Design - gettectonic.com - Page 12
Mapping Data Salesforce to Canva
26Sep

Mapping Data Salesforce to Canva

Mapping Data Fields in Salesforce for Canva Integration Salesforce administrators can map data fields from a brand template to Salesforce objects, enabling data from Salesforce to automatically populate placeholders in Canva designs. This feature is available exclusively for Canva Enterprise users and integrates with Salesforce Professional, Enterprise, or Unlimited editions. Mapping Data Salesforce to Canva. Steps for Mapping Data Fields in Salesforce: Pre-requisites: The following are the steps to set up field mapping using the Canva for Salesforce app. Step 1: Sync Brand Templates Before mapping fields, you need to sync brand templates from Canva to Salesforce. Here’s how: Step 2: Create a Template Mapping Template mapping connects data fields from a Salesforce object to placeholders in a Canva brand template, allowing Salesforce data to autofill the design. You need to create a separate template mapping for each Salesforce object. Unmapped Fields: You don’t have to map every field. If a field is unmapped, the placeholder in the Canva template will remain unchanged in the final design. Additional Information: Connecting Data Source Apps to Canva for Autofill You can connect data sources like Salesforce to Canva to autofill elements in your designs. Here’s a brief overview of how to connect and use Salesforce data: Creating Brand Templates for Salesforce To use Canva for Salesforce to generate sales collateral, brand designers must first create and publish a brand template. These templates include data fields that act as placeholders for Salesforce data. Mapping Data Salesforce to Canva With this setup, Salesforce admins can easily map data fields and auto-generate designs based on Salesforce data. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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September 26, 2024in Data, Enterprise, Salesforce
AI Agents Connect Tool Calling and Reasoning
26Sep

AI Agents Connect Tool Calling and Reasoning

AI Agents: Bridging Tool Calling and Reasoning in Generative AI Exploring Problem Solving and Tool-Driven Decision Making in AI Introduction: The Emergence of Agentic AI Recent advancements in libraries and low-code platforms have simplified the creation of AI agents, often referred to as digital workers. Tool calling stands out as a key capability that enhances the “agentic” nature of Generative AI models, enabling them to move beyond mere conversational tasks. By executing tools (functions), these agents can act on your behalf and tackle intricate, multi-step problems requiring sound decision-making and interaction with diverse external data sources. This insight explores the role of reasoning in tool calling, examines the challenges associated with tool usage, discusses common evaluation methods for tool-calling proficiency, and provides examples of how various models and agents engage with tools. Reasoning as a Means of Problem-Solving Successful agents rely on two fundamental expressions of reasoning: reasoning through evaluation and planning, and reasoning through tool use. While both reasoning expressions are vital, they don’t always need to be combined to yield powerful solutions. For instance, OpenAI’s new o1 model excels in reasoning through evaluation and planning, having been trained to utilize chain of thought effectively. This has notably enhanced its ability to address complex challenges, achieving human PhD-level accuracy on benchmarks like GPQA across physics, biology, and chemistry, and ranking in the 86th-93rd percentile on Codeforces contests. However, the o1 model currently lacks explicit tool calling capabilities. Conversely, many models are specifically fine-tuned for reasoning through tool use, allowing them to generate function calls and interact with APIs effectively. These models focus on executing the right tool at the right moment but may not evaluate their results as thoroughly as the o1 model. The Berkeley Function Calling Leaderboard (BFCL) serves as an excellent resource for comparing the performance of various models on tool-calling tasks and provides an evaluation suite for assessing fine-tuned models against challenging scenarios. The recently released BFCL v3 now includes multi-step, multi-turn function calling, raising the standards for tool-based reasoning tasks. Both reasoning types are powerful in their own right, and their combination holds the potential to develop agents that can effectively deconstruct complex tasks and autonomously interact with their environments. For more insights into AI agent architectures for reasoning, planning, and tool calling, check out my team’s survey paper on ArXiv. Challenges in Tool Calling: Navigating Complex Agent Behaviors Creating robust and reliable agents necessitates overcoming various challenges. In tackling complex problems, an agent often must juggle multiple tasks simultaneously, including planning, timely tool interactions, accurate formatting of tool calls, retaining outputs from prior steps, avoiding repetitive loops, and adhering to guidelines to safeguard the system against jailbreaks and prompt injections. Such demands can easily overwhelm a single agent, leading to a trend where what appears to an end user as a single agent is actually a coordinated effort of multiple agents and prompts working in unison to divide and conquer the task. This division enables tasks to be segmented and addressed concurrently by distinct models and agents, each tailored to tackle specific components of the problem. This is where models with exceptional tool-calling capabilities come into play. While tool calling is a potent method for empowering productive agents, it introduces its own set of challenges. Agents must grasp the available tools, choose the appropriate one from a potentially similar set, accurately format the inputs, execute calls in the correct sequence, and potentially integrate feedback or instructions from other agents or humans. Many models are fine-tuned specifically for tool calling, allowing them to specialize in selecting functions accurately at the right time. Key considerations when fine-tuning a model for tool calling include: Common Benchmarks for Evaluating Tool Calling As tool usage in language models becomes increasingly significant, numerous datasets have emerged to facilitate the evaluation and enhancement of model tool-calling capabilities. Two prominent benchmarks include the Berkeley Function Calling Leaderboard and the Nexus Function Calling Benchmark, both utilized by Meta to assess the performance of their Llama 3.1 model series. The recent ToolACE paper illustrates how agents can generate a diverse dataset for fine-tuning and evaluating model tool use. Here’s a closer look at each benchmark: Each of these benchmarks enhances our ability to evaluate model reasoning through tool calling. They reflect a growing trend toward developing specialized models for specific tasks and extending the capabilities of LLMs to interact with the real world. Practical Applications of Tool Calling If you’re interested in observing tool calling in action, here are some examples to consider, categorized by ease of use, from simple built-in tools to utilizing fine-tuned models and agents with tool-calling capabilities. While the built-in web search feature is convenient, most applications require defining custom tools that can be integrated into your model workflows. This leads us to the next complexity level. To observe how models articulate tool calls, you can use the Databricks Playground. For example, select the Llama 3.1 405B model and grant access to sample tools like get_distance_between_locations and get_current_weather. When prompted with, “I am going on a trip from LA to New York. How far are these two cities? And what’s the weather like in New York? I want to be prepared for when I get there,” the model will decide which tools to call and what parameters to provide for an effective response. In this scenario, the model suggests two tool calls. Since the model cannot execute the tools, the user must input a sample result to simulate. Suppose you employ a model fine-tuned on the Berkeley Function Calling Leaderboard dataset. When prompted, “How many times has the word ‘freedom’ appeared in the entire works of Shakespeare?” the model will successfully retrieve and return the answer, executing the required tool calls without the user needing to define any input or manage the output format. Such models handle multi-turn interactions adeptly, processing past user messages, managing context, and generating coherent, task-specific outputs. As AI agents evolve to encompass advanced reasoning and problem-solving capabilities, they will become increasingly adept at managing

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September 26, 2024in Data, Generative AI
CISA Launches New Services Portal
25Sep

CISA Launches New Services Portal

CISA Launches New Services Portal to Enhance Incident Reporting and Support In August, the Cybersecurity and Infrastructure Security Agency (CISA) introduced the CISA Services Portal, designed to streamline the process of reporting cybersecurity incidents and enhance information sharing. “The new CISA Services Portal improves the reporting process and offers more features for our voluntary reporters. We ask organizations reporting an incident to provide details such as the impacted entity, contact information, incident description, technical indicators, and mitigation steps,” a CISA spokesperson stated via email. By collecting detailed reports, CISA and its partners can assist victims in mitigating the effects of cyber incidents, prevent attackers from reusing tactics, and gain insights into the broader scope of adversary campaigns. This information-sharing benefits not just the initial victim but also helps protect other organizations from potential attacks. How the Portal Works The CISA Services Portal follows guidelines outlined in the NIST Special Publication 800-61 Revision 2, which defines a cyber incident as: In addition to cyber incidents, users can report malware, software vulnerabilities, threat indicators, and vulnerabilities in government websites. For reporting cyberattacks on critical infrastructure, users are directed to a different link as required by CIRCIA regulations. When using the portal, users are guided through a step-by-step reporting process, which includes identifying the affected organization, providing a detailed description of the incident, and outlining the technical details of the breach. What Makes CISA’s Portal Unique? While many breach reporting portals exist, CISA’s stands out for several reasons. It is a voluntary, stand-alone government portal available to all entities nationwide. It does not replace any breach reporting processes mandated by federal, state, local, or industry-specific regulations, such as those required by the FTC or FCC. The portal allows users to report incidents on behalf of their organization or as individual users. It also offers the option to set up an account for ongoing communication with CISA, where users can save, update, and share reports. What truly differentiates CISA’s portal is its capability to provide direct assistance in incident response and recovery. This is particularly valuable for small and medium-sized businesses that may lack the resources to effectively handle cyber incidents. Although reporting to CISA is not mandatory, the agency strongly encourages organizations to voluntarily report incidents or suspicious activity. CISA has also developed a guide to help prepare organizations for submitting reports, ensuring they have all necessary details related to the breach and their mitigation efforts. “Any organization experiencing a cyberattack or incident should report it—not only for their benefit but to help the broader community. CISA and our government partners have unique tools to assist with response and recovery, but we need to know about the incident to provide support,” said Jeff Greene, CISA Executive Assistant Director for Cybersecurity, in a statement announcing the portal. The new CISA Services Portal aims to strengthen collaboration, offering a more efficient and supportive environment for incident reporting and response. Salesforce comment: SAN FRANCISCO, Sept. 25, 2015—Salesforce (NYSE: CRM), the Customer Success Platform and world’s #1 CRM company, today issued the following statement on the proposed Cybersecurity Information Sharing Act of 2015 (“CISA”): “At Salesforce, trust is our number one value and nothing is more important to our company than the privacy of our customers’ data,” said Burke Norton, chief legal officer, Salesforce. “Contrary to reports, Salesforce does not support CISA and has never supported CISA.” Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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September 25, 2024in Data
Data Cloud and Autonomous Agents
25Sep

Data Cloud and Autonomous Agents

Salesforce is building momentum with Data Cloud, the heartbeat of its platform and foundation for Agentforce, fueled by strong business demand for unified data to deliver personalized, contextually relevant, and timely customer experiences across its Customer 360 applications, Flow, analytics, and Agentforce—Salesforce’s groundbreaking suite of autonomous AI agents. This week, Salesforce unveiled a major pivot in its AI strategy during its annual Dreamforce conference. The company is introducing AI tools that can handle tasks without human supervision, alongside a new pricing model. Customers will now pay US per conversation held by Salesforce’s new AI “agents,” which are designed to manage tasks such as customer service and scheduling sales meetings autonomously. This shift in strategy reflects Salesforce’s forward-thinking approach to AI and its potential to transform not only technology but also business models. By focusing on AI agents, Salesforce is responding to a market demand for increased workforce capacity without the need for full-time hires or gig workers—a point emphasized by CEO Marc Benioff during his keynote speech. Building on its predictive Einstein platform, Agentforce represents Salesforce’s next step in AI evolution. “Think of it as the next evolution of our AI wave,” said Muralidhar Krishnaprasad, Salesforce’s president and CTO. “We had AI wave one with Einstein’s predictive capabilities, AI wave two with generative AI copilots, and now we’re entering the age of agents.” Agentforce is designed to augment work by handling tasks across platforms, leveraging Salesforce’s Data Cloud to channel structured and unstructured data into agentic experiences. These agents, powered by the Atlas reasoning engine, use dynamic plans and Retrieval-Augmented Generation (RAG) techniques to address real-time customer questions and deliver actionable insights. Salesforce’s AI agents can operate autonomously, supporting businesses by handling a range of customer interactions and tasks with minimal human intervention. Adding to the AI-driven innovations, Salesforce introduced several new Data Cloud advancements that further enhance an organization’s ability to transform customer experiences using data and AI. These include: Data Cloud continues to drive impressive growth, with a 130% YoY increase in paid customers, processing 2.3 quadrillion records in the second quarter alone. Customers like The Adecco Group, Aston Martin, and Air India rely on Data Cloud to unify their data and deliver personalized, real-time customer experiences. For example, Air India uses Data Cloud to integrate data across its loyalty, reservations, and flight systems, allowing it to manage over 550,000 service cases each month. As AI reshapes the industry, Salesforce’s pivot to autonomous agents and a conversation-based pricing model shows its commitment to leading the charge in enterprise AI adoption, with Data Cloud as its driving force. Despite some software vendors struggling to capitalize on AI advancements, Salesforce’s new model positions it to thrive in a market where AI’s impact is just beginning to unfold. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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September 25, 2024in AI Tools, Data, Enterprise, Generative AI, Salesforce, Technology
Choose Salesforce for SMS
24Sep

Choose Salesforce for SMS

Why Integrating SMS with Salesforce Transforms Business Communication Effective communication is crucial in today’s fast-paced business environment. A company’s success often hinges on its ability to interact seamlessly with customers—whether through personalized service, timely updates, or the latest product offerings. Choose Salesforce for SMS. Today’s customers demand a seamless, omnichannel experience that goes beyond traditional communication methods like flyers and emails. They expect real-time, two-way interactions, which is where Salesforce SMS apps come into play. These apps, which integrate smoothly with existing CRM systems, are transforming how businesses engage with their customers. 5 Reasons to Integrate SMS with Salesforce Integrating SMS with Salesforce offers numerous benefits, primarily enhancing customer-facing efficiency and effectiveness. Here are five key advantages: SMS for Salesforce enables businesses to provide immediate customer support. For instance, logistics companies can use SMS to notify customers about delivery statuses or appointment updates in real time. SMS boasts an impressive open rate—over 95% within the first three minutes—making it a highly effective medium for increasing marketing engagement compared to email. You can even couple Salesforce SMS with tools like geofencing to send notifications via SMS when they are in the store. Integrating SMS with Salesforce allows for streamlined automation of processes such as order updates and appointment reminders. This reduces the need for manual intervention, boosts productivity, and frees up resources for more strategic tasks. Automated texts can be scheduled based on customer behavior or sales stages, optimizing workflows and enhancing efficiency. With a response rate of approximately 45%, SMS is highly effective for engaging customers. It facilitates prompt replies due to its immediate nature. Sales and marketing teams can leverage SMS for direct interactions, while retailers can use it to distribute discount codes and drive quick responses. Additionally, SMS is ideal for important notifications, enhancing customer service. By integrating SMS with Salesforce, businesses can tailor their messages to address specific customer needs and preferences. This personalization fosters stronger customer relationships and improves conversion rates. For example, a travel agency can send personalized vacation recommendations, while financial advisors can provide client-specific updates and advice. Salesforce’s integration with SMS allows for robust tracking and analysis of customer interactions and campaign effectiveness. Marketing teams can refine their strategies by reviewing metrics such as open rates, click-through rates, and conversion rates from SMS campaigns. Additionally, customer support teams can evaluate response times and resolution rates to improve service efficiency. How to Implement SMS in Salesforce To send and receive texts via Salesforce, you have several options: Salesforce offers two primary SMS solutions: Mobile Studio and Digital Engagement. For more tailored functionality, you can use Salesforce API or another API provider to develop a custom texting solution. While this offers greater flexibility and avoids extra costs, it involves significant development time and expense. Opting for a Salesforce-native SMS app from the Salesforce AppExchange can be advantageous. These apps, designed specifically for SMS within Salesforce, often offer: These native apps also come with dedicated customer support, making them a cost-effective and efficient choice. Best Practices for SMS Communication While SMS boasts high engagement rates, it’s essential to follow best practices to maintain a positive customer experience: Ensure compliance with data privacy regulations like GDPR and CCPA by securing clear consent from customers before sending SMS. Automate re-opt-in processes to maintain compliance. Send messages during the recipient’s regular business hours to avoid disturbing them at inconvenient times. Stay in touch with your audience regularly but avoid overwhelming them with excessive messages. Provide valuable content to keep engagement high. Use the same number for messaging to help customers recognize your communications and build trust. Respond promptly and courteously to customer replies. Provide clear, detailed responses to inquiries. Acknowledge and reward outstanding customer actions with thoughtful messages or gestures, such as donations to their favorite charities. Even a thank you for your purchase message can contain a surprise such as a coupon or a notification that a free gift is included with their order. Use SMS to highlight important announcements, events, or opportunities, tapping into the fear of missing out to drive engagement. SMS is the perfect omnichannel tool to incorporate into all your Salesforce journeys. Balance promotional content with conversational engagement to avoid appearing pushy and to keep the communication enjoyable for customers. People are much happier to get news they can use rather than advertisements. Encourage further engagement by including clear, actionable steps in your SMS messages, such as signing up for a free trial or using a discount code. A call to action must be designed with smaller screen views in mind. Include an easy way for recipients to unsubscribe from future messages to comply with legal requirements and respect customer preferences. By integrating SMS with Salesforce and adhering to these best practices, businesses can enhance their communication strategies, foster better customer relationships, and drive greater engagement. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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September 24, 2024in Salesforce
Introducing Marketing Cloud Advanced
24Sep

Introducing Marketing Cloud Advanced

Salesforce has unveiled a series of innovations in its Marketing Cloud, (Introducing Marketing Cloud Advanced) designed to empower businesses with AI-driven tools and enhanced data capabilities to elevate customer engagement. These new features aim to deepen customer relationships, improve team productivity, and boost operational efficiency. Introducing Marketing Cloud Advanced One of the standout innovations is Marketing Cloud Advanced, an upcoming edition that integrates advanced automation and AI. This edition is designed to connect marketing journeys with sales, service, and commerce workflows, offering a more personalized experience across multiple customer touchpoints. Additionally, the introduction of Agentforce for Marketing will bring generative and predictive AI into the marketing realm, helping marketers create comprehensive, end-to-end campaign experiences. Steve Hammond, Executive Vice President and General Manager of Marketing Cloud at Salesforce, commented: “Today’s most successful marketers engage customers on their terms and act as value multipliers across the entire customer experience—whether helping sales or service have more personalized conversations or re-engaging inactive customers. Built on Data Cloud, Marketing Cloud is the only solution that unifies data across every department and moment in the customer lifecycle, powered by Agentforce Agents and automation, driving growth, loyalty, and optimizing ROI.” Agentforce for Marketing introduces several capabilities that streamline marketing processes. Marketers can now plan, launch, and optimize campaigns with ease. Agentforce allows marketers to set campaign goals and brand guidelines, after which the AI generates campaign briefs, identifies target audience segments, and drafts initial emails and landing pages. The system continuously monitors performance and provides data-driven optimization suggestions based on key performance indicators (KPIs). A key addition is Einstein Marketing Intelligence (EMI), which helps marketers manage and optimize cross-channel campaign performance. EMI automates the process of data preparation, enrichment, harmonization, and visualization, enabling marketers to measure campaign effectiveness and make informed decisions to improve return on investment. Furthermore, Salesforce introduced Einstein Personalization, an AI-powered decision engine that delivers tailored customer experiences. This tool allows sales, service, and commerce teams to engage customers in real time based on live interactions and data. Using Flow’s A/B split testing feature, marketers can select dynamic email content for different audience segments and track performance to adjust strategies effectively. Sarah Lukins, General Manager of Digital at Fisher & Paykel Appliances, praised the new functionality: “Salesforce enables us to seamlessly access all of our marketing, commerce, service, sales, and external data in one place and leverage AI for more targeted audience engagement. We can now deliver more relevant and consistent personalized experiences across email, ads, web, social, and service engagements.” The Marketing Cloud Advanced Edition will roll out to customers in North America, Europe, and Latin America, while Agentforce Personalization is expected to become generally available by next summer. Additional releases include expanded Einstein multi-language support and unified SMS conversation capabilities. These innovations are part of Salesforce’s ongoing efforts to equip marketers with unified and actionable data, enhancing the performance of marketing teams and fostering deeper integration across organizations. Through AI and automation, Salesforce is helping businesses deliver more personalized, connected, and seamless customer experiences. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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September 24, 2024in Data, Salesforce Marketing Cloud
Oracle Advertising Sundown
23Sep

Oracle Advertising Sundown

Oracle Shifts Focus to B2B CX, Introduces New Fusion Cloud Features Despite winding down its online advertising products, Oracle is doubling down on its investment in customer experience (CX) technology, particularly in enabling B2B buying and supporting subscription and consumption models. During the Oracle CloudWorld conference on Wednesday, the company unveiled new capabilities for its Fusion Cloud Customer Experience and Unity Customer Data Platform. These enhancements empower Oracle CX users to analyze customer profiles to assemble B2B buying teams, leverage generative AI tools like native analytics, and utilize industry-specific accelerators to speed up the adoption of customer data tools. Key features include the ability to create self-service sites for individual accounts, enabling customers to review and summarize contracts using generative AI, receive quotes, and renew subscriptions. Other features enhance “assisted buying experiences,” blending self-service and human interaction, while tools like account onboarding and AI-powered email drafting simplify full-service sales processes. Subscription models, though still in their early stages for B2B, offer a streamlined alternative to traditional procurement processes. As Liz Miller, an analyst at Constellation Research, noted, subscription-based buying is easier and quicker, avoiding the lengthy procurement cycles many B2B buyers are familiar with. “The pain of traditional B2B buying is still fresh in everyone’s mind,” she said. Oracle Advertising Shuts Down Oracle’s advertising product support will end on September 30, as confirmed by CEO Safra Catz during the company’s June earnings call. The Oracle Advertising Data Management Platform (DMP), built from its BlueKai acquisition, is being retired, following in the footsteps of Salesforce, which discontinued its Audience Studio in 2021. Despite Oracle winding down its ad platform, this move shouldn’t be seen as a shift away from customer experience. Oracle founder Larry Ellison remains deeply involved in shaping the company’s CX strategy, with a focus on marketing tools and Apex low-code platforms, said Rob Pinkerton, Oracle’s senior vice president. Oracle’s modernized CX suite, built on the Fusion Cloud platform, has evolved significantly in recent years, though questions remain about whether it’s too late to regain market share. “Oracle as a CX platform has fallen off the radar for many buyers,” said Miller, adding that customers are no longer debating between Oracle, Microsoft, and Salesforce in the CX space. New Industry-Specific Tools for CX Oracle has also expanded its CX platform with industry-specific tools designed to accelerate the adoption of its customer data platform (CDP) across sectors such as high tech, manufacturing, professional services, telecommunications, utilities, financial services, travel, and retail. According to Rebecca Wettemann, CEO of research firm Valoir, Oracle’s Fusion platform has matured significantly and now supports the complexity of modern customer needs. Wettemann highlighted how common components like customer interaction summaries can be adapted for multiple industries, delivering faster results than traditional applications. Oracle’s Clinical Digital Assistant is one such example of this approach, illustrating the platform’s versatility and AI-driven enhancements. With these developments, Oracle continues to refine its CX offerings to better meet the unique demands of B2B customers, providing tools that streamline operations and enhance customer experiences across various industries. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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September 23, 2024in Generative AI
Open AI Update
23Sep

Open AI Update

OpenAI has established itself as a leading force in the generative AI space, with its ChatGPT being one of the most widely recognized AI tools. Powered by the GPT series of large language models (LLMs), as of September 2024, ChatGPT primarily uses GPT-4o and GPT-3.5. This insight provides an Open AI Update. In August and September 2024, rumors circulated about a new model from OpenAI, codenamed “Strawberry.” Initially, it was unclear if this model would be a successor to GPT-4o or something entirely different. On September 12, 2024, the mystery was resolved with the official launch of OpenAI’s o1 models, including o1-preview and o1-mini. What is OpenAI o1? OpenAI o1 is a new family of LLMs optimized for advanced reasoning tasks. Unlike earlier models, o1 is designed to improve problem-solving by reasoning through queries rather than just generating quick responses. This deeper processing aims to produce more accurate answers to complex questions, particularly in fields like STEM (science, technology, engineering, and mathematics). The o1 models, currently available in preview form, are intended to provide a new type of LLM experience beyond what GPT-4o offers. Like all OpenAI LLMs, the o1 series is built on transformer architecture and can be used for tasks such as content summarization, new content generation, question answering, and writing code. Key Features of OpenAI o1 The standout feature of the o1 models is their ability to engage in multistep reasoning. By adopting a “chain-of-thought” approach, o1 models break down complex problems and reason through them iteratively. This makes them particularly adept at handling intricate queries that require a more thoughtful response. The initial September 2024 launch included two models: Use Cases for OpenAI o1 The o1 models can perform many of the same functions as GPT-4o, such as answering questions, summarizing content, and generating text. However, they are particularly suited for tasks that benefit from enhanced reasoning, including: Availability and Access The o1-preview and o1-mini models are available to users of ChatGPT Plus and Team as of September 12, 2024. OpenAI plans to extend access to ChatGPT Enterprise and Education users starting September 19, 2024. While free ChatGPT users do not have access to these models at launch, OpenAI intends to introduce o1-mini to free users in the future. Developers can also access the models through OpenAI’s API, and third-party platforms such as Microsoft Azure AI Studio and GitHub Models offer integration. Limitations of OpenAI o1 As preview models, o1 comes with certain limitations: Enhancing Safety with OpenAI o1 To ensure safety, OpenAI released a System Card that outlines how the o1 models were evaluated for risks like cybersecurity threats, persuasion, and model autonomy. The o1 models improve safety through: GPT-4o vs. OpenAI o1 Here’s a quick comparison between GPT-4o and OpenAI’s new o1 models: Feature GPT-4o o1 Models Release Date May 13, 2024 Sept. 12, 2024 Model Variants Single model Two variants: o1-preview and o1-mini Reasoning Capabilities Good Enhanced, especially for STEM fields Mathematics Olympiad Score 13% 83% Context Window 128K tokens 128K tokens Speed Faster Slower due to in-depth reasoning Cost (per million tokens) Input: $5; Output: $15 o1-preview: $15 input, $60 output; o1-mini: $3 input, $12 output Safety and Alignment Standard Enhanced safety, better jailbreak resistance OpenAI’s o1 models bring a new level of reasoning and accuracy, making them a promising advancement in generative AI. 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 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

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September 23, 2024in AI Tools, Data, Generative AI, Technology
Salesforce Success Story
23Sep

Case Study: Salesforce Advanced Forcasting and Streamline Operations Yields Big Change and Bigger Results

Case Study: Salesforce Advanced Forcsting and Streamline Operations Yields Big Change and Bigger Results

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September 23, 2024in Data, Enterprise, Google, Mulesoft, Public Sector Solutions, Salesforce, Salesforce Einstein, Salesforce Experience Cloud, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud, Salesforce Implementation Services, Salesforce Manufacturing Automotive Energy, Salesforce Marketing Cloud, Salesforce Success Stories, Technology
Tableau Einstein is Here
22Sep

Tableau Einstein is Here

Tableau Einstein marks a new chapter for Tableau, transforming the analytics experience by moving beyond traditional reports and dashboards to deliver insights directly within the flow of a user’s work. This new AI-powered analytics platform blends existing Tableau and Salesforce capabilities with innovative features designed to revolutionize how users engage with data. The platform is built around four key areas: autonomous insight delivery through AI, AI-assisted development of a semantic layer, real-time data access, and a marketplace for data and AI products, allowing customers to personalize their Tableau experience. Some features, like Tableau Pulse and Tableau Agent, which provide autonomous insights, are already available. Additional tools, such as Tableau Semantics and a marketplace for AI products, are expected to launch in 2025. Access to Tableau Einstein is provided through a Tableau+ subscription, though pricing details remain private. Since being acquired by Salesforce in 2019, Tableau has shifted its focus toward AI, following the trend of many analytics vendors. In February, Tableau introduced Tableau Pulse, a generative AI-powered tool that delivers insights in natural language. In July, it also rolled out Tableau Agent, an AI assistant to help users prepare and analyze data. With AI at its core, Tableau Einstein reflects deeper integration between Tableau and Salesforce. David Menninger, an analyst at Ventana Research, commented that these new capabilities represent a meaningful step toward true integration between the two platforms. Donald Farmer, founder of TreeHive Strategy, agrees, highlighting that while the robustness of Tableau Einstein’s AI capabilities compared to its competitors remains to be seen, the platform offers more than just incremental add-ons. “It’s an impressive release,” he remarked. A Paradigm Shift in Analytics A significant aspect of Tableau Einstein is its agentic nature, where AI-powered agents deliver insights autonomously, without user prompts. Traditionally, users queried data and analyzed reports to derive insights. Tableau Einstein changes this model by proactively providing insights within the workflow, eliminating the need for users to formulate specific queries. The concept of autonomous insights, represented by tools like Tableau Pulse and Agentforce for Tableau, allows businesses to build autonomous agents that deliver actionable data. This aligns with the broader trend in analytics, where the market is shifting toward agentic AI and away from dashboard reliance. Menninger noted, “The market is moving toward agentic AI and analytics, where agents, not dashboards, drive decisions. Agents can act on data rather than waiting for users to interpret it.” Farmer echoed this sentiment, stating that the integration of AI within Tableau is intuitive and seamless, offering a significantly improved analytics experience. He specifically pointed out Tableau Pulse’s elegant design and the integration of Agentforce AI, which feels deeply integrated rather than a superficial add-on. Core Features and Capabilities One of the most anticipated features of Tableau Einstein is Tableau Semantics, a semantic layer designed to enhance AI models by enabling organizations to define and structure their data consistently. Expected to be generally available by February 2025, Tableau Semantics will allow enterprises to manage metrics, data dimensions, and relationships across datasets with the help of AI. Pre-built metrics for Salesforce data will also be available, along with AI-driven tools to simplify semantic layer management. Tableau is not the first to offer a semantic layer—vendors like MicroStrategy and Looker have similar features—but the infusion of AI sets Tableau’s approach apart. According to Tableau’s chief product officer, Southard Jones, AI makes Tableau’s semantic layer more agile and user-friendly compared to older, labor-intensive systems. Real-time data integration is another key component of Tableau Einstein, made possible through Salesforce’s Data Cloud. This integration enables Tableau users to securely access and combine structured and unstructured data from hundreds of sources without manual intervention. Unstructured data, such as text and images, is critical for comprehensive AI training, and Data Cloud allows enterprises to use it alongside structured data efficiently. Additionally, Tableau Einstein will feature a marketplace launching in mid-2025, which will allow users to build a composable infrastructure. Through APIs, users will be able to personalize their Tableau environment, share AI assets, and collaborate across departments more effectively. Looking Forward As Tableau continues to build on its AI-driven platform, Menninger and Farmer agree that the vendor’s move toward agentic AI is a smart evolution. While Tableau’s current capabilities are competitive, Menninger noted that the platform doesn’t necessarily set Tableau apart from competitors like Qlik, MicroStrategy, or Microsoft Fabric. However, the tight integration with Salesforce and the focus on agentic AI may provide Tableau with a short-term advantage in the fast-changing analytics landscape. Farmer added that Tableau Einstein’s autonomous insight generation feels like a significant leap forward for the platform. “Tableau has done great work in creating an agentic experience that feels, for the first time, like the real deal,” he said. Looking ahead, Tableau’s roadmap includes a continued focus on agentic AI, with the goal of providing each user with their own personal analyst. “It’s not just about productivity,” said Jones. “It’s about changing the value of what can be delivered.” Menninger concluded that Tableau’s shift away from dashboards is a reflection of where business intelligence is headed. “Dashboards, like data warehouses, don’t solve problems on their own. What matters is what you do with the information,” he said. “Tableau’s push toward agentic analytics and collaborative decision-making is the right move for its users and the market as a whole.” Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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September 22, 2024in Data, Enterprise, Generative AI, Salesforce
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

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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

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September 21, 2024in Salesforce Financial Services Solutions, Salesforce Nonprofit Cloud, Salesforce Optimization
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

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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.

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September 21, 2024in Data
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

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September 21, 2024in Data, Enterprise, Technology

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