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29Dec

ThoughtSpot AI agent Spotter enables conversational BI

ThoughtSpot Unveils Spotter: A Generative AI-Powered Data Agent ThoughtSpot, a leading analytics vendor, has launched Spotter, an advanced generative AI-powered agent designed to revolutionize how users interact with data. Spotter enables conversational data exploration, contextual understanding, and autonomous analysis, making it a significant leap forward in the analytics landscape. Spotter’s Role in ThoughtSpot’s Evolution Spotter replaces Sage, ThoughtSpot’s earlier generative AI-powered interface, which debuted in March 2023. Despite moving from private to public preview and gaining new capabilities, Sage never reached general availability. Spotter is now generally available for ThoughtSpot Analytics, while its embedded version is in beta testing. Unlike earlier AI tools that focused on question-and-answer interactions, such as Sage and Microsoft’s copilots, Spotter takes the concept further by integrating contextual awareness and autonomous decision-making. Spotter doesn’t just respond to queries; it suggests follow-up questions, identifies anomalies, and provides proactive insights, functioning more like a virtual analyst than a reactive chatbot. Key Features of Spotter Spotter is built to enhance productivity and insight generation through the following capabilities: Generative AI’s Growing Impact on BI ThoughtSpot has long aimed to make analytics accessible to non-technical users through natural language search. However, previous NLP tools often required users to learn specific vocabularies, limiting widespread adoption. Generative AI bridges this gap. By leveraging extensive vocabularies and LLM technology, tools like Spotter enable users of all skill levels to access and analyze data effortlessly. Spotter stands out with its ability to deliver proactive insights, identify trends, and adapt to user behavior, enhancing the decision-making process. Expert Perspectives on Spotter Donald Farmer, founder of TreeHive Strategy, highlighted Spotter’s autonomy as a game-changer: “Spotter is a big move forward for ThoughtSpot and AI. The natural language interface is more conversational, but the key advantage is its autonomous analysis, which identifies trends and insights without users needing to ask.” Mike Leone, an analyst at TechTarget’s Enterprise Strategy Group, emphasized Spotter’s ability to adapt to users: “Spotter’s ability to deliver personalized and contextually relevant responses is critical for organizations pursuing generative AI initiatives. This goes a long way in delivering unique value across a business.” Farmer also pointed to Spotter’s embedded capabilities, noting its growing appeal as an embedded analytics solution integrated with productivity tools like Salesforce and ServiceNow. Competitive Positioning Spotter aligns ThoughtSpot with other vendors embracing agentic AI in analytics. Google recently introduced Conversational Analytics in Looker, and Salesforce’s Tableau platform now includes Tableau Agent. ThoughtSpot’s approach builds on its core strength in search-based analytics while expanding into generative AI-driven capabilities. Leone observed: “ThoughtSpot is right in line with the market in delivering an agentic experience and is laying the groundwork for broader AI functionality over time.” A Step Toward the Future of Analytics With Spotter, ThoughtSpot is redefining the role of AI in business intelligence. The tool combines conversational ease, proactive insights, and seamless integration, empowering users to make data-driven decisions more efficiently. As generative AI continues to evolve, tools like Spotter demonstrate how businesses can unlock the full potential of their data. 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

December 29, 2024in AI Tools, Data, Enterprise, Generative AI, Google, Salesforce, Technology0 Comments

22Sep

Tableau Einstein is Here

Tableau Einstein marks a new chapter for Tableau, transforming the analytics experience by moving beyond traditional reports and dashboards to deliver insights directly within the flow of a user’s work. This new AI-powered analytics platform blends existing Tableau and Salesforce capabilities with innovative features designed to revolutionize how users engage with data. The platform is built around four key areas: autonomous insight delivery through AI, AI-assisted development of a semantic layer, real-time data access, and a marketplace for data and AI products, allowing customers to personalize their Tableau experience. Some features, like Tableau Pulse and Tableau Agent, which provide autonomous insights, are already available. Additional tools, such as Tableau Semantics and a marketplace for AI products, are expected to launch in 2025. Access to Tableau Einstein is provided through a Tableau+ subscription, though pricing details remain private. Since being acquired by Salesforce in 2019, Tableau has shifted its focus toward AI, following the trend of many analytics vendors. In February, Tableau introduced Tableau Pulse, a generative AI-powered tool that delivers insights in natural language. In July, it also rolled out Tableau Agent, an AI assistant to help users prepare and analyze data. With AI at its core, Tableau Einstein reflects deeper integration between Tableau and Salesforce. David Menninger, an analyst at Ventana Research, commented that these new capabilities represent a meaningful step toward true integration between the two platforms. Donald Farmer, founder of TreeHive Strategy, agrees, highlighting that while the robustness of Tableau Einstein’s AI capabilities compared to its competitors remains to be seen, the platform offers more than just incremental add-ons. “It’s an impressive release,” he remarked. A Paradigm Shift in Analytics A significant aspect of Tableau Einstein is its agentic nature, where AI-powered agents deliver insights autonomously, without user prompts. Traditionally, users queried data and analyzed reports to derive insights. Tableau Einstein changes this model by proactively providing insights within the workflow, eliminating the need for users to formulate specific queries. The concept of autonomous insights, represented by tools like Tableau Pulse and Agentforce for Tableau, allows businesses to build autonomous agents that deliver actionable data. This aligns with the broader trend in analytics, where the market is shifting toward agentic AI and away from dashboard reliance. Menninger noted, “The market is moving toward agentic AI and analytics, where agents, not dashboards, drive decisions. Agents can act on data rather than waiting for users to interpret it.” Farmer echoed this sentiment, stating that the integration of AI within Tableau is intuitive and seamless, offering a significantly improved analytics experience. He specifically pointed out Tableau Pulse’s elegant design and the integration of Agentforce AI, which feels deeply integrated rather than a superficial add-on. Core Features and Capabilities One of the most anticipated features of Tableau Einstein is Tableau Semantics, a semantic layer designed to enhance AI models by enabling organizations to define and structure their data consistently. Expected to be generally available by February 2025, Tableau Semantics will allow enterprises to manage metrics, data dimensions, and relationships across datasets with the help of AI. Pre-built metrics for Salesforce data will also be available, along with AI-driven tools to simplify semantic layer management. Tableau is not the first to offer a semantic layer—vendors like MicroStrategy and Looker have similar features—but the infusion of AI sets Tableau’s approach apart. According to Tableau’s chief product officer, Southard Jones, AI makes Tableau’s semantic layer more agile and user-friendly compared to older, labor-intensive systems. Real-time data integration is another key component of Tableau Einstein, made possible through Salesforce’s Data Cloud. This integration enables Tableau users to securely access and combine structured and unstructured data from hundreds of sources without manual intervention. Unstructured data, such as text and images, is critical for comprehensive AI training, and Data Cloud allows enterprises to use it alongside structured data efficiently. Additionally, Tableau Einstein will feature a marketplace launching in mid-2025, which will allow users to build a composable infrastructure. Through APIs, users will be able to personalize their Tableau environment, share AI assets, and collaborate across departments more effectively. Looking Forward As Tableau continues to build on its AI-driven platform, Menninger and Farmer agree that the vendor’s move toward agentic AI is a smart evolution. While Tableau’s current capabilities are competitive, Menninger noted that the platform doesn’t necessarily set Tableau apart from competitors like Qlik, MicroStrategy, or Microsoft Fabric. However, the tight integration with Salesforce and the focus on agentic AI may provide Tableau with a short-term advantage in the fast-changing analytics landscape. Farmer added that Tableau Einstein’s autonomous insight generation feels like a significant leap forward for the platform. “Tableau has done great work in creating an agentic experience that feels, for the first time, like the real deal,” he said. Looking ahead, Tableau’s roadmap includes a continued focus on agentic AI, with the goal of providing each user with their own personal analyst. “It’s not just about productivity,” said Jones. “It’s about changing the value of what can be delivered.” Menninger concluded that Tableau’s shift away from dashboards is a reflection of where business intelligence is headed. “Dashboards, like data warehouses, don’t solve problems on their own. What matters is what you do with the information,” he said. “Tableau’s push toward agentic analytics and collaborative decision-making is the right move for its users and the market as a whole.” 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

September 22, 2024in Data, Enterprise, Generative AI, Salesforce

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

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

10Jun

What is BI in Salesforce?

Salesforce BI helps to create fast, digestible reports to help you make informed decisions at the right time. Salesforce Einstein is a leading business intelligence software solution that will help streamline your operations. Read on in this insight to learn how Salesforce BI capabilities including Tableau rank in the Gartner Magic Quadrant. Make the right decision every time using analytics that go beyond business intelligence software. See why Gartner named Salesforce (Tableau) a Leader in the Gartner® Magic Quadrant™ for Analytics and Business Intelligence Platforms for the 11th consecutive year. Data and analytics leaders must use analytics and BI platforms to support the needs of IT, analysts, consumers and data scientists. While integration with cloud ecosystems and business applications is a key selection requirement, buyers also need platforms to support openness and interoperability. Analytics and business intelligence (ABI) platforms enable less technical users, including business people, to model, analyze, explore, share and manage data, and collaborate and share findings, enabled by IT and augmented by artificial intelligence (AI). For several years, the Magic Quadrant for Analytic and Business Intelligence Platforms has emphasized visual self-service for end users augmented by AI to deliver automated insights. While this remains a significant use case, the ABI platform market will increasingly need to focus on the needs of the analytic content consumer and business decision makers. To achieve this, automated insights must be relevant in context of a user’s goals, actions and workflow. Many platforms are adding capabilities for users to easily compose low-code or no-code automation workflows and applications. This blend of capabilities is helping to expand the vision for analytics beyond simply delivering datasets and presenting dashboards. Today’s ABI platforms can deliver enriched contextualized insights, refocus attention on decision-making processes and ultimately take actions that will deliver business value. In addition to the increasing consumer design focus trend, we see other key market trends, including the need for improved governance of analytic content creation and dissemination, and the demand for a headless, open architecture. For example, a headless ABI platform would decouple the metrics store from the front-end presentation layer, enabling more interoperability with competitive products. ABI platform functionality includes the following 12 critical capabilities, which have been updated to reflect areas of market change, differentiation and customer demand: Gartner added three new critical capabilities as part of our metrics store evaluation criteria this year: ABI platforms have always been about measurement. For decades, the slicing and dicing of measures by their dimensional attributes was synonymous with the act of performing business intelligence. However, over the last decade, the focus on metrics and measurement was overshadowed by data visualization. As data visualization became the most conspicuous capability, some business executives began to conflate ABI platforms with data visualization — as if ABI platforms are glorified chart wizards. This misconception minimizes much of the work performed and the business value delivered by ABI platforms. Establishing metrics stores as a critical capability to execute makes it clear that defining and communicating performance measures throughout an organization is one of the key purposes of an ABI platform. Analytics collaboration is a combination of many features (such as Slack/Teams integration, action frameworks) that collectively improve an organization’s ability to make decisions with consensus. Data science integration reflects the increasing likelihood that a business analyst may want to use data science to test certain hypotheses, and that data scientists will need to leverage features such as data prep and data visualization. In addition, Gartner is changing “catalogs” to “analytic catalogs” to emphasize a set of requirements that are not being met by ABI platform vendors today. Most large enterprises have thousands of reports built across multiple ABI platforms, but consumers in these organizations have no easy way to access these reports. The name change to analytic catalogs reflects the need for ABI platform vendors to deliver analytic content with the consumer in mind. Three critical capabilities were removed from our evaluation criteria: security, natural language generation (NLG; rolled into data storytelling) and cloud analytics (which will no longer be considered a platform capability, but instead a go-to-market strategy covered in the Magic Quadrant). And one of the security sub-criteria, about the granularity of authorization (e.g., row-based security) has been moved to the enterprise reporting capability. Salesforce (Tableau) Tableau, a Salesforce company, is a Leader in this Magic Quadrant. Its products are mainly focused on visual-based exploration that enables business users to access, prepare, analyze and present findings in their data. CRM Analytics, formerly Tableau CRM, provides augmented analytics capabilities for analysts and citizen data scientists. Tableau has global operations and serves clients of all sizes. In 2022, Tableau reinforced its augmented consumer vision to provide contextualized insights with deeper integration with Salesforce Data Cloud. IT also improved decision intelligence by bringing domain-aware insights into action with Revenue Intelligence and other Salesforce-native apps. The extensible design and x-platform integrations (Salesforce Flow, MuleSoft, UiPath and Looker) further enable composable analytics to bring insights into workflow with agility. Strengths Cautions 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

June 10, 2021in Artificial Intelligence, Data, Enterprise, Google, Mulesoft, Salesforce, Salesforce Data Cloud, Salesforce Einstein, Salesforce Flow, Salesforce Managed Services, Technology

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