AI Data Management

05Aug

July Changes to Preference Center

Privacy Center Update What’s the July Changes to Preference Center? Starting in July 2024, the Privacy Center app within the core platform now supports retention features. July Changes to Preference Center introduces a new Hyperforce-based retention store, allows for retention testing in sandboxes, and offers the option to mask data during retention. The new Hyperforce-based retention store can be provisioned using the core Privacy Center app, eliminating the need for Heroku or the Privacy Center managed package. The rollout of this new retention capability will be phased across regions, initially launching in Germany, Australia, and America East. You can spin up a retention store once it’s available in your region. For more details, refer to the Privacy Center’s Hyperforce-Based Retention Store FAQ. What action do I need to take? What if I don’t take any action? You can continue using the legacy Privacy Center app (managed package version) for data retention, but it will no longer be enhanced and will remain in maintenance mode. Heroku can still be used for managing data retention policies until the end of your contract. Where can I learn more about this upcoming change? Review the Privacy Center’s Hyperforce-Based Retention Store FAQ for more information. 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

August 5, 2024in Hyperforce

21Jun

AI and Consumer Goods Cloud

Salesforce’s latest “rolling thunder” of AI enhancements brings significant innovations to Consumer Goods Cloud, leveraging the power of the Einstein AI platform already integrated into Sales Cloud and Service Cloud. These enhancements are designed to optimize planning and execution for consumer goods companies. Salesforce Consumer Goods Cloud is an industry-specific solution that helps consumer goods companies streamline their route-to-market processes. By unifying trade promotion management and retail execution capabilities on a single platform, it enables seamless collaboration between headquarters and field teams. Utilizing Salesforce’s core CRM functionality and the Einstein AI platform, Consumer Goods Cloud empowers companies with data-driven insights and intelligent automation to drive profitable growth. “Consumer goods companies are laser-focused on profitable growth. With the latest Salesforce innovations for Consumer Goods Cloud, they can unify consumer and customer data to plan promotions precisely, equip every field rep with tools to increase sales and reduce downtime, and integrate trusted AI into every service agent’s workflow to solve problems and upsell more frequently,” explained Rob Garf, VP and GM of Retail and Consumer Goods at Salesforce. “In short, every consumer goods company can now transform into an AI Enterprise.” What’s New in Consumer Goods Cloud The latest updates in Consumer Goods Cloud focus on integrating Salesforce’s Data Cloud with Einstein generative AI capabilities, enhancing three key areas: Data Cloud for Consumer Goods: Account managers can now unify account and industry data to build rich customer profiles, segment accounts to the individual store level, and design hyper-localized assortment and promotion plans. For instance, a soft drink distributor can identify which citrus-flavored sodas are most popular in specific Mexican convenience stores and optimize replenishment accordingly. Einstein Copilot Account Summarization: Within the service console, agents can access AI-generated account summaries, eliminating the need to switch between screens and knowledge articles. Summaries include last interactions, order history, satisfaction scores, and promotion details, enabling agents to resolve inquiries quickly and upsell intelligently. Consumer Goods Cloud Einstein 1 for Sales: This AI-powered enhancement package provides sales managers, field reps, merchandisers, and delivery drivers with productivity and revenue-boosting insights. Real-time notifications and recommendations on stock levels, replenishment, special handling needs, and payment collection keep field teams responsive and effective. The Salesforce Embedded AI Difference Salesforce’s strategy of embedding AI via a unified Einstein platform offers several advantages: Consistency: With Einstein already integrated into Sales and Service Clouds, Salesforce can efficiently extend proven AI tools to industry-specific use cases, benefiting users with familiar interfaces and interaction paradigms. Completeness: Embedding AI at the platform level allows Salesforce to enhance the entire workflow from planning to execution. Consumer goods companies can apply intelligent insights to both back-office processes like promotion management and field activities like stock checks and payment collection. Continuous Innovation: The Einstein platform enables rapid deployment of Salesforce’s latest generative AI advancements across all clouds, ensuring customers always have access to state-of-the-art capabilities. Mars Snacking, one of the world’s largest consumer goods companies, is already benefiting from Salesforce’s AI-powered industry cloud. “At Mars Snacking, we are on an ambitious journey to rewire and almost double the size of our business by 2030,” said Bartek Kononiuk, Global Head of Product – Trade Promotion Management. “Consumer Goods Cloud and Trade Promotion Management will enable us to improve our business processes, data availability, and user experience in critical growth-enabling areas.” AI Innovation Comes at a Cost As the consumer goods industry strives to meet rapidly evolving buyer expectations, Salesforce’s embedded AI solutions for Consumer Goods Cloud offer timely advantages. By democratizing access to generative AI and data management capabilities, Salesforce enables companies of all sizes to optimize decision-making, boost field productivity, and drive profitable growth. However, these advanced functionalities come with significant costs. Salesforce’s Einstein AI enhancements often have substantial per-user surcharges, sometimes exceeding $100 per month. For large deployments involving thousands of employees, these expenses can quickly escalate. Consumer goods companies must carefully evaluate the productivity and revenue gains against the added licensing costs. Additionally, while Salesforce is leading the way in enterprise generative AI, the technology is still maturing. Early adopters may encounter instances where the AI delivers suboptimal results. Salesforce’s Trust Layer aims to mitigate these risks, but companies should approach generative AI with a clear understanding of its current limitations. The ongoing enhancements in Salesforce’s Einstein portfolio present a promising yet costly opportunity for customers to evolve into full-fledged AI Enterprises. As the costs and benefits become clearer, consumer goods companies will need to strategically decide where and how aggressively to deploy these advanced capabilities. Those that find the right balance could gain a significant competitive edge in the rapidly changing digital landscape. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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

June 21, 2024in Einstein 1 Platform, Generative AI, Salesforce

15May

Generative AI for Channel Sales Teams

Salesforce unveiled new AI, CRM, and Slack sales enhancements tailored for its Partner Relationship Management (PRM) solution. Generative AI for Channel Sales Teams. The latest generative and predictive AI is now accessible to channel managers, third-party resellers, brokers, and various indirect sellers. Generative AI introduces advanced capabilities to streamline channel management and expedite partner sales. Moreover, the AI seamlessly integrated into the CRM aids channel managers and partners in maintaining focus and alignment on top-priority opportunities. This announcement expands Einstein for Sales directly into the workflows of channel managers and their partners. Einstein Copilot streamlines operations and enables channel managers to focus on scalability by automating administrative tasks and sharing proactive insights on promising leads and opportunities. Furthermore, the integration of Slack AI and PRM for Slack facilitates swift collaboration and data sharing among internal and external partners. Why it’s significant: What’s new: Salesforce perspective: “New generative AI, data, and automation capabilities in a Slack-first PRM will offer channel sales teams practical tools to enhance partner and internal processes. This will help Salesforce partners and sellers increase collaboration, improve seller productivity on both sides, deepen relationships, and enable growth.” – Ryan Nunez, VP, Industry Solutions Customer perspective: “Thanks to Partner Relationship Management, our team can help our partners get up and running faster, and they can automate a lot of what they used to do previously. We’re also excited to see how the AI insights – such as lead scoring – will give our partners clear guidance on what to focus on much faster.” – Hooman Shahidi, Chief Executive Officer and Co-Founder, EVPassport Availability: Disclaimer: Any unreleased services or features referenced here are not currently available and may not be delivered on time or at all. Customers should make their purchase decisions based upon features that are currently available. 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

May 15, 2024in Einstein 1 Platform, Generative AI, Salesforce, Unified Knowledge

04Apr

Build a Culture of Data

What is a Data Culture? A Data Culture is the collective behaviors and beliefs of people who value, practice, and encourage the use of data to improve decision-making. As a result, data is woven into the operations, mindset, and identity of an organization. Why is a data culture important? It enables more informed decision-making. With a data culture in place, decisions at all levels of the organization are based on data-driven insights rather than intuition or guesswork. This leads to more effective strategies and better outcomes. What is the difference in data culture and data strategy? Gartner defines data strategy as “a highly dynamic process employed to support the acquisition, organization, analysis, and delivery of data in support of business objectives.” In contrast, the culture around data comes together with data talent, data literacy, and data tools. Build a Culture of Data Building a data culture is crucial for companies to unlock valuable insights and make smarter, more strategic decisions. Here’s what leaders need to know to foster a data-driven environment: By following these steps and prioritizing the development of a data culture, leaders can empower their organizations to make informed decisions, drive growth, and stay ahead of the competition in today’s data-driven world. Data Maturity Understanding data maturity is crucial for organizations as it provides a framework for assessing their current state of data management and analytics capabilities. It serves as a tool to guide decision-making and prioritize initiatives aimed at advancing the organization’s data capabilities. By evaluating data maturity, organizations can identify gaps, set goals, and determine the necessary steps to progress along their data journey. Data maturity assessment typically involves evaluating various aspects of data management, including data governance, data quality, data infrastructure, analytics capabilities, and organizational culture around data. Based on the assessment, organizations can identify areas of strength and weakness and develop a roadmap for improvement. Furthermore, understanding data maturity enables organizations to track their progress over time. By periodically reassessing data maturity, organizations can measure how much they have advanced and identify areas that still require attention. This iterative process allows organizations to continuously improve their data capabilities and adapt to evolving business needs and technological advancements. In summary, understanding data maturity allows organizations to: 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

April 4, 2024in Einstein 1 Platform, Salesforce

07Mar

Data Management and Data Maturity

Data Management and Data Maturity: Generative AI Raises Concerns About Data Ethics and Equity Harnessing the capabilities of generative AI is contingent on having comprehensive, unified, and accurate data, as indicated by more than half of IT leaders. However, several obstacles hinder progress. A recent survey unveils that a majority of IT leaders lack a unified data strategy, impeding the seamless integration of generative AI into their existing technology stack. Beyond technical challenges, generative AI also brings to the forefront serious ethical considerations. Key findings from the survey reveal: AI Illuminates Data Management While generative AI garners attention, more established AI applications, such as predictive analytics and chatbots, have long been advantageous for organizations. Technical leaders leveraging AI report significantly faster decision-making and operations. The benefits extend beyond speed, with analytics and IT leaders highlighting more time to address strategic challenges rather than being immersed in mundane tasks. Customers also reap the rewards, with technical leaders noting substantial improvements in customer satisfaction due to AI. Given the pivotal role of quality data in AI outcomes, it is unsurprising that nearly nine out of ten analytics and IT leaders consider new developments in AI to prioritize data management. Realized Benefits of AI Adoption Analytics and IT leaders cite several top benefits realized from AI adoption: Data Maturity Signals AI Preparedness Data maturity emerges as a foundational element for successful AI adoption, with high-maturity organizations boasting superior infrastructure, strategy, and alignment compared to their low-data-maturity counterparts. The disparities are particularly evident in terms of data quality, with high-maturity respondents being twice as likely as low-maturity respondents to possess the high-quality data required for effective AI utilization. Like2 Related Posts Salesforce Artificial Intelligence Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust Read more CRM Cloud Salesforce What is a CRM Cloud Salesforce? Salesforce Service Cloud is a customer relationship management (CRM) platform for Salesforce clients to Read more Salesforce’s Quest for AI for the Masses The software engine, Optimus Prime (not to be confused with the Autobot leader), originated in a basement beneath a West Read more How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more

March 7, 2024in Generative AI, Salesforce Data Cloud, Salesforce Einstein

30Jan

AI Capability Maturity Model

The AI Capability Maturity Model (AI CMM), devised by the Artificial Intelligence Center of Excellence within the GSA IT Modernization Centers of Excellence (CoE), functions as a standardized framework for federal agencies to evaluate their organizational and operational maturity levels. It is equally useful for private organizations in aligning them with predefined objectives. Instead of imposing normative capability assessments, the AI CMM concentrates on illuminating significant milestones indicative of maturity levels along the AI journey. The AI Capability Maturity Model focuses primarily on the development of AI capabilities within an organization. It evaluates an organization’s maturity across four main areas: data, algorithms, technology, and people. Serving as a valuable tool, the AI CMM assists organizations in shaping their unique AI roadmap and investment strategy. The outcomes derived from AI CMM analysis empower decision-makers to identify investment areas that address immediate goals for rapid AI adoption while aligning with broader enterprise objectives in the long run. Maturity vs capability models A maturity model tends to measure activities, such as whether a certain tool or process has been implemented. In contrast, capability models are outcome-based, which means you need to use measurements of key outcomes to confirm that changes result in improvements. AI development rooted in sound software practices underpins much of the content discussed in this and other chapters. Though not explicitly delving into agile development methodology, Dev(Sec)Ops, or cloud and infrastructure strategies, these elements are fundamental to the successful development of AI solutions. The AI CMM elaborates on how a robust IT infrastructure leads to the most successful development of an organization’s AI practice. What are the maturity levels of AI? What are the maturity levels of Artificial Intelligence? Or it can be measured this way. AI Maturity Model Why is AI maturity important? The AI Maturity Assessment is a process designed to help organizations evaluate their current AI capabilities, identify gaps and areas for improvement, and develop a roadmap to build a more effective AI program. Organizational Maturity Areas Organizational maturity areas represent the capacity to embed AI capabilities across the organization. Two approaches, top-down and user-centric, offer distinct perspectives on organizational maturity. Top-Down, Organizational View Bottom-Up, User-centric View Operational Maturity Areas Operational maturity areas represent organizational functions impacting the implementation of AI capabilities. Each area is treated as a discrete capability for maturity evaluation, yet they generally depend on one another. PeopleOps CloudOps DevOps SecOps DataOps MLOps AIOps AI Capability Maturity Model This comprehensive overview of organizational and operational maturity areas underlines the multifaceted nature of AI implementation and the critical role played by diverse elements in ensuring success across different layers of an organization. How AI is transforming the world? AI-powered technologies such as natural language processing, image and audio recognition, and computer vision have revolutionized the way we interact with and consume media. With AI, we are able to process and analyze vast amounts of data quickly, making it easier to find and access the information we need. Like1 Related Posts Salesforce Artificial Intelligence Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust Read more Salesforce’s Quest for AI for the Masses The software engine, Optimus Prime (not to be confused with the Autobot leader), originated in a basement beneath a West Read more How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more Sales Cloud Einstein Forecasting Salesforce, the global leader in CRM, recently unveiled the next generation of Sales Cloud Einstein, Sales Cloud Einstein Forecasting, incorporating Read more

January 30, 2024in Salesforce Einstein

03Jan

The Long and Winding Data Success Road

Long and Winding Data Success Road Fostering a Data-Driven Culture for Informed Decision-Making Enhancing trust in data goes beyond technical solutions; it hinges on cultivating a culture that instills confidence and fosters widespread adoption. Data culture, defined as the collective behaviors and beliefs of individuals who value, practice, and promote data usage for improved decision-making, empowers all members of an organization with insights to address complex business challenges. Key Insights: Redefining Data Governance for Trustworthiness Data governance extends beyond a mere set of rules and restrictions; strategically employed, it becomes a vital tool for reinforcing data trustworthiness. An impressive 85% of analytics and IT leaders use data governance to ensure and certify baseline data quality. It entails establishing rules or policies governing the collection, management, storage, measurement, and communication of information, setting parameters for data access, accuracy, privacy, security, and retention. Governance in Action: A Multi-Pronged Approach Defying Data Gravity Data gravity, the notion that accumulating large data volumes in a specific location or system attracts additional applications and services, poses challenges for data relocation. Leaders in analytics and IT adopt a multi-pronged approach, employing an average of 3.2 different strategies to counteract data gravity. Strategies to Mitigate Data Gravity: Like1 Related Posts CRM Cloud Salesforce What is a CRM Cloud Salesforce? Salesforce Service Cloud is a customer relationship management (CRM) platform for Salesforce clients to Read more How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more Capture Initial Traffic Source With Google Analytics To ensure the proper sequencing of Tags, modify the Tag sequencing in the Google Analytics preview Tag settings. The custom Read more Snowflake and Salesforce with Embed Snowflake has deepened its partnership with investor Salesforce by introducing two tools that seamlessly connect their cloud-native systems. Snowflake and Read more

January 3, 2024in Salesforce Data Cloud, Salesforce Tableau

13Sep

The Crucial Role of Data and Integration in AI at Dreamforce

Understanding The Crucial Role of Data and Integration in AI at Dreamforce At this year’s Dreamforce, AI is the star of the show, but two essential supporting actors are data and integration. Enterprises are increasingly recognizing the importance of unifying their diverse data sources for effective analysis and swift action, and the race to harness AI makes this integration even more critical. Integration is key not only for merging data but also for automating end-to-end processes, enabling organizations to move faster and deliver better outcomes to customers. Crucial Role of Data and Integration in AI at Dreamforce. It’s no surprise that MuleSoft, acquired by Salesforce five years ago, is now a major contributor to Salesforce’s growth. Brian Millham, President and COO at Salesforce, highlighted this during the company’s recent Q2 earnings call: “In Q2, nearly half of our greater than $1 million deals included MuleSoft. As customers integrate data from all sources to drive efficiency, growth, and insights, MuleSoft has become mission-critical and was included in half of our top 10 deals.” Breaking Down Silos Param Kahlon, EVP and General Manager for Automation and Integration at Salesforce, recently discussed the investments customers are making in data and integration. He emphasized the importance of breaking down operational silos: “We are in the business of breaking silos across systems to ensure that data can travel seamlessly through multiple systems and people for processes like order-to-cash or procure-to-pay. Our technology connects these dots.” The surge in AI interest has increased the urgency to act, as Kahlon explained: “Creating data repositories for AI algorithms requires real-time data across silos, driving significant demand for our integration solutions.” Consolidating Data Enterprises have long struggled with data consolidation due to monolithic application stacks with separate data stores. This has been a challenge even within Salesforce’s own products. Last year, Salesforce introduced a Customer Data Platform (CDP) called Data Cloud, which includes a real-time data layer named Genie. Kahlon elaborated on its significance: “Data Cloud’s strength lies in its understanding and storage of Salesforce metadata. This native integration allows for real-time actions within Salesforce, enhancing the ability to aggregate, reason over, and act on data.” For example, when a customer contacts a bank, Data Cloud can compile their ATM usage, website interactions, and recent support cases, providing the agent with a comprehensive view to better assist the customer. Leveraging Metadata for AI Salesforce’s metadata layer, which has been fine-tuned over two decades, gives it a distinct advantage. Kahlon noted: “This metadata-based architecture allows us to create meaningful AI algorithms that are natively consumed within Salesforce, enabling visualization and action based on real-time data.” This is crucial for training the underlying Large Language Model (LLM) accurately, ensuring generated content is contextually grounded and trustworthy. Kahlon emphasized: “The trust layer is essential. We need to ensure no hallucination or toxicity in the LLM’s responses, and that communications align with our company’s values.” Real-Time Data and API Management Data Cloud’s ability to connect to other data sources like Snowflake without duplicating data is a significant benefit. Kahlon commented: “Duplicating data is not desirable. Customers need real-time access to the actual source of truth.” On the integration front, APIs have simplified connecting applications and data sources. However, managing API sprawl is crucial. Kahlon explained: “Standardizing API use and publishing them in a centralized portal is essential for reusability and consistency. Low-code platforms and connectors are becoming increasingly relevant, enabling business users to access data without relying on IT.” Automation and AI The demand for automation is growing, and low-code tools are vital. Instead of integration experts being overwhelmed, organizations should establish Centers for Excellence to focus on creating reusable connectors and automations. Kahlon added: “Companies need low-code tools to involve more business users in the transformation journey without slowing down due to legacy applications.” In the future, AI may further ease the workload on integration specialists. MuleSoft recently introduced an API Experience Hub to make APIs discoverable, and AI might eventually help monitor execution logs and manage APIs more effectively. Kahlon concluded: “AI could help developers find and use APIs efficiently, enhancing security and governance while simplifying access to data across the organization.” Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

September 13, 2023in Mulesoft, Salesforce, Salesforce Einstein, Snowflake

05Jul

Data Management for AI

AI Data Management is the strategic and systematic handling of an organization’s data assets through the integration of AI technology. The primary goal is to enhance data quality, analysis, and decision-making processes. This encompasses the implementation of procedures, guidelines, and technical methodologies for the efficient collection, organization, storage, and utilization of data. While Generative AI receives considerable attention, more established AI applications, such as predictive analytics and chatbots, have long proven beneficial for organizations. Technical leaders leveraging AI report notable improvements in decision-making speed and operational efficiency. Beyond speed, analytics and IT leaders find more time to address strategic challenges rather than being immersed in routine tasks. Customers also experience significant enhancements in satisfaction due to AI. With AI outcomes heavily reliant on data quality, nearly nine in 10 analytics and IT leaders prioritize data management as a high concern amidst new AI developments. Artificial Intelligence quietly contributes to data management by addressing aspects like quality, accessibility, and security. As organizations accelerate digital transformation, AI and Machine Learning are increasingly harnessed to maximize data value. Effective data management is pivotal in creating an environment where data becomes a valuable asset throughout the organization. It mitigates issues arising from poor data, such as friction, inaccurate predictions, and accessibility challenges, ideally preventing them proactively. The labor-intensive nature of data management involves cleaning, extracting, integrating, cataloging, labeling, and organizing data. AI plays a crucial role in organizing data by analyzing extensive datasets and identifying relevant and high-quality content based on predefined criteria. It assists in tagging, categorizing, and summarizing content, simplifying user access to needed information. AI significantly contributes to various data management areas, including classification, cataloging, quality improvement, security, and data integration. It excels in tasks such as obtaining, extracting, and structuring data, locating data, reducing errors, ensuring security, and building master lists. In the realm of database management systems, AI is integrated, particularly machine learning, for automatic diagnosis, monitoring, alerting, and protection of databases. This advancement allows software to manage these tasks autonomously. ML data management applies data quality practices and debugging solutions to machine learning processes. Techniques such as embeddings/similarity search, active learning, meta-learning, and reinforcement learning are utilized for understanding data. AI databases play a crucial role in meeting the complex querying needs of AI systems, providing flexibility and power to enhance innovation and progress. AI-powered solutions contribute to data management by analyzing access patterns, detecting anomalies, and ensuring compliance with privacy regulations through anonymization or pseudonymization of sensitive data. Like1 Related Posts Salesforce Artificial Intelligence Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust Read more Salesforce’s Quest for AI for the Masses The software engine, Optimus Prime (not to be confused with the Autobot leader), originated in a basement beneath a West Read more Salesforce Data Studio Data Studio Overview Salesforce Data Studio is Salesforce’s premier solution for audience discovery, data acquisition, and data provisioning, offering access Read more How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more

July 5, 2022in Salesforce Einstein

July Changes to Preference Center

AI and Consumer Goods Cloud

Build a Culture of Data

Data Management and Data Maturity

AI Capability Maturity Model

The Long and Winding Data Success Road

Data Management for AI

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