{"id":238500,"date":"2026-04-08T15:04:37","date_gmt":"2026-04-08T13:04:37","guid":{"rendered":"https:\/\/www.itta.net\/?post_type=formations&#038;p=238500"},"modified":"2026-04-12T18:59:03","modified_gmt":"2026-04-12T16:59:03","slug":"leveraging-data-with-artificial-intelligence","status":"publish","type":"formations","link":"https:\/\/www.itta.net\/en\/trainings\/artificial-intelligence\/leveraging-your-data-with-artificial-intelligence\/","title":{"rendered":"Leveraging Your Data with Artificial Intelligence"},"content":{"rendered":"\r\n<p class=\"wp-block-paragraph\">The Leveraging Your Data with Artificial Intelligence training course explains how AI can help organizations better exploit their data without immediately entering an advanced development or data science approach. It helps participants identify concrete use cases, spot opportunities for value creation and structure a first approach to AI-augmented data.<\/p>\r\n\r\n\r\n\r\n<p>An accessible course connecting data, business use cases and artificial intelligence<\/p>\r\n\r\n\r\n\r\n<p class=\"wp-block-paragraph\">During this course, participants discover how AI can facilitate the analysis, summarization, exploration and exploitation of business data. They learn to better understand what AI can realistically bring to a decision-making context, to distinguish simple approaches from more advanced projects and to identify useful applications for their work.<\/p>\r\n","protected":false},"featured_media":0,"parent":0,"menu_order":0,"template":"","editeurs":[4257,1488,1493],"domaine":[2982,4250],"class_list":["post-238500","formations","type-formations","status-publish","hentry","editeurs-itta-artificial-intelligence","editeurs-microsoft","editeurs-power-bi-microsoft","domaine-artificial-intelligence","domaine-data-science-and-applied-ai"],"acf":{"reference":"AI-05-01","duree_nombre":1,"duree_unite":"jour","prix_virtuel":750,"prix_presentiel":800,"prix_blended":"","prix_elearning":"","views":82,"niveau":["Fondamental"],"role":[],"certifiant":"","pdus":"","lieux":["Gen\u00e8ve","Lausanne"],"formats_dapprentissage":["presentiel","virtuel"],"examen_inclus":"Non certifiant","garanti":[],"financement":["Temptraining"],"description_temptraining":"","description_caf":"","description_title":"Leveraging Your Data with Artificial Intelligence Training","objectifs":"<ul>\r\n \t<li>Understand how AI can be applied to data exploitation<\/li>\r\n \t<li>Identify concrete use cases related to analysis and decision-making<\/li>\r\n \t<li>Distinguish between data, analysis, automation and predictive models<\/li>\r\n \t<li>Spot improvement opportunities in your own business area<\/li>\r\n \t<li>Communicate more effectively with data, IT or business teams<\/li>\r\n \t<li>Prepare an initial approach to data valorization with AI<\/li>\r\n<\/ul>","connaissances":"<ul>\r\n \t<li>No prior technical knowledge is required<\/li>\r\n \t<li>Familiarity with business data and office tools is a plus<\/li>\r\n<\/ul>","profils_participants":"<ul>\r\n \t<li>Managers<\/li>\r\n \t<li>Department heads<\/li>\r\n \t<li>Project managers<\/li>\r\n \t<li>Support functions<\/li>\r\n \t<li>Professionals who work with data in their daily activities<\/li>\r\n \t<li>Anyone looking to understand how to use AI to better exploit data<\/li>\r\n<\/ul>","examen":"","documentation":"<ul>\n \t<li>Support de cours num\u00e9rique inclus<\/li>\n<\/ul>","lab__exercices":"<ul>\n \t<li>This course includes workshops on identifying use cases, analyzing needs, reviewing data-driven scenarios and exploring how to better leverage business information with AI.<\/li>\n<\/ul>","contenu_cours":"<strong>Module 1: Understanding the Value of Data in Business<\/strong>\r\n<ul>\r\n \t<li>The role of data in decision-making<\/li>\r\n \t<li>Structured and unstructured data<\/li>\r\n \t<li>Data quality<\/li>\r\n \t<li>Difference between dashboards, analysis and artificial intelligence<\/li>\r\n \t<li>Positioning AI in a data valorization logic<\/li>\r\n<\/ul>\r\n<strong>Module 2: What AI Can Do with Data<\/strong>\r\n<ul>\r\n \t<li>Exploring trends<\/li>\r\n \t<li>Summarizing information<\/li>\r\n \t<li>Identifying correlations<\/li>\r\n \t<li>Detecting weak signals<\/li>\r\n \t<li>Assisting with segmentation or prioritization<\/li>\r\n \t<li>Preparing analyses more quickly<\/li>\r\n<\/ul>\r\n<strong>Module 3: Business Use Cases<\/strong>\r\n<ul>\r\n \t<li>Sales<\/li>\r\n \t<li>Marketing<\/li>\r\n \t<li>Human resources<\/li>\r\n \t<li>Finance<\/li>\r\n \t<li>Operations<\/li>\r\n \t<li>Customer service<\/li>\r\n \t<li>Identifying situations where AI can support business analysis<\/li>\r\n<\/ul>\r\n<strong>Module 4: From Data to Action<\/strong>\r\n<ul>\r\n \t<li>Moving from a volume of data to clearer decisions<\/li>\r\n \t<li>Transforming raw data into useful information<\/li>\r\n \t<li>Structuring a business-oriented exploitation approach<\/li>\r\n \t<li>Identifying management or improvement levers<\/li>\r\n<\/ul>\r\n<strong>Module 5: Limitations and Success Factors<\/strong>\r\n<ul>\r\n \t<li>Data quality<\/li>\r\n \t<li>Interpreting results<\/li>\r\n \t<li>Bias<\/li>\r\n \t<li>The need for business judgment<\/li>\r\n \t<li>Required maturity level depending on use cases<\/li>\r\n \t<li>Human validation of analyses<\/li>\r\n<\/ul>\r\n<strong>Module 6: Preparing a Data Valorization Approach<\/strong>\r\n<ul>\r\n \t<li>Identifying a first use case<\/li>\r\n \t<li>Engaging the right stakeholders<\/li>\r\n \t<li>Framing a need<\/li>\r\n \t<li>Starting with simple use cases<\/li>\r\n \t<li>Evolving the approach over time<\/li>\r\n<\/ul>","cours_recommandes":[238501,238502,238503,46883,238506],"infos_additionnelles":"<h3>Data as the Raw Material of Artificial Intelligence<\/h3>\r\n<p>Organizations accumulate growing volumes of data without always knowing how to exploit them effectively. Artificial intelligence provides powerful tools to transform this raw data into actionable information, strategic insights and competitive advantages.<\/p>\r\n<p>Understanding how AI exploits data allows professionals to better valorize their organization's information assets and make more informed decisions across all business functions.<\/p>\r\n\r\n<h3>Preparing Your Data for AI<\/h3>\r\n<p>The quality of AI results depends directly on the quality of input data. Collection, cleaning, structuring and enrichment are essential preliminary steps. Missing, inconsistent or biased data produce erroneous analyses, regardless of the sophistication of the tool used.<\/p>\r\n<p>The training covers data management best practices and methods for evaluating a dataset's quality before exploiting it with AI tools. Participants learn to identify common data quality issues and apply practical remediation techniques.<\/p>\r\n\r\n<h3>Analyzing Your Data with Generative AI<\/h3>\r\n<p>Language models can analyze data tables, identify trends, detect anomalies and produce interpretations in natural language. This capability makes data analysis accessible to professionals who are not proficient with traditional statistical tools.<\/p>\r\n<p>Integrating AI into tools like Excel, Google Sheets or Power BI enables querying data in natural language and getting immediate answers, which considerably accelerates the analysis process. This conversational approach to data analysis represents a fundamental shift in how business professionals interact with their information.<\/p>\r\n\r\n<h3>Visualizing and Communicating Results<\/h3>\r\n<p>AI helps produce relevant visualizations and tell the story that data reveals. Selecting the right chart type, formatting dashboards and writing analytical commentary are tasks that AI can assist effectively.<\/p>\r\n<p>The ability to transform raw data into compelling presentations is an increasingly valued skill. AI enables achieving professional-level quality in data-driven communication, making every team member more capable of presenting insights persuasively to stakeholders.<\/p>\r\n\r\n<h3>Toward an AI-Augmented Data Culture<\/h3>\r\n<p>Exploiting data with AI is not just a matter of tools but also of organizational culture. Companies that develop a solid data culture get the most out of AI. Team training, implementation of best practices and data governance are the pillars of this transformation.<\/p>\r\n<p>ITTA supports companies in French-speaking Switzerland in this data maturity journey with practical training delivered in Geneva and Lausanne, combining tool mastery and data exploitation methodology.<\/p>\r\n\r\n\r\n<h3>AI-Augmented Data Analysis in the Swiss Context<\/h3>\r\n<p>Switzerland is a country where data culture is particularly well developed, especially in the finance, healthcare and industrial sectors. Artificial intelligence enables further valorization of this data by making it accessible to a wider audience within organizations. Augmented analysis tools democratize access to data insights and allow every employee to contribute to informed decision-making.<\/p>\r\n<p>Swiss requirements for data quality, analytical rigor and privacy protection create a demanding but beneficial framework for AI deployment. Organizations that train their teams in AI-powered data exploitation gain a lasting competitive advantage. The skills acquired are cross-functional and applicable regardless of industry or company size.<\/p>\r\n\r\n\r\n<p>Participants leave with a clear methodology for exploiting their organization's data with AI tools. They have identified the most promising data sources and are able to produce relevant analyses using generative AI tools. This skill enables them to contribute to their company's data culture and make better-informed decisions within their area of responsibility.<\/p>\r\n\r\n<p><strong>Are statistics skills needed to use AI on data?<\/strong><\/p>\r\n<p>No, current AI tools allow data analysis in natural language without prior statistical knowledge. However, understanding basic concepts improves the relevance of analyses and interpretation of results.<\/p>\r\n\r\n<p><strong>Can AI analyze Excel data?<\/strong><\/p>\r\n<p>Yes, tools like ChatGPT and Claude can directly analyze Excel and CSV files. They identify trends, create visualizations and formulate conclusions in natural language.<\/p>\r\n\r\n<p><strong>What data should not be submitted to AI?<\/strong><\/p>\r\n<p>Sensitive personal data, confidential financial information and trade secrets should not be submitted to public AI tools. Enterprise versions offer appropriate confidentiality guarantees.<\/p>\r\n\r\n<p><strong>Can AI replace a data analyst?<\/strong><\/p>\r\n<p>AI makes basic data analysis accessible to everyone, but does not replace a data analyst's expertise for complex analyses, advanced statistical modeling and data system design.<\/p>\r\n\r\n<p><strong>How should you start exploiting data with AI?<\/strong><\/p>\r\n<p>The first step is to identify available data and the business questions they could answer. The training provides a structured methodology for moving from raw data to actionable insight.<\/p>","inclus_dans_ce_cours_virtuel":"<ul>\n \t<li>Training provided by a domain expert<\/li>\n \t<li>Digital documentation and support materials<\/li>\n \t<li>Achievement badge<\/li>\n<\/ul>","inclus_dans_ce_cours_presentiel":"<ul>\n \t<li>Training provided by a domain expert<\/li>\n \t<li>Digital documentation and support materials<\/li>\n \t<li>Achievement badge<\/li>\n<\/ul>","inclus_dans_ce_cours_blended":"","inclus_dans_ce_cours_elearning":"","inclus_dans_ce_cours_surmesure":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.5 (Yoast SEO v27.5) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Leveraging Data with AI Training<\/title>\n<meta name=\"description\" content=\"Data and AI training in Geneva and Lausanne. 1 day to better leverage your data with artificial intelligence. Business use cases. 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