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What is GovSlack

What is GovSlack?

GovSlack: Streamlined, Secure Collaboration for Government GovSlack brings together your team, tools, and partners into one secure platform, specifically designed to meet government regulations for processing and storing sensitive data. By integrating GovSlack, organizations can eliminate internal and external communication silos while empowering employees with the flexibility to excel in their roles. What is GovSlack?GovSlack is a tailored version of Slack that allows agencies, contractors, citizens, and partners to collaborate in a centralized, secure workspace. It complies with the strictest security and operational standards required by public sector clients. Is Slack FedRAMP approved?Yes, Slack holds FedRAMP Moderate authorization to meet public sector compliance needs. GovSlack goes further, earning FedRAMP JAB High authorization and is actively pursuing DoD CC SRG IL4 compliance to cater to defense sector clients. Does the federal government use Slack?Security is crucial for public sector agencies and their partners. Federal agencies trust Slack as their secure digital HQ, helping teams from the U.S. Department of Veterans Affairs to the GSA’s 18F office enhance collaboration while meeting stringent data privacy requirements. GovSlack Key FeaturesGovSlack, launched in July 2022 after extensive testing and feedback from government clients, offers several features to ensure secure and compliant collaboration: GovSlack integrates seamlessly with other government-specific tools such as Salesforce Government Cloud Plus, creating a powerful CRM and collaboration ecosystem to accelerate agency goals and improve constituent services. Case Study: Army Software Factory (SWF)The Army SWF, using agile methods to upskill soldiers and develop cutting-edge digital solutions, required a flexible and secure collaboration platform. By adopting GovSlack, they fostered improved collaboration without compromising compliance with stringent DoD policies. Partner IntegrationsLeading companies such as Lockheed Martin and Box are leveraging GovSlack to streamline collaboration while maintaining compliance with federal regulations. For example, Lockheed Martin is using GovSlack to manage projects with government partners while ensuring sensitive data remains protected. ThoughtsGovSlack is built for secure, government-specific collaboration, providing agencies and their partners with the flexibility to work more effectively while meeting compliance needs. By integrating with key tools and offering enhanced security features, GovSlack supports the digital transformation efforts of public sector organizations across the globe. 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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crm analytics

Build Better Tableau Dashboards

The effort made to build better Tableau dashboards pays tenfold in there readability and usability. “Dashboard design is not about making dashboards ‘pretty. It’s making them functional and helping the user to get the information they need as efficiently as possible.” ALEXANDER WALECZEK, ANALYTICS PRACTICE LEAD AND TABLEAU AMBASSADOR Effective communication with your audience involves considering their needs from start to finish. The key lies in posing the right questions. To convey information to your readers in an engaging manner, it is crucial to grasp fundamental aspects, such as: Possibly, when tailoring content for a time-pressed salesperson with only 15 seconds to spare for crucial performance indicators, it is imperative to present the most vital information in a glance. Additionally, ensuring that the dashboard is mobile-friendly and loads swiftly becomes essential. On the other hand, if your target audience consists of a team set to review quarterly dashboards over an extended period, offering more detailed views of the data might be advisable. Build Better Tableau Dashboards for Your Audience Take into account the expertise level of your audience. Gain a deeper understanding of their skill set by inquiring about their priorities and data consumption habits. This insight is crucial for determining the most effective way to present data, guiding key design decisions. For instance, a novice may require more action-oriented labels for filters or parameters compared to an advanced user. Here are four effective methods to assess the dashboard and data proficiency of your audience: Adjust Your Narrative Adjust your narrative accordingly. Tailoring your dashboards to suit the intended audience enhances their impact. Below are three visualizations depicting the distribution of tornadoes in the United States for the first nine months of the year. The distinction lies in the level of visual information employed to convey the narrative. There might e an extremely minimal presentation, progressing in complexity towards the right. None of these approaches is inherently superior to the others. The minimal visualization on the left might be ideal for audiences well-versed in the subject matter, appreciating simplicity and the elimination of redundancy. On the other hand, for newcomers or individuals viewing the visualization just once, the explicitness of the visualization on the right could be more effective. Determining what constitutes clutter versus essential information is where collaboration with colleagues becomes crucial. Crafting persuasive dashboards involves making a lasting impact on partnership. By closely collaborating with line-of-business stakeholders, you can secure the buy-in and engagement needed to tailor the dashboard to their specific requirements and expectations. This collaborative approach forms the essence of dashboard persuasion. A Work in Progress Demonstrate your process and embrace iterative refinement. Establishing a culture of analytics should be accompanied by a culture of supportive and frequent critique. Creating multiple versions of your work and actively seeking feedback throughout the process will contribute to a superior final product. Avoid isolation and stagnation; share your progress with others, use the feedback to refine your work, and repeat the process until you achieve a satisfactory result. Much like the formation of a diamond requiring extraordinary heat, pressure, and time, the outcome is worth the effort. Encouraging critiques is essential for cultivating a culture of constructive feedback. Trust among colleagues is important, arguably it enables mutual respect and trust in each other’s feedback. Developing a thick skin is also necessary, focusing on designing dashboards that cater to users and clients’ needs rather than personal preferences. Similar to writers who must “kill their darlings,” designers must prioritize the overall effectiveness of the dashboard, making honest assessments and adjustments when needed. “It also helps to have a public place—on a real or virtual wall—for sharing work. Making work public creates constant opportunities for feedback and improvements.” Tableau 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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Salesforce Revenue Lifecycle Management

Salesforce Revenue Lifecycle Management

Seamless Revenue Lifecycle Management Powered by Salesforce Revenue Cloud Is your company struggling to manage complex revenue streams, manual billing processes, or compliance with ASC 606 and IFRS 15 standards? Tectonic specializes in implementing Salesforce Revenue Lifecycle Management solutions through Salesforce Revenue Cloud. We offer tailored strategies for mid-market and enterprise companies across industries like High Tech, SaaS, Manufacturing, Hospitality, and Life Sciences. Industries We Serve The Challenges You Face Managing complex revenue streams can be overwhelming without the right systems. If your business is facing challenges like: Tectonic’s Tailored Solutions – Salesforce Revenue Lifecycle Management We leverage Salesforce Revenue Cloud to automate and streamline your Salesforce Revenue Lifecycle Management, helping companies overcome these challenges with ease. Key Use Cases for Salesforce Revenue Lifecycle Management (RLM) Content updated September 2024. 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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How to Implement AI for Business Transformation

How to Implement AI for Business Transformation

Harnessing the Power of AI for Business Transformation The age of artificial intelligence (AI) is here. How to Implement AI for Business Transformation? Once a niche technology confined to research labs and the realm of science fiction, AI has now become a mainstream force. Today, an estimated 35% of businesses are leveraging AI to enhance products, boost efficiency, and gain a competitive edge. However, for companies yet to begin their AI journey, the path to implementation can seem daunting. So how can organizations navigate the complexities of AI and unlock its potential to drive success? This comprehensive guide is designed to empower businesses to confidently adopt AI. We’ll break down what AI is, assess your organization’s readiness, help you develop a robust AI strategy, and explore how to implement and integrate AI across operations. Ultimately, this insight will show you how to embrace AI for continuous innovation, helping automate tasks, uncover insights, and future-proof your business. AI Era Demands an Intelligent Data Infrastructure AI consulting services and digital transformation partners like Tectonic underscore the technology’s immense value, helping organizations evaluate, implement, and scale AI initiatives. However, knowing where to start and who to trust can be challenging. This guide will provide best practices for planning and executing AI projects, helping you make informed decisions when selecting solutions and partners. By the end, your organization will be equipped with the knowledge and confidence needed to make AI a powerful competitive advantage. Understanding the AI Landscape Before diving into AI implementation, it’s important to understand what artificial intelligence is and the wide array of applications it offers. What is Artificial Intelligence? Artificial intelligence (AI) refers to software and machines designed to perform tasks that typically require human intelligence—such as visual perception, speech recognition, decision-making, and language translation. AI is already deeply integrated into many everyday products and services, including: Machine Learning Basics At the core of most AI systems is machine learning (ML), which involves training algorithms on vast datasets, enabling them to learn from examples without being explicitly programmed for every scenario. There are three main types of machine learning: Beyond ML, fields like natural language processing (NLP) focus on understanding human language, while computer vision analyzes visual content such as images and video. Real-World AI Applications Understanding the fundamentals of AI helps organizations align their needs with its capabilities. Common business use cases for AI include: Armed with this knowledge, businesses can better evaluate how AI fits into their goals and operations. Developing a Comprehensive AI Strategy Once you understand the AI landscape, the next step is developing a strategic plan to guide implementation. Establishing an AI Vision and Objectives AI adoption must align with clear financial and operational goals. Leadership teams should identify: Aligning stakeholders and executive leaders around specific use cases will drive urgency, investment, and commitment. AI Ethics and Governance AI adoption also requires guidelines for ethical usage, transparency, and accountability. Organizations should consider: Establishing these frameworks early ensures responsible and transparent AI usage. Resourcing an AI Program AI implementation requires the right talent and resources. Budget considerations should include: A Phased AI Adoption Roadmap Rather than attempting to scale AI all at once, organizations should adopt a phased approach: This roadmap balances short-term impact with long-term scalability. Choosing the Right AI Implementation Approach With your strategy in place, the next decision is how to implement AI. Three primary approaches are: The choice depends on your organization’s internal capabilities, desired level of customization, and timeline. Integrating AI into Your Operations Successful AI implementation requires careful planning and integration with existing operations. Develop an Integration Plan Consider how AI will interact with existing systems and workflows: Address Security and Privacy Ensure that AI systems comply with data privacy regulations and security protocols, especially when handling sensitive information. Drive Adoption Through Training Help staff understand how AI will augment their roles by providing training on how the algorithms work and how to interact with AI systems effectively. Monitor for Model Decay Implement processes to monitor and retrain models as needed to ensure continued performance and reliability. Embracing AI for Continuous Improvement AI should be viewed as an ongoing investment, driving continuous improvement across the organization. Encourage a Data-Driven Culture Empower teams to identify new AI use cases and experiment with AI-driven solutions. Provide the tools and frameworks to facilitate this culture of innovation. Foster Responsible AI Ensure that AI systems are transparent, accountable, and designed to augment human decision-making responsibly. Commit to Reskilling As AI capabilities evolve, continually upskill employees to ensure your workforce remains at the forefront of technological advancements. Unlocking the Future of AI The potential of AI to revolutionize businesses is clear. However, achieving success requires more than just technical capabilities. It demands thoughtful planning, strategic alignment, and a commitment to continuous improvement. By following this guide, your organization can confidently implement AI to unlock powerful data-driven insights, automate tasks, and achieve lasting competitive advantage. The future of AI is full of possibilities—are you ready to seize them? Tectonic is ready to help. How to Implement AI for Business Transformation 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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Marketing Cloud Growth and Advanced Editions

Marketing Cloud Growth and Advanced Editions

While Growth Edition is tailored to small businesses looking to get started with robust marketing automation, Advanced Edition caters to companies that need more sophisticated tools to scale personalization efforts, improve customer engagement, and streamline workflows. It offers additional features, including real-time journey testing, predictive AI for customer scoring, and advanced SMS capabilities, allowing businesses to enhance every touchpoint with their customers.

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AI Transparency

AI Transparency Explained

Understanding AI Transparency AI transparency is about making the inner workings of an AI model clear and understandable, allowing us to see how it arrives at its decisions. It involves a variety of tools and practices that help us comprehend the model, the data it’s trained on, how errors and biases are identified and categorized, and how these issues are communicated to developers and users. As AI models have become more advanced, the importance of transparency has grown. A significant concern is that more powerful models are often more opaque, leading to the so-called “black box” problem. “Humans naturally struggle to trust something they can’t understand,” said Donncha Carroll, partner and chief data scientist at Lotis Blue Consulting. “AI hasn’t always proven itself to be unbiased, which makes transparency even more critical.” Defining AI Transparency AI transparency is essential for building trust, as it allows users to understand how decisions are made by AI systems. Since AI models are trained on data that can carry biases or risks, transparency is crucial for gaining the trust of users and those affected by AI decisions. “AI transparency is about clearly explaining the reasoning behind the output, making the decision-making process accessible and comprehensible,” said Adnan Masood, chief AI architect at UST. “It’s about demystifying AI and providing insight into its decision-making process.” Transparency is becoming increasingly vital due to its role in fostering trust, enabling auditability, ensuring compliance, and helping to identify and address potential biases. Without it, AI systems risk perpetuating harmful biases, making opaque decisions, or causing unintended consequences in high-risk scenarios, Masood added. Explainability and Interpretability in AI Transparency AI transparency is closely related to concepts like explainability and interpretability, though they are distinct. Transparency ensures that stakeholders can understand how an AI system operates, including its decision-making and data processing. This clarity is essential for building trust, especially in high-stakes applications. Explainability, on the other hand, provides understandable reasons for AI’s decisions, while interpretability refers to how predictable a model’s outputs are based on its inputs. While both are crucial for achieving transparency, they don’t fully encompass it. Transparency also involves openness about how data is handled, the model’s limitations, potential biases, and the context of its usage. Ilana Golbin Blumenfeld, responsible AI lead at PwC, emphasized that transparency in process, data, and system design complements interpretability and explainability. Process transparency involves documenting and logging key decisions during system development and implementation, while data and system transparency involves informing users that an AI or automated system will use their data, and when they are interacting with AI, like in the case of chatbots. The Need for AI Transparency AI transparency is crucial for fostering trust between AI systems and users. Manojkumar Parmar, CEO and CTO at AIShield, highlighted the top benefits of AI transparency: Challenges of the Black Box Problem AI models are often evaluated based on their accuracy—how often they produce correct results. However, even highly accurate models can be problematic if their decision-making processes are opaque. As AI’s accuracy increases, its transparency often decreases, making it harder for humans to trust its outcomes. In the early days of AI, the black box problem was somewhat acceptable, but it has become a significant issue as algorithmic biases have been identified. For example, AI models used in hiring or lending have been found to perpetuate biases based on race or gender due to biased training data. Even highly accurate models can make dangerous mistakes, such as misclassifying a stop sign as a speed limit sign. These errors highlight the importance of understanding how AI reaches its conclusions, especially in critical applications like healthcare, where a misdiagnosis could be life-threatening. Transparency in AI makes it a better partner for human decision-making. In regulated industries, like banking, explainability is often a legal requirement before AI models can be deployed. Similarly, regulations like GDPR give individuals the right to understand how decisions involving their private data are made by AI systems. Weaknesses of AI Transparency While AI transparency offers many benefits, it also presents challenges: As AI models continuously evolve, they must be monitored and evaluated to maintain transparency and ensure they remain trustworthy and aligned with their intended outcomes. Balancing AI Transparency and Complexity Achieving AI transparency requires a balance between different organizational needs. When implementing AI, organizations should consider the following factors: Best Practices for Implementing AI Transparency Achieving AI transparency requires continuous collaboration and learning within an organization. Leaders and employees must clearly understand the system’s requirements from a business, user, and technical perspective. Blumenfeld suggests that providing AI literacy training can help employees contribute to identifying flawed responses or behaviors in AI systems. Masood recommends prioritizing transparency from the beginning of AI projects. This involves creating datasheets for datasets, model cards for models, rigorous auditing, and ongoing analysis of potential harm. Key Use Cases for AI Transparency AI transparency has many facets, and teams should address each potential issue that could hinder transparency. Parmar suggests focusing on the following use cases: The Future of AI Transparency AI transparency is an evolving field as the industry continually uncovers new challenges and develops better processes to address them. “As AI adoption and innovation continue to grow, we’ll see greater AI transparency, especially in the enterprise,” Blumenfeld predicted. However, approaches to transparency will vary based on the needs of different industries and organizations. Carroll anticipates that AI transparency efforts will also be shaped by factors like insurance premiums, particularly in areas where AI risks are significant. These efforts will be influenced by an organization’s overall system risk and evidence of best practices in model deployment. Masood believes that regulatory frameworks, like the EU AI Act, will play a key role in driving AI transparency. This shift toward greater transparency is crucial for building trust, ensuring accountability, and responsibly deploying AI systems. “The journey toward full AI transparency is challenging, with its share of obstacles,” Masood said. “But through collective efforts from practitioners, researchers, policymakers, and society, I’m optimistic that

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Reshaping the Automotive Industry With Salesforce

Changing customer expectations are reshaping the automotive industry, compelling dealerships to reevaluate their approach to business. With only 1% of buyers fully satisfied with their vehicle purchase experience, dealerships face a significant barrier to fostering loyalty. This dissatisfaction jeopardizes long-term profitability, as customers may turn elsewhere for future service or vehicle needs. Delivering exceptional customer experiences has become more critical than ever. However, rising operational costs present the challenge of achieving more with fewer resources — and doing so quickly. To drive sustainable growth, dealerships must prioritize relationship-building alongside achieving sales goals. Central to this effort is creating personalized digital touchpoints, especially for millennial and Gen Z shoppers, who now dominate the market. These younger consumers seek seamless, consistent experiences — from online browsing to in-person showroom visits. Turning them into lifelong customers requires a unified view of customer data, encompassing their digital shopping habits, service requests, and communications across all platforms. Fortunately, new tools can help dealerships meet these changing demands while reducing costs and improving productivity. To succeed, however, dealerships must adopt a mindset shift, moving beyond transactional practices to focus on customer-centric strategies. Digital Storefronts Are Falling Short Research reveals that fewer than 20% of original equipment manufacturers (OEMs) and retailers consider their digital storefronts engaging and mobile-friendly. For more insights into the industry’s challenges and opportunities, check out the “Trends in Automotive” report, based on feedback from 500 industry leaders. Beyond 30-Day Sales Goals: Building Lasting Relationships Dealerships have long operated in 30-day cycles, dictated by monthly sales goals from OEMs. However, successful dealerships now balance these targets with efforts to nurture long-term relationships. This involves more than sporadic emails about promotions or tune-ups. Instead, it’s about providing consistent, valuable interactions that address customer needs year-round. For example, keeping customers informed with personalized communications—such as alerts about service offers or recommendations for vehicle upgrades—can enhance their overall experience and build trust. Four Steps to Build Customer Loyalty The Path to Loyalty: A 360-Degree Customer View Sustaining long-term profitability hinges on extending customer loyalty beyond individual car sales. With Americans now keeping vehicles for an average of 12 years, dealerships must create enduring relationships across the vehicle’s lifecycle. Salesforce Automotive Cloud empowers dealerships with a 360-degree view of customer data, enabling teams to deliver personalized, seamless experiences. This unified approach helps sales teams close deals faster and service teams provide tailored consultations, ultimately fostering loyalty. Salesforce Sales and Service Cloud provide the same 360-degree view with powerful sales and service tools, including automated agents. The goal? To ensure customers think of your dealership first—whether for service, upgrades, or their next vehicle purchase. By placing the customer at the center of your business and leveraging advanced technology, dealerships can adapt to the evolving landscape and thrive in the future. 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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Communicating With Machines

Communicating With Machines

For as long as machines have existed, humans have struggled to communicate effectively with them. The rise of large language models (LLMs) has transformed this dynamic, making “prompting” the bridge between our intentions and AI’s actions. By providing pre-trained models with clear instructions and context, we can ensure they understand and respond correctly. As UX practitioners, we now play a key role in facilitating this interaction, helping humans and machines truly connect. The UX discipline was born alongside graphical user interfaces (GUIs), offering a way for the average person to interact with computers without needing to write code. We introduced familiar concepts like desktops, trash cans, and save icons to align with users’ mental models, while complex code ran behind the scenes. Now, with the power of AI and the transformer architecture, a new form of interaction has emerged—natural language communication. This shift has changed the design landscape, moving us from pure graphical interfaces to an era where text-based interactions dominate. As designers, we must reconsider where our focus should lie in this evolving environment. A Mental Shift In the era of command-based design, we focused on breaking down complex user problems, mapping out customer journeys, and creating deterministic flows. Now, with AI at the forefront, our challenge is to provide models with the right context for optimal output and refine the responses through iteration. Shifting Complexity to the Edges Successful communication, whether with a person or a machine, hinges on context. Just as you would clearly explain your needs to a salesperson to get the right product, AI models also need clear instructions. Expecting users to input all the necessary information in their prompts won’t lead to widespread adoption of these models. Here, UX practitioners play a critical role. We can design user experiences that integrate context—some visible to users, others hidden—shaping how AI interacts with them. This ensures that users can seamlessly communicate with machines without the burden of detailed, manual prompts. The Craft of Prompting As designers, our role in crafting prompts falls into three main areas: Even if your team isn’t building custom models, there’s still plenty of work to be done. You can help select pre-trained models that align with user goals and design a seamless experience around them. Understanding the Context Window A key concept for UX designers to understand is the “context window“—the information a model can process to generate an output. Think of it as the amount of memory the model retains during a conversation. Companies can use this to include hidden prompts, helping guide AI responses to align with brand values and user intent. Context windows are measured in tokens, not time, so even if you return to a conversation weeks later, the model remembers previous interactions, provided they fit within the token limit. With innovations like Gemini’s 2-million-token context window, AI models are moving toward infinite memory, which will bring new design challenges for UX practitioners. How to Approach Prompting Prompting is an iterative process where you craft an instruction, test it with the model, and refine it based on the results. Some effective techniques include: Depending on the scenario, you’ll either use direct, simple prompts (for user-facing interactions) or broader, more structured system prompts (for behind-the-scenes guidance). Get Organized As prompting becomes more common, teams need a unified approach to avoid conflicting instructions. Proper documentation on system prompting is crucial, especially in larger teams. This helps prevent errors and hallucinations in model responses. Prompt experimentation may reveal limitations in AI models, and there are several ways to address these: Looking Ahead The UX landscape is evolving rapidly. Many organizations, particularly smaller ones, have yet to realize the importance of UX in AI prompting. Others may not allocate enough resources, underestimating the complexity and importance of UX in shaping AI interactions. As John Culkin said, “We shape our tools, and thereafter, our tools shape us.” The responsibility of integrating UX into AI development goes beyond just individual organizations—it’s shaping the future of human-computer interaction. This is a pivotal moment for UX, and how we adapt will define the next generation of design. Content updated October 2024. 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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Big Data and Data Visualization

Big Data and Data Visualization Explained

Data Visualization: Turning Complex Data into Clear Insights Data visualization is the practice of converting information into visual formats, such as maps or graphs, to make data more accessible and understandable. The primary purpose of data visualization is to highlight patterns, trends, and outliers within large data sets, allowing users to quickly glean insights. The term is often used interchangeably with information graphics, information visualization, and statistical graphics. The Role of Data Visualization in Data Science Data visualization is a crucial step in the data science process. After data is collected, processed, and modeled, it must be visualized to draw meaningful conclusions. It’s also a key component of data presentation architecture, a discipline focused on efficiently identifying, manipulating, formatting, and delivering data. Importance Across Professions Data visualization is essential across various fields. Teachers use it to display student performance, computer scientists to explore AI advancements, and executives to communicate information to stakeholders. In big data projects, visualization tools are vital for quickly summarizing large datasets, helping businesses make informed decisions. In advanced analytics, visualization is equally important. Data scientists use it to monitor and ensure the accuracy of predictive models and machine learning algorithms. Visual representations of complex algorithms are often easier to interpret than numerical outputs. Historical Context of Data Visualization Data visualization has evolved significantly over the centuries, long before the advent of modern technology. Today, its importance is more pronounced, as it enables quick and effective communication of information in a universally understandable manner. Why Data Visualization Matters Data visualization provides a straightforward way to communicate information, regardless of the viewer’s expertise. This universality makes it easier for employees to make decisions based on visual insights. Visualization offers numerous benefits for businesses, including: Advantages of Data Visualization Key benefits include: Challenges and Disadvantages Despite its advantages, data visualization has some challenges: Data Visualization in the Era of Big Data With the rise of big data, visualization has become more critical. Companies leverage machine learning to analyze vast amounts of data, and visualization tools help present this data in a comprehensible way. Big data visualization often employs advanced techniques, such as heat maps and fever charts, beyond the standard pie charts and graphs. However, challenges remain, including: Examples of Data Visualization Techniques Early computer-based data visualizations often relied on Microsoft Excel to create tables, bar charts, or pie charts. Today, more advanced techniques include: Common Use Cases for Data Visualization Data visualization is widely used across various industries, including: The Science Behind Data Visualization The effectiveness of data visualization is rooted in how humans process information. Daniel Kahneman and Amos Tversky’s research identified two methods of information processing: Visualization Tools and Vendors Data visualization tools are widely used for business intelligence reporting. These tools generate interactive dashboards that track performance across key metrics. Users can manipulate these visualizations to explore data in greater depth, and indicators alert them to data updates or important events. Businesses might use visualization of data software to monitor marketing campaigns or track KPIs. As tools evolve, they increasingly serve as front ends for sophisticated big data environments, assisting data engineers and scientists in exploratory analysis. Popular data visualization tools include Domo, Klipfolio, Looker, Microsoft Power BI, Qlik Sense, Tableau, and Zoho Analytics. While Microsoft Excel remains widely used, newer tools offer more advanced capabilities. Data visualization is a vital subset of the broader field of data analytics, offering powerful tools for understanding and leveraging business data across all sectors. 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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multi-channel campaigns

Multi-Channel Campaigns

Leveraging Salesforce for Multi-Channel Campaign Management with Tectonic In today’s dynamic marketing landscape, businesses need to connect with their audience across multiple channels—email, social media, web, and beyond. Managing these campaigns effectively can feel like juggling too many balls at once, especially when aiming to maintain consistent messaging, track performance, and maximize ROI. That’s where Salesforce, paired with expertise from Tectonic, makes all the difference. Salesforce simplifies multi-channel campaign management, helping businesses stay organized, boost engagement, and achieve measurable results. Let’s dive into how this powerful platform, supported by Tectonic’s expertise, takes campaign management to the next level. The Challenge Marketing teams often face obstacles like: The Solution: Streamlined Multi-Channel Campaign Management Salesforce, with Tectonic as your trusted partner, transforms the complexity of multi-channel campaigns into a streamlined and effective process. By integrating channels, automating workflows, and delivering real-time insights, businesses can engage their audience and achieve exceptional results. 1. Centralized Campaign Planning Salesforce Marketing Cloud provides a centralized platform for planning and managing campaigns across channels—email, SMS, social media, and paid ads—all from one dashboard. This unified view ensures messaging and branding stay consistent while fostering seamless collaboration across teams. Tectonic enhances this process by ensuring your Salesforce environment is optimized for your specific needs. 2. Personalized Messaging at Scale Salesforce’s AI-powered tools, such as Einstein AI, analyze customer data to craft personalized content for every audience segment. These tools allow businesses to create tailored emails, ads, and social media posts that resonate with specific groups. With Tectonic’s guidance, you can implement personalization strategies that drive engagement and conversions to new heights. 3. Real-Time Performance Tracking Salesforce consolidates campaign performance data into a single platform, providing real-time metrics like open rates, click-through rates, social engagement, and conversions. Customizable dashboards allow your team to monitor results at a glance, enabling quick adjustments for maximum impact. Tectonic can help design these dashboards for clarity and actionable insights. 4. Automation for Efficiency Managing multi-channel campaigns involves countless tasks, but Salesforce automates processes such as scheduling, triggering emails, and personalizing messaging based on user behavior. By streamlining these repetitive tasks, your team can focus on creativity and strategy. Tectonic’s expertise ensures these automations are tailored to fit your business workflows perfectly. 5. Seamless Integration Across Channels Salesforce integrates with platforms like Google Ads, Facebook, LinkedIn, and Instagram, consolidating data into one ecosystem. This eliminates the need for manual data collection, saving time and improving accuracy. Tectonic ensures these integrations are set up seamlessly, so you can focus on running impactful campaigns. Strategies for Success with Salesforce Why Tectonic + Salesforce? Tectonic combines deep Salesforce expertise with a keen understanding of marketing challenges to help you fully unlock the platform’s potential. Together, Salesforce and Tectonic empower businesses to achieve more efficient, impactful, and ROI-driven campaigns. Ready to revolutionize your multi-channel marketing strategy? Let Tectonic guide you to Salesforce success. Content updated December 2024. 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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Benefits of Salesforce Experience Cloud

Benefits of Salesforce Experience Cloud

Salesforce Experience Cloud: Transforming Digital Customer Engagement To understand the Benefits of Salesforce Experience Cloud we must understand what a customer or partner portal is intended to do. Salesforce Experience Cloud, previously known as Community Cloud, is a powerful digital experience platform (DXP) designed to help organizations create and deliver exceptional, customer-centric experiences across multiple channels. This platform goes beyond community management, offering tools for building and managing websites, portals, mobile apps, and integrating social media. Benefits of Salesforce Experience Cloud explored. Built on Salesforce Customer 360, Experience Cloud gives businesses a comprehensive view of their customers by connecting data from various sources. With these insights, businesses can create personalized experiences tailored to each customer’s preferences and needs. Organizations can use Experience Cloud to design portals, websites, and communities, providing seamless access to relevant information, collaboration tools, and resources. The platform’s flexibility allows businesses to enhance customer satisfaction, improve partner collaboration, and boost employee productivity. Key Benefits of Salesforce Experience Cloud Salesforce Experience Cloud offers numerous benefits that help businesses deliver seamless experiences across the customer journey. Here are some of its key advantages: 1. Seamless Integration Experience Cloud integrates effortlessly with other Salesforce products like Sales Cloud and Service Cloud, providing a unified platform for comprehensive customer management. 2. Scalability and Customization The platform is highly scalable, allowing businesses to expand their communities as they grow. With extensive customization options, businesses can tailor the platform to meet their specific needs and branding requirements. 3. Security and Trust Salesforce is known for its robust security features, ensuring customer data is protected at all times. Businesses can confidently manage sensitive customer information within Experience Cloud. 4. Extensive AppExchange Ecosystem Salesforce’s AppExchange marketplace provides access to a wide range of pre-built integrations and apps that enhance the functionality of Experience Cloud, allowing businesses to customize and extend their platform capabilities. Real-World Uses of Salesforce Experience Cloud Salesforce Experience Cloud is used by businesses across various industries to improve customer engagement, enhance collaboration, and boost productivity. Some key use cases include: 1. Partner Portals Experience Cloud enables businesses to create dedicated partner portals where partners can collaborate with internal teams, access resources, and share leads. This accelerates partner engagement and streamlines business processes. 2. Self-Service Portals Businesses can offer 24/7 self-service portals, allowing customers to access product information, troubleshoot common issues, and track their interactions. These portals help reduce the workload on support teams and enhance customer satisfaction. 3. Customer Communities Experience Cloud allows businesses to create customer communities where users can find personalized content, engage with other users, and access self-service resources. This promotes collaboration and reduces the strain on customer support teams. 4. Employee Communities Internal employee communities serve as hubs for company-wide communication, training, and collaboration. Employees can access resources, share knowledge, and seek support, ultimately boosting engagement and productivity. 5. Branded Mobile Apps Businesses can use Experience Cloud to develop branded mobile apps that give customers, partners, and employees convenient access to services, resources, and information on the go. 6. Social Media Integration Experience Cloud integrates with popular social media platforms, allowing businesses to engage with customers directly, share content, and respond to inquiries. Top Features of Salesforce Experience Cloud Salesforce Experience Cloud is packed with features that enhance customer engagement, streamline operations, and improve overall efficiency: Companies Using Salesforce Experience Cloud Nike and PUMA leverage Experience Cloud for personalization. Nike’s loyalty program and Puma’s mobile shopping experience are enhanced by the platform’s built-in mobile UX design and technical architecture, resulting in better customer engagement and increased sales. Bank of America and Wells Fargo use Experience Cloud to offer customer support through self-service portals and community forums, improving customer satisfaction and gathering valuable feedback. IBM uses the platform to create collaborative communities for employees and customers alike. With integrated tools like Salesforce Einstein and IBM Watson, the company has enhanced internal collaboration and customer service. Hulu uses Salesforce to power its Help Center, where customers can find answers, engage with other viewers, and leave feedback that shapes Hulu’s content. OpenTable relies on Experience Cloud for its Diner Help portal, a one-stop shop for dining-related queries, enhancing the user experience and operational efficiency. Choosing the Right Salesforce Experience Cloud Partner for Implementation When implementing Salesforce Experience Cloud, choosing the right partner is crucial to ensure success. Look for a partner with: With the right partner, like Tectonic, businesses can fully grasp the power of Salesforce Experience Cloud to deliver exceptional digital experiences that foster customer loyalty, drive business growth, and improve operational efficiency. Content updated October 2024. 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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Salesforce Einstein and Einstein Automate

Einstein Trust

Generative AI, Salesforce, and the Commitment to Trust The excitement surrounding generative AI is palpable as it unlocks new dimensions of creativity for individuals and promises significant productivity gains for businesses. Engaging with generative AI can be a great experience, whether creating superhero versions of your pets with Midjourney or crafting pirate-themed poems using ChatGPT. According to Salesforce research, employees anticipate saving an average of 5 hours per week through the adoption of generative AI, translating to a substantial monthly time gain for full-time workers. Whether designing content for sales and marketing or creating a cute version of a beloved story, generative AI is a tool that helps users create content faster. However, amidst the enthusiasm, questions arise, including concerns about the security and privacy of data. Users ponder how to leverage generative AI tools while safeguarding their own and their customers’ data. Questions also revolve around the transparency of data collection practices by different generative AI providers and ensuring that personal or company data is not inadvertently used to train AI models. Additionally, there’s a need for assurance regarding the accuracy, impartiality, and reliability of AI-generated responses. Salesforce has been at the forefront of addressing these concerns, having embraced artificial intelligence for nearly a decade. The Einstein platform, introduced in 2016, marked Salesforce’s foray into predictive AI, followed by investments in large language models (LLMs) in 2018. The company has diligently worked on generative AI solutions to enhance data utilization and productivity for their customers. The Einstein Trust Layer is designed with private, zero-retention architecture. Emphasizing the value of Trust, Salesforce aims to deliver not just technological capabilities but also a responsible, accountable, transparent, empowering, and inclusive approach. The Einstein Trust Layer represents a pivotal development in ensuring the security of generative AI within Salesforce’s offerings. The Einstein Trust Layer is designed to enhance the security of generative AI by seamlessly integrating data and privacy controls into the end-user experience. These controls, forming gateways and retrieval mechanisms, enable the delivery of AI securely grounded in customer and company data, mitigating potential security risks. The Trust Layer incorporates features such as secure data retrieval, dynamic grounding, data masking, zero data retention, toxic language detection, and an audit trail, all aimed at protecting data and ensuring the appropriateness and accuracy of AI-generated content. Salesforce proactively provided the ability for any admin to control how prompt inputs and outputs are generated, including reassurance over data privacy and reducing toxicity. This innovative approach allows customers to leverage the benefits of generative AI without compromising data security and privacy controls. The Trust Layer acts as a safeguard, facilitating secure access to various LLMs, both within and outside Salesforce, for diverse business use cases, including sales emails, work summaries, and service replies in contact centers. Through these measures, Salesforce underscores its commitment to building the most secure generative AI in the industry. Generating content within Salesforce can be achieved through three methods: CRM Solutions: Einstein Copilot Studio: Einstein LLM Generations API: An overarching feature of these AI capabilities is that every Language Model (LLM) generation is meticulously crafted through the Trust Layer, ensuring reliability and security. At Tectonic, we look forward to helping you embrace and utilize generative AI with Einstein save time. 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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Salesforce Field Service

Understanding Salesforce Field Service

Salesforce Field Service is an expansive addition to Salesforce Service Cloud, offering a 360 degree perspective on workforce management, especially in scenarios where services extend beyond the office or work site into the field. This typically involves mobile employees such as service technicians, alongside service agents, dispatchers, and service managers. Let’s dive into the nuances of Salesforce Field Service, addressing key aspects like its definition, user roles, reporting, scheduling, optimization, and more. Salesforce Field Service is also a popular tool in public sector solutioning. Understanding Salesforce Field Service will help to decide if it is right for your service teams. Understanding Salesforce Field Service: Salesforce Field Service, formerly known as Field Service Lightning (FSL), is an extension of Service Cloud designed to provide a comprehensive workforce management view. It seamlessly manages services delivered in the field, such as a electrical installer arriving to fulfill a customer’s order. Field Service Lightning evolves with each Salesforce release, continually enhancing functionality. Salesforce Field Service enhances customer engagement with real-time personalization, optimization of field teams with a scheduling engine, and access to offline data. Components of Salesforce Field Service: Field Service comprises various components, including Service Appointments, Salesforce Service Cloud integration, Territory and Scheduling functionalities, the Field Service Dispatcher Console, and the Field Service Mobile App for Android and iOS with offline capabilities. A detailed data model diagram illustrates the relationship between Service Cloud and Field Service. Users of Salesforce Field Service: Salesforce Field Service is particularly valuable for organizations with mobile field technicians supporting customers. It excels in scenarios with intricate service delivery involving multiple individuals like dispatchers, field technicians, service agents, and service managers. Each user persona interacts with Salesforce Field Service in distinct ways, utilizing tools like the Dispatcher Console or Field Service Mobile App tailored to their roles. Use Cases for Salesforce Field Service: Field Service proves beneficial in diverse service scenarios, including repairs, maintenance, sales visits, and healthcare care visits. It facilitates efficient coordination among dispatchers, field technicians, service agents, and service managers, streamlining processes and enhancing customer satisfaction. Key Terms in Salesforce Field Service: Understanding key terms such as Work Orders, Service Appointments, Contracts, Entitlements, Work Types, Service Territories, Operating Hours, Resources, and Scheduling Policies is essential. These terms collectively form the foundation of Salesforce Field Service’s robust data model and optimized usability. Scheduling and Optimization: Scheduling in Salesforce Field Service is managed through the Dispatcher Console, empowering dispatchers to make informed decisions. Various methods, from manual to fully automated, enable the creation of service appointments, optimizing resource allocation and ensuring efficient service delivery. Forecasting and Planning: Use real-time data to quickly assess the impact of global or in-day optimization on travel time and resource utilization. Refine scheduling policies, get an instant view of optimization results, and KPI changes. Asset Service Management: Move from reactive to proactive service with real-time asset tracking. Monitor service outcomes and create preventive maintenance plans based on asset use, condition, age, and specific criteria. Service Territory Design: Service Territories, categorized as Primary, Secondary, and Relocation Territories, define where teams can perform work. Well-defined territories are crucial for efficient dispatcher satisfaction, requiring thoughtful initial design and ongoing maintenance. Field Service Mobile App: The Salesforce Field Service Mobile app is essential for on-the-move workforces, offering technicians essential information to optimize job execution. Offline capabilities ensure uninterrupted work, and the Visual Remote Assistant feature enables two-way video and audio communication between agents, technicians, and customers. Slack for Field Service: Empower your mobile workforce with seamless connectivity and timely assistance whenever it is needed. Mobilize for service appoints and tap into the expertise of colleagues throughout the organization with the user-friendly Slack interface. Reporting in Salesforce Field Service: Monitoring service efficiency and success involves leveraging Salesforce reports, list views, and other platform analytics. Work Order List Views and Field Service Report Types provide insights into service call-outs and overall performance. Enabling Field Service Lightning: Setting up Salesforce Field Service involves installing it as a Managed Package, assigning licenses to users, and configuring standard objects like Products, Assets, Service Contracts, and Entitlements. Creating records for Service Territories, Service Resources, and Operating Hours establishes the framework for the service model. Field Service Deployments: Deployments in Salesforce Field Service can be time-consuming due to its reliance on object data. Considerations for complex relational data movement between environments and potential risks, such as resistance to automation, require careful attention. Tectonic, as a Salesforce Consulting Partner, can assist your company in deploying Salesforce Field Service. Salesforce Field Service projects, while intricate, can be highly valuable when executed correctly providing a large ROI. Adequate planning, phased implementations, and a long-term perspective contribute to the success of organizations leveraging the capabilities of Salesforce Field Service. If you are looking for a more robust solution, consider Field Service Plus combining dispatch and technician functionality along with Service Cloud and Sales Cloud. Tectonic looks forward to helping your field service team utilize this Salesforce tool. Tectonic is please to announce Salesforce Service Cloud Implementation Solutions. Like2 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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