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Visualize any data and integrate the visuals into the apps you use every day with Power BI, a unified platform for self-service and business intelligence.

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Dataiku launches generative AI tool for dynamic data storytelling - SiliconANGLE

Dataiku launches generative AI tool for dynamic data storytelling Artificial intelligence data analytics startup Dataiku Inc. today announced the launch of Dataiku Stories, a generative artificial intelligence-powered data storytelling solution that allows businesses to generate insights and transform company data into visual presentations. Designed to bridge the gap between static slide presentations and enterprise dashboards, Dataiku Stories allows businesses to use simple prompts to discover and share powerful decision-making insights based on the latest, most trusted company information. According to the company, the service does so without sacrificing speed or accuracy within the Dataiku Universal AI Platform. The service seeks to address the issue wherein presentations rely on static screenshots of data visuals that quickly become outdated as a result of constantly changing underlying data, limiting their effectiveness in business decision-making. A Dataiku global survey of 150 data team leaders at enterprise organizations revealed that nearly all data teams need to update presentations with new data at least quarterly, with about one-third updating them weekly. Despite the widespread ability to create presentations, Dataiku argues, the expertise needed to translate raw data into actionable business narratives is less common. The survey found that over one-third of data teams make six or more monthly requests to other departments for data access. Dataiku Stories democratizes data access by allowing more individuals within organizations to access, analyze and share business insights confidently and accurately, with the end goal of facilitating faster and more informed decision-making. "The magic of Dataiku Stories is the ability to instantly create presentations using trusted, real-time data from projects already built and managed on the Dataiku Universal AI Platform, ensuring full analytics continuity," Sophie Dionnet, global vice president of product and business solutions at Dataiku, said in a statement. "Since this can all be done without any coding skills or waiting on a request from overburdened data scientists or business analysts, Stories effectively solves the common challenge of 'last mile' data analytics -- unlocking even more business value from AI across companies." Respondents in Dataiku's survey also said that their teams can spend up to 42 hours on average per month building and revising presentations. With its generative AI assistance, Dataiku Stories can instantly generate entire presentations with simple prompts, the company said, as well as iterate content through suggestions for relevant data points and automatically generated imagery. The dynamic presentations made with Dataiku Stories also automatically update with new, fresh data so presentations remain accurate and up to date, even after they're shared. Dataiku Stories is now available to qualified Dataiku customers.

SiliconANGLE

Wed, 11 Dec, 4:41 PM UTC

Future of Analytics: Leveraging Generative AI for Data Visualization

The integration of Generative AI into data visualization is not just a trend but a paradigm shift in how we approach data analytics. It empowers businesses and researchers to communicate insights more effectively, make informed data-driven decisions, and uncover patterns and trends that were previously hidden. Generative AI is revolutionizing data analytics by automating data generation, enhancing predictive modeling, and providing deeper insights. It creates synthetic data to supplement real datasets, improving model training and addressing data scarcity. Generative AI can identify patterns and anomalies, making predictions more accurate and robust. It also assists in creating advanced visualizations and narratives, turning complex data into comprehensible formats. What are the future uses of generative AI? The future uses of generative AI are vast and transformative. In healthcare, it can design new drugs, personalize treatment plans, and simulate medical scenarios. In entertainment, it will create immersive virtual worlds, realistic characters, and personalized content. In education, generative AI can develop tailored learning experiences and intelligent tutoring systems. In business, it will enhance customer service through advanced chatbots, optimize supply chains, and automate complex tasks. Which AI tool is best for data analysis? Several AI tools excel in data analysis, each offering unique features. Python-based tools like TensorFlow and PyTorch are highly regarded for their flexibility and extensive libraries, ideal for advanced machine learning and deep learning tasks. For more user-friendly platforms, RapidMiner and KNIME provide drag-and-drop interfaces, making data analysis accessible without extensive coding knowledge. IBM Watson offers robust AI-driven insights with powerful natural language processing capabilities. How can generative AI help you improve your data visualization charts? Generative AI can significantly enhance data visualization charts by automatically generating visually appealing and insightful representations. It can identify key patterns and trends within the data, suggesting optimal visualization types for clarity and impact. AI can also create interactive elements, enabling deeper exploration of data insights. By automating the design process, generative AI ensures consistency and accuracy while saving time. Can generative AI create dashboards? Generative AI can indeed create dashboards by leveraging its ability to analyze data patterns and generate visual representations. Using algorithms like GANs (Generative Adversarial Networks) or neural networks, AI can process complex datasets and generate interactive dashboards that visualize trends, insights, and predictions. These dashboards can be customized based on user requirements, offering dynamic updates and intuitive interfaces. This capability not only enhances data exploration but also supports decision-making processes across various industries, from finance and healthcare to marketing and logistics.

Analytics Insight

Fri, 28 Jun, 6:06 PM UTC

Business Intelligence Trends in 2025: What's Next?

Organizations that embrace these trends and invest in the right tools and strategies will be well-positioned to leverage data for competitive advantage, drive innovation, and make more informed decisions. Whether it's through the adoption of real-time analytics, the implementation of self-service BI, or the prioritization of ethical AI practices, the future of business intelligence promises to be dynamic, data-driven, and full of opportunities for those ready to embrace the change. What will be the future of business intelligence? The future of business intelligence (BI) will be driven by advanced analytics, artificial intelligence, and machine learning. Real-time data processing, predictive analytics, and enhanced data visualization will empower businesses to make more informed decisions. Additionally, greater accessibility and user-friendly tools will democratize BI, making it integral to all business operations. What is the scope of business intelligence? The scope of business intelligence (BI) encompasses data collection, integration, analysis, and reporting to support decision-making. BI tools help organizations optimize operations, identify market trends, improve customer experiences, and drive strategic planning. It spans various industries, enhancing performance and competitiveness through data-driven insights and informed business strategies. What are the 5 stages of business intelligence? The five stages of business intelligence (BI) are crucial for transforming raw data into actionable insights. It begins with data collection, where data is gathered from various sources. This data is then integrated and stored in a central repository during the data integration stage. Next, data analysis is conducted to identify patterns and insights within the data. These findings are subsequently presented through dashboards and reports in the reporting stage. Finally, the insights derived from this process are used to guide business strategies and actions, facilitating informed decision-making. What does a business intelligence role do? A business intelligence role involves analyzing data to support decision-making processes. Responsibilities include data collection, integration, and analysis, creating reports and dashboards, identifying trends and insights, and providing strategic recommendations. BI professionals ensure data accuracy and accessibility, enabling organizations to make data-driven decisions and improve overall performance. What is the main objective of business intelligence? The main objective of business intelligence (BI) is to transform data into actionable insights that support informed decision-making. BI aims to improve organizational performance by identifying trends, uncovering opportunities, optimizing processes, and guiding strategic planning. Ultimately, BI enhances efficiency, competitiveness, and profitability through data-driven analysis.

Analytics Insight

Sun, 30 Jun, 8:01 AM UTC

Leveraging Snowflake data cloud integration for business intelligence - SiliconANGLE

Streamlining business intelligence: Empowering users with AI-driven tools In the changing landscape of data integration and business intelligence, companies are increasingly turning to solutions like Snowflake's data cloud integration to streamline data accessibility and analytics. The integration of platforms such as Domo with Snowflake's data cloud exemplifies how organizations can leverage these tools to enhance data management, improve business processes and drive strategic decisions, according to RJ Tracy (pictured, left), senior vice president of strategic development and channel at Domo Inc. "Domo really has a pretty broad platform and we wrap around Snowflake. So we start with an integration layer. We have over a thousand pre-built connectors that help people get data," Tracy said. "We also have a full ETL layer that allows you to prepare your data...and then on the other side, we have a full visual layer and a low-code, no-code app platform. All of that's wrapped around an AI service layer that allows us to leverage all the really innovative things that Snowflake's doing with AI and serve it out to the end-of-business user." Tracy and David Damitz (right), global business intelligence team lead at TaylorMade Golf Co. spoke with theCUBE's Rebecca Knight and Dave Vellante at Data Cloud Summit, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed the integration of Domo with Snowflake for enhanced data management, the challenges of breaking down data silos, automating repetitive tasks and empowering business users with self-service analytics and artificial intelligence tools. (* Disclosure below.) The automation of repetitive tasks and the empowerment of business users through self-service analytics and AI-driven tools are an important part of the process. The common challenges organizations face, such as data silos and manual data handling, and the steps taken to overcome these hurdles, are in focus now, according to Damitz. "We have to make data assets available to everyone in the most easy-to-use way possible," he said. "One of our main issues or challenges was finding all these kind of data silos that were throughout the organization and being able to put them into one place so that people could then take those different data assets and those data sets and kind of play them against each other in different ways that they weren't able to do previously when they were more siloed." AI is transforming customer service through chatbots that access information and Domo's platform, which uses AI and analytics to save time for representatives. Customers can now fully manage their large language models, according to Tracy. "I think one I was mentioning, in customer service, we have a lot of documents and things that our reps have to refer to to get information to their customers about," he said. "Those are things that previously they'd have to go and fish through a bunch of PDF documents and things like that. We're creating a chatbot on top of a lot of those documents now that'll go and be able to read those so they can just ask the question, 'Hey, what is this?' Bring it up and they can communicate that to the customer." Here's the complete video interview, part of SiliconANGLE's and theCUBE Research's coverage of Data Cloud Summit:

SiliconANGLE

Tue, 16 Jul, 4:02 PM UTC

Top AI Tools for Business Analysts: Enhancing Data-Driven Decision Making

A comprehensive overview of essential AI tools that business analysts should be familiar with to improve their data analysis and decision-making processes.

1 Sources

Thu, 29 Aug, 12:10 AM UTC

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