2022-04-05に更新

4 Types of Data Analytics to Improve Decision-Making

The field of data analytics is vast. Data analytics is classified into four types: descriptive, diagnostic, predictive, and prescriptive analytics. Each category has a distinct aim and role in the data analysis process. These are the most common data analytics applications in business.

  • Descriptive analytics aids in answering inquiries about what occurred. These strategies are used to summarize huge datasets in order to convey outcomes to stakeholders. These tactics can help track successes and failures by establishing key performance indicators (KPIs). In various industries, metrics such as return on investment (ROI) are used. To track success in certain industries, specialized metrics are devised. This procedure necessitates the acquisition of relevant data, data processing, data analysis, and data display. This procedure provides critical information about previous performance.
  • Diagnostic analytics can assist answer questions about what happened and why. These strategies are intended to supplement more basic descriptive analytics. They employ descriptive analytics insights to delve deeper into the cause. The performance indicators are further studied to see why they improved or deteriorated. This generally occurs in three steps:
  1. Identify anomalies in the data. These may be unexpected changes in a metric or a particular market.
  2. Data that is related to these anomalies is collected.
  3. Statistical techniques are used to find relationships and trends that explain these anomalies.
  • Predictive analytics assists in answering questions about what will occur in the future. These strategies use historical data to discover trends and predict whether they will reoccur. Predictive analytical tools provide significant insight into what may occur in the future, and their approaches encompass a wide range of statistical and machine learning techniques, such as neural networks, decision trees, and regression.
  • Prescriptive analytics assists in determining what should be done. Data-driven decisions can be made utilizing predictive analytics insights. In the face of uncertainty, this enables firms to make educated judgments. Machine learning systems that can detect trends in massive datasets are used in predictive analytics techniques. The chance of various outcomes can be estimated by evaluating previous decisions and events.

These forms of data analytics give firms the insight they require to make effective and efficient decisions. When used together, they provide a comprehensive picture of a company's needs and potential.

A good Data Analytics Training will teach you how to get started in your Data Analytics career.

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Nathan Martin

Python Developer

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