Business Analytics and Data and Statistical Methods

Business Analytics and Data and Statistical Methods

1. Business Analytics

   • Business Analytics is the application of quantitative techniques to interpret data and support strategic and operational decision-making in organizations. It is classified into three types:
     • Descriptive Analytics: summarizes and describes historical data to understand what happened.
     • Predictive Analytics: uses statistical models to forecast future trends.
     • Prescriptive Analytics: recommends optimal actions based on predictive results.
     • This analytics interacts with the 5 Vs of Big Data:
       • Volume: large quantities of data.
         • Velocity: speed of data generation and processing.
           • Variety: diversity of formats and sources.
             • Veracity: quality and trustworthiness of data. Value: usefulness data brings to the organization.
     • Examples
       • Marketing: PepsiCo used predictive analytics on consumer data to understand regional demand and optimize product distribution, resulting in significant sales growth for specific products.
         • Finance: Financial institutions utilize analytics for credit scoring and risk assessment, improving loan approval decisions and managing default risks.
           • Production: Manufacturers use business analytics to identify bottlenecks in production lines and forecast maintenance needs to reduce downtime.

2. Statistical Methods

   • These are mathematical techniques for organizing, analyzing, and interpreting large datasets. They include classical methods like statistical inference, regression, hypothesis testing, and multivariate analysis, adapted for big data through approaches such as subsampling, divide-and-conquer, and online updating. Includes techniques such as:
     • Descriptive Analysis: Summary statistics (mean, median, mode, standard deviation) to describe dataset properties quickly. Used widely for data summarization and trend spotting.
       • Inferential Statistics: Draw conclusions about populations from samples using hypothesis testing and confidence intervals. Useful when full data collection is infeasible.
     • Regression Analysis: Models relationships between variables to predict outcomes, including linear, multiple, and logistic regression. Critical for forecasting and impact assessment.
       • Cluster Analysis: Groups data points based on similarity to identify natural segments without prior labels. Common in customer segmentation and pattern discovery.
     • Time Series Analysis: Examines data points collected over time to identify trends, seasonality, and forecast future values. Applied heavily in production and sales forecasting.
       • Sentiment Analysis: Extracts opinion and emotions from text data, e.g. social media or customer reviews, using natural language processing techniques.

3. References

   • QuestionPro. (2024). Métodos de análisis estadísticos: ¿Cuáles utilizar? https://www.questionpro.com/blog/es/metodos-de-analisis-estadisticos/.Datasur. (2025). Análisis Estadístico para Empresas: Guía Definitiva. https://www.datasur.com/analisis-estadistico-de-datos/ SAP News. (2023). La importancia del análisis de datos en una empresa. https://news.sap.com/spain/2023/08/la-importancia-del-analisis-de-datos-en-una-empresa/ Harvard Business School Online. (2021). 3 Statistical Analysis Methods You Can Use to Make Decisions. https://online.hbs.edu/blog/post/statistical-analysis-methods IBM. (2023). ¿Qué es la analítica empresarial? https://www.ibm.com/es-es/think/topics/business-analytics EBSCO Research Starters. Estadística y análisis de datos. https://www.ebsco.com/research-starters/business-and-management/statistics-and-data-analysis InforGES. (2025). ¿Qué es el análisis de datos? Ejemplos reales en empresas. https://inforges.es/blog/que-es-el-analisis-de-datos/

4. Data

   • Data is the essential foundation of both statistical methods and business analytics, as it represents the facts and observations that allow us to understand reality and make well-informed decisions. In statistical methods, data enables the application of techniques that describe patterns, estimate behaviors, and validate hypotheses, ensuring accurate and reliable results. In business analytics, those same data are transformed into valuable information that drives strategy, optimizes processes, and enhances organizational competitiveness. Therefore, the quality, diversity, and volume of data are key factors that connect statistical rigor with the effectiveness of business analytics in generating useful knowledge. Data Types:
     • Structured Data: Well-organized information stored in relational databases (tables with rows and columns).
       • Unstructured Data: Data without a fixed format like text, images, videos, and social media.
         • Semi-structured Data: Combines qualities of both, e.g., JSON or XML files.