The data management function is a fundamental set of business operations, similar to finance, human resources, or facilities management, that offers resources to support the development and adoption of best practises across data management disciplines. It will always be necessary throughout the entire lifecycle of patient demographic data.
Depending on the size of the organisation and the amount of its control over patient demographic data assets, the data management function’s scope will change. This task might be partially carried out by a single person in a small organisation. A multi-person organisation might be required for larger enterprises with a wider scope of data governance. However, the key concept is to assign accountability for the work products that an organisation develops and keeps over time. Documentation of data management policies and procedures as well as quality standards created to improve the calibre of patient demographic data are examples of possible deliverables.
A variety of tasks and procedures are included in data management with the goal of efficiently managing data throughout its lifecycle. The following are the main duties of data management:
Gathering data from multiple sources, including databases, applications, sensors, or external systems, is known as data collection. It entails locating the pertinent data sources, guaranteeing the accuracy and quality of the data, and extracting the necessary information.
Data storage is the process of organising data and placing it in a convenient location for quick access, retrieval, and manipulation. This entails deciding on the best storage options, such as databases, data warehouses, or cloud storage, and putting effective data organisation techniques into practise.
Data integration is the process of bringing together data from various sources and formats to create a single, cohesive perspective. It entails ensuring consistency by transforming and mapping data, resolving data disputes, and building an extensive and coherent data repository.
Making sure that data is accurate, complete, consistent, and dependable is known as data quality management. It involves data scrubbing, validation, standardisation, and the creation of measurements and guidelines for data quality. Data integrity and usability are two goals of data quality management.
Data security is the process of protecting data from loss, theft, and unauthorised access. It entails putting security precautions in place including data masking, encryption, and access limits. A part of data security is adhering to all applicable laws and privacy rules.
Data governance is the process of creating rules, guidelines, and practises for handling data assets. Data governance makes sure that data is utilised, shared, and kept properly, and that data-related choices are in line with corporate goals and legal requirements. Definitions of data ownership, roles, and duties are also made, as well as data management frameworks.
Data analytics is the process of drawing conclusions and information from data to inform choices and influence company results. It encompasses methods like predictive modelling, machine learning, statistical analysis, and data mining. Finding patterns, trends, and correlations in the data is made easier by data analytics.
Data Privacy and Compliance: The responsibility for assuring adherence to privacy laws like the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR). To safeguard people’s privacy rights, it entails putting in place data anonymization, consent management, data retention regulations, and auditing measures.
Data archiving and retention is the process of keeping data for future reference, legal compliance, or long-term retention. Retention policies specify how long data should be kept and when it can be safely erased, while archiving entails shifting rarely accessed material to affordable storage.
Data Access and Sharing: Providing secure access to and sharing of data by authorised users. In order to achieve this, it is necessary to manage user permissions, put in place data access controls, create data sharing agreements, and offer tools for sharing and collaborating on data.
The availability, integrity, security, and usability of data are all ensured by these processes, allowing businesses to efficiently use their data assets and make wise decisions.