BIG DATA ANALYTICS

Big data describes large sets of diverse data—structured, unstructured, and semi-structured—that are continuously generated at high speed and in high volumes. Big data is typically measured in terabytes or petabytes. One petabyte is equal to 1,000,000 gigabytes. To put this in perspective, consider that a single HD movie contains around 4 gigabytes of data. One petabyte is the equivalent of 250,000 films. Large datasets measure anywhere from hundreds to thousands to millions of petabytes.

Big data analytics is the process of finding patterns, trends, and relationships in massive datasets. These complex analytics require specific tools and technologies, computational power, and data storage that support the scale.

Data analysis

This is the step in which raw data is converted to actionable insights. The following are four types of data analytics:

1. Descriptive analytics

Data scientists analyze data to understand what happened or what is happening in the data environment. It is characterized by data visualization such as pie charts, bar charts, line graphs, tables, or generated narratives.

2. Diagnostic analytics

Diagnostic analytics is a deep-dive or detailed data analytics process to understand why something happened. It is characterized by techniques such as drill-down, data discovery, data mining, and correlations. In each of these techniques, multiple data operations and transformations are used for analyzing raw data.

3. Predictive analytics

Predictive analytics uses historical data to make accurate forecasts about future trends. It is characterized by techniques such as machine learning, forecasting, pattern matching, and predictive modeling. In each of these techniques, computers are trained to reverse engineer causality connections in the data.

4. Prescriptive analytics

Prescriptive analytics takes predictive data to the next level. It not only predicts what is likely to happen but also suggests an optimum response to that outcome. It can analyze the potential implications of different choices and recommend the best course of action. It is characterized by graph analysis, simulation, complex event processing, neural networks, and recommendation engines.