Data Silos

03Dec

MoEngage Unveils New Tools to Help Marketers Adapt to Consumer Trends

MoEngage, a leading cross-channel customer engagement platform, has launched new features designed to help marketers quickly adapt to shifting consumer behaviors. These updates, introduced at the bi-annual MoEngage NEXT event, include Connected Apps for seamless data integration, a Salesforce CRM integration for streamlined data exchange, and Coupons for managing single-use discounts. “Our new capabilities reinforce our commitment to empowering marketers with tools to understand and adapt to evolving consumer expectations,” said Raviteja Dodda, CEO and Co-Founder of MoEngage. “These innovations enable our clients to scale personalized engagement based on individual preferences and behaviors.” Tackling Fragmented Engagement Tools Marketers often struggle to deliver personalized experiences due to disconnected engagement tools and data silos. To bridge this gap, MoEngage introduced Connected Apps, a low-code framework that integrates data across messaging platforms, advertising channels, IVR systems, data warehouses, and chatbots. Enhanced Integration with Salesforce CRM The new bi-directional native integration with Salesforce CRM simplifies data exchange between the two platforms. Marketers can now trigger real-time personalized campaigns without needing costly custom integrations. This integration not only improves efficiency but also reduces operational costs. Streamlining Coupon Management To enhance customer engagement, MoEngage launched Coupons, a feature that helps marketers allocate and manage single-use discount codes from a centralized dashboard. The tool includes real-time updates on coupon status, alerts for shortages and expiration dates, and ingestion tracking, ensuring smooth campaign execution while optimizing costs. Driving Scalable and Personalized Engagement With these innovations, MoEngage continues to solidify its position as a go-to platform for marketers seeking to adapt quickly to consumer trends. By addressing common pain points like data fragmentation and inefficient tools, MoEngage enables marketers to deliver meaningful, personalized customer experiences at scale. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

December 3, 2024in Data, Salesforce, Third Party Apps0 Comments

03Dec

Transform Customer Experiences

How to Transform Customer Experiences with AI and Sub-Second E2E Real-Time Data Sync Introducing Data Cloud’s Sub-Second E2E Real-Time FeatureDeliver hyper-personalized experiences in real time, no matter how or where customers engage with your brand. Exceptional customer experiences hinge on unifying interactions across every touchpoint. Yet, fragmented data dispersed across systems, channels, and clouds often stands in the way. Salesforce Data Cloud eliminates these silos by delivering a synchronized, real-time customer data ecosystem, enabling brands to create personalized, seamless experiences instantly—regardless of how or where customers connect. We’re excited to announce that the Sub-Second E2E Real-Time feature in Salesforce Data Cloud is now available. This innovation processes and analyzes data as it’s generated, empowering brands to make immediate, data-driven decisions. Combined with Einstein Personalization—which leverages advanced machine learning (ML) and rules-based automation—businesses can deliver individualized experiences across all channels, driving deeper engagement and improved outcomes. What is Sub-Second Real-Time? Sub-second real-time refers to the ability to process and deliver data or responses in less than one second, ensuring ultra-low latency and near-instantaneous results. This capability is critical for applications requiring immediate data updates, such as live analytics, responsive user interfaces, and time-sensitive decision-making. The Sub-Second E2E Real-Time feature empowers industries like fraud detection, predictive maintenance, and real-time marketing with instant insights. By synchronizing data across systems, channels, and clouds, Data Cloud ensures a unified, real-time customer view, giving businesses a competitive edge. Real-World Examples of Sub-Second Real-Time in Action 1. Real-Time Web Personalization Imagine a user browsing a website. As they interact with products, Data Cloud instantly captures this activity and updates their customer profile. Using Einstein Personalization, the system processes this data in milliseconds to tailor their browsing experience. For instance, personalized product recommendations can appear as the user clicks, leveraging insights from their behavior across platforms such as websites, point-of-sale systems, mobile apps, and other data sources. This seamless personalization is made possible by Data Cloud’s integrations, including zero-copy ingestion from major data warehouses like Snowflake, Databricks, and Redshift. The result? A continuously updated, 360-degree customer view that enhances every touchpoint. 2. Real-Time Support with Agentforce Now, consider a customer engaging in a live chat for assistance. As they browse, their actions are captured and updated in real time. When they initiate a chat, whether through Agentforce AI agents or human support, the agent has immediate access to their full activity history, updated within milliseconds. This enables the agent to provide tailored responses and solutions, ensuring a frictionless and engaging customer support experience. Why Sub-Second Real-Time Matters From personalization to support, the Sub-Second E2E Real-Time feature in Data Cloud ensures every customer interaction feels relevant, timely, and connected. By bridging the gap between data silos and intelligent automation, businesses can unlock new opportunities to exceed customer expectations—at scale and in real time. Explore how Salesforce Data Cloud can transform your customer experience today. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

December 3, 2024in Data, Salesforce, Salesforce Data Cloud, Snowflake0 Comments

26Nov

Salesforce Strategies to Improve a Nonprofit

Transforming Nonprofit Operations with Salesforce: Lessons from a Real-Life Success Story Actionable insights for nonprofits to streamline operations and amplify impact-Salesforce Strategies to Improve a Nonprofit Running a nonprofit is challenging enough without the added frustration of disjointed systems. Many nonprofits grapple with scattered databases, isolated email tools, and incompatible fundraising platforms, resulting in inefficiencies and operational headaches. When systems operate in silos, teams waste time on manual data entry and backtracking, which hinders program delivery and donor engagement—putting the mission at risk. Enter Salesforce Nonprofit Cloud, a transformative platform designed to centralize operations, improve donor communication, and provide actionable insights. With 93% of Salesforce users reporting positive ROI, the platform empowers nonprofits to focus on what matters most: driving impact. Salesforce can revolutionize nonprofit operations. Case Study: Supporting Families Through Salesforce Client: Children’s Organization for displaced children in Ukraine Mission: To help children separated from their families during the war in Ukraine by providing bilingual, family-narrated audiobooks and beautifully illustrated storybooks. Challenge:While Better Time Stories had a meaningful mission, their operational processes were a roadblock. Their delivery system struggled with: The Approach 1. Goals Set Results With these optimizations, Better Time Stories significantly improved delivery success: Continuous system support ensured seamless operations and enhanced the organization’s ability to meet its mission. Key Strategies for Nonprofits Using Salesforce 1. Automate Donation and Impact Tracking 2. Personalize Donor Journeys 3. Create Custom Workflows 4. Integrate Salesforce with Other Tools 5. Enable Advanced Reporting 6. Build Volunteer and Beneficiary Portals 7. Leverage AI for Strategic Decisions 8. Design Scalable Data Architecture 9. Conduct Regular Health Checks Conclusion Nonprofits need solutions that simplify operations and maximize impact. Salesforce Nonprofit Cloud offers the tools to centralize processes, enhance donor engagement, and drive mission-critical outcomes. By following these strategies and working with an experienced implementation partner, your nonprofit can achieve operational excellence and focus on delivering meaningful results. Ready to transform your nonprofit operations with Salesforce? Let’s make it happen! 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

November 26, 2024in Data, Mulesoft, Salesforce, Salesforce Einstein, Salesforce Experience Cloud, Salesforce Flow, Salesforce Nonprofit Cloud0 Comments

11Nov

AI Readiness in Salesforce

This new capability simplifies data management, empowering teams to focus on deriving insights rather than wrestling with data silos.

November 11, 2024in AI Tools, Data, Mulesoft, Salesforce, Salesforce Einstein0 Comments

31Oct

Pitfall of Process Optimization

In 1963, Peter Drucker wrote one of the most influential articles on business, Managing for Business Effectiveness. Much like Fred Brooks’ 1975 classic, The Mythical Man-Month, it has profound lessons. However, through today’s lens of AI and automation, it seems we may have misinterpreted Drucker’s insights, inadvertently industrializing the problem rather than solving it. Pitfall of process optimization. Pitfalls of process optimization. One pivotal point from Drucker’s essay (highlighted by Dave Duggal) is: “The major problem is the confusion between effectiveness and efficiency. There is nothing more useless than doing efficiently what should not be done at all. Yet our tools — especially accounting concepts and data — all focus on efficiency. What we need is a way to identify areas of effectiveness and a method to concentrate on them.” While Drucker emphasized focusing on results and making data-driven decisions, his warning that “our data and accounting focus on efficiency” has been largely overlooked. Instead of addressing this, businesses have industrialized the pursuit of efficiency at the expense of effectiveness. The Efficiency Trap Drucker’s assertion that “there is nothing more useless than doing with great efficiency what should not be done at all” remains true, yet much of the business and IT landscape has fixated on eliminating steps, even if the return on this effort is minimal. He warned that too much focus is placed on problems rather than opportunities and on areas where even exceptional performance yields little impact. This mirrors many process optimization efforts, where the goal is often to remove unnecessary steps, focusing on efficiency rather than true effectiveness. The Pitfall of Process Optimization Entire business methodologies were built around simplifying processes and eliminating redundant steps. Companies created cultures centered on optimization, believing that by cutting out inefficiencies, they would achieve success. Yet, as Drucker noted, this focus on efficiency has often resulted in neglecting broader opportunities. Poor Data, Poor Outcomes Drucker’s concerns about tools and data have proven strangely prophetic. Instead of focusing on effectiveness, many organizations now face data problems, often rooted in over-optimized processes. Some of the firms most dedicated to process optimization are the very ones known for slow responses to market changes, as their data fails to keep pace with business needs. Focusing on Process, Missing the Bigger Picture When businesses focus narrowly on processes, they overlook key information needed downstream. This might improve micro-level efficiency, but it often damages macro-level outcomes. For instance, optimizing an order submission process may mean critical data isn’t captured, leading to issues further along in the supply chain. This process-driven thinking fosters data silos—disconnected systems that, while progressing individual steps, fail to offer the necessary insights for broader business decisions. Effectiveness Requires Understanding Reality AI amplifies these challenges. To fully leverage AI, businesses must shift from process-centric to reality-based thinking. Companies that can manage their digital reality, enabling AI to make smart, outcome-driven decisions, will outperform those stuck in outdated process mentalities. AI won’t just optimize individual steps like restocking inventory; it will manage complex tasks such as provisioning networks, negotiating with suppliers, or resolving customer complaints. To support this, businesses must move beyond step-based optimization and embrace new approaches that focus on multi-dimensional KPIs and AI-driven outcomes. A Shift from Process to Reality The future of business optimization will require understanding KPIs in a multi-dimensional way, embedding AI into operations, and allowing it to drive business outcomes. This will necessitate a shift in data architecture, with a focus on operational reality rather than reporting. The Dangers of Ignoring the Shift Businesses that cling to process thinking may find isolated success with AI but risk falling behind competitors that embrace a broader transformation. Like retailers who tried to compete with Amazon by merely launching websites without addressing underlying fulfillment challenges, companies may see short-term gains but falter in the long run. The Cultural Challenge of Transformation Switching from process-focused thinking to a reality-based approach will be difficult. Since Drucker’s 1963 essay, the industrialization of step-elimination has become deeply ingrained in business culture. Processes are comfortable; they allow for focused problem-solving in isolated areas. Moving to a mindset that prioritizes operational reality, dependencies, and cross-functional collaboration is a significant cultural shift. Embracing the Change However, the businesses that make this transition will gain a competitive advantage. Those that recognize the scale of change required—making cultural, organizational, and architectural shifts—will operate in a different league than those who don’t. By shifting from efficiency-driven processes to reality-based effectiveness, organizations can unlock the full potential of AI, ensuring not just operational improvements but transformational business success. You can avoid the pitfalls of process optimization. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

October 31, 2024in Salesforce

18Oct

Latest on AI, CRM, and Data Innovations

What’s Happening at Salesforce? The Latest on AI, CRM, and Data Innovations OneMagnify and CX Today have collaborated to explore the latest advancements in AI, CRM, and data at Salesforce. The Salesforce suite is evolving rapidly, driven by the emergence of generative AI, large language models, and increasingly diverse customer demands. Discover how Salesforce is adapting to this dynamic landscape, what the future holds for the industry giant, and how business leaders can maximize the potential of the Salesforce platform. Adam MacDonald, a Salesforce Solution Engineer at OneMagnify, emphasizes, “Organizations often struggle with Salesforce implementation when they fail to align internally and address data silos as the first step in their digital transformation. Defining the solution with the end goal in mind, while allowing for quick, focused wins, is a solid strategy for securing the long-term organizational buy-in essential for successful implementation.” Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

October 18, 2024in Data, Generative AI, Salesforce

14Oct

Gap Between Data and Decisions

Bridging the Gap Between Data and Decisions One of the biggest challenges in enterprise technology is the fragmentation of data across various systems, commonly referred to as data silos. Salesforce research shows that 81% of IT leaders believe these silos prevent their organizations from fully utilizing data, while 94% of business leaders feel they should be extracting more value from their data. Despite the wealth of available information, only 30% of employees report using data to make decisions. Tableau Einstein addresses this challenge by enabling self-service analytics through a unified metadata framework. This framework ensures consistent data definitions and structures across an organization, allowing data architects and analysts to create semantic models. These models simplify complex datasets, making them more accessible to business users. Additionally, the platform integrates AI-powered agents into the data authoring process, automatically suggesting relationships between data objects and providing business context to improve the reliability of insights. Integrating AI for Proactive Insights Southard Jones, Chief Product Officer of Tableau, highlights the need for analytics tools to deliver proactive and autonomous insights within users’ workflows. “Employees often miss critical insights because they lack the time or expertise to analyze complex data sets,” Jones said. “With predictive and generative AI, Tableau Einstein can explore your data and surface insights you might not have considered.” Features like Pulse and Tableau Agent provide insights within familiar platforms like Salesforce, Slack, and third-party applications such as Workday. For example, a manager using Workday might receive real-time notifications about team performance metrics or potential indicators of employee attrition, allowing for timely intervention. Enhancing Trust Through Transparency Building trust in data analytics is key to adoption. Tableau Einstein introduces Tableau Semantics, which acts as a bridge between raw data and business terminology. “With Tableau Semantics, we provide clear explanations and data lineage, showing users exactly how metrics are created,” Jones explained. This transparency demystifies complex calculations, making it easier for users to understand metrics like campaign ROI. By aligning technical data with business understanding, the platform seeks to make analytics more accessible and trustworthy. Potential Challenges While AI-driven analytics holds great promise, it raises questions about data privacy, implementation complexity, and the learning curve for employees. Although AI-powered insights can revolutionize how organizations use data, it’s essential to address data governance concerns and ensure employees are trained to interpret AI-generated insights accurately. The competitive landscape for AI analytics tools is growing, with alternatives like Microsoft’s Power BI offering similar capabilities. Organizations will need to compare Tableau Einstein with other options in terms of features, integration potential, and overall cost. Facilitating Collaboration and Customization Tableau Einstein’s open, API-driven architecture enables developers to create customized analytical agents tailored to specific enterprise needs. “By opening our APIs, we allow developers to interact with the platform programmatically,” Jones noted. This flexibility supports the creation of custom analytics solutions that can seamlessly integrate with other business applications. The platform also introduces marketplace capabilities for sharing and discovering data models and analytics assets, fostering collaboration and innovation while reducing redundant efforts across organizations. Real-World Applications and Early Feedback Early adopters are already exploring how Tableau Einstein enhances decision-making processes. Leslie Bercher, Senior Director of Performance Marketing & Analytics at Arvest Bank, noted, “Embedding these insights into our daily applications empowers teams to make informed decisions quickly. It’s not just about data; it’s about having actionable insights when and where we need them.” Despite the potential, adopting advanced analytics tools may require investment in training and change management to ensure successful implementation. Without the right support, organizations may struggle to unlock the full potential of these technologies. Looking Ahead: The Future of AI-Powered Analytics Tableau Einstein is available through Tableau+, with future integrations planned across Salesforce products, including Sales, Service, Marketing, and Commerce Cloud. Salesforce is offering expert guidance, migration support, and a reimagined partner network to help businesses adopt the platform efficiently. As companies continue their digital transformation journeys, AI-powered analytics tools like Tableau Einstein will be pivotal in making data more accessible and actionable. Success will depend on how well organizations address challenges related to data privacy, employee training, and system integration. “The autonomous revolution in analytics is just beginning,” Jones observed. “The key will be how companies use these tools to gain insights and drive strategic actions that lead to measurable business outcomes.” Salesforce’s launch of Tableau Einstein marks a significant step in democratizing data analytics within organizations. By embedding AI-driven insights into daily workflows and increasing transparency in data metrics, the platform has the potential to transform operations. However, realizing this potential will require thoughtful implementation, strong employee engagement, and ongoing support. As AI and analytics evolve, companies that harness these technologies effectively will gain a competitive advantage in a data-driven world. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

October 14, 2024in Generative AI

09Oct

Smartsheet and AWS Collaborate

Smartsheet and AWS Collaborate to Enhance AI-Driven Decision-Making with New Amazon Q Business Connector October 8, 2024 — During its annual ENGAGE customer conference, Smartsheet (NYSE: SMAR), the enterprise work management platform, announced a partnership with AWS to introduce a new connector that integrates Smartsheet data with Amazon Q Business. This generative AI-powered assistant can answer questions, provide summaries, generate content, and securely complete tasks using data from customers’ enterprise systems. This integration will allow Amazon Q Business users to access insights about their projects and processes managed in Smartsheet, facilitating a cohesive search experience that empowers employees to make informed, data-driven decisions. Smartsheet and AWS Collaborate. As organizations increasingly recognize the importance of data-driven decisions, data silos remain a major hurdle. Research from Salesforce in 2024 indicates that only about 28% of business applications are interconnected. The new connector aims to address this issue by securely merging Smartsheet data with other sources integrated into Amazon Q Business, such as Salesforce, Slack, Microsoft Teams, and AWS. This will benefit over 13 million Smartsheet users globally, including around 85% of the 2024 Fortune 500 companies, allowing them to access their work management data, including sheets, conversations, and files, through AWS’s generative AI-powered assistant. This integration enhances decision-making, productivity, and efficiency. Smartsheet and AWS Collaborate “The Smartsheet connector furthers our strategy to securely integrate Smartsheet with leading enterprise AI tools, allowing customers to work seamlessly across their business applications,” said Ben Canning, SVP of Product Experiences at Smartsheet. “By combining our flexible data model with Amazon Q Business, we’re unlocking access to work management data for our mutual customers, enabling them to focus on achieving business outcomes without worrying about data storage.” For instance, service operations managers can utilize the new connector to manage complex projects more effectively. By posing specific questions to the Amazon Q Business assistant, teams can gain insights from various data sources, including sheets, conversations, and attachments in Smartsheet. The AI assistant conducts thorough searches while respecting access permissions, saving time and enhancing project oversight. This streamlined approach improves client retention, accuracy, and overall service quality. “Generative AI presents a unique opportunity for organizations to transform their internal workflows. The key is securely accessing their own data, regardless of its location or format,” stated Dilip Kumar, Vice President of Amazon Q Business at AWS. “Many enterprises use Smartsheet as their primary collaboration hub, storing billions of rows of data. Allowing Amazon Q Business users to interact with their Smartsheet data in a simple, secure manner boosts productivity, analysis, and decision-making.” “Generative AI is driving a significant shift in how enterprise knowledge is stored, accessed, and utilized,” noted Dion Hinchcliffe, VP of the CIO Practice at The Futurum Group. “This transition offers a chance to redefine what’s possible in data management. A strategic, informed approach to adopting this technology is crucial. By integrating work management data into Amazon Q Business, Smartsheet and AWS are creating a unified AI search experience across their knowledge base, unlocking the true potential of their data.” Empowering Teams to Achieve More with Generative AI Smartsheet is collaborating with industry leaders like AWS to develop AI capabilities that help enterprises manage their critical tasks more strategically and efficiently. Earlier this year, Smartsheet implemented Amazon Q Business internally to enhance knowledge management and boost employee productivity in the cloud. The Smartsheet connector exemplifies how both organizations are delivering powerful AI tools that revolutionize team workflows. Smartsheet continues to integrate generative AI throughout its platform, designed with practicality, transparency, and customer needs in mind. Smartsheet’s AI tools enable organizations to swiftly extract insights from data, create automated processes, generate text and summaries, and accomplish more with the AI assistant. Through the end of December, Smartsheet is offering its entire suite of AI tools to all customers, allowing everyone to leverage AI’s capabilities within the platform. The Smartsheet connector is currently available to Amazon Q Business customers in public preview. About Smartsheet Smartsheet is a modern enterprise work management platform trusted by millions globally, including approximately 85% of the 2024 Fortune 500 companies. As a pioneering leader in its category, Smartsheet delivers powerful solutions that drive performance and foster innovation. Visit www.smartsheet.com 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

October 9, 2024in AI Tools, Data, Enterprise, Generative AI, Salesforce, Technology

07Oct

Tableau Einstein Alliance

We’re expanding our commitment to the future of data and analytics with the launch of the Tableau Einstein Alliance, an initiative designed to foster an ecosystem of visionary and innovative partners. These partners will help integrate Agentforce across every facet of analytics, positioning our customers to harness the full potential of AI. The future of data-driven insights is here, and our partners play a vital role in this journey. —Ryan Aytay, CEO, Tableau Salesforce Introduces Tableau Einstein Alliance Salesforce recently unveiled the Tableau Einstein Alliance, a new partner community for its Tableau Einstein users. This initiative aims to empower partners to excel in the “agent era” by offering exclusive benefits to support their development and implementation of AI-driven solutions and analytics agents. Members of the Alliance will gain access to product roadmaps, in-house expertise, and dedicated marketing support, as well as opportunities for co-selling. Moreover, Salesforce is offering these partners the ability to leverage the Alliance for building AI agents, apps, and solutions that maximize their clients’ investments in AI and data. What is Tableau Einstein? Launched in September, Tableau Einstein is an AI-powered visual analytics platform designed to scale and enhance data-driven workflows. It seamlessly integrates with Salesforce tools like Agentforce and its privacy framework, providing data professionals with the ability to create semantic models using real-time customer data. Tableau Einstein also features a marketplace where organizations can share analytical assets, and its APIs facilitate seamless collaboration. Teams can easily work together on data models, visualizations, and dashboards in a unified, drag-and-drop interface. Why Tableau Einstein Matters In discussing the platform, Ryan Aytay highlighted its transformative capabilities: “By leveraging high-performance AI to connect data, actions, and humans, autonomous and assistive agents are redefining business efficiency. They ensure that the data foundation you’re already using will continue to support your needs into the future. You no longer need to sift through data silos or be a specialist to access critical insights—data is now accessible to everyone.” With Tableau Einstein, Salesforce is setting a new standard for how businesses can achieve success with AI-powered, data-driven insights. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

October 7, 2024in Data, Salesforce, Salesforce Tableau

02Oct

AI Agents and Work

With AI tools becoming increasingly prevalent in workplaces worldwide, the focus has shifted from their novelty to optimizing productivity and effectiveness. AI Agents and Work. At Dreamforce 2024, AI agents were a hot topic, particularly with the launch of Salesforce’s new Agentforce platform. However, Slack also made its mark in the AI space, keen to demonstrate its role in transforming the future of work. TechRadar Pro sat down with Slack CEO Denise Dresser to explore how AI is reshaping the platform and why businesses should embrace this transformation. “Work is Broken” “Work, fundamentally, is broken,” Dresser stated, explaining that many workers spend too much time on what she describes as “the work of work.” This often involves switching between disconnected enterprise apps and navigating through data silos just to access the basic information needed to perform their jobs. Dresser emphasized that AI is poised to relieve much of this burden. She highlighted the “incredible wave” of AI adoption across industries, with over 13,000 AI-powered apps built on Slack. “We’re really investing in ensuring AI works for every worker, driving more productivity,” she said, noting that we’re in one of the most significant periods of change in the workplace, full of both opportunity and questions. Slack’s AI-Powered Enhancements With the support of its parent company, Salesforce, Slack has fully embraced AI, offering a broad range of tools designed to improve productivity. Dresser pointed to Slack’s summarization tools, which streamline workflows by surfacing important items from channels and conversations—tools she finds invaluable in her own workday. In addition, Slack’s Huddles—quick, impromptu meetings—now come with AI enhancements, including a new canvas sidebar that generates summaries, notes, and action items in real-time from a live transcript. Slack AI is also integrated into Workflow Builder, allowing users to automate processes using natural language, eliminating the resistance that some workers may feel toward adopting AI. “Slack is the natural place for work,” Dresser said, explaining that the future of work will involve searching, collaborating, and taking action all within the flow of daily tasks. “That vision has never been more true, and never been more real, because this moment is here.” The Era of AI Agents AI agents represent another major step forward for Slack. Dresser noted that third-party integrations with tools like Adobe, Box, and Workday will help facilitate seamless conversations across multiple apps without the need to switch between them. “The era of agents is a big idea, and it’s happening now,” she remarked. However, Dresser stressed the importance of ensuring a smooth transition, noting that onboarding customers effectively will be key to fulfilling this vision. “We think that’s the future.” Looking Ahead: AI at the Core of Work With the technology now in place, the responsibility lies with workers and managers to leverage AI to enhance their daily routines. “What we focus on is making sure we build a product that people love,” Dresser said, adding, “there’s really no one in the world that can offer the breadth of product that can operate across your entire business at the scale we can.” Dresser concluded with optimism about the future of work: “We’re really excited about this innovation meeting this moment. I don’t think there could be a better time for us, and we’re very optimistic about what’s ahead.” Slack’s AI-powered future is designed to break down barriers, streamline workflows, and make work more efficient for everyone. 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

October 2, 2024in Agentforce Platform, AI Tools, Data, Enterprise, Salesforce, Technology

30Sep

Predictive Analytics in Salesforce

Predictive Analytics in Salesforce: Enhancing Decision-Making with AI In an ever-changing business environment, companies seek tools to forecast trends and anticipate challenges, enabling them to remain competitive. Predictive analytics, powered by Salesforce’s AI capabilities, offers a cutting-edge solution for these needs. In this guide, we’ll explore how predictive analytics works and how Salesforce empowers businesses to make smarter, data-driven decisions. What is Predictive Analytics? Predictive analytics uses historical data, statistical modeling, and machine learning to forecast future outcomes. With the vast amount of data organizations generate—ranging from transaction logs to multimedia—unifying this information can be challenging due to data silos. These silos hinder the development of accurate predictive models and limit Salesforce’s ability to deliver actionable insights. The result? Missed opportunities, inefficiencies, and impersonal customer experiences. When organizations implement proper integrations and data management practices, predictive analytics can harness this data to uncover patterns and predict future events. Techniques such as logistic regression, linear regression, neural networks, and decision trees help businesses gain actionable insights that enhance planning and decision-making. Einstein Prediction Builder A key component of the Salesforce Einstein Suite, Einstein Prediction Builder enables users to create custom AI models with minimal coding or data science expertise. Using in-house data, businesses can anticipate trends, forecast customer behavior, and predict outcomes with tailored precision. Key Features of Einstein Prediction Builder Note: Einstein Prediction Builder requires an Enterprise or Unlimited Edition subscription to access. Predictive Model Types in Salesforce Salesforce employs various predictive models tailored to specific needs: Building Custom Predictions Salesforce supports custom predictions tailored to unique business needs, such as forecasting regional sales or calculating appointment attendance rates. Tips for Building Predictions Prescriptive Analytics: Turning Predictions into Actions Predictive insights are only as valuable as the actions they inspire. Einstein Next Best Action bridges this gap by providing context-specific recommendations based on predictions. How Einstein Next Best Action Works Data Quality: The Foundation of Accurate Predictions The effectiveness of predictive analytics depends on the quality of your data. Poor data—whether due to errors, duplicates, or inconsistencies—can skew results and undermine trust. Best Practices for Data Quality Modern tools like DataGroomr can automate data validation and cleaning, ensuring that predictions are based on trustworthy information. Empowering Smarter Decisions with Predictive Analytics Salesforce’s AI-driven predictive analytics transforms decision-making by providing actionable insights from historical data. Businesses can anticipate trends, improve operational efficiency, and deliver personalized customer experiences. As predictive analytics continues to evolve, companies leveraging these tools will gain a competitive edge in an increasingly dynamic marketplace. Embrace the power of predictive analytics in Salesforce to make faster, more strategic decisions and drive sustained success. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

September 30, 2024in Data, Enterprise, Salesforce, Salesforce Einstein

28Sep

Data Data Data

With the unprecedented surge in data—expected to grow by 22% this year according to the Salesforce State of Data and Analytics 2023 report—and the rapid evolution of AI, maintaining data quality and security has become more critical than ever. The AI and Information Management 2024 Report highlights that 52% of organizations encountered data quality issues during AI implementation, while 71% expressed serious concerns about data privacy and security in AI deployments. The cornerstone of success in any data-driven initiative is strong data governance, ensuring data quality, trust, and privacy. The report also emphasized that effective data governance maximizes the value of data assets. Beyond compliance and quality control, it democratizes data access across an organization, making it essential for long-term success. This insight explores common challenges undermining Salesforce data quality and outlines how robust data governance can lead to high-quality, reliable data. Challenges to Maintaining Quality Salesforce Data Challenge 1: Data Silos Create Fragmented Customer Information Use Case: In the Consumer Packaged Goods (CPG) sector, nearly half of companies struggle to collect and integrate the massive amount of data required for AI adoption. Data fragmentation across supply chains, product categories, and SKUs creates silos, leading to inconsistent customer information. For instance, sales and marketing teams might work with different data sets, leading to disjointed customer interactions and ineffective campaigns. This results in missed sales opportunities, inefficient resource use, and poor customer experiences, ultimately weakening strategic decision-making and competitiveness. Solution: Implementing an information lifecycle management system can address data fragmentation. It provides a unified view for managing content across repositories, helping companies maintain a holistic customer view. Challenge 2: Poor Data Quality Erodes Trust Use Case: The Office of the National Coordinator for Health Information Technology found that at least 7 out of 100 medical records are mismatched or duplicated. A healthcare organization using Salesforce without proper data governance may face incomplete or inaccurate patient information, resulting in incorrect treatments and inefficient care, which damages patient trust and increases legal risks. Solution: You leverages AI to eliminate duplicate records, ensuring clean and accurate data. It classifies content based on metadata and assigns governance policies to prevent duplication, ensuring high-quality data across systems. Additionally, Backup for Salesforce tools detect suspicious anomalies or potential ransomware activity, proactively preserving data integrity. Challenge 3: Data Growth Increases Cyber Risks Use Case: As 65% of e-commerce organizations aim to become fully data-driven by 2026, increasing data volumes introduce greater risks. Without robust protection strategies, companies face potential data loss and operational disruptions. Solution: Tectonic’s solution offers automatic daily backups and on-demand restores, safeguarding data integrity and minimizing downtime in the event of cyberattacks or data breaches. Building a Strong Data Governance Foundation To overcome these challenges, organizations must adopt a comprehensive approach to data governance. Here are seven essential steps to establish effective Salesforce data governance: Champion Salesforce Data Governance with Tectonic Effective Salesforce data governance requires collaboration across all levels of the organization. With over two decades of experience, Tectonic empowers organizations to enhance their digital operations, mitigate risks, and achieve compliance in a dynamic data landscape. By establishing a strong data governance framework, your organization can confidently navigate the challenges of data growth, security, and quality. 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 28, 2024in Salesforce

23Sep

Next Gen Commerce Cloud

Salesforce has launched the next generation of Commerce Cloud, delivering a unified platform that connects B2C, DTC, and B2B commerce, along with Order Management, Payments, and more, to drive seamless customer experiences and revenue growth. With these innovations, businesses can scale across digital and physical channels while leveraging trusted AI and enterprise-wide data for smarter operations. Next Gen Commerce Cloud. Key features include Autonomous Agentforce Agents, which enhance commerce for merchants, buyers, and shoppers by automating tasks such as product recommendations and order tracking. Companies like MillerKnoll have seen success by using Commerce Cloud’s innovations to scale their workforce and drive revenue across multiple channels. New Agentforce Agents for Commerce — Merchant, Buyer, and Personal Shopper — autonomously manage tasks and improve the customer journey. They handle tasks without human intervention, such as product recommendations or order lookups, drawing insights from rich data sources like customer interactions, inventory, orders, and reviews. By tapping into unified data, these agents augment employees, offering tailored experiences and increasing efficiency, while strictly adhering to privacy and security standards. Salesforce’s Commerce Cloud now natively integrates every part of the commerce journey, helping businesses break down data silos and offer consistent, personalized interactions. As Michael Affronti, SVP and GM of Commerce Cloud, highlights: “Unified commerce is the future, breaking down silos to deliver seamless experiences across all channels.” Key new features and functionalities include: With these advancements, Commerce Cloud empowers businesses to create seamless, AI-powered experiences that drive customer loyalty, operational efficiency, and revenue growth across every touchpoint. 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 23, 2024in Data, Enterprise, Google, Salesforce, Salesforce Commerce Cloud

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

September 21, 2024in Data, Enterprise, Technology

20Sep

Salesforce Underwriting Solutions

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

September 20, 2024in Salesforce Development Services, Salesforce Financial Services Solutions

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