{"id":6305,"date":"2026-06-30T09:00:00","date_gmt":"2026-06-30T07:00:00","guid":{"rendered":"https:\/\/noeliabermudez.com\/?p=6305"},"modified":"2026-05-01T10:13:58","modified_gmt":"2026-05-01T08:13:58","slug":"what-is-the-data-literacy-competency-how-can-we-develop-it","status":"publish","type":"post","link":"https:\/\/noeliabermudez.com\/en\/what-is-the-data-literacy-competency-how-can-we-develop-it\/","title":{"rendered":"What is the Data Literacy Competency? How can we develop it?"},"content":{"rendered":"\n

The data literacy competency<\/strong> is about the ability to analyze, interpret, and use data for strategic decision-making<\/strong>, this aspect is essential in environments where AI is part of workflows.<\/p>\n\n

People apply this competence by reading and interpreting data in reports, performing basic analysis with digital tools, using data in problem solving and drawing conclusions that guide action in the company. This encourages evidence-based decision-making<\/a> and improves organizational efficiency. <\/p>\n

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\"What<\/a><\/figure>\n<\/div>\n

Data Literacy in Today’s Era<\/h3>\n\n

In today’s digital age, where artificial intelligence dominates the professional environment, this competence is increasingly in demand by companies in order to be more innovative, productive and adapt to changes.<\/p>\n\n

It’s not just about understanding graphs or figures, but about knowing how to interpret, analyze, and use information to make sound decisions. Nowadays, companies handle a lot of data for decision-making, therefore, those who know how to analyze, interpret and relate them have a great advantage, both in the workplace and in everyday life. <\/p>\n\n

In the company, it translates, for example, into:<\/p>\n\n

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  • Identify market trends<\/strong> , analyze KPIs, and create visualizations to support business strategies.<\/li>\n\n\n\n
  • Make data-driven decisions<\/strong>, reducing decisions based on intuition and using quantitative evidence to support business strategies, digital transformation, and resource allocation.<\/li>\n\n\n\n
  • Optimize processes:<\/strong> analyzing operational data helps identify opportunities to improve efficiency and control quality in areas such as production, logistics, sales, or customer service.<\/li>\n\n\n\n
  • Innovate and drive competitive advantages<\/strong>: A culture of data literacy encourages the development of new products, personalized services, and market-tailored solutions, anticipating trends and customer needs.<\/li>\n<\/ul>\n\n

    This competence is related to other competences seen above such as analytical capacity<\/strong>, autonomy<\/strong> and critical thinking<\/strong>, as well as the ability to innovate<\/strong> and solve problems<\/strong>. <\/p>\n\n

    HOW CAN I DEVELOP MY DATA LITERACY?<\/u><\/strong><\/h2>\n\n

    To develop the data literacy competency we can use, for example, the DALI framework<\/strong>, which was created using the Grounded Theory and the Delphi method. Its framework has these main ingredients: <\/p>\n\n

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    • Understanding data<\/strong>: encompasses knowledge about what data is, where it comes from, how it is collected, processed, stored, and what role algorithms and security play.<\/li>\n\n\n\n
    • Acting on data<\/strong>: involves using data to solve problems, answer questions, and make informed decisions, encouraging practical application in real life.<\/li>\n\n\n\n
    • Data engagement<\/strong>: This focuses on using data for active participation and encouraging critical thinking to assess data quality, sources, and potential biases.<\/li>\n\n\n\n
    • Ethics and privacy:<\/strong> a cross-cutting element that influences others, emphasizing ethical and legal aspects, as well as the importance of privacy and equity in data processing.<\/li>\n<\/ul>\n\n

      Data Literacy: Training<\/h3>\n\n

      The first fundamental step to develop this competence is to train<\/strong>: courses, workshops or training on data analysis and visualization, basic statistics and use of digital tools.<\/p>\n\n

      Some analysis tools: Platforms such as Excel, Google Sheets, Tableau or Power BI, Google Data Studio are essential for basic analysis and data visualization in the corporate environment.<\/p>\n\n

      Advanced systems: As the need grows, SQL databases, languages such as Python and predictive analysis environments are incorporated to exploit large volumes of information.<\/p>\n\n

      Collaborative platforms: Digital spaces where teams can share results or maintain data repositories.<\/p>\n\n

      Data Literacy: Familiarity<\/h3>\n\n

      The next step is to become familiar in practice with tools<\/strong> and formats to better understand what the data is telling us.<\/strong> Practice interpreting data: Analyze reports, graphs, or actual data sets, looking for patterns, trends, and potential anomalies. It is not enough to have numbers, we have to make sense of them, be critical and know how to communicate what we discover. <\/u><\/strong><\/p>\n\n

      Likewise, another important aspect is to encourage data-based decision-making<\/strong> , apply critical thinking when analyzing information and make decisions based on analysis.<\/p>\n\n

      This comprehensive approach seeks to empower people to navigate safely in an increasingly data-driven world and to make conscious, informed decisions.<\/p>\n\n

      “Remember to continue developing your talent to reach your best version”<\/strong><\/strong><\/p>\n\n

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