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How to Connect Multiple Data Sources in Power BI Desktop

How to Connect Multiple Data Sources in Power BI Desktop

In today’s data-driven world, the ability to analyze diverse data sources can set a business apart. With Power BI Desktop, a Microsoft tool, analysts can seamlessly integrate data from various platforms and transform raw information into actionable insights. For instance, you could combine Excel-based sales figures with financial data from SQL Server and customer information from Salesforce into an interactive report. Mastering these techniques can be easier through structured learning, such as Microsoft Power BI courses, which offer practical insights into leveraging this powerful tool. This guide will help you connect, combine, and visualize multiple data sources in Power BI Desktop to make smarter, data-driven decisions. Why Combine Multiple Data Sources? Organizations often face the challenge of managing data stored across disparate systems. Financial records may reside in SQL Server, sales data in Excel, and customer information in cloud platforms like Salesforce. Insights from these datasets are often hidden unless they are integrated. Using Power BI Desktop, you can load multiple data sources into a unified model, providing a comprehensive view that enables better analysis and decision-making. Getting Started with Power BI Desktop Before integrating datasets, ensure you have Power BI Desktop installed. The tool is available for download from the official Power BI website. Once installed, launch Power BI Desktop to begin connecting your data sources. Step-by-Step Guide 1. Connecting Your First Data Source Follow these steps to connect to your first data source: At this stage, you can use Power Query Editor to clean and transform the data as needed. 2. Adding Additional Data Sources Enhance your report by adding more data sources: For example, you could link an Excel file for sales data, a SQL Server database for product details, and Azure for supplementary information, all within a single report. 3. Combining Data from Multiple Sources To merge data from different sources into a cohesive model: This process creates a unified data model that allows for cross-tabulation and advanced visualizations. 4. Using Power Query Editor for Data Transformation Before combining datasets, you may need to clean and transform the data. Use Power Query Editor to: Access Power Query Editor by selecting Transform Data on the Home tab. 5. Creating Visualizations with Combined Data With your unified data model, you can create compelling visualizations: 6. Refreshing Data Connections Power BI Desktop enables you to refresh data connections effortlessly, ensuring your reports stay updated: Best Practices for Connecting Multiple Data Sources Conclusion Integrating multiple data sources in Power BI Desktop empowers businesses to uncover deep insights and make informed decisions. By following these steps, you can connect, aggregate, and visualize diverse datasets with ease. To further enhance your expertise, explore free resources or consider professional courses to master the versatility of Power BI Desktop—a vital tool for data professionals and business analysts. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Cohesity Data Explore

Cohesity has introduced Data Explore, a new feature in its Gaia generative AI platform, aimed at simplifying data search within backups for any employee. The update, launched this week, adds keyword search capabilities and data visualization through topic word clouds, enhancing user access to valuable information. Previously, users could interact with Gaia’s conversational AI interface to ask questions about stored data. Data Explore now extends this by enabling users to browse frequent keywords within data sets and receive search suggestions to help refine their queries. This addition is particularly valuable for users who may not know exactly what to ask when exploring backup data. As part of the update, Gaia’s support for file storage systems has also expanded. Gaia now integrates with both on-premises and cloud-based file servers, such as Dell Technologies’ PowerScale and NetApp systems, in addition to existing support for Microsoft 365 services like Outlook, SharePoint, and OneDrive. This enhanced search functionality reflects a broader trend among backup vendors to deliver greater utility from stored data, according to Simon Robinson of TechTarget’s Enterprise Strategy Group. He noted that tools making data accessible to non-experts bring businesses closer to the goal of actionable insights. “You don’t need to be a corporate librarian to use this stuff,” Robinson said. Data Explore’s semantic indexing, similar to internet search engines, aids users by automatically surfacing keywords, questions, and suggestions, making backup data more searchable and actionable. According to Krista Case, an analyst at Futurum Group, this helps reduce AI hype by grounding Gaia in practical use cases, facilitating faster insights for end users. Since Gaia’s launch as a SaaS add-on for Cohesity Data Cloud, its features have evolved to offer deeper insights beyond simple chatbot interactions. Greg Statton, Cohesity’s VP of AI solutions, shared that the platform aims to be more than a support agent for backup queries. The vision is to provide advanced AI tools that enhance data discovery, flag abnormal events, and reduce alert fatigue, giving IT administrators actionable intelligence that is more contextually aware of their tasks. Ultimately, Cohesity’s Data Explore feature exemplifies generative AI’s potential in unlocking business value from backup data. By making this data accessible and understandable, Cohesity is helping organizations achieve the long-awaited promise of deriving value from stored data – a milestone Robinson believes backup vendors are now on the verge of realizing. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Where Will the Data Scientists Go

Where Will the Data Scientists Go

What Is to Become of the Data Scientist Role? This question frequently arises among executives, particularly as they navigate the changing roles of data teams, such as those at DataRobot. Where Will the Data Scientists Go may not be as relevant as what new places can they go with AI? The short answer? While tools may evolve, the core of data science remains steadfast. As the field of data science continues to expand, the role of the data scientist becomes increasingly vital. The need will grow, even as the role changes. Trust in AI is dependant upon human oversight. Beyond the Hype of Consumer AI The surge in consumer AI products has raised concerns among data scientists about the implications for their careers. However, these technologies are built on data and generate vast amounts of new data, presenting numerous opportunities. The real transformative potential lies in enterprise-scale automation. Enterprise-Scale Automation: The Data Scientist’s Domain Enterprise-scale automation involves creating large-scale, reliable systems. Data scientists are crucial in this effort, as they bring expertise in data exploration and systematic inference. They are uniquely positioned to identify automation opportunities, design testing and monitoring strategies, and collaborate with cross-functional teams to bring AI solutions from concept to implementation. As automation grows, the role of the data scientist is essential in ensuring these systems function effectively and safely, particularly in environments without human oversight. New Skills for Data Scientists: The Guardians of AI Applications Data scientists will need to acquire new skills to manage automation at scale, including securing the systems they build. Generative AI introduces new risks, such as potential vulnerabilities to prompt injections or other security threats. Governance and ensuring positive business impacts will become increasingly important, requiring a data science mindset. Building Great Data Teams in the Age of AI The future of data science will not be about automation replacing data scientists but about the evolution of roles and skills. Data scientists need to focus on the core foundations of their discipline rather than the specific tools they use, as tools will continue to evolve. Teams must be built intentionally, encompassing a range of skills and personalities necessary for successful enterprise automation. Business Leaders: Navigating the AI Landscape Business leaders will need to excel in decision-making, understanding the problems they aim to solve, and selecting the appropriate tools and teams. They will also need to manage evolving regulations, particularly those related to the design and deployment of AI systems. Data Scientists: Precision Thinkers at the Forefront Contrary to the belief that AI could replace coding skills, the essence of data science lies in precise thinking and clear communication. Data scientists excel in translating business needs into data-driven decisions and AI applications, ensuring that solutions are not only technically sound but also aligned with business objectives. This skill set will be crucial in the era of AI, as data scientists will play a key role in optimizing workflows, designing AI safety nets, and protecting their organization’s brand and reputation. The Evolving Role of Data Science The demand for precise, data-literate thinkers will only grow with the rise of enterprise AI systems. Whether they are called data scientists or another name, professionals who delve deeply into data and provide critical insights will remain essential in navigating the complexities of modern technology and business landscapes. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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