Table of ContentsHistoryPrinciples of Good Data ManagementData PolicyData OwnershipEstablishing a Data PolicyData AcquisitionData Care – StewardshipImplementationChampion of data managementData managerBenefitsBenefits for data providersBenefits for users and customersExample case studiesFuture trendsReal-time analyticsAn increased focus on securityConclusionThis essay will discuss the importance of database management, its origin, its key principles, its implementation and its benefits for an organization or society. Say no to plagiarism. Get a Custom Essay on “Why Violent Video Games Should Not Be Banned”?Get an Original EssayWhat is Database Management? Database management is simply the manipulation and control of data to meet the user's needs, usually using a program. A database, in simple terms, is an organized collection of data. A database is a system that allows data to be easily stored, accessed, manipulated and updated. Meanwhile, data is basically facts, events, transactions that have been recorded. So why is database management important? Due to limited resources, databases must be managed in the most efficient and cost-effective manner to ensure the sustainability of an organization. If the database is not managed properly, it can lead to inefficiencies, misuse, or lost/stolen or unusable data. HistoryIn the beginning, IBM created a program called IMS. It provided less redundant data, data independence and much more. However, it was difficult to implement and there were no standards to follow. Then there was a networked database management system. CODASYL invented a program called COBOL with the CODASYL approach. Thanks to this approach, it was possible to use the DBMS for professional purposes. However, it remained complex with design and maintenance issues. The relational DBMS was therefore invented to fill this void. This was a new system for entering data and working with large databases using a record table system. Make it easier to find or delete items and make the program more intuitive. Principles of Good Data Management So, what are the requirements to properly manage your database? Good data management is vital for the correct use of an organization's information resources throughout its structure. Below are some of the key concepts.Data PolicyA good foundation in data policy will ensure good data management. Each policy may differ from organization to organization, but there must be adherence to a standard for data management to work. Moreover, in this day and age, data policies need to be reviewed from time to time to deal with current or future threats. This must not stagnate. Data ownership The data owner must be clear. Owners are those who first owned the data and have custody of its control, legal rights and financial aspects. Owners have the right to do anything with the data. and even destroy them. If the owner is not clear, the data risks being misused, used for free, or even lost. It is therefore importantthat Data Owners take steps to establish and document: Ownership of their data for backup purposes. The legal and non-statutory obligation linked to their nature of use to ensure that the data is compliant. Departmental policies such as data security. etc. should be periodically updated with current and future threats or issues. Users and customers must agree on how the data is used and let the agreement be signed by both parties. Establish a data policy Here are some data policy statement templates prescribed by the IGGI organization, to use and adapt to your own needs. Data acquisitionWith projects and activities, data will be created and if it already existed and can be used, or if new data is needed. Before projects are approved, they must indicate how the data received will be maximally used, the responsibility for the use of the data and how the benefits will be maximized and shared. Proper data handling and storage is necessary in order to fully utilize the data. Data Maintenance – Stewardship The steward ensures that procedures are followed. Data will be kept in a secure database and stored for a maximum lifecycle. All data will be verified and quality controlled before being used or stored. The data will be easily accessible to staff and customers. Data that does not need to be retained will be investigated to see if it can provide benefits and be justified as to the cost of retaining it. ImplementationFor an organization to be successful, best data management practices must be implemented across the organization, led by the Data Management Champion with Data Management and Data Managers to support you. Data Management Champion The Champion is responsible for: Verifying that the data management policy complies with the law and government policy. Feedback to manager on data improvement progress. quality and results of good data management. If the organization is large, having multiple branches across a large geographic area will require a data management steering group. Data Manager The Data Manager along with his local counterpart will fulfill these responsibilities: Develop and maintain data policy reporting and corporate guidance. Lead the development, implementation and maintenance of detailed data policies, standards, procedures and guidelines across the organization. Appoint and monitor data managers. Provide advice and training to staff. Standardize local practices in each company. .Ensure maintenance of the central metadata resource.Data StewardsData Stewards are responsible for applying standards to each dataset: The dataset must be written to the organization's catalog according to metadata discovery standards , to allow ownership, intellectual property rights, management and accessibility to be determined. The policy for using the data set and making it available to third parties must be subject to mutual written consent. The dataset and its terms of use must comply with all legal and non-statutory obligations of the organization. Data must follow standard categorization and definitions, where applicable, and must adhere to all standards, ethics of practice and other related protocols. The data must be completely.