BIG DATA 1

BIG DATA 1

1. Big Data is defined as extremely large and complex data sets that cannot be managed, stored, or analyzed using traditional information processing tools. It involves the use of modern technologies such as Hadoop, Streams, and cloud microservices to process data in real time, and goes beyond the data itself to include the infrastructure needed to store, manage, and analyze it efficiently and scalably, representing an evolution in the way organizations handle information and make decisions.

   • Business Analytics relies on Statistical and Data Methods to transform raw information into evidence-based strategic decisions.

2. BUSINESS ANALYTICS

   • Business Analytics is a strategic and systematic approach that integrates data, advanced statistical methods, and analytical technologies to transform massive and complex volumes of information into actionable, understandable insights that guide decision-making within organizations. Its fundamental purpose is to convert uncertainty into knowledge, enabling companies to anticipate scenarios, optimize processes, and create sustainable competitive advantages. (Holmes, 2018; Sedkaoui, 2018; IBM, 2023).
     • DESCRIPTIVE ANALYSIS
       • It forms the basis of Business Analytics and focuses on summarizing, organizing, and presenting historical or current data using descriptive statistics, key performance indicators (KPIs), dashboards, and visualizations that enable an understanding of an organization's past or present behavior.
     • PREDICTIVE ANALYSIS
       • It is built on descriptive analysis and uses advanced statistical models, machine learning algorithms, and data mining techniques to identify hidden patterns in historical data and project future scenarios with a high degree of reliability, allowing trends, behaviors, or risks to be anticipated before they occur.
     • PRESCRIPTIVE ANALYSIS
       • This focuses on recommending optimal actions to achieve specific organizational objectives. It uses simulation techniques, mathematical optimization, business rules, and artificial intelligence algorithms, including reinforcement learning and recommendation systems, to evaluate multiple possible scenarios, consider operational constraints, and propose concrete decisions that maximize desired outcomes.

3. DATA AND STATISCTICAL METHODS

   • The goal of these methods is to extract valid, reliable, and actionable knowledge, ensuring that data is not only processable but also meaningful and useful for informed decision-making. This rigorous approach guarantees the quality, integrity, and relevance of information, making it the essential foundation upon which business analytics, machine learning, and evidence-based strategies are built in big data environments.
     • STATISTICAL METHODS
       • Are defined as quantitative techniques that enable data to be analyzed and interpreted using descriptive statistics such as means and graphs, statistical inference such as hypothesis testing and confidence intervals, and predictive modeling such as regression, clustering, and time series, in order to identify patterns, validate hypotheses, and generate reliable projections for decision-making.
     • DATA PROCESSING METHODS
       • These are essential activities for preparing reliable information that is ready for analysis. This includes cleaning, such as removing duplicates, integration, and transformation, such as standardization and the creation of new variables. Finally, ensuring data quality in terms of accuracy, completeness, consistency, and timeliness.
     • VISUALIZATION METHODS
       • Consists of the graphical representation of results using interactive dashboards, dynamic graphs, and heat maps, using tools such as Power BI, Tableau, Looker, or Python libraries, with the aim of communicating insights in a clear, intuitive, and accessible way to non-technical audiences, thus facilitating data-driven decision-making.