Data Visualization Archives - gettectonic.com

29Aug

Connected Vehicles

Revolutionizing the Automotive Industry: Salesforce’s Connected Car App The automotive industry has always been a beacon of innovation, consistently pushing the boundaries to enhance the driving experience. From the iconic Model T and the assembly line to today’s electric and autonomous vehicles, the evolution of automobiles has been driven by an unyielding pursuit of progress. I actually purchased a new-to-me car today, and with the connected vehicle on the horizon I’m kind of glad I’ll be able to upgrade in a couple years. Bluetooth and back up cameras are great. But a car that can tell the dealership to get me on the horn before some automotive calamity occurs? The future is here, my friends. Connected Vehicles for Better Experiences Now, as digital transformation reshapes industries, a new chapter is emerging in automotive innovation: the connected car. Leading this charge is Salesforce, a global powerhouse in customer relationship management (CRM), with the introduction of its groundbreaking Connected Car App, poised to redefine in-car experiences for both drivers and passengers. From my personal buying experience today, the car business could use some customer relationship management! The Future of In-Car Connectivity Salesforce’s Connected Car App is more than just a technological enhancement; it represents a fundamental shift in how we interact with our vehicles. By leveraging Salesforce’s Customer 360 platform, this app creates personalized, engaging experiences that go far beyond traditional automotive features. The Connected Car App is designed to make every journey more intuitive and efficient, offering real-time insights and services tailored to the unique needs of each driver. Whether it’s maintenance alerts, optimized route suggestions based on traffic, or personalized entertainment options, the app transforms the car into a truly smart companion on the road. A GPS feature? I guess I can plan on deleting Waze off my phone in the near future! Powered by Salesforce Customer 360 At the heart of the Connected Car App is Salesforce’s Customer 360 platform, which delivers a comprehensive, 360-degree view of each customer. This integration ensures that the app provides tailored experiences based on a deep understanding of the driver’s preferences, habits, and history. It isn’t going to just know you by a vehicle loan number, a VIN number, or even just an email address. For instance, a driver who frequently takes long road trips might receive customized recommendations for rest stops, dining options, and attractions along their route. Meanwhile, commuters could benefit from real-time updates on traffic, weather, and parking availability. The app’s ability to anticipate and respond to the driver’s needs in real time distinguishes it from traditional in-car systems. I can just hear my car now, advising me it has been one hour since I stopped for coffee, and she’s worried about my sanity. Enhancing Customer Loyalty and Satisfaction with Connected Vehicles The Connected Car App offers significant potential to boost customer loyalty and satisfaction. By delivering a personalized driving experience, automakers can strengthen relationships with customers, transforming each driving journey into an opportunity to build brand loyalty. If Toyota is suddenly going to treat me like Shannan Hearne instead of customer # xxxxx would be ecstatic. Additionally, the app’s capability to collect and analyze data in real time opens new avenues for automakers to engage with their customers. Predictive maintenance reminders, targeted promotions, and special offers are just a few examples of how the app fosters a deeper connection between the brand and the driver. Oh, yeah. My connected vehicle app is DEFINITELY going to be talking to me about changing my oil (I’m not exactly diligent), how great the latest model of Toyota is (I drove a Corolla for 18 years and have also owned a Tacoma, a Tundra, and a Prius), and if it would add coffee coupons I would be golden. A New Era of Automotive Innovation Salesforce’s Connected Car App marks a pivotal moment in the automotive industry’s digital transformation. As vehicles become increasingly connected, the opportunities for innovation are boundless. Salesforce is at the forefront with a solution that not only enhances the driving experience but also empowers automakers to build stronger, more meaningful relationships with their customers. In a world where customer expectations are constantly growing, the Connected Car App is a game-changer. Customers, even car owners, expect their brands to know them and recognize them. By integrating Salesforce’s CRM capabilities directly into vehicles, the app creates a seamless, personalized experience that stands out. As we look ahead, it’s clear that the Connected Car App is just the beginning of an exciting new era of automotive innovation. As a marketer at heart and a technologist by trade, I’m really excited about the potential here. Connected Vehicle: A Unified Digital Foundation Salesforce’s Connected Vehicle platform provides automakers with a unified, intelligent digital foundation, enabling them to reduce development time and roll out features and updates faster than ever before. This platform allows seamless integration of vehicle, Internet of Things (IoT), driver, and retail data from various sources, including AWS IoT FleetWise and Snapdragon® Car-to-Cloud Connected Services Platform, to enhance driver experiences and ensure smooth vehicle operation. Can you imagine a smart app like the Connected Vehicle talking to your loyalty apps for gas stations, convenience stores, and grocery stores? I would be driving down the interstate and the app will tell me there is a Starbucks ahead AND I have a 10% off coupon. Automakers and mobility leaders like Sony Honda Mobility are already exploring the use of Connected Vehicle to deliver better experiences for their customers. The platform’s ability to access and integrate data from any source in near real time allows automakers to personalize driver experiences, in-car offers, and safety upgrades. Why It Matters By 2030, every new vehicle sold will be connected, and the advanced, tech-driven features they provide will be increasingly important to consumers. A recent Salesforce survey revealed that drivers already consider connected features to be nearly as important as a car’s brand. Connected Vehicle accelerates this evolution, enabling automakers to immediately deliver branded, customized experiences tailored to

August 29, 2024in Salesforce Implementation Services, Salesforce Managed Services, Salesforce Manufacturing Automotive Energy

24Aug

Pulse for Salesforce

Salesforce Unveils Pulse for Salesforce: Integrating Tableau Analytics with CRM to Revolutionize Data-Driven Decision-Making In today’s data heavy business world, where data-driven decision-making is essential for success, the fusion of advanced analytics with customer relationship management (CRM) systems is more crucial than ever. Addressing this need, Salesforce has introduced Pulse for Salesforce, a groundbreaking tool that integrates Tableau’s powerful analytics directly into the Salesforce CRM environment. Meeting the Demand for Actionable Insights This launch aligns with a broader trend in the business intelligence (BI) market, where companies strive to make data analytics more accessible and actionable for non-technical users. Recent studies indicate that while 80% of business leaders view data as critical to decision-making, nearly one-third feel overwhelmed by the sheer volume of information available. Moreover, 91% of these leaders believe their organizations would significantly benefit from generative AI (Gen AI) technologies. Pulse for Salesforce marks a significant milestone in Salesforce’s ongoing strategy following its $15.7 billion acquisition of Tableau in 2019. Tableau, a leader in data visualization and BI since its founding in 2003, has been central to Salesforce’s mission of enhancing customer data management and analysis. The integration of Tableau’s capabilities within Salesforce’s CRM platform represents a major step forward in providing a comprehensive, data-driven solution. Ryan Aytay, President and CEO of Tableau, on the New Integration “Historically, sales leaders and teams have lacked personalized, accessible data insights in their daily flow of work, and analysts often spend considerable time on ad hoc requests and repetitive queries, slowing down decision-making and business growth,” says Ryan Aytay, CEO of Tableau. “By integrating Tableau Pulse’s AI-driven insights into Salesforce, we’re addressing these needs and enhancing data-driven decision-making to help businesses accelerate growth.” Boosting CRM Productivity with Salesforce’s AI Platform Pulse for Salesforce is built on Salesforce’s Einstein 1 AI Platform and leverages Gen AI to provide contextual metrics and insights directly within the Salesforce interface. This seamless integration streamlines decision-making for sales teams by reducing the need for manual data searches or reliance on analysts for ad-hoc queries. Key Features of Pulse for Salesforce Practical Applications and Data Security A practical application of Pulse for Salesforce is performance monitoring. Sales leaders can track team win rate trends directly from their homepage, quickly identifying areas or individuals needing additional support. Similarly, individual sales representatives can monitor their conversion rates and use natural language queries to analyze data by industry, potentially leading to more targeted sales efforts. The integration also addresses data security concerns, a critical issue in the age of AI-powered analytics. Pulse for Salesforce employs the Einstein Trust Layer, a secure AI architecture built into the Einstein 1 Platform, ensuring that customer data remains protected while benefiting from the advanced capabilities of generative AI. Collaboration Salesforce partnered with key industry players and partners to bring this innovative solution to market. With Pulse for Salesforce, organizations can now fully harness the power of integrated analytics and CRM to drive informed decision-making, enhance productivity, and ultimately accelerate business growth. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

August 24, 2024in Einstein 1 Platform, Einstein Insights, Generative AI, Salesforce Development Services, Salesforce Einstein, Salesforce Optimization, Salesforce Tableau

21Aug

Salesforce and the Connected Car

The concept of the Connected Car has been a topic of discussion for years, often accompanied by ambitious predictions from consultants about its market potential. For example, McKinsey in 2021 projected that by 2030, Connected Cars would constitute 95% of all vehicles on the road. Central to the success of these vehicles is data, with each one generating approximately 25 GB of data per hour. That’s a lot of data. Like a whole truckload of data! Salesforce and the Connected Car is uniquely a perfect fit. However, this raises two critical questions. First, do consumers actually understand what a Connected Car is? Second, if they do, are they comfortable sharing their personal data with automakers to enhance their driving experience? In January, Salesforce conducted a study of 2,188 car owners in the U.S., revealing some unsettling insights. A significant portion of drivers—over two-thirds (65%)—are unfamiliar with the concept of a Connected Car. Even more telling, over a third (37%) had never heard the term before. As of now, two-thirds of respondents either don’t have connected features in their cars or are not using them if they do. This includes features like Apple CarPlay and others. Personally, while shopping for a car I look for all those connected bells and whistles. On the flip side, this presents a considerable opportunity for automakers. According to Salesforce’s data, drivers expressed a willingness to pay a premium for advanced features, such as driver assistance, touchscreens, and smartphone integration. When it comes to sharing personal data, however, there’s still work to be done. While over half of respondents (54%) are comfortable with cars collecting data on vehicle diagnostics or seatbelt usage (35%), fewer are okay with data collection on driving speed (34%) or route history (31%). The discomfort grows when it comes to more sensitive data like voice recordings (17%), biometrics (13%), or text messages (12%). There are incentives that could encourage data sharing. For example, over two-thirds of respondents (67%) would be willing to exchange personal data for better insurance rates. Other incentives include advanced driver personalization (43%), such as customized seat and mirror settings, and enhanced personal safety features like real-time health monitoring (36%). Introducing Salesforce Connected Vehicle Salesforce for the Automotive IndustryIn response to these trends, Salesforce has introduced Connected Vehicle, a new application within the Automotive Cloud, alongside new partnerships with Qualcomm and AWS. These innovations aim to help automakers create the cars of the future. According to Salesforce: With a single console and a ready-to-use set of industry-specific, low-code/no-code development tools, Connected Vehicle helps automakers roll out new services and features to drivers faster. It enables bidirectional, over-the-air (OTA) capabilities for data sharing and software updates between the cloud and the vehicle via wireless or cellular networks. Key features for automakers include: Connected Vehicle is available today, with additional features like Connected Vehicle Summary, Interaction Summary, Warranty Summary, and Sales Agreement expected to be generally available in the fall. Why Now? The Connected Car has been around in some form since 2005, but according to Achyut Jajoo, SVP & GM of Automotive at Salesforce, connectivity is just the beginning. He explains: “One big challenge for automakers was that once a car left the factory, it was difficult to update its software or add new capabilities. The car was limited to the features it shipped with, unless you took it back to the dealership. But today, the fundamental architecture of these vehicles is changing. I often describe it as a phone on wheels. With standardized chips and modules, data can now be pushed to the cloud, allowing for real-time control and updates.” This shift means that once a vehicle leaves the factory, its performance and features can evolve over time. This “software-defined vehicle” revolution allows for continuous enhancements and the deployment of new capabilities that weren’t possible before. While connectivity is an essential piece, this software-driven approach is the real game changer. As for Salesforce’s role and its partnerships with AWS and Qualcomm, Jajoo says: “We are known for taking data and creating customer experiences. When we looked at the automotive market, we saw how complicated it is. We heard horror stories and realized that by partnering with other tech powerhouses, we could provide an end-to-end solution. This approach was shaped by our discussions with customers who said they struggled with these challenges and would love for us to collaborate.” Final Thought We may not be close to McKinsey’s 95% prediction quite yet, but the trajectory is clear. With data being central to success—and consumer trust in how that data is used—Salesforce’s collaboration with AWS and Qualcomm seems like a smart move. Apparently I am the ideal customer as I want my vehicle connected! By Tectonic’s Senior Consultant, Shannan Hearne Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

August 21, 2024in Salesforce Automotive Cloud, Salesforce Development Services, Salesforce Manufacturing Automotive Energy, Salesforce Optimization

18Aug

Salesforce on AI

Marketing success hinges on delivering consistent, timely, and engaging content. According to the Salesforce State of Marketing report, 78% of high-performing marketers identify data as their most critical asset for creating cohesive customer journeys. Yet, only 49% report having a unified view of customer data sources. This disconnect highlights a significant challenge many marketing teams face in effectively leveraging their data. For organizations already invested in Salesforce, incorporating AI-driven business intelligence (BI) tools offers numerous benefits. These include reduced time to deliver insights, enhanced automation, increased innovation, improved agility, and cost savings. However, realizing these benefits depends on having high-quality data and robust data strategies. This insight explores AI-driven BI from a Salesforce perspective, highlighting its benefits, applications, and future trends. By understanding the potential of AI in BI, organizations can better harness their data to drive success and innovation. The Role of AI in Business Intelligence Integrating AI into BI systems elevates data analysis by offering deeper insights and predictive capabilities. Here’s how AI enhances BI: These examples demonstrate AI’s ability to improve BI systems by enhancing data accuracy, providing real-time insights, and improving forecasting. Salesforce’s AI Capabilities in BI Salesforce’s AI capabilities in BI are embodied in the versatile tool, Salesforce Einstein. Easily integrated with BI, Einstein automates tasks and delivers personalized insights. Companies using Einstein have reported a 20% increase in sales productivity. Here’s how Einstein can be utilized in various scenarios: These examples illustrate how Salesforce’s AI tools, particularly Einstein, can transform BI by automating routine tasks and delivering personalized insights, ultimately driving customer satisfaction and business growth. Future Trends in AI and BI The future of AI and BI promises even more advanced capabilities and innovations. As AI evolves, so too will the tools for BI. Here are some trends expected to emerge in the near future: These trends show that AI and BI are evolving rapidly. Companies that stay ahead of these developments will be well-positioned to leverage AI for greater innovation and efficiency. Next Steps AI-powered BI, especially with Salesforce, is transforming how businesses operate by providing deeper insights and better decision-making capabilities. To stay competitive and foster innovation, organizations must embrace AI. The quest is no longer just to be data-driven. It is to be data-decisioned. Here are some actionable steps: By taking these steps, businesses can fully leverage AI-driven BI and maintain a competitive edge in the fast-evolving digital playinf field of AI. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

August 18, 2024in Salesforce Einstein

28Jul

Leeds and Other Heatmap Solutions

With over 80% of people shopping online – and the numbers are bound to rise – it’s important to know how your would-be customers behave on your website: where they click, how they scroll, and what motivates them to take specific actions. Heatmap analytics does it, allowing you to dominate CRO and UX through effective behavior data interpretation. This insight will look at Leeds and Other Heatmap Solutions. Powered by heatmap software and heatmap tools, heatmap analytics can help you convert customers at scale by optimizing their on-site and mobile experience. Make no mistake: the quality of user behavior tracking can make a difference between a closed sale and a bounce. Leads Heatmap Software is an innovative tool that transforms complex lead data into easy-to-understand, color-coded heatmaps within Salesforce CRM. This solution uses advanced data visualization techniques, enabling users to quickly identify high-potential leads. Interactive Heatmaps Leverage dynamic, real-time heatmaps to visualize lead density and quality, making it easier to pinpoint high-potential areas. Real-Time Updates Stay up-to-date with the latest information as heatmaps automatically refresh with new leads or changes to existing data, ensuring you always have the most current view. Enhanced Analytics Dive deeper into lead behavior and trends with comprehensive analytics tools that provide detailed reports and predictive insights. Detailed Lead Profiles Access in-depth lead profiles directly from the heatmap, including contact details, engagement history, and quick shortcuts for a complete view of each lead. Online Chat Integration Interact with leads instantly using integrated online chat, facilitating immediate and personalized communication. All website pages have a purpose, whether that purpose is to drive further clicks, qualify visitors, provide a solution, or even a mix of all of those things. Heatmaps and recorded user sessions allow you to see if your page is serving that purpose or going against it. What Is a Heatmap? Generally speaking, heatmaps are graphical representations of data that highlight value with color. On a website heatmap, the most popular areas are showcased in red (hot) and the least popular are in blue (cold). The colors range on a scale from red to blue. Heatmaps are an excellent method of collecting user behavior data and converting it into a deep analysis of how visitors engage with your website pages. It can analyze: That information will help you identify user trends and key into what should be optimized to up engagement. Setting up website heatmapping software is a great start to refining your website design process and understanding your users. When to Use Heatmaps The truth is that heatmaps can actually be invaluable when testing and optimizing user experiences and conversion opportunities. There are many times you should be using them. Redesigning Your Website Updating, or even upgrading, your website isn’t just a task on your to do list. Careful thought, attention, and creativity should be put into the revamp if you want it to be worth the time and resources. Heatmaps can help with studying your current design to identify what your visitors are engaging with and what they’re ignoring. You’ll be tapped into what makes your visitors tick so that you can build a site meant specifically for your unique audience. Analyzing Webpage Conversions Trying to figure out why certain pages aren’t converting the way you thought they would? Use a heatmap. You’ll be able to identify exactly what’s attracting attention and deduce why. The same goes for buttons and pages that are showing a higher rate of conversion than anticipated. By keying into the design, copy, and other elements that are working for you, you’ll know exactly how to optimize your under-performing webpages. Testing New Updates As your business grows and you develop new ideas, naturally you’ll want to test them. A/B testing allows you to measure and analyze visitor response to a project or design, but you can take it a step further with heatmapping. Leverage the data graph by examining exactly what captures your visitors’ attention. At the end of the testing period, you may be able to pull designs or elements that received high levels of engagement from the page that didn’t perform as well into the successful one. How To Analyze Visually Using the color-coded visualizations, you can read your webpage for engagement levels and attention “hot spots.” Where the map reads red, that’s where visitors are showing the highest points of interactivity. Blue reflects low numbers. You can spot design issues or opportunities to move buttons, forms, and the like with a visual read. Data Points Reviewing raw data tables will give you more specific insights into your page’s performance. You can examine HTML elements and pixel locations of clicks to really understand what’s drawing people in. You can even filter your clicks and views in order of popularity with certain software. This takes the guessing out of your redesign and testing efforts. Tableau has instant, real-time reporting in place for users looking for actionable insights. With smart dashboards and a drag and drop interface, navigating the product is easy. Their cloud storage means omni-channel data access from anywhere. You can perform ad hoc analyses whenever it’s convenient for you. You can also share your reports with anyone to boost business impact. With built in A/B testing and consolidated heatmaps, Freshmarketer puts in the extra effort to plot out visitor interactions. Recorded in real time, you can analyze heatmaps based by device, which the software automatically detects. Offering scrollmaps and click maps, Freshmarketer strives to “go beyond traditional heatmaps.” Looker offers similar services to the other software options listed, but they also supply a unique security management feature to protect your data. Also partnered with Google Cloud, you’ll have access to reporting from anywhere in the world. Primarily a data analysis solution, you’ll have access to other data intelligence and visualization features as well. Hotjar is one of the most popular website analytics software suites, offering free heatmaps for desktop, mobile, and tablet within its basic subscription plan. You can create heatmaps and synergize them with other free features like user session recordings, surveys, and

July 28, 2024in Google Analytics

11Jul

Generative AI for Tableau

Tableau’s first generative AI assistant is now generally available. Generative AI for Tableau brings data prep to the masses. Earlier this month, Tableau launched its second platform update of 2024, announcing that its first two GenAI assistants would be available by the end of July, with a third set for release in August. The first of these, Einstein Copilot for Tableau Prep, became generally available on July 10. Tableau initially unveiled its plans to develop generative AI capabilities in May 2023 with the introduction of Tableau Pulse and Tableau GPT. Pulse, an insight generator that monitors data for metric changes and uses natural language to alert users, became generally available in February. Tableau GPT, now renamed Einstein Copilot for Tableau, moved into beta testing in April. Following Einstein Copilot for Tableau Prep, Einstein Copilot for Tableau Catalog is expected to be generally available before the end of July. Einstein Copilot for Tableau Web Authoring is set to follow by the end of August. With these launches, Tableau joins other data management and analytics vendors like AWS, Domo, Microsoft, and MicroStrategy, which have already made generative AI assistants generally available. Other companies, such as Qlik, DBT Labs, and Alteryx, have announced similar plans but have not yet moved their products out of preview. Tableau’s generative AI capabilities are comparable to those of its competitors, according to Doug Henschen, an analyst at Constellation Research. In some areas, such as data cataloging, Tableau’s offerings are even more advanced. “Tableau is going GA later than some of its competitors. But capabilities are pretty much in line with or more extensive than what you’re seeing from others,” Henschen said. In addition to the generative AI assistants, Tableau 2024.2 includes features such as embedding Pulse in applications. Based in Seattle and a subsidiary of Salesforce, Tableau has long been a prominent analytics vendor. Its first 2024 platform update highlighted the launch of Pulse, while the final 2023 update introduced new embedded analytics capabilities. Generative AI assistants are proliferating due to their potential to enable non-technical workers to work with data and increase efficiency for data experts. Historically, the complexity of analytics platforms, requiring coding and data literacy, has limited their widespread adoption. Studies indicate that only about one-quarter of employees regularly work with data. Vendors have attempted to overcome this barrier by introducing natural language processing (NLP) and low-code/no-code features. However, NLP features have been limited by small vocabularies requiring specific business phrasing, while low-code/no-code features only support basic tasks. Generative AI has the potential to change this dynamic. Large language models like ChatGPT and Google Gemini offer extensive vocabularies and can interpret user intent, enabling true natural language interactions. This makes data exploration and analysis accessible to non-technical users and reduces coding requirements for data experts. In response to advancements in generative AI, many data management and analytics vendors, including Tableau, have made it a focal point of their product development. Tech giants like AWS, Google, and Microsoft, as well as specialized vendors, have heavily invested in generative AI. Einstein Copilot for Tableau Prep, now generally available, allows users to describe calculations in natural language, which the tool interprets to create formulas for calculated fields in Tableau Prep. Previously, this required expertise in objects, fields, functions, and limitations. Einstein Copilot for Tableau Catalog, set for release later this month, will enable users to add descriptions for data sources, workbooks, and tables with one click. In August, Einstein Copilot for Tableau Web Authoring will allow users to explore data in natural language directly from Tableau Cloud Web Authoring, producing visualizations, formulating calculations, and suggesting follow-up questions. Tableau’s generative AI assistants are designed to enhance efficiency and productivity for both experts and generalists. The assistants streamline complex data modeling and predictive analysis, automate routine data prep tasks, and provide user-friendly interfaces for data visualization and analysis. “Whether for an expert or someone just getting started, the goal of Einstein Copilot is to boost efficiency and productivity,” said Mike Leone, an analyst at TechTarget’s Enterprise Strategy Group. The planned generative AI assistants for different parts of Tableau’s platform offer unique value in various stages of the data and AI lifecycle, according to Leone. Doug Henschen noted that the generative AI assistants for Tableau Web Authoring and Tableau Prep are similar to those being introduced by other vendors. However, the addition of a generative AI assistant for data cataloging represents a unique differentiation for Tableau. “Einstein Copilot for Tableau Catalog is unique to Tableau among analytics and BI vendors,” Henschen said. “But it’s similar to GenAI implementations being done by a few data catalog vendors.” Beyond the generative AI assistants, Tableau’s latest update includes: Among these non-Copilot capabilities, making Pulse embeddable is particularly significant. Extending generative AI capabilities to work applications will make them more effective. “Embedding Pulse insights within day-to-day applications promises to open up new possibilities for making insights actionable for business users,” Henschen said. Multi-fact relationships are also noteworthy, enabling users to relate datasets with shared dimensions and informing applications that require large amounts of high-quality data. “Multi-fact relationships are a fascinating area where Tableau is really just getting started,” Leone said. “Providing ways to improve accuracy, insights, and context goes a long way in building trust in GenAI and reducing hallucinations.” While Tableau has launched its first generative AI assistant and will soon release more, the vendor has not yet disclosed pricing for the Copilots and related features. The generative AI assistants are available through a bundle named Tableau+, a premium Tableau Cloud offering introduced in June. Beyond the generative AI assistants, Tableau+ includes advanced management capabilities, simplified data governance, data discovery features, and integration with Salesforce Data Cloud. Generative AI is compute-intensive and costly, so it’s not surprising that Tableau customers will have to pay extra for these capabilities. Some vendors are offering generative AI capabilities for free to attract new users, but Henschen believes costs will eventually be incurred. “Customers will want to understand the cost implications of adding these new capabilities,”

July 11, 2024in Generative AI, Salesforce Data Cloud

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-

June 15, 2024in Generative AI

10Jun

Democratize Data with Einstein Copilot for Tableau

Most workers today recognize the importance of rich data analytics in their jobs. However, 33% struggle to understand and generate insights from data. To address this, Salesforce has introduced Einstein Copilot for Tableau, which allows users of all skill levels to create complex data visualizations without extensive learning or coding. Democratize Data with Einstein Copilot for Tableau. Launched in April 2024, the beta version of this AI assistant features a user-friendly interface that simplifies the process with questions or simple commands. This facilitates the quick creation of comprehensive data presentations, including reports, dashboards, and various charts. Democratize Data with Einstein Copilot for Tableau Einstein Copilot for Tableau leverages a combination of AI technologies—natural language processing (NLP), machine learning (ML), and generative AI—to provide actionable insights. NLP enables conversational and intuitive interactions, while ML models process user queries and analyze data. Generative AI drives cognitive reasoning, planning, and creates insights, recommendations, and diagrams based on user inputs. By integrating with Tableau Cloud, Einstein Copilot accesses historical proprietary data, enables advanced data analysis, and translates user intent into actionable insights. It relies on Tableau’s analytics infrastructure to execute code and displays results through user-friendly visualizations and dashboards. Additionally, the Einstein Trust Layer secures and protects private data in Einstein Copilot. It authorizes inbound requests, ensuring users have necessary permissions to access specific data and safeguards model outputs to prevent the disclosure of confidential information. How Einstein Copilot for Tableau Transforms Requests into Insights To understand how Einstein Copilot for Tableau turns requests into actionable insights, let’s walk through each step of the interaction process: Einstein Copilot for Tableau democratizes access to data analytics, enabling all users to harness the power of data without needing extensive technical knowledge. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

June 10, 2024in Einstein 1 Platform, Salesforce Tableau

AI Agents

Lessons Learned in the First Year of Developing AI Agents In the first year of working on AI agents, valuable insights emerged from direct collaboration with engineers and UX designers, as they iterated on the overall product experience. The objective was to create a platform for customers to use standard data analysis agents and build custom agents tailored to specific tasks and data structures relevant to their business. This platform integrates connectors to databases like Snowflake and BigQuery with built-in security, supports RAG over a metadata layer describing database contents, and facilitates data analysis through SQL, Python, and data visualization tools. Feedback on the effectiveness of these developments came from both internal evaluations and customer insights. Users from Fortune 500 companies utilize these agents daily to analyze their internal data. Key Insights on AI Agents Additional Insights Further insights on code and infrastructure include: These lessons underscore the importance of focusing on reasoning, iterative improvements to the agent-computer interface, understanding model limitations, and building robust supporting infrastructure to enhance AI agent performance and user satisfaction. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

June 4, 2024in Salesforce Einstein

04Jun

Trust Einstein Copilot for Tableau

Are you prepared to utilize the capabilities of Einstein Copilot to expand your organization’s analytical advantages? This robust tool facilitates data exploration, insights generation, and visualization development at an unprecedented pace. However, before immersing yourself in its capabilities, it’s crucial to grasp how Einstein Copilot upholds Tableau and Salesforce’s core value: Trust. Let’s discover how the Einstein Trust Layer safeguards your data, ensures result accuracy, and facilitates auditing, addressing common questions and concerns raised by our customers.Trust Einstein Copilot for Tableau. What is Einstein Copilot for Tableau? Using generative AI and statistical analysis, Einstein Copilot for Tableau is able to understand the context of your data to create and suggest relevant business questions to help kickstart your analysis. A smart, conversational assistant for Tableau users, Einstein Copilot for Tableau automates data curation—the organization and integration of data collected from various sources—by generating calculations and metadata descriptions. Einstein Copilot for Tableau can fill data gaps and enhance analysis by creating synthetic datasets where real data is limited. Einstein Copilot helps you anticipate outcomes with predictive analytics that simulate diverse scenarios and uncover hidden correlations. Additionally, generative models can increase data privacy by producing non-traceable data for analysis. Fulfilling the promise of generative AI, Einstein Copilot for Tableau presents an efficient, insightful, and ethical approach to data analytics. Think of it as an intelligent assistant integrated into the Tableau suite of products to make everyone successful in their analysis workflow—whether they’re an experienced data analyst or a data explorer. As your intelligent analytics AI assistant, Einstein Copilot for Tableau guides you through the process of creating data visualizations in Tableau by assisting you with recommended questions, conversational data exploration, guided calculation creation, and more. Understanding the Einstein Trust Layer The Einstein Trust Layer constitutes a secure AI architecture embedded within the Salesforce platform. Comprising agreements, security technology, and data privacy controls, it ensures the safety of your data while exploring generative AI solutions. Built upon the Einstein Trust Layer, Einstein Copilot for Tableau and other Tableau AI features inherit its security, governance, and Trust capabilities. The Einstein Trust Layer is a secure AI architecture, built into the Salesforce platform. It is a set of agreements, security technology, and data and privacy controls used to keep your company safe while you explore generative AI solutions. Tableau has been on the journey to help people see and understand their data for over two decades. Thanks to data analysts, this mission has been a success and will continue to be a success. Data analysts are the backbone of organizations that champion data culture, capture business requirements, prep data, and create data content for end users. Data Access and Privacy Who Accesses Your Data? A primary concern among our customers revolves around data access. Rest assured, the Einstein Trust Layer enforces strict policies to safeguard your organization’s data. Third-party LLM providers, including Open AI and Azure Open AI, adhere to a zero data retention policy. This means that data sent to LLMs isn’t stored; once processed, both the prompt and response are promptly forgotten. Additionally, each Einstein Copilot for Tableau customer receives their own Data Cloud instance, securely storing prompts and responses for auditing purposes. Data Residency and Access Control Einstein Copilot for Tableau respects permissions, row-level security, and data policies within Tableau Cloud, ensuring that only authorized personnel within your organization access specific data. Whether using Einstein Copilot or not, data access is restricted based on organizational roles and permissions. Data Handling and Processing Data Sent Outside of Tableau Cloud Site Einstein Copilot for Tableau operates within the confines of your Tableau site, scanning connected data sources to create a summary context. This summarized data is sent to third-party LLM providers for vectorization, enabling accurate interpretation of user queries. Importantly, the zero data retention policy ensures that summarized data is forgotten post-vectorization. Personally Identifiable Information (PII) Data To enhance data privacy, Einstein Copilot for Tableau employs data masking for PII data. This technique replaces sensitive information with placeholder text, ensuring privacy without sacrificing context. While our detection models strive for accuracy, continuous evaluation and refinement are paramount to maintain trust. Result Trustworthiness Ensuring Safe and Accurate Results Einstein Copilot for Tableau employs Toxicity Confidence Scoring to identify harmful inputs and responses. By combining rule-based filters and AI models, potentially harmful content is filtered and flagged for review. Furthermore, accuracy benchmarks ensure that generated results align closely with human-authored ones, bolstering trust in the platform. Future Trust Enhancements Trust remains an ongoing focus for our teams. Initiatives such as a BYO LLM solution and improved disambiguation capabilities are underway to further enhance trustworthiness. Continuous feedback, testing, and iteration drive our efforts to maintain your trust in Einstein Copilot for Tableau and the Einstein Trust Layer. Data analysis and data-driven decision-making have been part of the vocabulary in organizations over the years. And, while data analysis is one of the most in-demand tech skills sought by employers today, not everyone in an organization has “analyst” in their job title—myself included. Yet, so many of us use data daily to make informed decisions. The rise of generative AI presents a significant opportunity for us to bring transformative benefits to analytics. Businesses are eager to embrace generative AI because it can help save time, provide faster insights, and empower analysts to be even more productive with an AI assistant—freeing analysts to focus on delivering high-quality, data-driven insights. Is Tableau replacing Einstein analytics? Einstein Analytics has a new name. Say hello to Tableau CRM. Everything about how it works stays the same, just with that snazzy new name. When Tableau joined the Salesforce family, we brought together analytics capabilities of incredible depth and power. What is the difference between Einstein analytics and Tableau? If you’re only planning on analyzing Salesforce data, Einstein Analytics would probably make the most sense for you. However, if you need to analyze information that is coming from all over the place, Tableau will give your users more options. Tableau GPT infuses automation in every part of analytics – from preparation to communicating

June 4, 2024in Einstein 1 Platform, Salesforce Tableau

18May

AI Hallucinations

Generative AI (GenAI) is a powerful tool, but it can sometimes produce outputs that appear true but are actually false. These false outputs are known as hallucinations. As GenAI becomes more widely used, concerns about these hallucinations are growing, and the demand for insurance coverage against such risks is expected to rise. The market for AI risk hallucination insurance is still in its infancy but is anticipated to grow rapidly. According to Forrester’s AI predictions for 2024, a major insurer is expected to offer a specific policy for AI risk hallucination. Hallucination insurance is predicted to become a significant revenue generator in 2024. AI hallucinations are false or misleading responses generated by AI models, caused by factors such as: These hallucinations can be problematic in critical applications like medical diagnoses or financial trading. For example, a healthcare AI might incorrectly identify a benign skin lesion as malignant, leading to unnecessary medical interventions. To mitigate AI hallucinations: AI hallucination, though a challenging phenomenon, also offers intriguing applications. In art and design, it can generate visually stunning and imaginative imagery. In data visualization, it can provide new perspectives on complex information. In gaming and virtual reality, it enhances immersive experiences by creating novel and unpredictable environments. Notable examples of AI hallucinations include: Preventing AI hallucinations involves rigorous training, continuous monitoring, and a combination of technical and human interventions to ensure accurate and reliable outputs. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

May 18, 2024in Generative AI

24Apr

Summer 24 Analytics Release Notes

Analytics summer 24 enhancements include new and updated features for Lightning reports and dashboards, Data Cloud reports and dashboards, CRM Analytics, Intelligent apps, and Tableau. Summer 24 Analytics Release Notes. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

April 24, 2024in Salesforce, Salesforce Marketing Cloud, Salesforce Tableau

26Oct

RAG – Retrieval Augmented Generation in Artificial Intelligence

Salesforce has introduced advanced capabilities for unstructured data in Data Cloud and Einstein Copilot Search. By leveraging semantic search and prompts in Einstein Copilot, Large Language Models (LLMs) now generate more accurate, up-to-date, and transparent responses, ensuring the security of company data through the Einstein Trust Layer. Retrieval Augmented Generation in Artificial Intelligence has taken Salesforce’s Einstein and Data Cloud to new heights. These features are supported by the AI framework called Retrieval Augmented Generation (RAG), allowing companies to enhance trust and relevance in generative AI using both structured and unstructured proprietary data. RAG Defined: RAG assists companies in retrieving and utilizing their data, regardless of its location, to achieve superior AI outcomes. The RAG pattern coordinates queries and responses between a search engine and an LLM, specifically working on unstructured data such as emails, call transcripts, and knowledge articles. How RAG Works: Salesforce’s Implementation of RAG: RAG begins with Salesforce Data Cloud, expanding to support storage of unstructured data like PDFs and emails. A new unstructured data pipeline enables teams to select and utilize unstructured data across the Einstein 1 Platform. The Data Cloud Vector Database combines structured and unstructured data, facilitating efficient processing. RAG in Action with Einstein Copilot Search: RAG for Enterprise Use: RAG aids in processing internal documents securely. Its four-step process involves ingestion, natural language query, augmentation, and response generation. RAG prevents arbitrary answers, known as “hallucinations,” and ensures relevant, accurate responses. Applications of RAG: RAG offers a pragmatic and effective approach to using LLMs in the enterprise, combining internal or external knowledge bases to create a range of assistants that enhance employee and customer interactions. Retrieval-augmented generation (RAG) is an AI technique for improving the quality of LLM-generated responses by including trusted sources of knowledge, outside of the original training set, to improve the accuracy of the LLM’s output. Implementing RAG in an LLM-based question answering system has benefits: 1) assurance that an LLM has access to the most current, reliable facts, 2) reduce hallucinations rates, and 3) provide source attribution to increase user trust in the output. Retrieval Augmented Generation in Artificial Intelligence Content updated July 2024. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

October 26, 2023in Einstein 1 Platform, Generative AI, Salesforce, Salesforce Data Cloud

20Aug

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

August 20, 2023in Salesforce

15Jul

Salesforce Analytics Acronyms

Here is a helpful list of analytics acronyms you may encounter when discussing Salesforce. Salesforce Analytics Acronyms Acronym Meaning Definition AWS Amazon Web Services AWS offers “reliable, scalable, and inexpensive cloud computing services…the world’s most comprehensive and broadly adopted cloud, offering over 200 fully featured services from data centers globally.” (source)Multiple Salesforce products use AWS; however, it’s prominent in the analytics arena, where organizations rely on AWS to:”Extract, prepare, and analyze Salesforce.com data using Amazon AppFlow, AWS Glue DataBrew, and Amazon Athena.” (source) BYOAI Bring Your Own AI ‘Bring your own AI (model)’ is Salesforce’s way to meet their customers where they’re at. With Data Cloud, Salesforce not only supports Einstein (their own LLM), but also other ones like Sagemaker, Vertex AI, etc. BYOM Bring Your Own Model ‘Bring your own AI (model)’ is Salesforce’s way to meet their customers where they’re at. With Data Cloud, Salesforce not only supports Einstein (their own LLM), but also other ones like Sagemaker, Vertex AI, etc. CRMA CRM Analytics This is the analytics/business intelligence (BI) solution on the Salesforce platform, integrating Salesforce data with external data and provides the ability to analyze large datasets while performing predictive analytics. (Previously known as Wave/Einstein Analytics/Tableau CRM). EA Einstein Analytics The former name for CRM Analytics (also formerly Tableau CRM). You may still come across EA in outdated documentation. GPT Generative Pre-trained Transformer GPT is a technology concept, although it’s often mistaken for a product name (with the rise of the wildly popular ChatGPT). LLM Large Language Model An AI model that has been trained using lots of data. Salesforce has their own LLM that outperformed expectations during testing. Salesforce’s LLM leverages Data Cloud, or can be plugged into a different data lake. Organizations can also opt to their own LLM alongside their Salesforce data. NBA Einstein Next Best Action An AI-driven Salesforce product that makes recommendations to users when they are working with records in Salesforce. It appears within a Lightning component that’s typically placed on the record page sidebar. The user can choose to Accept or Reject the recommendation. NLP Natural Language Processing “Artificial intelligence that focuses on how computers can understand, interpret, and generate human language. NLP allows customers to interact with systems using normal human language rather than complex commands.” (source) SAQL Structured Query Language “Use SAQL to access data in Tableau CRM dataset. Tableau CRM uses SAQL behind the scenes in lenses, dashboards, and explorer to gather data for visualizations.” (source) TCRM Tableau CRM Tableau CRM is a self-service data visualization and business intelligence (BI) platform that integrates Salesforce data with external data. Formerly known as Einstein Analytics, and now CRM Analytics, this product boasts some of the most powerful data analysis capabilities in the market. XMD Extended Metadata “Enables you to customize the formatting of dataset fields and their values in Analytics dashboards and lenses.” (source) Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

July 15, 2023in Salesforce

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