provide deeper insights into behavior, and enhance the performance of AI agents within Agentforce by improving response accuracy. IDC’s Hayley Sutherland highlighted that vectorization of this content enables retrieval-augmented generation (RAG), enhancing the use of large language models (LLMs) to generate more effective and context-aware responses. 2. 50 New Data Connectors Salesforce has expanded its connector library with 50 new options, raising the total to 200. This helps businesses integrate diverse data sources, facilitating improved personalization and decision-making. These connectors are also critical for feeding data to AI agents, enabling them to generate more accurate insights. 3. Sub-second Data Layer for Real-time Responses A new sub-second data layer has been rolled out, allowing enterprises to ingest, unify, analyze, and act on data in real time. This layer powers Einstein Personalization, enabling instant AI-driven recommendations and analytics. It’s especially important for the low-latency needs of Agentforce’s autonomous AI agents, ensuring faster, more accurate customer interactions. 4. Governance and Security Enhancements Salesforce has introduced AI tagging and classification features for unstructured data, automating labeling and organization to ensure secure access. Policy-based governance will offer granular control over data access based on tags, metadata, and user attributes. Additionally, customer-managed encryption keys ensure data security, while the newly available private connect feature allows secure data sharing between Data Cloud and public cloud environments. 5. Data Cloud One and Hybrid Search Data Cloud One, a new feature, will allow enterprises to connect multiple Salesforce accounts across different departments or regions into a single instance, simplifying data management. The hybrid search feature, which combines vector and keyword search, is designed to improve the speed and accuracy of finding industry-specific information. Other updates include Tableau Semantics, which helps organize data based on its meaning, and a new data community platform to foster collaboration among data professionals. These updates reflect Salesforce’s commitment to enhancing data-driven decision-making and delivering powerful AI capabilities for enterprises. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
September 19, 2024in Data, Enterprise

Subscribe to our mailing list. Join our mail list to receive our newsletter

We'll keep you up to date with our latest news, insights, free resources, and much more.
Don't worry, we won't spam you.
gettectonic.com