Data analytics is the process of transforming raw data into actionable business decisions. It helps enterprises analyse, interpret, and visualize raw, large, and complex datasets, identify insights that matter, and make better decisions. It bridges the gap between businesses and their data, replaces gut or intuition-based decisions to data-driven decisions, and makes raw data resourceful by statistical, machine learning, and other techniques helpful for transforming data from its idle state.
It goes without saying that data analytics has extensive use cases and applicability across industries. Given its ability to help businesses extract insights from data, it helps logistics providers get complete visibility over their supply chain, eliminate bottlenecks, healthcare providers deliver better and personalised patient care, financial service providers detect fraud and deliver seamless digital experiences, and manufacturers connect production and supply chain data for better visibility. The main purpose of data analytics is to help businesses make sense of their data and use it to improve and transform how they function, cater to customers, and scale.
How Does Data Analytics Work?
Data analytics follows a five-step process to gathering and analyzing data to help enterprises identify actionable insights for decision making.
- Data Collection: Data is collected from multiple sources like transactions, operational data bases, and so on. Collecting data from all business sources ensures that you get a complete picture without missing out on key insights.
- Data Cleansing: Data cleaning helps you identify, eliminate, and fix errors, missing values, duplicates, and other inconsistencies from collected data. This ensures that only accurate data is pushed downstream for analysis.
- Data Transformation: This is where your data is standardized, formatted, and structured for analysis. It eliminates any compatibility or readiness issue in your data and ensures it is analysis-ready, transforming it from its original state.
- Data Modeling: This is where statistical models are applied to data to help you detect trends, identify meaningful patterns, and predict future outcomes with informed data.
- Data Visualization: Data is made visually accessible through intuitive charts, reports, and dashboards. Data visualization tools are used to create intuitive data stories that cut through the noise and help businesses focus on what matters, simplifying complex data into easy-to-understand business concepts.
