Big Data analytics refers is the systematic process by which meaningful insights are extracted from large and complex data sets. These large volumes of data are known as big Data, and the processes, tools, and technologies used to derive contextual meaning from them form the process of big data analytics.
Imagine how much data is generated via IoT devices with connected experiences? This data is ever growing, and needs you to stay ahead of it by identifying trends and patterns helpful for decision making. Without big data analytics, your enterprise could drown in these data volumes and complexity, and lose the most valuable asset it has to make better decisions.
Difference Between Big Data and Traditional Data
Traditional data analytics focus on structured data. Structured data is typically stored in relational databases, or simply in databases with data organized for a computer to understand.
Big data analytics deal with large volumes of structured, unstructured, and semi structured data, and needs techniques like machine learning to gain meaningful insights from large datasets.
Key Stages of Big Data Analytics
The process of extracting trends and insights from large datasets goes through the following stages:
1. Data Collection
Data from multiple sources is gathered and integrated into a central repository like a data lake, which makes data more accessible and manageable. Data at this stage can be structured or unstructured.
2. Data Processing
At this stage, raw data is converted to a format fit for analysis. It involves systematically organization, extraction, transformation, and loading into a storage system.
3. Data Cleansing
Data cleaning ensures that your data does not have missing values, duplicates, or other inconsistencies. This is the foundation for accurate outputs and data reliability.
4. Data Analysis
Predictive analytics, deep learning, and machine learning are techniques used to discover patterns and derive intelligence from data. This serves as the foundation for data-driven decision making.
Analysis Methods Within Big Data
Based on the context, use, and unique business goals, there are 4 types of data analytics:
- Descriptive Analytics: Answers “what happened” from historical data analysis by using data mining techniques. Example: weekly on monthly sales reports.
- Predictive Analytics: Answers “what could happen” using machine learning and statistical techniques to make informed future forecasts based on historical data.
- Diagnostic Analytics: Answers “why something happened “ using data discovery, drill-down, and data mining techniques, ensuring businesses find targeted and actionable answers for ongoing problems.
- Prescriptive Analytics: Answers “what should be done” using data analysis and simulation algorithms to recommend high-priority actions.
