Reasons for Performing Discovery in Your Data Management Process

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You need to analyze the data to find out what information needs to be cleansed and how the data is to be structured.

This helps to define the reports and dashboards that will support the business processes you seek to manage.

Discovery is an important step in the data management process as it helps in identifying the relevant data for an organization and ensures that it is used effectively. There are several reasons why discovery should be done in the data management process, including:

1. Identifying the right data: Discovery helps in identifying the right data for an organization, which is essential for making informed decisions. It helps in understanding the nature of the data and its relevance to the organization's goals and objectives.

2. Eliminating irrelevant data: Discovery also helps in eliminating irrelevant data, which can be a burden on an organization's resources. By identifying the data that is not relevant, organizations can focus on the data that is important for their operations.

3. Improving data quality: Discovery can also help in improving the quality of data by identifying any errors or inconsistencies in the data. By addressing these issues, organizations can ensure that their data is accurate and reliable.

4. Streamlining processes: Discovery can also help in streamlining processes by identifying areas where automation or other improvements can be made. By identifying these areas, organizations can save time and resources and improve their overall efficiency.

Overall, discovery is a crucial step in the data management process as it helps in identifying relevant data, eliminating irrelevant data, improving data quality, and streamlining processes. By prioritizing discovery, organizations can ensure that their data is used effectively and efficiently, leading to better decision-making and improved performance.