Data Management Practices
In data management, data protection and security should be at the top of the list. Even a minor data breach can be devastating for an organization's reputation. In addition, an organization's data management system should be designed to minimize duplication. Duplication of data leads to inaccurate business decisions and analysis, so a system that eliminates this risk is a necessity. Organizations should also establish an auditing process to detect and correct mistakes.
Data protection and security rules and regulations
Data protection and security rules and regulations are a vital part of protecting your personal data. There are various federal laws in place to protect the privacy of your data, and there are also numerous state and local laws that affect the privacy of your data. In the United States, the federal government has implemented hundreds of laws to protect your privacy and personal information. For example, the federal law known as the Federal Trade Commission Act (FTC Act) protects consumers against unfair or deceptive practices and enforces federal privacy rules.
GDPR is an EU law that requires entities to follow certain guidelines in the processing of personal data. The GDPR does not specify thresholds, but it requires compliance by entities based in the EU. Generally, if your business makes at least 50% of its revenue from selling personal data, you must abide by GDPR rules.
Systematic nomenclature and annotation of variables
Systematic nomenclature and annotation of variable names are an important part of data management practices. This step helps researchers to describe the meaning of a variable and provide a standardized and consistent naming convention for data. Variable names should be descriptive and easily understood by readers.
Variables are used for many purposes and may be referred to as "data producers." This refers to advanced consumers who produce new information at a higher level. This new information may include actual data or simple information. However, it must always be traceable to its source. Data management practices comprise a variety of processes for managing and delivering datasets. Each step in the data management value chain has a role that is defined by the needs of the organization.
Quality assurance practices for data management
Quality assurance is the process of ensuring that data is processed correctly. This involves applying a quality assurance plan to prevent defects and ensuring that data meets a specified quality standard. The quality plan will cover the data acquisition, processing, and data use stages. It is also called data-quality management or DQM. Quality assurance practices refer to defect prevention and detection, and are applied to data both before and after it is acquired. A 'defect' is any issue with the data, which may affect its fitness for use.
As the world's data collection and management technologies have evolved, data quality issues have become more complex. The rise of big data systems and cloud computing has increased the scope of these problems. Quality assurance is now essential for organizations that need to ensure the accuracy, reliability, and security of their data. Quality assurance is an important part of the software development lifecycle.
Transferability of best data management practices
Transferability of best data management practices is a key factor in effective data management. This process involves translating best practices from one part of an organization to another, enabling reuse and data sharing. This process also facilitates enterprise standardization and helps organizations improve key metrics. Ideally, the best data management practices should be easily transferable and accessible to other members of the organization.
Effective data management can result in increased customer satisfaction, increased operational efficiency, increased revenue, and improved profitability. Best data management practices are key to effective decision-making. A digital marketer with over a decade of experience in the tech industry, Silvia Valcheva writes on emerging technologies, big data, artificial intelligence, the Internet of Things, and process automation.