{"id":7430,"date":"2025-03-04T10:23:09","date_gmt":"2025-03-04T08:23:09","guid":{"rendered":"https:\/\/dev.test-it.hu\/?p=7430"},"modified":"2025-03-04T10:24:36","modified_gmt":"2025-03-04T08:24:36","slug":"data-analysis-and-business-intelligence","status":"publish","type":"post","link":"https:\/\/dev.test-it.hu\/en\/aidata\/adatelemzes-es-uzleti-intelligencia\/","title":{"rendered":"Data analysis and business intelligence"},"content":{"rendered":"

Data analysis and business intelligence<\/h2>\n\n\n\n

Modern companies generate vast amounts of data, yet a significant portion remains unused. According to some estimates, up to 55% of corporate data goes unutilised, even though analysing this information can be crucial for informed decision-making and business intelligence. This is why well-executed data analysis is becoming increasingly important in corporate operations.<\/p>\n\n\n\n

Data analysis in 7 steps<\/h2>\n\n\n\n

Data analysis is a structured process that consists of seven main steps. <\/p>\n\n\n\n

    \n
  1. Goal setting<\/li>\n\n\n\n
  2. Data collection<\/li>\n\n\n\n
  3. Data organisation<\/li>\n\n\n\n
  4. Data cleaning<\/li>\n\n\n\n
  5. Analysis<\/li>\n\n\n\n
  6. Data visualization<\/li>\n\n\n\n
  7. Conclusion<\/li>\n<\/ol>\n\n\n\n

    Now, let's take a closer look at the steps of data analysis.<\/p>\n\n\n\n

    \"Data
    Data analysis enables informed decision-making.<\/figcaption><\/figure>\n\n\n\n

    1. Goal setting<\/h3>\n\n\n\n

    The first step is to clearly define the objectives. This determines what data is needed and the criteria by which it will be analyzed. It is important to formulate a hypothesis that will be either confirmed or rejected during the analysis. <\/p>\n\n\n\n

    2. Data collection <\/h3>\n\n\n\n

    Data collection can come from internal sources such as databases, surveys, or emails, as well as external sources like social media, reviews, or reports. In the era of Big Data, the volume and diversity of data are constantly increasing, making data collection increasingly complex. <\/p>\n\n\n\n

    3. Data organisation<\/h3>\n\n\n\n

    The collected data must be organized for better transparency. It is advisable to create documentation and remove unnecessary or redundant data to make the subsequent analysis easier. <\/p>\n\n\n\n

    4. Data cleaning<\/h3>\n\n\n\n

    Data cleaning (along with the previous phase of data collection) is one of the most time-consuming steps. For textual data, typos and irrelevant responses need to be filtered out, while for numerical data, outliers must be identified and missing values addressed. <\/p>\n\n\n\n

    5. Analysis<\/h3>\n\n\n\n

    During the analysis, different methods can be applied depending on the objectives. These include descriptive statistics, regression analysis, time series analysis, classification, and clustering. With the help of these techniques, we can examine whether the formulated hypothesis is valid. <\/p>\n\n\n\n

    6. Data visualization<\/h3>\n\n\n\n

    Presenting the results is essential for decision-makers. Data visualization tools such as Power BI or Tableau help display data in a clear and comprehensible manner. For more specific charts, programming languages like Python or R can also be used. <\/p>\n\n\n\n

    7. Conclusion<\/h3>\n\n\n\n

    Based on the analysis results, we draw conclusions that support better decision-making and contribute to the advancement of business intelligence.<\/p>\n\n\n\n

    \"Rengeteg
    Most companies have a vast amount of unused data.<\/figcaption><\/figure>\n\n\n\n

    Data analysis and artificial intelligence<\/h2>\n\n\n\n

    Artificial Intelligence (AI) significantly enhances data analysis through the following methodologies: <\/p>\n\n\n\n