Data Visualization

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 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

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 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

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 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

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 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

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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

October 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 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

August 20, 2023in Salesforce

14Jul

CRM Analytics and Tableau

Whether you’re exploring data visualization tools or delving into the realm of analytics, Tableau and Salesforce CRM Analytics (formerly known as Tableau CRM) likely appear on your radar, both under the Salesforce umbrella. In this discussion, we’ll scrutinize the key disparities between these solutions and discern when one triumphs over the other. Firstly, let’s clarify the essence of both platforms: Tableau stands as a standalone, user-centric business intelligence platform, offering a suite of products like Tableau Prep, Tableau Desktop, and Tableau Online, tailored for data preparation, visualization, and dissemination. Salesforce CRM Analytics embeds analytics and reporting within Salesforce, furnishing insights seamlessly within your CRM workflow, predominantly drawing data from your Salesforce environment while accommodating certain external data sources. Here’s a comparative glimpse: Tableau: Salesforce CRM Analytics: Moreover, Salesforce offers Einstein Discovery, an AI-powered analytics tool augmenting data analysis with machine-learning models and statistical analysis. It enables swift detection of correlations, prediction of outcomes, and recommendation of improvement strategies, enhancing proactive decision-making. This plug-in seamlessly integrates with both Salesforce CRM Analytics and Tableau, subject to appropriate licensing. In contemplating between Tableau and CRM Analytics, Charlotte Bayart, Data & AI consultant at delaware, emphasizes the level of reporting: “For business reports on a management level, Tableau will likely excel due to its versatility and powerful visualizations. However, for operational reporting within CRM workflows necessitating real-time insights and immediate actionability, CRM Analytics proves indispensable. With embedded solutions like CRM Analytics, users gain direct access to detailed insights without additional layers, facilitating prompt decision-making and action.” Ultimately, organizations leveraging Salesforce as a CRM platform might find synergy in employing both Tableau and CRM Analytics concurrently, optimizing their analytical prowess across various reporting needs. 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

July 14, 2022in Salesforce

23Nov

Why We Love Pie But Not Pie Charts

Everybody loves pie, but not all of us love pie charts (except when it’s a chart about pie). It turns out, our brains have a hard time comparing the area of shapes. When it comes to visualizing data, we prefer simple and easy to understand. Consider making that pie chart in your next presentation a bar chart. This unsavory position against pie charts reflects Tectonic’s passion for effective data visualizations. When done properly, visualizations help us quickly see new things and digest the size and scale of your business and market. Visualizations that allow you to interact with data and easily see areas where you need to focus, make decision making easier. But many of us are stuck managing from charts of numbers, or at best static visualizations in a power point. You know those presentations that someone spends weeks creating each month to describe the events in the prior month. Is this the kind of efficiency we thought 2018 technology would deliver? When you finally get the information, how do you correctly tie individual results to the trends in your business and markets so you can draw the right conclusions and make decisions? For example: are we generating enough leads, in the right industries, for the right products to generate the revenue we need in Q4? In other words, how do you translate how much pie you have eaten into how full you are? On the surface, it seems easy: If I’m eating pie, it’s late in the meal and I’m probably already full…but what if those assumptions don’t hold true? Are you willing to risk your comfort on it…or your business on it? At Tectonic, we help you align your desired business results with the events and activities in your business. These “analytical pathways” make it simpler to use data to drive your business. We can show you how to unlock the trends in your business and use data to drive new results. Happy Pie Season! 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 How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more A World Series Lesson for Your Business The Houston Astros won the World Series last night. The first time the organization has won the World Series since Read more Why Your Company Isn’t Like a Baseball Team Recently, Chris shared an excellent post about the new World Series Champion Houston Astros. In short, it was a reminder Read more

November 23, 2017in Salesforce Implementation Services

Generative AI for Tableau

The AI Tech Stack

AI Agents

Trust Einstein Copilot for Tableau

AI Hallucinations

Summer 24 Analytics Release Notes

RAG – Retrieval Augmented Generation in Artificial Intelligence

CRM Analytics and Tableau

Why We Love Pie But Not Pie Charts

Recent Posts

Understanding the Causes of Disillusionment with Generative AI

Meta Joins the Race to Reinvent Search with AI

Google Prepares AI-Powered Jarvis Agent

Is the Future Agentic for ERP?

AI is Transforming Threat Detection

Contact Us

Be in touch today — and start your business on a path to success.

Category

Archives