Data-Driven - gettectonic.com - Page 8
Where Will the Data Scientists Go
13Jul

Where Will the Data Scientists Go

What Is to Become of the Data Scientist Role? This question frequently arises among executives, particularly as they navigate the changing roles of data teams, such as those at DataRobot. Where Will the Data Scientists Go may not be as relevant as what new places can they go with AI? The short answer? While tools may evolve, the core of data science remains steadfast. As the field of data science continues to expand, the role of the data scientist becomes increasingly vital. The need will grow, even as the role changes. Trust in AI is dependant upon human oversight. Beyond the Hype of Consumer AI The surge in consumer AI products has raised concerns among data scientists about the implications for their careers. However, these technologies are built on data and generate vast amounts of new data, presenting numerous opportunities. The real transformative potential lies in enterprise-scale automation. Enterprise-Scale Automation: The Data Scientist’s Domain Enterprise-scale automation involves creating large-scale, reliable systems. Data scientists are crucial in this effort, as they bring expertise in data exploration and systematic inference. They are uniquely positioned to identify automation opportunities, design testing and monitoring strategies, and collaborate with cross-functional teams to bring AI solutions from concept to implementation. As automation grows, the role of the data scientist is essential in ensuring these systems function effectively and safely, particularly in environments without human oversight. New Skills for Data Scientists: The Guardians of AI Applications Data scientists will need to acquire new skills to manage automation at scale, including securing the systems they build. Generative AI introduces new risks, such as potential vulnerabilities to prompt injections or other security threats. Governance and ensuring positive business impacts will become increasingly important, requiring a data science mindset. Building Great Data Teams in the Age of AI The future of data science will not be about automation replacing data scientists but about the evolution of roles and skills. Data scientists need to focus on the core foundations of their discipline rather than the specific tools they use, as tools will continue to evolve. Teams must be built intentionally, encompassing a range of skills and personalities necessary for successful enterprise automation. Business Leaders: Navigating the AI Landscape Business leaders will need to excel in decision-making, understanding the problems they aim to solve, and selecting the appropriate tools and teams. They will also need to manage evolving regulations, particularly those related to the design and deployment of AI systems. Data Scientists: Precision Thinkers at the Forefront Contrary to the belief that AI could replace coding skills, the essence of data science lies in precise thinking and clear communication. Data scientists excel in translating business needs into data-driven decisions and AI applications, ensuring that solutions are not only technically sound but also aligned with business objectives. This skill set will be crucial in the era of AI, as data scientists will play a key role in optimizing workflows, designing AI safety nets, and protecting their organization’s brand and reputation. The Evolving Role of Data Science The demand for precise, data-literate thinkers will only grow with the rise of enterprise AI systems. Whether they are called data scientists or another name, professionals who delve deeply into data and provide critical insights will remain essential in navigating the complexities of modern technology and business landscapes. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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July 13, 2024in Generative AI
What is Salesforce Health Cloud
11Jul

Explore Salesforce Health Cloud

Empower Your Healthcare Team with Salesforce Health Cloud Equip your healthcare team with comprehensive 360-degree views that help connect and engage every patient, member, employee, and partner. Explore Salesforce Health Cloud Explore Health Cloud Understanding the capabilities of this platform is the first step to transforming your organization’s patient management. Let’s explore what Health Cloud offers to various types of healthcare organizations. Introducing Salesforce Health Cloud: A CRM Solution for Patient Management Over 600 companies, including industry leaders like Lilly, Pacific Clinics, United Healthcare, Progyny, Stanley Healthcare, and Humana, trust Salesforce Health Cloud for their patient management needs. As the healthcare industry rapidly evolves, effective patient information management is essential. This insight looks into Salesforce Health Cloud’s capabilities, features, integration options, and benefits, including its security architecture. What is Health Cloud? Salesforce Health Cloud is a cloud-based technology designed specifically for the healthcare industry. It centralizes patient information, giving healthcare professionals a complete view of patient records, enabling more effective treatments and better patient care. Key Capabilities of Salesforce Health Cloud Salesforce Health Cloud is a robust platform offering key capabilities such as: Salesforce in the Healthcare Industry Salesforce is increasingly popular among healthcare organizations for several reasons: Salesforce Health Platform Features Salesforce Health Cloud offers three main sets of features: Salesforce Health Cloud Architecture The architecture of Salesforce Health Cloud includes: Salesforce Health Cloud Security Salesforce Health Cloud is designed to securely manage healthcare data, featuring: Revolutionizing Healthcare Delivery with Salesforce Health Cloud Salesforce Health Cloud is designed for healthcare organizations to automate processes and provide personalized patient care. Since its launch in 2016, Health Cloud has evolved to address the complexities of the healthcare industry, including the introduction of Customer 360 for Health, an AI-driven healthcare solution. Why Choose Salesforce Health Cloud? Salesforce Health Cloud connects healthcare teams to ensure that patients receive the right care, supported by multi-layered security to protect sensitive patient data. It integrates clinical and non-clinical patient data, streamlining workflows and enhancing patient satisfaction. Top Features of Salesforce Health Cloud Key features include Patient 360, Care Plans, Care Coordination, Health Timeline, and Einstein Analytics for Healthcare, among others. Salesforce has also introduced AI-powered innovations under the Patient 360 for Health initiative, enhancing patient care and operational efficiency. Integration with MuleSoft Salesforce Health Cloud’s integration with MuleSoft allows organizations to connect with existing healthcare systems, ensuring accurate and up-to-date patient information, unlocking the full potential of their data, and improving decision-making. Conclusion Salesforce Health Cloud is more than just a platform—it’s a comprehensive solution for managing doctor-patient interactions, recordkeeping, and delivering personalized care. By leveraging Health Cloud, healthcare organizations can transform patient experiences, streamline processes, and ensure data security and compliance, positioning themselves for a brighter future in healthcare. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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July 11, 2024in Mulesoft, Salesforce Health Cloud, Salesforce Marketing Cloud, Salesforce Shield
Content Marketing Lessons
11Jul

Content Marketing Lessons

Content Marketing Lessons: Beyond Creativity Content marketing requires more than just creativity; it demands a strategic approach rooted in collaboration, consistency, and data-driven insights. Salesforce, a leader in customer relationship management, exemplifies how to revolutionize content marketing to achieve meaningful business outcomes. Centralize Content Strategy for Consistency One of the key takeaways from Salesforce’s content marketing evolution is the power of centralization. Jessica Bergmann, Vice President of Content and Customer Marketing at Salesforce, led a shift that elevated content marketing to a strategic function within the company. By centralizing content operations, Salesforce ensured consistency in voice, tone, and messaging across all channels. This centralization wasn’t about controlling content but about creating a unified narrative that resonates with customers at every touchpoint. Empower Teams with Strategic Roles To bridge the gap between audience needs and Salesforce’s business objectives, Jessica introduced two pivotal roles: content strategists and editorial leads. These roles are embedded within brand, persona, and industry teams, ensuring content aligns with business goals and is tailored to the specific needs of different customer segments. This approach underscores the importance of empowering teams with the right expertise and tools to deliver impactful content. Leverage Technology for Seamless Operations Salesforce’s centralized content operations team plays a crucial role in managing the company’s content ecosystem. By utilizing a central content operations tool, the team oversees real-time editorial calendars, workflows, and a global measurement dashboard. This technological foundation allows Salesforce to streamline content production and maintain a cohesive strategy across its global teams. For any organization aiming to scale content marketing efforts, investing in the right technology is essential. Integrate Cross-Functional Collaboration A key to Salesforce’s success is its emphasis on cross-functional collaboration. By working closely with product marketing, creative, and campaigns teams, the content marketing function at Salesforce is integral to the broader marketing strategy. This integrated approach ensures content is not created in isolation but as part of a larger, cohesive effort to educate customers and drive business growth. Measure What Matters In content marketing, measurement is everything. Salesforce’s content performance dashboard provides visibility into how content is performing across the organization. By tracking metrics like traffic, engagement, and progression, Salesforce ensures its content efforts align with business objectives. This focus on actionable metrics helps teams make informed decisions about optimizing, promoting, or cutting content. Prioritize Strategic Initiatives Salesforce’s ability to manage multiple high-impact projects, such as Dreamforce, Salesforce+, and the #TeamEarth campaign, demonstrates its strategic prioritization process. Using the V2MOM framework (vision, values, methods, obstacles, and measures), Salesforce aligns its content marketing efforts with the company’s broader goals. This structured approach allows Salesforce to allocate resources effectively and ensure content initiatives deliver maximum impact. Focus on Audience-First Content At the heart of Salesforce’s content marketing strategy is an unwavering focus on the audience. By adopting an “audience-first” mindset, Salesforce’s content teams strive to create content that addresses customer needs while earning the right to market to them. This approach is crucial in today’s content-saturated environment, where businesses must offer genuine value to stand out. Develop Long-Range Content Plans Content marketing isn’t just about quick wins; it’s about building long-term relationships with your audience. Salesforce’s commitment to long-range content planning, integrating thought leadership, search, and editorial efforts, ensures the company remains top-of-mind for customers throughout their buying journey. This long-term focus is key to nurturing leads and converting them into loyal customers. Invest in Content Marketing Talent Hiring the right talent is vital for a successful content marketing strategy. Salesforce’s experience highlights the importance of bringing in content marketing experts who can execute the strategy effectively. These experts bring fresh ideas and ensure the content marketing function is respected and prioritized within the organization. Show Early Wins to Build Momentum Finally, one of the most important lessons from Salesforce’s content marketing journey is the value of showcasing early wins. By focusing on quick victories that demonstrate the impact of content marketing, Jessica and her team built momentum and secured buy-in from senior leadership. This approach is essential for any content marketing team seeking to establish itself as a strategic function within the organization. Conclusion Salesforce’s content marketing transformation offers valuable insights for businesses at any stage of their content marketing journey. By centralizing content strategy, empowering teams with strategic roles, leveraging technology, and focusing on audience-first content, Salesforce has created a content marketing engine that drives real business results. For organizations looking to elevate their content marketing efforts, these lessons provide a clear roadmap to 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

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July 11, 2024in Salesforce
Recurring Donations in NPSP
19Jun

Recurring Donations in NPSP

This insight will resonate with those familiar with Salesforce, the Nonprofit Success Pack (NPSP), its various iterations, and other related Salesforce automation features. Recurring Donations in NPSP create a special challenge, Tectonic has found. For those less engaged in these topics, there are still valuable ideas to discuss. For over a decade, there has been an ongoing challenge with handling donations processed through multiple transactions. These Multi-Installment Donation Scenarios (MIDS) include: Tracking donations where the commitment and transaction occur simultaneously is straightforward. For example, credit card donations through online forms require creating a donor record (if it doesn’t already exist) and a gift record. However, when there is a time lag between commitment and transaction, similar processes can be used. The challenge arises when commitments result in multiple checks or credit card transactions over time, breaking standard rules. This has puzzled fundraisers, operations personnel, accountants, and database admins for years, leading to various approaches. Each role may prefer different methods, adding complexity. Even Salesforce has changed its approach to MIDS, resulting in multiple models/modules in circulation. If you feel overwhelmed by MIDS, you’re not alone. MIDS are one of many complex scenarios to record in a donor database. However, other scenarios are not covered in this insight, such as: There are several options for modeling MIDS in Salesforce: Donation With Payments: Commonly used for grants, NPSP automates payment scheduling according to interval rules. This is recommended for organizations using accrual accounting. Legacy Recurring Donations: Allows setting donation schedule rules, with Salesforce automatically creating donations accordingly. Suitable for projection reports and updating each donation when the transaction occurs. Enhanced Recurring Donations: Introduced by Salesforce in 2020, this module solves issues like skipping months or changing future payment amounts by creating one installment ahead. When the installment is paid, the next one is created according to the schedule/rules. Custom “Pledge” Object: Useful for grouping donations according to pledges, without needing automation for scheduling installments. Ideal for tracking multiple data points about each pledge. Our Opinion: Tectonic has a strong preference for Legacy Recurring Donations over Enhanced Recurring Donations, although it’s not a significant issue to use either. Donations with Payments are problematic and should be avoided. Here’s why: Problems with Donations with Payments: The official documentation suggests this model for accrual accounting, but this recommendation is questionable. Many readers lack a foundational understanding of accounting models. Furthermore, dividing a donation into parts (payments) complicates reporting and rollup fields. Recurring Donations Benefits: The Recurring Donations (RD) model is smart and elegant. NPSP’s automations for both Legacy and Enhanced RD modules are strategic and reliable. Using the RD model for more than just traditional recurring donations (like monthly donations) can be beneficial. Rebranding “Recurring Donation” to “Pledge Fulfillment Schedule” can naturally incorporate various MIDS types, such as grants and pledges. Top Tips for Configuring Legacy Recurring Donations: One Odd Thing: In moves management, if a donor switches from a lump sum to installment gifts, create an RD and auto-create installment donations. Delete the first donation in the series and connect the Moves Management donation to the Recurring Donation record. Legacy Recurring Donations are a preferred option for modeling MIDS. This data model is suitable for all scenarios. With proper setup, it can become a powerful tool for a data-driven fundraising department or a streamlined option for tricky use cases. Two principles of good design—“like goes with like” and “measure twice, cut once”—explain why transaction-oriented information should stay on the Donation record, and pledge-oriented information on the Recurring Donation record. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 19, 2024in Salesforce Development Services, Salesforce for Nonprofits, Salesforce Implementation Services, Salesforce Nonprofit Cloud, Salesforce Public Sector Solutions
Time to Reset AI Expectations
17Jun

Time to Reset AI Expectations

AI is often portrayed as either the ultimate solution to all our problems or a looming threat that must be handled with extreme caution. These are the two polar extremes of a debate that surrounds any transformative technology, and the reality likely lies somewhere in the middle. Time to Reset AI Expectations. At the recent 2024 MIT Sloan CIO Symposium, AI was the central theme, with numerous keynotes and panels devoted to the topic. The event also featured informal roundtable discussions that touched on legal risks in AI deployment, AI as a driver for productivity, and the evolving role of humans in AI-augmented workplaces. Time to Reset AI Expectations A standout moment was the closing keynote, “What Works and Doesn’t Work with AI,” delivered by MIT professor emeritus Rodney Brooks. Brooks, who directed the MIT AI Lab from 1997 to 2003 and was the founding director of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) until 2007, offered insights to distinguish between the hype and reality of AI. A seasoned robotics entrepreneur, Brooks founded several companies, including iRobot, Rethink Robotics, and Robust.AI. In his keynote, Brooks introduced his “Three Laws of Artificial Intelligence,” which serve to ground our understanding of AI: Brooks reminded the audience that AI has been a formal academic discipline since the 1950s when its pioneers believed that nearly every aspect of human intelligence could, in principle, be encoded as software and executed by increasingly powerful computers. Decades of Efforts In the 1980s, leading AI researchers were confident that within a generation, AI systems capable of human-like cognitive abilities could be developed. They secured government funding to pursue this vision. However, these projects underestimated the complexities of replicating human intelligence, particularly cognitive functions like language, thinking, and reasoning, in software. After years of unmet expectations, these ambitious projects were largely abandoned, leading to the so-called AI winter—a period of reduced interest and funding in AI. AI experienced a resurgence in the 1990s with a shift towards a statistical approach that analyzed patterns in vast amounts of data using sophisticated algorithms and high-performance supercomputers. This data-driven approach yielded results that approximated intelligence and scaled far better than the earlier programming-based models. Over the next few decades, AI achieved significant milestones, including Deep Blue’s 1997 victory over chess grandmaster Garry Kasparov, Watson’s 2011 win in the Jeopardy! Challenge, and AlphaGo’s 2016 triumph over Lee Sedol, one of the world’s top Go players. AI also made strides in autonomous vehicles, as evidenced by the successful completion of the 2007 DARPA Grand Challenge and the 2012 DARPA Robotics Challenge for disaster response robots. Is It Different Now? Following these achievements, AI seemed poised to “change everything,” according to Brooks. But is it really? Since 2017, Brooks has published an annual Predictions Scorecard, comparing predictions for future milestones in robotics, AI, machine learning, self-driving cars, and human space travel. “I made my predictions because, then as now, I saw an immense amount of hype surrounding these topics,” Brooks said. He observed that the media and public were making premature conclusions about the impact of AI on jobs, road safety, space exploration, and more. “My predictions, complete with timelines, were meant to temper expectations and inject some reality into what I saw as irrational exuberance.” So why have so many AI predictions missed the mark? Brooks, who has a penchant for lists, attributes this to what he calls the Seven Deadly Sins of Predicting the Future of AI. In a 2017 essay, he described these “sins”: The takeaway? While AI has made remarkable progress, there’s still a long journey ahead. It’s Time to Reset AI Expectations. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 17, 2024in Salesforce
The AI Tech Stack
15Jun

The AI Tech Stack

The AI tech stack integrates various tools, libraries, and solutions to enable applications with generative AI capabilities, such as image and text generation. Key components of the AI stack include programming languages, model providers, large language model (LLM) frameworks, vector databases, operational databases, monitoring and evaluation tools, and deployment solutions. The infrastructure of the AI tech stack leverages both parametric knowledge from foundational models and non-parametric knowledge from information sources (PDFs, databases, search engines) to perform generative AI functions like text and image generation, text summarization, and LLM-powered chat interfaces. This insight explores each component of the AI tech stack, aiming to provide a comprehensive understanding of the current state of generative AI applications, including the tools and technologies used to develop them. For those with a technical background, this Tectonic insight includes familiar tools and introduces new players within the AI ecosystem. For those without a technical background, the article serves as a guide to understanding the AI stack, emphasizing its importance across business roles and departments. Content Overview: The AI layer is a vital part of the modern application tech stack, integrating across various layers to enhance software applications. This layer brings intelligence to the stack through capabilities such as data visualization, generative AI (image and text generation), predictive analysis (behavior and trends), and process automation and optimization. In the backend layer, responsible for orchestrating user requests, the AI layer introduces techniques like semantic routing, which allocates user requests based on the meaning and intent of the tasks, leveraging LLMs. As the AI layer becomes more effective, it also reduces the roles and responsibilities of the application and data layers, potentially blurring the boundaries between them. Nevertheless, the AI stack is becoming a cornerstone of software development. Components of the AI Stack in the Generative AI Landscape: The emphasis on model providers over specific models highlights the importance of a provider’s ability to update models, offer support, and engage with the community, making switching models as simple as changing a name in the code. Programming Languages Programming languages are crucial in shaping the AI stack, influencing the selection of other components and ultimately determining the development of AI applications. The choice of a programming language is especially important for modern AI applications, where security, latency, and maintainability are critical factors. Among the options for AI application development, Python, JavaScript, and TypeScript (a superset of JavaScript) are the primary choices. Python holds a significant market share among data scientists, machine learning, and AI engineers, largely due to its extensive library support, including TensorFlow, PyTorch, and Scikit-learn. Python’s readable syntax and flexibility, accommodating both simple scripts and full applications with an object-oriented approach, contribute to its popularity. According to the February 2024 update on the PYPL (PopularitY of Programming Language) Index, Python leads with a 28.11% share, reflecting a growth trend of +0.6% over the previous year. The PYPL Index tracks the popularity of programming languages based on the frequency of tutorial searches on Google, indicating broader usage and interest. JavaScript, with an 8.57% share in the PYPL Index, has traditionally dominated web application development and is now increasingly used in the AI domain. This is facilitated by libraries and frameworks that integrate AI functionalities into web environments, enabling web developers to leverage AI infrastructure directly. Notable AI frameworks like LlamaIndex and LangChain offer implementations in both Python and JavaScript/TypeScript. The GitHub 2023 State of Open Source report highlights JavaScript’s dominance among developers, with Python showing a 22.5% year-over-year increase in usage on the platform. This growth reflects Python’s versatility in applications ranging from web development to data-driven systems and machine learning. The programming language component of the AI stack is more established and predictable compared to other components, with Python and JavaScript/TypeScript solidifying their positions among software engineers, web developers, data scientists, and AI engineers. Model Providers Model providers are a pivotal component of the AI stack, offering access to powerful large language models (LLMs) and other AI models. These providers, ranging from small organizations to large corporations, make available various types of models, including embedding models, fine-tuned models, and foundational models, for integration into generative AI applications. The AI landscape features a wide array of models that support capabilities like predictive analytics, image generation, and text completion. These models can be broadly classified into closed-source and open-source categories. Closed-Source Models have privatized internal configurations, architectures, and algorithms, which are not shared with the model consumers. Key information about the training processes and data is withheld, limiting the extent to which users can modify the model’s behavior. Access to these models is typically provided through an API or a web interface. Examples of closed-source models include: Open-Source Models offer a different approach, with varying levels of openness regarding their internal architecture, training data, weights, and parameters. These models foster collaboration and transparency in the AI community. The levels of open-source models include: Open-source models democratize access to advanced AI technology, reducing the entry barriers for developers to experiment with niche use cases. Examples of open-source models include: Open-Source vs. Closed-Source Large Language Models (LLMs) AI engineers and machine learning practitioners often face the critical decision of whether to use open- or closed-source LLMs in their AI stacks. This choice significantly impacts the development process, scalability, ethical considerations, and the application’s utility and commercial flexibility. Key Considerations for Selecting LLMs and Their Providers Resource Availability The decision between open- and closed-source models often hinges on the availability of compute resources and team expertise. Closed-source model providers simplify the development, training, and management of LLMs, albeit at the cost of potentially using consumer data for training or relinquishing control of private data access. Utilizing closed-source models allows teams to focus on other aspects of the stack, such as user interface design and data integrity. In contrast, open-source models offer more control and privacy but require significant resources to fine-tune, maintain, and deploy. Project Requirements The scale and technical requirements of a project are crucial in deciding whether to use open-

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June 15, 2024in Generative AI
Zero ETL
13Jun

Zero ETL

What is Zero-ETL? Zero-ETL represents a transformative approach to data integration and analytics by bypassing the traditional ETL (Extract, Transform, Load) pipeline. Unlike conventional ETL processes, which involve extracting data from various sources, transforming it to fit specific formats, and then loading it into a data repository, Zero-ETL eliminates these steps. Instead, it enables direct querying and analysis of data from its original source, facilitating real-time insights without the need for intermediate data storage or extensive preprocessing. This innovative method simplifies data management, reducing latency and operational costs while enhancing the efficiency of data pipelines. As the demand for real-time analytics and the volume of data continue to grow, ZETL offers a more agile and effective solution for modern data needs. Challenges Addressed by Zero-ETL Benefits of ZETL Use Cases for ZETL In Summary ZETL transforms data management by directly querying and leveraging data in its original format, addressing many limitations of traditional ETL processes. It enhances data quality, streamlines analytics, and boosts productivity, making it a compelling choice for modern organizations facing increasing data complexity and volume. Embracing Zero-ETL can lead to more efficient data processes and faster, more actionable insights, positioning businesses for success in a data-driven world. Components of Zero-ETL ZETL involves various components and services tailored to specific analytics needs and resources: Advantages and Disadvantages of ZETL Comparison: Z-ETL vs. Traditional ETL Feature Zero-ETL Traditional ETL Data Virtualization Seamless data duplication through virtualization May face challenges with data virtualization due to discrete stages Data Quality Monitoring Automated approach may lead to quality issues Better monitoring due to discrete ETL stages Data Type Diversity Supports diverse data types with cloud-based data lakes Requires additional engineering for diverse data types Real-Time Deployment Near real-time analysis with minimal latency Batch processing limits real-time capabilities Cost and Maintenance More cost-effective with fewer components More expensive due to higher computational and engineering needs Scale Scales faster and more economically Scaling can be slow and costly Data Movement Minimal or no data movement required Requires data movement to the loading stage Comparison: Zero-ETL vs. Other Data Integration Techniques Top Zero-ETL Tools Conclusion Transitioning to Zero-ETL represents a significant advancement in data engineering. While it offers increased speed, enhanced security, and scalability, it also introduces new challenges, such as the need for updated skills and cloud dependency. Zero-ETL addresses the limitations of traditional ETL and provides a more agile, cost-effective, and efficient solution for modern data needs, reshaping the landscape of data management and analytics. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 13, 2024in Snowflake
Cost of Free Analytics
20May

Cost of Free Analytics

Is It Time to Upgrade Your Web Analytics? For years, you might have relied on free web analytics tools, thinking they do the job or resigning yourself to an “it is what it is” mindset. But what if there’s a better way to truly understand your customers and supercharge your marketing efforts? Upgrading to a premium analytics solution could be a game changer for your brand and your peace of mind. What is the Cost of Free Analytics? It’s time to move beyond those so-called free tools (which aren’t really free when you factor in hidden costs) and invest in a robust analytics solution. The right tool can transform your approach—imagine saying goodbye to the hassle of patching together data or juggling disparate reports. With clear, comprehensive insights into customer interactions, you’ll make smarter, data-driven decisions across your business. The Pitfalls of Free Analytics Tools While free analytics tools might seem like a cost-effective choice, they come with significant drawbacks. They often offer limited functionality, delayed or incomplete data, siloed reporting, and compliance risks. Relying on these tools can lead to guesswork and errors, resulting in costly mistakes. Limited Functionality Free analytics tools barely skim the surface of what’s possible with data collection and reporting. They depend on third-party cookies and route your data through their servers, providing you with only partial insights. Essential features like persistent digital identity tracking, profile building, journey mapping, predictive analytics, and machine learning capabilities are typically missing. In contrast, premium tools leverage advanced algorithms and machine learning to unearth valuable data patterns and insights. For instance, a premium tool might reveal that users who view a product page after watching a related video are significantly more likely to make a purchase—information that could greatly influence your marketing strategy. Subpar Data Quality Free tools often lag in delivering real-time data, giving you an outdated snapshot of customer interactions. Timely data is crucial for agile marketing—without it, you risk missing out on opportunities and wasting ad spend. Stale data leads to missed chances and inefficiencies. Reporting Silos and Inaccuracies Free analytics solutions typically don’t integrate data across your organization, resulting in fragmented and siloed information. Different teams may have access to unaligned reports, often requiring multiple tools to piece together insights. This lack of a unified source of truth makes it impossible to get a comprehensive view of customer interactions across various touchpoints. Organizational Inefficiencies Managing free tools can be resource-intensive. They often require extensive tagging and manual upkeep, leading to increased costs and the risk of inaccurate data due to broken or altered tags. This inefficiency can impact long-term business decisions and strategic planning. Compliance Risks Free tools often involve sending your data to external servers, raising concerns about data loss, latency, and compliance with privacy regulations. These tools process your digital engagement and Personally Identifiable Information (PII) on their servers, complicating the task of maintaining regulatory standards and ensuring data security. The True Cost of Free Tools The reality is, “free” isn’t really free. The hidden costs and risks associated with free analytics tools can outweigh their benefits. While premium analytics solutions may seem expensive at first glance, they offer superior insights and performance improvements that provide a competitive edge. With accurate, real-time data and advanced features, investing in a premium tool is a decision that pays off. Remember, the old adage “nothing’s free” rings true—don’t jeopardize your brand’s success with subpar tools that end up costing more in the long run! Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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May 20, 2024in Salesforce
Salesforce Success Story
19May

Case Study: Salesforce Health Services Modernization

Modernizing Public Health using Salesforce Client is the largest tribally-operated health care system in the United States, with almost 160 full-time providers and over 2,200 health services employees. Committed to improving patient access, this Tribal Nation has invested substantially in technology, building new facilities and expanding existing locations. The primary hospital and nine health centers are dedicated to providing world-class patient care. Salesforce Health Services Modernization “The Gadugi Portal and Salesforce technology is helping us get to our citizens quicker by streamlining the application process, and enabling us to have updated records that are online and easily accessible to our citizens to truly help them recover in their greatest time of need” Client Implemented – Case Study: Salesforce Health Services Modernization The Problem: The Solution: The Results: Ready to explore a Salesforce implementation with Health Cloud, MuleSoft, and Experience Cloud for your government or public sector entity? Contact Tectonic today. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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May 19, 2024in Mulesoft, Public Sector Solutions, Salesforce, Salesforce Experience Cloud, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud, Salesforce Implementation Services, Salesforce Marketing Cloud, Salesforce Success Stories
Education Cloud AI Innovations
14May

Education Cloud AI Innovations

Salesforce AI Innovations: Empowering Student Success and Faculty Efficiency Salesforce is introducing new Education Cloud AI Innovations, AI-powered tools designed to streamline the educational journey, enabling students to chart clear paths to graduation, translate their coursework into resume-ready skills, and connect with mentors who can guide them toward their career goals. Enhancing Faculty and Staff Efficiency with AI New generative AI capabilities are set to automate time-consuming tasks for faculty and staff, allowing them to focus on what matters most—driving student success. Personalizing Student Experiences with AI Institutions like the University of Nevada Las Vegas and Texas Tech are leveraging Salesforce for Education to create personalized student experiences and improve staff efficiency through AI-driven solutions. Salesforce Introduces AI-Powered Student Success Tools for Education Cloud Today, Salesforce unveiled cutting-edge AI tools for Education Cloud, including Intelligent Degree Planning and Skills Generator. These innovations are designed to help institutions craft personalized graduation pathways, translate coursework into tangible skills, and facilitate impactful mentorship programs. Additionally, Salesforce introduced Data Cloud for Education and Einstein Copilot Recruitment & Admissions Actions. These tools will enable institutions to automate routine tasks, enhance recruitment and enrollment processes, and bolster student support. Why It Matters Education professionals face the highest burnout rates across industries, and students are feeling the impact—only 11% of college students believe they are workforce-ready. As the education sector approaches an enrollment cliff, confidence in the value of a college degree is declining, and educators are leaving the profession in significant numbers. Schools must find ways to reduce staff workload while improving student experiences and outcomes. Explore Education Cloud Elevate the educational experience with the #1 AI CRM for learner and institution success. DIVE IN AI Innovation for Lifelong Student Success The new AI capabilities for Education Cloud, built on Salesforce’s Einstein 1 Platform, will help higher education and K-12 institutions unlock the power of their data to deliver trusted AI solutions. These innovations are designed to improve staff efficiency while enhancing student experiences and learning outcomes. Key features include: AI in Action for Faculty and Staff Salesforce is delivering new AI and data tools to automate tasks related to recruitment, enrollment, and student experience management. New features include: With these industry-specific AI and data tools, Education Cloud is poised to help K-12 and higher education institutions offer more individualized support to every student while increasing operational efficiency and reducing staff burnout. The Salesforce Perspective “Every institution wants to provide the best possible experiences for their students and staff. With industry-specific AI and data tools, Education Cloud will help K-12 and higher ed institutions offer more personalized support to every student while increasing efficiency and helping to reduce staff burnout. This will free educators and staff to focus on improving student outcomes, such as career readiness, well-being, and graduation rates.”— Bala Subramanian, VP & GM of Education Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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May 14, 2024in Einstein 1 Platform, Generative AI, Salesforce Education Cloud, Salesforce Education Solutions, Salesforce Einstein, Salesforce Managed Services, Salesforce Optimization, Salesforce Shield
Salesforce-Powered Nonprofit Events
08May

Salesforce-Powered Nonprofit Events

Salesforce has cemented its position as the #1 CRM, becoming an essential tool for businesses improving their marketing, sales, and support processes. But there’s another powerful feature you should explore: managing events directly within Salesforce. Salesforce’s versatility allows you to organize a successful event of any size, whether it’s online, offline, or a hybrid meeting. However, like any business endeavor, a well-thought-out strategy is crucial to ensure your event planning turns into a major success. In this insight, you’ll learn how events contribute to a business’s lifecycle, how to manage them effectively, and the essential tools you’ll need for successful event management in Salesforce. The Role of Events in Nonprofits If your company hasn’t yet ventured into event organization, it’s time to reconsider. Events are a great way to fuel engagement, foster data-driven decisions, and build a thriving community. On a larger scale, event planning offers a range of business benefits, including: Common Event Management Challenges Even with the best intentions, event planning comes with challenges that can derail your efforts if not addressed. Let’s review some common challenges and how Salesforce can help: How Salesforce Elevates Event Management Salesforce brings significant advantages to event planning, offering: Your Salesforce Event Planning Checklist To ensure your event management with Salesforce is effective, follow this simple checklist: Our Thoughts Building a robust event management process with Salesforce is not only a logical choice but a strategic one. It ensures smooth event execution, supports business growth, and boosts your brand’s equity. By leveraging the right tools, such as Salesforce, your event planning process becomes more efficient, data-driven, and ultimately more successful. For expert support in managing Salesforce-powered events, consider reaching out to a partner like Tectonic to guide you through the process. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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May 8, 2024in Salesforce Einstein, Salesforce Experience Cloud, Salesforce for Nonprofits, Salesforce Implementation Services, Salesforce Nonprofit Cloud
Harnessing Sales Data
12Apr

Harnessing Sales Data

Harnessing Sales Data for Better Insights and Rapid Deal Closure Sales data is a critical asset for gaining insights and closing deals swiftly. With the ever-expanding data footprint, including customer response rates, leads in the pipeline, and quota attainment, tracking these metrics is essential. Ignoring them can be detrimental, as nearly all sales professionals recognize the importance of real-time data in meeting customer expectations, according to the Trends in Data and Analytics for Sales Report. However, concerns about data setup for generative AI and data accuracy persist. Sixty-three percent of sales professionals report that their company’s data isn’t optimized for AI, and only 42% are confident in their data’s accuracy. The demand for sales data has become a focal point for sales leaders and representatives, who increasingly rely on data to enhance customer engagement and productivity through trusted sources and AI integration. What is Sales Data? Sales data encompasses two main categories: external data, which includes information about prospects such as demographics, interests, behavior, and engagement; and internal sales data, including deal attributes and sales performance metrics. This data helps inform deal actions, assess progress toward sales targets or key performance indicators (KPIs), and supports tools like AI to enhance efficiency. Why is Sales Data Important? Sales data provides a measurable framework for all sales activities, enabling the setting of performance benchmarks and targets. It helps identify risks in the pipeline and highlights opportunities for upselling or fostering competition among sales reps. The data is also crucial for leveraging generative AI, which can automate tasks such as email drafting and sales pitch creation, provided the data is accurate and well-organized. Types of Sales Data Collecting and Utilizing Sales Data To effectively collect and utilize sales data, invest in a CRM system that serves as a centralized data repository with analytics capabilities. Automate data collection within the CRM, integrate data from other tools, and prioritize the security of sensitive information. Visualizing data through dashboards can help track progress toward business goals and make informed decisions. Real-Life Application: A Case Study A global consulting firm used sales data to enhance win rates and accelerate deal velocity. By integrating CRM analytics with data from various sources, the firm identified key deal attributes impacting success and adjusted strategies accordingly. The use of AI-driven “opportunity scores” further enabled the firm to monitor deal health and optimize resource allocation. Essential Tools for Harnessing Sales Data Turning Sales Data into Actionable Insights Regularly reviewing CRM-generated insights and adjusting strategies based on these insights is crucial for closing more deals and delivering consistent value to customers. By focusing on data-driven decision-making, sales teams can stay competitive and meet evolving customer needs. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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April 12, 2024in Generative AI
What is Einstein Used for in Salesforce?
14Mar

What is Einstein Used for in Salesforce?

Salesforce Einstein is an AI-powered platform that can be used in various ways to enhance customer experiences and streamline business operations: SalesSalesforce Einstein can help sales teams better understand customers, improve conversion rates, and close deals more quickly. For instance, it can generate sales call summaries, draft emails using customer data, and provide real-time predictions. Customer ServiceEinstein helps customer service agents resolve cases faster and provide customers with relevant information during interactions. MarketingSalesforce Einstein enables marketers to create personalized experiences and send the right content to the right customer at the right time. ITSalesforce empowers IT teams to embed intelligence across the business and create smarter apps for customers and employees. CommerceSalesforce assists retailers by recommending the best products to each customer. Salesforce also includes features to protect data privacy and security, such as the Tectonic GPT Trust Layer, which provides AI bias detection, data security, and regulatory compliance. Salesforce Einstein is the first all-inclusive AI for CRM. It’s an integrated set of AI technologies that makes the Customer Success Platform smarter and brings AI to Salesforce users everywhere. Salesforce is the only comprehensive AI for CRM. It is: Tectonic and Salesforce allow businesses to become AI-first, providing the ability to anticipate customer needs, improve service efficiency, and enable smarter, data-driven decision-making. Sales teams can anticipate next opportunities and exceed customer needs,Service teams can proactively resolve issues before they occur,Marketing teams can create predictive journeys and personalize experiences like never before,IT teams can embed intelligence everywhere and create smarter apps. AI that works for your business.Drive business productivity and personalization with predictive AI, generative AI, and agents across the Customer 360 platform. Create and deploy assistive AI experiences natively in Salesforce, allowing your customers and employees to converse directly with Agentforce to solve issues faster and work smarter. Empower service reps, agents, marketers, and others with AI tools safely grounded in your customer data to make every customer experience more impactful. What is Salesforce Einstein?As of 2024, this groundbreaking AI-based product remains a leader in the CRM industry since its release in 2016. It combines a range of AI technologies, including advanced machine learning, natural language processing (NLP), predictive analytics, and image recognition, enabling businesses to improve productivity and sustain growth. Salesforce AI BenefitsThe most significant benefits of AI are the time and efficiency gains it offers to business processes. By automating tasks, employees can focus on more strategic work. Additionally, automating repetitive tasks reduces errors and enhances operational efficiency. Saleesforce provides robust reporting features that generate valuable insights to support decision-making, helping businesses understand customer needs and identify opportunities. From a customer perspective, Salesforce ensures more meaningful and personalized experiences through advanced NLP capabilities and machine learning to better understand customer behavior. Salesforce AI FeaturesSalesforce is a feature-rich platform that leverages AI’s capabilities in Natural Language Processing, Machine Learning, and image processing. Some of the key features include: Salesforce PricingCosts depend on the required features and the size of the business. Pricing starts at $50 per user per month, with potential increases based on the specific capabilities needed. Salesforce Tectonic ChallengesAlthough Salesforce Tectonic offers numerous benefits, companies may face challenges during integration, such as aligning it with existing systems and ensuring proper training for employees to maximize its use. How to Prepare for Salesforce Tectonic IntegrationUsing an implementation partner like Tectonic can help ensure seamless integration. A partner will assess your current Salesforce setup, recommend the right features, and guide you through the integration process. ConclusionSalesforce is a cutting-edge platform that empowers businesses to transform operations with comprehensive AI capabilities. It provides tailored solutions for sales, service, marketing, and commerce teams, enabling better customer interactions, data-driven decision-making, and increased productivity. With the right implementation partner like Tectonic, businesses can seamlessly integrate and leverage Tectonic to stay ahead in a competitive landscape. Content updated November 2024. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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March 14, 2024in AI Tools, Data, Generative AI, Salesforce, Salesforce Einstein

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