Types of Big Data Analytics
Data Analytics:
Data Analytics is the discovery and communication of meaningful patterns in data. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantity performance. Data Analytics often favors data visualization to communicate insight.
In a nutshell, Data analytics is the scientific process of transforming data into insight for making better decisions. By using techniques such as mathematical modeling to analyze complex situations, analytics gives executives the power to make more effective decisions and build more productive systems based on:
1. More complex data
2. Consideration of all available options
3. Careful predictions of outcomes and estimates of risk
4. The latest decision tools and techniques
Types of Data Analytics:
There are mainly four types of data analytics in big data:
- Predictive Analytics
- Descriptive Analytics
- Prescriptive Analytics
- Diagnostic Analytics
Predictive Analytics:
Predictive Analytics turns data into valuable, actionable information. It uses the data to determine the probable future outcome of an event or a likelihood of a situation occurring.
It encompasses a variety of statistical techniques from modeling, machine learning, data mining and game theory that analyze current and historical facts to make predictions about future events.
Descriptive Analytics:
Descriptive Analytics looks at data and analyzes past events for insight as to how to approach the future. It looks at past performance and understands that performance by mining historical data to look for the reasons behind past success or failure. Almost all management reporting such as sales, marketing, operations and finance, uses this type of post-mortem analysis.
Descriptive models quantity relationships in data in a way that is often used to classify customers or prospects into groups. Unlike predictive models that focus on predicting a single customer behavior, descriptive models identity many different relationships between customers or products. Theses models don’t rank-order customers by their likelihood of taking a particular action the way predictive models do.
Prescriptive Analytics:
Prescriptive Analytics automatically synthesizes big data, mathematical sciences, business rules and machine learning to make predictions and then decision options to take advantage of the predictions.
It goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing the decision maker the implications of each decision option. These analytics not only anticipates what will happen and when it will happen, but also why it will happen.
Diagnostic Analytics:
Diagnostic analytics is one of the more advanced types of big data analytics that you can use to investigate data and content. Through this type of analytics, you use the insight gained to answer the question, “Why did it happen?”. So, by analyzing data, you can comprehend the reasons for certain behaviors and events related to the company you work for, their customers, employees, products, and more.
