Data Analytics Archives - gettectonic.com

13Mar

Why Its Good to be Data-Driven

The Power of Data-Driven Decision Making Success in business hinges on the ability to make informed decisions. Every operational aspect, from minor choices like office furniture selection to critical investments such as multi-million-dollar marketing campaigns, is shaped by a series of interrelated decisions. While instinct and intuition may play a role, most business choices rely on relevant data—covering aspects such as objectives, pricing, technology, and potential risks. However, excess irrelevant data can be just as detrimental as insufficient accurate data. Why Its Good to be Data-Driven organization… The Evolution of Data-Driven Decision Making Organizations that prioritize data-driven strategies rely on accurate, relevant, complete, and timely data. Simply amassing large volumes of information does not equate to better decision-making; companies must democratize data access, ensuring it is available to all employees rather than limited to data analysts. The practice of using data to inform business decisions gained traction in the mid-20th century when researchers identified decision-making as dynamic, complex, and often ambiguous. Early techniques like decision trees and prospect theory emerged in the 1970s alongside computer-aided decision-making models. The 1980s saw the rise of commercial decision support systems, and by the early 21st century, data warehousing and data mining revolutionized analytics. However, without clear governance and organizational policies, these vast data stores often fell short of their potential. Today, the goal of data-driven decision-making is to combine automated decision models with human expertise, creativity, and critical thinking. This approach requires integrating data science with business operations, equipping managers and employees with powerful decision-support tools. Characteristics of a Data-Driven Organization A truly data-driven organization understands the value of its data and maximizes its potential through structured alignment with business objectives. To safeguard and leverage data assets effectively, businesses must implement governance frameworks ensuring compliance with privacy, security, and integrity standards. Key challenges in establishing a data-driven infrastructure include: The Benefits of a Data-Driven Approach Businesses recognize that becoming data-driven requires more than just investing in technology; success depends on strategy and execution. According to KPMG, four critical factors contribute to the success of data-driven initiatives: A data-driven corporate culture accelerates decision-making, enhances employee engagement, and increases overall business value. Integrating ethical considerations into data usage is crucial for mitigating biases and maintaining data integrity. Transitioning to a Data-Driven Business With the rapid advancement of generative AI, data-driven organizations are poised to unlock trillions of dollars in economic value. McKinsey estimates that AI-driven decision-making could add between .6 trillion and .4 trillion annually across key sectors, including customer operations, marketing, software engineering, and R&D. To successfully transition into a data-driven organization, companies must: By embracing a data-driven model, organizations enhance their ability to make automated yet strategically sound decisions. With seamless data integration across CRM, ERP, and business applications, companies empower human decision-makers to apply their expertise to high-quality, actionable insights—driving innovation and competitive advantage in a rapidly evolving marketplace. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

March 13, 2025in Data, Generative AI, Technology0 Comments

07Mar

Aligning Strategy and Goals

Aligning Strategy and Goals: Bridging the Gap Between Data and Business Success Aligning data strategy with business goals is critical—but easier said than done. 41% of business leaders report that their data strategy is only partially or not at all aligned with their objectives. Here’s how to close the gap and make data a true driver of business success. 1. Define Your Business Goals Collaboration between business and IT stakeholders is essential. Start by identifying and prioritizing objectives that drive success, such as revenue growth, customer satisfaction, cost reduction, and market expansion. Business Goal How Data Supports It Revenue Growth Use analytics to identify high-value customers and optimize marketing strategies for higher conversions. Customer Satisfaction Leverage trusted customer data to personalize experiences and improve engagement. Cost Reduction Analyze operational data to streamline processes and improve efficiency. Market Expansion Use market and customer insights to identify new growth opportunities. 2. Determine Key Metrics Once goals are clear, define key performance indicators (KPIs) to measure progress. Business Goal Key Metric Revenue Growth Conversion Rate: Measures the percentage of leads converted into paying customers. Customer Satisfaction Retention Rate: Tracks the percentage of returning customers over time. Cost Reduction Operational Efficiency Ratio: Compares operational costs to revenue. Market Expansion Customer Acquisition Rate: Measures the rate of new customer growth. 3. Assess Resources and Budget Evaluate whether you have the systems, tools, and budget needed to support your goals. If customer personalization is a priority, you may need solutions like Data Cloud to unify and leverage customer insights. A strong CRM or data analytics platform may also be required to track specific KPIs. 4. Build a Data-Driven Culture Data maturity is not just about tools—it’s about people. Empower teams with the skills, training, and mindset to leverage data effectively. Change management initiatives and ongoing education will help integrate data into daily decision-making. See how F5 is building a data-driven culture with Tableau:“Data has been transforming our corporate culture right before our eyes. Every day, I wake up learning something new about data.”— Amie Bright, Former RVP of Enterprise Data Strategy and Insights, F5 5. Align Teams for Success Use this handy checklist to ensure alignment across your organization: ✅ Collaborate with business and IT teams to define and prioritize objectives.✅ Develop key data KPIs in partnership with internal stakeholders.✅ Survey team leaders to assess the tools, systems, and budgets needed.✅ Invest in training and change management to build a data-driven culture.✅ Join a data leadership community to gain insights and best practices. Want to accelerate your data strategy? Reach out to Tectonic to get started 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

March 7, 2025in Data, Enterprise, Salesforce Data Cloud, Technology0 Comments

06Mar

Google and Salesforce Expand Partnership

Google and Salesforce Expand Partnership to Enhance AI Agent Capabilities Google and Salesforce are deepening their collaboration to provide customers with greater flexibility in AI agent deployment. This expanded partnership will integrate Google Gemini within Salesforce’s Agentforce platform, enabling AI agents to process images, audio, and video with advanced multimodal capabilities. Enhanced AI Functionality with Gemini Through this integration, AI agents will gain access to Gemini’s powerful models, allowing them to handle complex tasks with extended context windows and leverage real-time insights from Google Search via Vertex AI. This collaboration aims to empower businesses with AI solutions that are not limited to a single model provider, offering crucial flexibility in AI customization. Srini Tallapragada, Salesforce’s President and Chief Engineering and Customer Success Officer, emphasized that the integration offers customers the ability to choose the applications and models that best suit their needs. “Salesforce offers a complete enterprise-grade agentic AI platform that makes it easy to deploy new capabilities quickly and realize business value fast. Google Cloud is a pioneer in enterprise agentic AI, offering some of the most powerful models, agents, and AI development tools on the planet. Together, we are creating the best place for businesses to scale with digital labor.” Key Benefits of the Integration The partnership is set to deliver significant advantages for businesses, as outlined in the official announcement: Thomas Kurian, CEO of Google Cloud, highlighted the benefits of this collaboration: “Our mutual customers have asked for seamless integration across Salesforce and Google Cloud. This expanded partnership enables them to accelerate AI transformations with state-of-the-art AI models, agentic AI, and advanced data analytics.” Strengthening Customer Service Integrations The partnership will also enhance the connection between Salesforce Service Cloud and Google Cloud’s Customer Engagement Suite, providing AI-driven improvements to customer support. Key upcoming features include: Expanding AI-Powered Decision-Making Beyond Gemini, Agentforce will integrate Google Search through Vertex AI, leveraging secure connections between Salesforce Data Cloud and Google BigQuery. This will enable AI agents to access real-time information for improved accuracy and decision-making. For example, in supply chain management, AI can track shipments, monitor inventory in Salesforce Commerce Cloud, and anticipate disruptions using real-time data on weather, port congestion, and geopolitical events. Additionally, joint customers will be able to utilize Salesforce’s unified platform—including Agentforce, Data Cloud, and Customer 360—on Google Cloud’s AI-optimized infrastructure. This integration ensures enhanced security through dynamic grounding, zero data retention, and toxicity detection via the Einstein Trust Layer. Businesses will also soon have the option to purchase Salesforce products via the Google Cloud Marketplace. More AI Innovations from Google and Salesforce Google recently announced the development of a personalized AI-powered chatbot that will be integrated into its devices, including smartphones, laptops, and tablets. This tool will automatically answer calls, process requests, and respond on behalf of users. Meanwhile, Salesforce’s Service Assistant—formerly known as Salesforce Service Planner—has launched on Service Cloud. Designed to support live agents, it generates step-by-step plans for resolving customer inquiries by analyzing intent, case history, and customer context. For optimal performance, Salesforce recommends integrating it with Data Cloud and the contact center knowledge base. With this expanded partnership, Google and Salesforce are setting the stage for businesses to leverage cutting-edge AI technology, driving innovation and operational efficiency across industries. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

March 6, 2025in Agentforce Platform, Agentic AI, Data, Enterprise, Google, Salesforce, Salesforce Commerce Cloud, Salesforce Data Cloud, Salesforce Service Cloud, Technology0 Comments

01Mar

The Evolution of Data

Data’s Evolution: Once Oil Now Compute Power For years, data was dubbed the new oil, a valuable resource that could fuel innovation. However, with the rise of artificial intelligence (AI), compute power has taken center stage as the key driver of business success. Sequoia has not hesitated to outline AI’s transformative potential: “The fields that generative AI addresses — knowledge work and creative work — comprise billions of workers. If generative AI can make these workers at least 10% more efficient and/or creative, they become not only faster and more efficient but more capable than before. Therefore, generative AI has the potential to generate trillions of dollars of economic value.” — Sonya Huang, Pat Grady, Generative AI: A Creative New World While markets continue to assess the validity of this claim, the parallels to past technological revolutions—such as the internet and mobile phones—are undeniable. AI’s influence on knowledge work, particularly in data analytics, is poised to be profound, reshaping how organizations extract value from data. The Rise of Data Analytics and AI Integration The data analytics market is expanding at an unprecedented rate, with a projected CAGR of 27.3% through 2030. A decade ago, data science degrees were rare; professionals often transitioned from related analytical fields like physics, mathematics, accounting, and economics. However, the landscape has shifted dramatically. According to the National Center for Education Statistics, bachelor’s degrees in data science surged by 968% between 2020 and 2022, reflecting the growing demand for analytics expertise. Despite its rapid evolution, modern data analytics presents unique challenges. Unlike software engineering—where code is deterministic—data pipelines evolve based on ingestion processes, requiring a different approach to governance and tooling. AI’s introduction into this space offers a chance to accelerate insights, but only if organizations integrate it strategically into their data frameworks. Modernizing Data Practices: From ETL to ELTL Historically, organizations relied on extract-transform-load (ETL) processes for data management. However, inefficiencies led to the rise of extract-load-transform (ELT), empowering analysts to transform data within cloud-based environments. Developer tools like dbt further streamlined this process, enabling analysts to create data models without relying on centralized data architects. Yet, with increased speed came challenges—primarily, the proliferation of inconsistent data sources. This issue underscores the importance of a final load step (ELTL) that incorporates governance measures to ensure accuracy before presenting data to users. Without proper governance, discrepancies between dashboards can erode trust in analytics. Data Governance and the Role of the Semantic Layer To mitigate inconsistencies, organizations have embraced semantic layers—standardized frameworks that define key business metrics. dbt’s semantic layer and emerging solutions like SQLMesh help enforce governance, ensuring AI-powered analytics operate on reliable data. AI’s effectiveness hinges on a well-documented semantic layer, built on metadata that defines relationships between data objects. Many AI systems utilize retrieval-augmented generation (RAG), which retrieves relevant context before generating responses. However, for AI-driven analytics to succeed, organizations must implement safeguards such as primary key checks and join definitions, ensuring structured and accurate outputs. AI-Powered Precision Analytics The integration of AI into data analytics is driving a shift toward precision analytics—delivering exactly the right data transformation tailored to user needs. This approach mirrors the concept of precision medicine in healthcare, where treatments are customized for individuals. With AI, organizations can move beyond dashboards overloaded with filters and instead provide instant, context-aware insights. Data Objects: A New Paradigm Traditionally, analytics relied on dashboards, but AI enables a more flexible approach through “data objects.” A data object represents a distinct SQL query that answers a user’s prompt. These objects can take three primary forms: This shift allows users to generate and compare data objects dynamically, reducing reliance on static reports and fostering a more agile analytical environment. AI Data Analytics: The Next Frontier Organizations now have the opportunity to revolutionize data analytics through AI. Broadly, AI-driven analytics solutions fall into two categories: Text-to-SQL technology has made significant strides, with execution benchmarks like Spider achieving over 91% accuracy. However, new challenges such as Spider 2.0 highlight the complexity of multi-step analytical workflows. AI systems must incorporate rigorous governance mechanisms to ensure accuracy and reliability. Trust, Governance, and the Human Element Trust is paramount in AI-driven analytics. Without proper governance, AI models risk generating misleading insights, undermining confidence in data-driven decision-making. Organizations must establish clear metadata standards, enforce rigorous validation checks, and maintain a human-in-the-loop approach to AI oversight. An AI-powered data agent should function much like a skilled data analyst—asking clarifying questions, referencing well-documented schemas, and leveraging business glossaries to interpret queries accurately. When implemented correctly, AI-driven analytics can transform data accessibility, making insights available in seconds rather than weeks. The Future of AI-Driven Analytics AI is poised to revolutionize data analytics, offering real-time insights with unparalleled accuracy. However, success hinges on a strong foundation of governance, well-defined data models, and reliable metadata. Organizations that embrace AI with a strategic approach will unlock new efficiencies, reduce bottlenecks, and redefine how data powers decision-making. By integrating AI into analytics responsibly, businesses can move beyond dashboards and embrace true precision analytics—where every insight is timely, relevant, and trustworthy. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

March 1, 2025in Artificial Intelligence, Data, Generative AI, Technology0 Comments

24Feb

Salesforce and Google Announcement

Salesforce (NYSE:CRM) has entered into a deal with Google (NASDAQ:GOOGL) to offer its customer relations management software, Agentforce artificial intelligence assistants, and Data Cloud offerings through Google Cloud, the companies announced today. Google and Salesforce already have many of the same clients, and this new deal will allow for more product integration between Google Workspace and Salesforce’s customer relationship management and AI offerings. Salesforce already uses Amazon (AMZN) Web Services for much of its cloud computing. “Our mutual customers have asked us to be able to work more seamlessly across Salesforce and Google Cloud, and this expanded partnership will help them accelerate their AI transformations with agentic AI, state-of-the-art AI models, data analytics, and more,” said Thomas Kurian, CEO of Google Cloud. The deal is expected to total $2.5B over the next seven years, according to a report by Bloomberg. Salesforce and Google today announced a major expansion of their strategic partnership, delivering choice in the models and capabilities businesses use to build and deploy AI-powered agents. In today’s constantly evolving AI landscape, innovations like autonomous agents are emerging so quickly that businesses struggle to keep pace. This expanded partnership provides crucial flexibility, empowering customers to develop tailored AI solutions that meet their specific needs, rather than being locked into a single model provider. Google Cloud is at the forefront of enterprise AI innovation with millions of developers building with Google’s cutting-edge Gemini models and on Google Cloud’s AI-optimized infrastructure. This expanded partnership will empower Salesforce customers to build Agentforce agents using Gemini and to deploy Salesforce on Google Cloud. This is an expansion of the existing partnership that allows customers to use data from Data Cloud and Google BigQuery bi-directionally via zero-copy technology—further equipping customers with the data, AI, trust, and actions they need to bring autonomous agents into their businesses. Additionally, this integration empowers Agentforce agents with the ability to reference up-to-the-minute data, news, current events, and credible citations, substantially enhancing their contextual awareness and ability to deliver accurate, evidence-backed responses. For example, in supply chain management and logistics, an agent built with Agentforce could track shipments and monitor inventory levels in Salesforce Commerce Cloud and proactively identify potential disruptions using real-time data from Google Search, including weather conditions, port congestion, and geopolitical events. Availability is expected in the coming months. AI: Unlocking the Power of Choice and Flexibility with Gemini and Agentforce Businesses need the freedom to choose the best models for their needs rather than be locked into one vendor. In 2025, Google’s Gemini models will also be available for prompt building and reasoning directly within Agentforce. With Gemini and Agentforce, businesses will benefit from: For example, an insurance customer can submit a claim with photos of the damage and an audio voicemail from a witness. Agentforce, using Gemini, can then help the insurance provider deliver better customer experiences by processing all these inputs, assessing the claim’s validity, and even using text-to-speech to contact the customer with a resolution, streamlining the traditionally lengthy claims process. Availability is expected this year. Trust: Salesforce Platform deployed on Google Cloud Customers will be able to use Salesforce’s unified platform (Agentforce, Data Cloud, Customer 360) on Google Cloud’s highly secure, AI-optimized infrastructure, benefiting from features like dynamic grounding, zero data retention, and toxicity detection provided by the Einstein Trust Layer. Once Salesforce products are available on Google Cloud, customers will also have the ability to procure Salesforce offerings through the Google Cloud Marketplace, opening up new possibilities for global businesses to optimize their investments across Salesforce and Google Cloud and benefiting thousands of existing joint customers. Action: Enhanced Employee Productivity and Customer Service with AI-Powered Integrations Millions use Salesforce and Google Cloud daily. This partnership prioritizes choice and flexibility, enabling seamless cross-platform work. New and deeper connections between platforms like Salesforce Service Cloud and Google Cloud’s Customer Engagement Suite, as well as Slack and Google Workspace, will empower AI agents and service representatives with unified data access, streamlined workflows, and advanced AI capabilities, regardless of platform. Salesforce and Google Cloud are deeply integrating their customer service platforms—Salesforce Service Cloud and Google Cloud’s Customer Engagement Suite—to create a seamless and intelligent support experience. Expected later this year, this unified approach empowers AI agents in Service Cloud with: Salesforce and Google Cloud are also exploring deeper integrations between Slack and Google Workspace, boosting productivity and creating a more cohesive digital workspace for teams and organizations. The companies are currently exploring use cases such as: Expanding Partnership Capabilities and Integrations This partnership goes beyond core product integrations to deliver a more connected and intelligent data foundation for businesses. Expected availability throughout 2025: This landmark partnership between Salesforce and Google represents a strategic paradigm shift in enterprise AI deployment, emphasizing infrastructure innovation, AI capability enhancement, and enterprise value. The integration of Google Search grounding provides a unique competitive advantage, offering real-time, factual responses backed by the world’s most comprehensive search engine. The companies are committed to ongoing innovation and deeper collaboration to empower businesses with even more powerful solutions. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

February 24, 2025in Agentic AI, Artificial Intelligence, Data, Enterprise, Google, Salesforce, Salesforce Commerce Cloud, Salesforce Service Cloud, Technology0 Comments

20Feb

Navigating the CRM Split for Drugmakers

Salesforce vs. Veeva: Navigating the CRM Split for Drugmakers The long-standing partnership between Salesforce and Veeva is coming to an end, forcing pharmaceutical companies to decide which platform best suits their evolving needs. A Strategic Decision, Not Just an IT Shift As the contract between the two companies expires this September, drugmakers have until 2030 to choose their path. While some view the shift as a simple migration, industry leaders warn that this decision carries deeper strategic implications. “Sometimes this is being seen as just an IT migration—but no, if you’re just migrating, you’re missing the strategic importance of this,” said Nancy Phelan, SVP and Head of Customer Engagement at Trinity Life Sciences. “Leaders are realizing this is a much bigger decision, requiring thoughtful consideration of timing, approach, and long-term business impact.” A Messy Divorce? In some ways, the split has turned into a battle, with both companies scrambling to win over clients. By the end of December, Salesforce had reportedly poached several major customers from Veeva, which currently holds around 80% market share in life sciences. Both companies are adapting to drastic changes in the healthcare landscape, including an explosion of data, increasingly complex therapies, and evolving customer needs. From what Phelan has observed, drugmakers aren’t gravitating toward one side or the other based on company size, pipeline, or core focus. Instead, both platforms offer distinct advantages that could shape the user experience in different ways. Why the Split? Veeva’s decision to leave the Salesforce platform stems from mounting limitations and risks that made a standalone approach more appealing. According to a report by Everest Group, the separation will shrink Salesforce’s footprint in life sciences, but its broader market presence may fuel faster development of next-generation technologies. Veeva, on the other hand, is doubling down on its industry-specific capabilities, aiming to enhance its tailored solutions for pharma and biotech companies. A Changing Landscape For nearly two decades, Salesforce and Veeva have been intertwined, with Veeva building its life sciences CRM on Salesforce’s platform. Now, both companies are introducing new solutions, reflecting shifts in the pharmaceutical business model. “Companies like Pfizer or Novartis last made this decision more than 15 years ago,” Phelan noted. “Back then, specialty pharmacy complexities, field reimbursement challenges, and patient affordability concerns weren’t as prominent as they are today.” Additionally, the rise of AI and big data analytics has transformed the role of CRM platforms, making the Salesforce-Veeva decision more complex than ever. Two Roads, Two Strategies The key difference between the platforms moving forward will be how they align with drugmakers’ priorities: What’s Next for Pharma? As the transition nears, both Veeva and Salesforce are putting their best foot forward. Fortunately, pharma companies still have time to evaluate their options. “How we’re advising companies is, you’ve got a window of time and a future that is radically different from the last time you made this decision,” Phelan said. “You need to strategically assess the pieces that are important to you.” With the deadline approaching, drugmakers must determine which path aligns best with their long-term vision. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

February 20, 2025in Data, Life Sciences Cloud, Salesforce0 Comments

01Feb

Basejump on AI Analytics

The Evolution of Data and AI: A New Frontier Once considered the new oil, data is no longer the sole focus of business transformation. The emergence of artificial intelligence (AI) has shifted the conversation toward compute power as the fundamental resource driving innovation. Sequoia Capital emphasizes AI’s transformative impact, stating: “The fields that generative AI addresses — knowledge work and creative work — comprise billions of workers. [If] Generative AI can make these workers at least 10% more efficient and/or creative: they become not only faster and more efficient, but more capable than before. Therefore, Generative AI has the potential to generate trillions of dollars of economic value.”— Sonya Huang, Pat Grady, Generative AI: A Creative New World Whether this vision materializes remains to be seen, but the comparisons to past technological revolutions—such as the internet and mobile computing—are hard to ignore. The Role of AI in Knowledge Work and Data Analytics Data analytics has seen unprecedented growth, prompting businesses to reassess its value. With a projected compound annual growth rate (CAGR) of 27.3% through 2030, the market’s rapid expansion has led to a push for better tools and standards. Data analytics has evolved from a discipline primarily rooted in physics, mathematics, accounting, and economics to a more structured academic field. This shift is evident in the surge of data science degrees. The National Center for Education Statistics reported a 968% increase in data science bachelor’s degrees awarded from 2020 to 2022, highlighting growing demand. Modern Data Analytics: From ETL to ELTL Traditional extract-transform-load (ETL) processes have struggled to keep up with modern analytical demands. The introduction of extract-load-transform (ELT) architectures, championed by tools like dbt, empowered data teams by shifting transformation ownership from data architects to analysts. However, this speed came at a cost: fragmented data governance and inconsistencies in reporting. A more refined approach—ELTL (extract-load-transform-load)—adds a critical governance step, ensuring structured data models before visualization and reporting. Data Governance, the Semantic Layer, and AI Unstructured data delivery has led to discrepancies across dashboards and reports. To address this, solutions such as dbt’s semantic layer and SQLMesh provide standardized testing, metadata management, and auditing mechanisms. AI plays a crucial role here, offering: AI-Driven Precision Analytics AI is set to revolutionize data querying by replacing cumbersome dashboards with precise, real-time insights. Borrowing from the concept of precision medicine, “precision analytics” ensures that businesses receive tailored data transformations instantly. This shift reduces reliance on static filters and rigid dashboards, allowing AI to dynamically generate and refine queries. The Rise of Data Objects AI-generated SQL queries—what we term “data objects”—are distinct from traditional dashboards in that they are: AI Solutions: Retrieving and Querying Data AI-powered data analytics falls into two broad categories: Building Trust in AI-Driven Analytics AI-generated analytics must be trustworthy, requiring: The Future of AI in Data Analytics AI will not replace data teams but will enhance their efficiency by reducing bottlenecks and accelerating insights. Organizations that integrate AI with strong data governance will achieve a competitive edge, enabling faster, more accurate, and more relevant analytics. The shift toward AI-driven data analytics is inevitable. The question is: will organizations embrace the change and refine their strategies, or will they be left behind in the wake of innovation? 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, 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

February 1, 2025in Artificial Intelligence, Data, Enterprise, Generative AI, Technology

07Jan

Why Choose Salesforce as Your Mortgage CRM?

Centralized Data Storage & Management Salesforce Mortgage CRM consolidates all your mortgage data into a single platform, offering a unified view of client information, loan details, and transaction history. This eliminates the confusion of scattered data, ensuring brokers have quick access to crucial information. For instance, if a client calls with questions about their loan, brokers can immediately retrieve accurate details, enhancing trust and improving the client experience. Accurate Loan Forecasting With AI-driven data analytics, Salesforce provides actionable insights into future loan opportunities, potential risks, and projected income. By analyzing historical data, brokers can predict client behavior, such as the likelihood of refinancing. This proactive approach allows brokers to deliver tailored offers, increasing conversions and client satisfaction. Seamless Collaboration & Secure Data Sharing Effective communication within an organization is essential, and Salesforce Mortgage CRM excels at connecting team members across locations and devices. Features like document management ensure efficient workflows through version control, rights management, and advanced search capabilities. Brokers can also share select records or documents securely with external stakeholders by granting limited access, ensuring confidentiality while streamlining the lending process. Top-Notch Security Salesforce Mortgage CRM prioritizes data security with multi-level protocols, including multi-factor authentication, data encryption, 24/7 monitoring, and an advanced Trust Layer. These features ensure that sensitive client information, such as financial and personal data, is safeguarded, fostering confidence among potential borrowers. Personalized Client Interactions Every client’s journey is unique, and Salesforce empowers brokers to deliver personalized experiences. For example, if a client’s fixed-rate term is nearing its end, Salesforce can automate outreach with refinancing options. This level of personalization improves client satisfaction and strengthens retention strategies. In-Depth Analytics and Visualization Salesforce offers robust analytics tools to provide mortgage professionals with insights into client behavior, market trends, operational performance, and risk factors. Interactive dashboards and customizable reports enable brokers to visualize metrics like loan-to-value ratios, debt-to-income ratios, and delinquency rates. These insights help streamline operations, improve profitability, and mitigate risks. Automation and AI-Powered Assistance Salesforce integrates cutting-edge AI tools to automate routine tasks, segment clients, and deliver tailored recommendations. Brokers can use these tools to identify refinancing opportunities, streamline workflows, and increase efficiency. While advanced AI features may require premium subscriptions, Salesforce also provides no-code automation capabilities. Tools like Salesforce Flow allow users to create automated workflows for tasks such as data entry, follow-ups, and approvals. For complex workflows, Flow Orchestrator enables brokers to coordinate multi-step processes seamlessly. These automation features eliminate manual tasks, freeing brokers to focus on high-value activities. Streamline Your Lending Process with Salesforce Mortgage CRM Whether you’re looking to centralize your data, enhance client relationships, or improve operational efficiency, Salesforce Mortgage CRM offers a comprehensive solution. Its combination of AI, analytics, and automation ensures that your team can work smarter, not harder. Ready to transform your mortgage business? Let Salesforce help you achieve greater efficiency and client satisfaction today. Unlock the Full Potential of Mortgage Lending with Salesforce Mortgage CRM Automation Without AI Salesforce Mortgage CRM streamlines repetitive tasks through its no-code automation capabilities, removing the need for AI. Salesforce Flow allows brokers to create automated workflows triggered by specific events, such as follow-up tasks, reminders, or status updates. For more intricate workflows, Flow Orchestrator provides seamless coordination for multi-step processes, including approvals, ensuring a smooth lending experience. Whether sending monthly statements or routing documents between stakeholders in the approval process, Salesforce Mortgage CRM automates these tasks based on your predefined rules. For enterprises, Financial Services Cloud (FSC) offers pre-built templates to manage mortgage applications. For SMBs, building tailored flows from scratch can be a more efficient and cost-effective solution, offering greater customization without overpaying for unused FSC features. An excellent example of Salesforce’s versatility is integrating scheduling tools like Calendly with borrower portals built in Experience Cloud. These portals allow clients to book appointments while considering time zone differences and send automated reminders, ensuring seamless borrower engagement. Platform Customization and Scalability Salesforce is an agile platform that adapts to your business needs. Need custom fields for underwriting criteria? A new object to track loan pipeline opportunities? Or automated workflows to manage approvals? Salesforce’s intuitive point-and-click configuration tools empower you to customize your system without complex coding, making it a perfect fit for businesses of any size. Seamless Integration with Third-Party Tools Salesforce’s vast ecosystem, powered by the AppExchange, offers over 7,000 add-ons to enhance your CRM’s functionality. From digital signature solutions to mortgage rate comparison tools, these integrations eliminate the need for extensive custom development. If your unique needs aren’t met by existing options, Salesforce’s certified AppExchange partners can help create tailored solutions. Salesforce Products Tailored for Mortgage Lending Firms 1. Financial Services Cloud (FSC) FSC is purpose-built for the financial services industry, including mortgage lending. It provides a 360-degree view of clients, integrating their financial holdings, activities, and loan details. With FSC, lenders can offer personalized recommendations, streamline the underwriting process, and stay compliant with industry regulations using built-in risk management tools. 2. Marketing Cloud Account Engagement (Pardot) Pardot empowers lenders to engage potential borrowers effectively. For example, a mortgage firm targeting first-time homebuyers can use Pardot to create automated, personalized email campaigns, nurturing leads with tailored content about loans, interest rates, and homebuying tips.Real-time insights and Einstein-powered predictive lead scoring help mortgage professionals prioritize high-value leads and track engagement through detailed campaign performance metrics, such as Multi-Touch Attribution Reports. 3. Experience Cloud For mortgage firms looking to provide a self-service option, Experience Cloud allows for building custom portals and websites. Borrowers can use these portals to upload documents, track application statuses, and access loan details. The drag-and-drop design tools and pre-built templates make it easy to create branded, user-friendly interfaces without requiring extensive coding. 4. Service Cloud Customer service is critical in the mortgage process, and Salesforce Service Cloud enhances this experience with omnichannel support across email, chat, and phone. Borrowers can quickly get answers to questions about their applications, while automation retrieves and shares relevant data instantly. This improves response times, reduces manual effort, and ensures borrowers receive

January 7, 2025in AI Tools, Data, Enterprise, Salesforce, Salesforce Development Services, Salesforce Experience Cloud, Salesforce Financial Services Solutions, Salesforce Flow, Salesforce Marketing Cloud, Salesforce Service Cloud

01Jan

AI Captivates the World

In the late 1990s, a transformative moment unfolded that expanded the world to enquiring minds—the screeching of a dial-up modem, followed by a pixelated “Welcome” screen that connected users to a vast, invisible network spanning the globe. The internet revolution redefined how people interacted with information and one another, permanently reshaping digital communication. Fast forward to 2024, and a similar wave of innovation is underway. Artificial intelligence is captivating the world with its ability to understand, create, and process information. Massive datasets can now be uploaded to AI tools, which instantly distill complex insights—tasks that once took teams of analysts weeks to complete are now executed in seconds. Just as the internet linked people and information, AI is deepening connectivity across all aspects of life, from healthcare and finance to workplaces and homes. In this evolving digital divide, designers hold a critical role—not only in making AI usable but in ensuring it remains understandable, trustworthy, and human-centered. As Fei-Fei Li, Co-Director of Stanford’s Human-Centered AI Institute, states, “If we want machines to think, we need to teach them to see.” The traditional linear process of problem ideation, design, prototyping, and delivery is no longer sufficient for AI design. Instead, designers find themselves on an “AI design rollercoaster”—a dynamic cycle of constant iteration. One day, a seemingly impossible feature is prototyped, and the next, the entire approach pivots due to breakthroughs in large language model (LLM) capabilities. Many teams develop working prototypes before even defining their target audience. It is akin to painting a landscape from a moving train—compelling, challenging, and occasionally bewildering. However, this state of flux is where innovation thrives. Strategies for Designers: Understanding AI’s Capabilities and Limitations Designing for AI requires an understanding of its strengths and weaknesses. While designers do not need to become machine learning engineers, they must grasp AI fundamentals to communicate effectively with technical teams. For example, neural networks excel at recognizing patterns in unstructured data but often struggle with logical reasoning. Recognizing these limitations prevents the development of features that sound promising in theory but fail in practice. Strategies for Designers: Designing for Data Scalability Data is the lifeblood of AI systems, yet its quality and availability fluctuate over time. Designers must create interfaces that can adapt to changing data landscapes. For instance, an AI-powered personal finance app may initially rely on basic transaction data but later incorporate richer datasets for advanced investment recommendations. Interfaces should be modular and scalable, capable of accommodating evolving AI functionalities. Strategies for Designers: The Role of Prototyping in AI Design Static wireframes and basic mockups are insufficient for AI-driven products. AI prototypes must capture the responsive, dynamic nature of intelligent systems. Interactive prototypes offer stakeholders a tangible preview of AI’s potential, highlighting both opportunities and challenges early in the design process. Strategies for Designers: Developing AI Design Intuition To navigate AI design effectively, professionals must cultivate an “AI design sixth sense”—an intuitive understanding of what works well in AI-driven interactions. Immersing in AI experiences, exploring different tools, and analyzing emerging design patterns help build this expertise. Strategies for Designers: Pushing Boundaries in AI Design There are no established rulebooks for AI design—only a vast frontier waiting to be explored. The absence of rigid norms offers designers the freedom to experiment and push boundaries. Some of the most groundbreaking innovations stem from unconventional ideas once deemed impractical. Strategies for Designers: Strengthening Collaboration Between Design and Engineering In AI product design, the traditional “design then handoff” model is giving way to a more integrated approach. Designers and engineers increasingly work in tandem, refining AI experiences through continuous iteration. Some of the most effective design solutions emerge from close collaboration with technical teams. Strategies for Designers: The Next Frontier of Design As AI design continues to evolve, the parallels to the early days of the internet are striking. The excitement, potential, and magnitude of change are reminiscent of Web 1.0, yet amplified in scope. Looking ahead, the field must address profound questions: Will AI become indistinguishable from human intelligence? Will designers craft interfaces for AI-human hybrids yet to be imagined? Designers play an essential role in shaping this future—not as passive observers, but as architects of the next digital revolution. The experiences they create will define humanity’s interactions with artificial intelligence. This responsibility should inspire innovation, challenge conventions, and push the boundaries of what is possible. Call to Action Begin the AI design journey today. Choose an AI tool, explore its interface, and analyze its capabilities. Identify strengths, weaknesses, and opportunities for improvement. Share insights with fellow designers and contribute to the evolving conversation on AI design. The next breakthrough may arise from a single moment of curiosity. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

January 1, 2025in AI Tools, Artificial Intelligence, Data

23Dec

AI Transforming Precision Medicine

How AI-Driven Data Curation is Transforming Precision Medicine Precision medicine—a healthcare approach that personalizes disease prevention and treatment based on insights into a patient’s genes, environment, and behavior—holds incredible promise. However, its success depends on high-quality, curated data from sources like electronic health records (EHRs). This reliance creates significant challenges for healthcare providers and researchers. Can artificial intelligence (AI) help address these hurdles? AI-enabled data curation is already making strides in advancing precision medicine, particularly in oncology. By analyzing vast datasets, including structured and unstructured information, AI is helping healthcare organizations accelerate research and improve patient outcomes. Data Curation Challenges in Precision Medicine Real-world data (RWD) is a key driver of precision medicine, but processing this data is fraught with challenges. According to Dr. C.K. Wang, Chief Medical Officer at COTA, Inc., EHRs provide unprecedented access to detailed patient information, enabling deeper insights into care patterns. However, much of this data resides in unstructured formats, such as clinicians’ notes, making it difficult to extract and analyze. “To transform this unstructured data into actionable insights, significant human expertise and resources are required,” Wang explained. While AI tools like COTA’s CAILIN, which uses advanced search capabilities, streamline this process, human involvement remains essential. Wang emphasized that even with the rapid advancements in AI, healthcare data curation requires expert oversight to ensure quality and reliability. “The adage ‘junk in, junk out’ applies here—without high-quality training data, AI cannot generate meaningful insights,” he noted. PHI and COTA: A Collaborative Approach to AI-Driven Curation To overcome these challenges, Precision Health Informatics (PHI), a subsidiary of Texas Oncology, partnered with COTA to enhance their data curation capabilities. The collaboration aims to integrate structured and unstructured data, including clinician notes and patient-reported outcomes, into a unified resource for precision medicine. PHI’s database, which represents 1.6 million patient journeys, provides a rich resource for hypothesis-driven studies and clinical trial enrichment. However, much of this data was siloed or unstructured, requiring advanced tools and expert intervention. Lori Brisbin, Chief Operating Officer at PHI, highlighted the importance of partnering with a data analytics leader. “COTA’s strong clinical knowledge in oncology allowed them to identify data gaps and recommend improvements,” she said. This partnership is yielding significant results, including a high data attrition rate of 87%—far surpassing the industry average of 50% for similar projects. The Role of AI in Cancer Care AI tools like CAILIN are helping PHI and COTA refine data curation processes by: Brisbin likened the role of AI to sorting images: “If you’re looking for German shepherds, AI will narrow the search but might include similar images, like wolves or huskies. Experts are still needed to validate and refine the results.” Building the Foundation for Better Outcomes The integration of high-quality RWD into analytics efforts is reshaping precision medicine. While clinical trial data offers valuable insights, it often lacks the variability seen in real-world scenarios. Adding RWD to these datasets helps expand the scope of research and ensure broader applicability. For instance, cancer care guidelines developed with RWD can account for diverse patient populations and treatment approaches. COTA’s work with PHI underscores the value of collaborative data curation, with AI streamlining processes and human experts ensuring accuracy. The Future of AI in Precision Medicine As healthcare organizations invest in data-driven innovation, AI will play an increasingly pivotal role in enabling precision medicine. However, challenges remain. Wang noted that gaps in EHR data, such as missing survival metrics, can undermine oncological outcomes research. Advances in interoperability and external data sources will be key to addressing these issues. “The foundation of our partnership is built on leveraging data insights to enhance care quality and improve operational efficiency,” Wang said. Through AI-powered tools and meaningful partnerships, precision medicine is poised to deliver transformative results, empowering providers to offer tailored treatments that improve patient outcomes 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 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, 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 23, 2024in AI Tools, Artificial Intelligence, Data

22Nov

Value-Based Care Technologies

Essential Technologies for Value-Based Care Success As healthcare providers increasingly adopt value-based care, they must invest in the right technologies and resources to succeed in this model, which incentivizes high-quality, cost-effective care. Value-Based Care Technologies tie reimbursement to care quality, making providers accountable for patient outcomes while providing resources to enhance care. As of 2021, nearly 60% of healthcare payments were already tied to value-based models, according to the Health Care Payment Learning and Action Network (HCP LAN). While partnerships can initiate value-based care, providers must invest in the right technology to fully achieve the intended outcomes. Health Information Exchange (HIE) A robust health information exchange (HIE) is fundamental to value-based care, as it enables providers and payers to access high-quality data seamlessly. HIE allows healthcare professionals to share patients’ medical information electronically across organizations, promoting care coordination by giving providers a comprehensive view of patient needs. For patients, HIE enables more informed involvement in their care by making their health data accessible across specialists, labs, and pharmacies. While joining an HIE may involve new technology investments and workflow adjustments, it ultimately enhances provider access to critical health data. Population Health Management Tools Population health management tools help providers assess health outcomes within groups rather than focusing on individuals alone. These tools aggregate and analyze data, allowing practices to identify high-risk patients and create targeted interventions. This not only enhances health outcomes but can also reduce costs by avoiding expensive treatments. Patient engagement tools, such as telehealth and remote patient monitoring, are essential in population health management, especially for monitoring high-risk patients when in-person care is not feasible. Digital surveys integrated within patient portals can provide insights into social determinants of health, adding a broader context to patient needs. Data Analytics Data analytics transform healthcare data into actionable insights across four types: descriptive, diagnostic, predictive, and prescriptive. Providers can use these analytics to reduce hospital readmissions, predict diseases, and identify chronic illnesses. Data integration and risk stratification capabilities are especially valuable in value-based care, enabling providers to track patient health outcomes effectively and prioritize high-risk cases. Artificial Intelligence & Machine Learning AI and machine learning support many data analytics functions, helping identify patient needs and easing administrative burdens. Given staffing shortages and burnout—reported by 63% of physicians in 2021, according to the American Medical Association (AMA)—AI can automate tasks like documentation, charting, and scheduling, allowing providers to focus more on patient care. Additionally, AI-driven automation in revenue cycle management tasks, such as billing and coding, can reduce the administrative workload associated with value-based care. Price Transparency Technology Price transparency empowers patients to seek cost-effective care, a core principle of value-based models. When providers comply with transparency regulations, patients can better understand their costs and make informed decisions. For providers, leveraging price transparency tools ensures compliance and facilitates partnerships with payers by enabling more effective negotiation, which supports the overall goals of value-based care. As healthcare continues shifting to value-based models, investing in these technologies is critical for providers aiming for long-term success. While these tools rdo equire substantial investment, they are essential for improving patient outcomes, optimizing care quality, and ensuring sustainability in value-based care. When evaluating and choosing healthcare technology tools, contact Tectonic for help. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

November 22, 2024in AI Tools, Artificial Intelligence, Data, Life Sciences Cloud, Salesforce, Salesforce Development Services, Salesforce Foundations, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud, Salesforce Implementation Services, Salesforce Optimization, Technology

07Nov

Data Analytics for Disease Management

Healthcare IT advancements, especially electronic health records (EHRs), have made it easier to gather and store data, which, in turn, fuels population health initiatives and improves patient outcomes. The Agency for Healthcare Research and Quality highlights that using health IT tools can significantly enhance chronic disease management by promoting efficient care delivery, information-sharing, and patient education. However, selecting and adopting the right analytics tools remains challenging. Here are five essential data analytics tools that healthcare providers can leverage for effective chronic disease management.

November 7, 2024in AI Tools, Data, Salesforce Data Cloud, Salesforce Health and Life Sciences Solutions, Salesforce Implementation Services, Salesforce Optimization, Salesforce Public Sector Solutions

31Oct

Cool and New AI Cool and New AI

AI is revolutionizing the way we work, offering a wide range of tools beyond ChatGPT that can enhance efficiency, creativity, and productivity. Whether you’re working with data, code, marketing, videos, images, AI bots, or research, here are the top AI tools that can transform your workflow. Cool and New AI. Don’t get spooked. There will be a cornucopia more in November. 🌟 Code 1️⃣ GlideTurn spreadsheets into powerful mobile apps without writing a single line of code. Glide makes it easy for non-developers to create professional apps with minimal effort. 2️⃣ BubbleA visual programming platform that allows users to build web applications without any coding knowledge. Ideal for entrepreneurs and startups looking to launch digital products quickly. 3️⃣ AskCodiThis AI coding assistant speeds up coding tasks, offers helpful suggestions, and simplifies debugging for developers, making it a must-have tool for coding professionals. 🌟 Data 1️⃣ BasedLabsA robust data analytics platform designed for scientists and engineers. BasedLabs offers complex data processing and model building with exceptional precision. 2️⃣ Coral AIPerfect for data-driven professionals, Coral AI provides efficient edge AI tools for processing large datasets and delivering insights with on-device intelligence, speeding up tasks. 3️⃣ JuliusAn AI-powered tool for market researchers and data analysts, Julius streamlines data processes and offers powerful insights into market trends. 🌟 Marketing 1️⃣ Sprout SocialThis all-in-one social media management platform leverages AI to help marketers optimize their social presence, engage with audiences, and track detailed analytics. 2️⃣ AdCreative AIEnhance your marketing campaigns with AI-generated ads that convert. AdCreative AI allows marketers to design high-impact, creative ads effortlessly. 3️⃣ Jasper AIA top tool for content creators, Jasper AI assists in crafting high-conversion marketing copy, blogs, and ad texts at scale, making it indispensable for digital marketing. 🌟 Video 1️⃣ SynthesiaCreate professional videos without the need for cameras or actors. Synthesia’s AI avatars enable you to produce multilingual videos, making it ideal for corporate and educational content. 2️⃣ HeygenThis AI tool simplifies video production by allowing users to create AI-generated videos, perfect for marketing campaigns or training materials. 3️⃣ Opus ClipOpus Clip transforms long-form video content into short, engaging clips optimized for social media, helping creators repurpose content easily. 🌟 Image 1️⃣ Getimg.AIAutomate image creation with Getimg.AI, which enhances your visual content by generating high-quality images in minutes, speeding up the design process. 2️⃣ PicsartA versatile image editing and design platform with AI tools that make creating stunning visuals effortless, making it ideal for social media, advertising, and creative projects. 3️⃣ Leonardo AIA powerful AI-driven tool for creators, Leonardo AI helps generate high-quality images, illustrations, and graphics, making it an essential tool for designers and artists. 🌟 AI Bot 1️⃣ LiveChatAn AI-powered live chat solution that integrates seamlessly into websites to provide real-time customer support, enhancing business communication. 2️⃣ LandbotThis tool helps create conversational experiences with AI-powered chatbots for customer support, sales, and lead generation, automating client interactions. 3️⃣ CustomGPTA customizable GPT-powered AI chatbot tailored for specific industries and businesses, perfect for providing personalized customer support. 🌟 Research 1️⃣ ChatPDFTurn PDFs into interactive documents with ChatPDF, allowing users to easily navigate and extract information using an AI-based assistant. 2️⃣ VidIQVidIQ provides AI-powered tools to optimize YouTube content for better engagement and visibility, making it invaluable for content creators. 3️⃣ SemrushAn advanced SEO platform powered by AI, Semrush gives marketers and researchers deep insights into online visibility, helping boost content performance. AI extends far beyond ChatGPT. This diverse range of tools is designed to make your work more efficient and productive, whether you’re coding, marketing, creating content, or conducting research. Embrace these AI tools to unlock new levels of creativity and efficiency. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

October 31, 2024in AI Tools, Data

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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

October 14, 2024in Generative AI

09Oct

Enterprise AI

Enterprise AI: Revolutionizing Business Operations for a Competitive Edge Enterprise AI refers to the suite of advanced artificial intelligence technologies—such as machine learning, natural language processing (NLP), robotics, and computer vision—that organizations use to transform operations, enhance efficiency, and gain a competitive advantage. These technologies demand high-quality data, skilled expertise, and adaptability to rapid advancements. Businesses increasingly adopt enterprise AI because of its ability to automate critical processes, reduce costs, optimize operations, and enable data-driven decision-making. According to McKinsey’s 2024 report, 72% of organizations now integrate AI into their operations, a significant increase from 50% just six years ago. However, implementing AI presents challenges, such as employee mistrust, data biases, lack of explainability, and managing AI’s fast evolution. Successful adoption requires aligning AI initiatives with organizational goals, fostering data trust, and building internal expertise. This guide provides a strategic roadmap for embracing enterprise AI, covering foundational concepts, advanced use cases, and ways to navigate common pitfalls. Why AI Matters in the Enterprise Enterprise AI is a transformative force, similar to how the internet revolutionized global businesses. By integrating AI into their operations, organizations can achieve: AI-driven applications are reshaping industries by enabling hyper-personalized customer experiences, optimizing supply chains, and automating repetitive tasks to free employees for higher-value contributions. The rapid pace of AI innovation requires leaders to consistently re-evaluate its alignment with their strategies while maintaining effective data management and staying informed on evolving tools and regulations. AI’s Transformational Impact on Business AI’s potential is as groundbreaking as electrification in the 20th century. Its immediate influence lies in automating tasks and augmenting human workflows. For example: Generative AI tools like ChatGPT and Copilot further accelerate adoption by automating creative and intellectual tasks. Key Benefits of Enterprise AI Challenges of Enterprise AI Despite its benefits, AI adoption comes with hurdles: Ethical concerns, such as workforce displacement and societal impacts, also demand proactive strategies. AI and Big Data: A Symbiotic Relationship AI thrives on large, high-quality datasets, while big data analytics leverage AI to extract deeper insights. The rise of cloud computing amplifies this synergy, enabling scalable, cost-effective AI deployments. Evolving AI Use Cases AI continues to redefine industries, turning complex tasks into routine operations: Future AI Trends to Watch Building the Future with Responsible AI As AI advances, organizations must prioritize responsible AI practices, balancing innovation with ethical considerations. Developing robust frameworks for transparency and governance is essential to maintaining trust and fostering sustainable growth. AI’s future offers vast opportunities for businesses willing to adapt and innovate. By aligning AI initiatives with strategic goals and investing in robust ecosystems, enterprises can unlock new efficiencies, drive innovation, and lead in their industries. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

October 9, 2024in AI Tools, Artificial Intelligence, Data, Enterprise, Generative AI

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