Almost every organization uses data in some form for different organizational processes. However, the data used and stored by an organization is in very large quantities.
According to data analytics surveys, an average company uses 162.9TB of data annually, which is a lot! And this data quantity keeps increasing with time.
With such a large quantity of data in every organization, it’s very difficult for employees to manage and use it effectively. Therefore, many organizations use data management to optimize data before using it. This blog discusses everything you need to know about data management.
What is Data Management?
In simple words, data management is the discipline that encapsulates all fields that turn data into a useful resource. In data management, the data collected or created by an organization is stored, organized, and maintained. The main purpose of data management is to ensure that every organization uses only valuable and correct data in its business processes. Data management techniques also aim to make data more accessible for the people working in an organization.
Data management processes and techniques vary in different industries. Scroll down to read how different industries use different data management types.
In master data management, the data is collected from all the available sources and ingested into one constant source. Afterwards, the organization only works on that constant source of data. The employees on all levels make important decisions based on that one constant data source only.
In data stewardship, data stewards follow certain data management guidelines to ensure that the data in an organization is safe, accessible, usable, and trusted. Data stewardship has certain regulations on how an organization can use data. Furthermore, data stewardship ensures the quality of an organization’s data for long-term usage. Data stewards also hold an organization accountable for its data usage.
The data quality management field is a subdomain of data stewardship. As the name suggests, data quality management revolves around combing the collected data. Through combing techniques, all the underlying issues in the data, like duplicate records, are removed.
Protected data is one of the most important aspects of data management. The data is protected against unauthorized access, encryption management, and accidental deletion or movement in data security. Security specialists ensure that the data protection techniques are applied to every stage of data storage and transportation.
Data Governance sets the laws for the data in any organization. A data governance framework contains rules that state the policies for collecting, flowing, and protecting data in an organization.
Big data management is responsible for collecting, analyzing, storing, and using a very large quantity of digital data. In big data management, raw data is collected and analyzed to use in business intelligence procedures.
This type of data management is responsible for deriving a large amount of data from sources like application log files. The data is further used in business intelligence and analytics activities.
Importance of Data Management
Following are some reasons why data management is important for any organization.
As mentioned above, organizations deal with a large quantity of data. Therefore, accessing the right data for every procedure is difficult. Through data management, people can quickly find the data they need for their analysis. Finding instant data can make your employees more organized and productive.
Since the data is used to make major marketing and business decisions, it must be completely correct and trustable. Data management helps minimize any potential errors so that the end data is accurate.
Many organizations contain confidential and highly sensitive data. Data management protects the data from any unwanted breaches by following the latest encryption techniques. Your company’s data is safe from data loss, theft, or unauthorized access with data management.