Data analytics is a tool that businesses use to extract insights from the data they generate. There are four types of data analytics: descriptive, diagnostic, predictive, and prescriptive analytics. Each type serves a different purpose and can be used in various business contexts. Descriptive analytics summarizes past events and provides an overview of what has happened in the past. It can be used in management presentations to present historical data and provide insights into past trends, patterns, and anomalies.

A data dashboard is a visualization tool that displays key business metrics and KPIs in real-time. It provides an at-a-glance overview of a company's performance and enables users to quickly identify areas that require attention. A data dashboard typically displays data in the form of charts, graphs, and tables, and can be customized to meet the specific needs of the business.

Diagnostic analytics is an essential category of data for a data dashboard. It is used to identify the root cause of a problem and to help businesses understand why something has happened. By analyzing historical data and trends, diagnostic analytics can provide valuable insights into the factors that contributed to a particular outcome. For example, if a company's sales have decreased in a particular region, diagnostic analytics can help identify the reasons for the decline, such as changes in the competitive landscape or a shift in consumer preferences.

Predictive analytics is another crucial category of data that should be included in a data dashboard. It uses statistical models and machine learning algorithms to forecast future trends and identify potential problems before they occur. Predictive analytics can help businesses anticipate changes in demand or identify potential supply chain disruptions, allowing them to take proactive measures to mitigate their impact. For example, if a company uses predictive analytics to identify a potential disruption in the supply chain, they can make adjustments to inventory levels or sourcing strategies to ensure uninterrupted production.

Prescriptive analytics is the final category of data that should be included in a data dashboard. It provides recommendations for actions that businesses can take to improve their performance or to address specific problems. Prescriptive analytics uses a combination of historical data, machine learning algorithms, and business rules to identify the best course of action in a given situation. For example, if a company's sales have been declining in a particular region, prescriptive analytics may recommend adjusting pricing strategies or launching a targeted marketing campaign to increase demand.

A data dashboard is a powerful tool that can provide businesses with real-time insights into their performance. Diagnostic, predictive, and prescriptive analytics are the categories of data that are most appropriate for a data dashboard. By including these categories of data in their data dashboard, businesses can identify problems, anticipate trends, and take proactive measures to improve their performance.