Data analysis is the process of identifying, cleaning, transforming, and modeling data to discover meaningful and useful information. The data is then crafted into a story through reports for analysis to support the critical decision-making process.
Descriptive analytics
Descriptive analytics help answer questions about what has happened based on historical data.
Developing key performance indicators (KPIs), these strategies can help track the success of key objectives. Metrics such as return on investment (ROI) and specialized metrics are developed to track performance in specific industries. Generating reports to provide a view of an organization’s sales and financial data.
Diagnostic analytics
Diagnostic analytics answer questions about why events happened.
Use the findings from descriptive analytics to discover the cause of these events. This process occurs in three steps:
- Identify anomalies in the data.
- Collect data that is related to these anomalies.
- Use statistical techniques to discover relationships and trends that explain these anomalies.
Predictive analytics
Predictive analytics answer questions about what will happen in the future. Predictive analytics techniques use historical data to identify trends and determine if they’re likely to recur. Techniques include a variety of statistical and machine learning techniques such as neural networks, decision trees, and regression.
Prescriptive analytics
Prescriptive analytics answer questions about which actions should be taken to achieve a goal.
By using insights from predictive analytics, organizations can make data-driven decisions. Prescriptive analytics techniques rely on machine learning strategies to find patterns in large datasets. By analyzing past decisions and events, organizations can estimate the likelihood of different outcomes.
Cognitive analytics
Cognitive analytics draw inferences from existing data and patterns, derive conclusions based on existing knowledge bases, and then add these findings back into the knowledge base for future inferences, a self-learning feedback loop.
Cognitive analytics helps you learn what might happen if circumstances change and determine how you might handle these situations.
Inferences are unstructured hypotheses that are gathered from several sources and expressed with varying degrees of confidence.
Example
By enabling reporting and data visualizations, a retail business uses descriptive analytics to look at patterns of purchases from previous years to determine what products might be popular next year. The company might also look at supporting data to understand why a particular product was popular and if that trend is continuing, which will help them determine whether to continue stocking that product.
A business might determine that a certain product was popular over a specific timeframe. Then, they can use this analysis to determine whether certain marketing efforts or online social activities contributed to the sales increase.
