Data orchestration is the automated process of collecting, managing and integrating data from various sources to ensure the right data is available to the right applications when you need it. To successfully orchestrate data, you must coordinate data workflows across different systems, often in real time. Data management platforms and orchestration tools provide the infrastructure to manage data pipelines and ensure efficient processing and movement without manual intervention. Effective data orchestration streamlines operations and reduces latency to maximize the benefits of data management.

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There are a variety of data management practices, but here are the four primary types.

1. Data governance: This is the process of establishing policies and procedures to ensure you’re efficiently using data. Data governance focuses on data quality, data security and regulatory compliance. It’s a framework for how you manage data across your organization.
2. Data integration: Integrating data is combining data types, including unstructured data, from various sources into a single, unified view. This process is essential for creating a landscape that supports your data analytics and decision-making. Techniques such as extract, transform, load (ETL), data virtualization and data warehousing are commonly used for integration.
3. Data quality management: Sustaining data quality is about maintaining its accuracy, completeness, reliability and timeliness. Data quality management involves data cleansing, validation and monitoring processes that detect and correct errors or inconsistencies.
4. Data security: Protecting your data from unauthorized access, breaches and other threats is a must in today’s data-driven and highly regulated business environment. It’s necessary to implement measures such as encryption, access controls and data masking to safeguard your data privacy.

A data management strategy is a comprehensive plan that outlines how your organization will manage data assets to meet business objectives and regulatory requirements. A good strategy will include policies, processes and technologies to ensure your data stays accurate, secure, accessible and valuable. There are several key components of a well-executed data management strategy:

• Data governance framework: To establish roles, responsibilities and policies for defining data ownership, stewardship and quality standards
• Data architecture: To design the infrastructure for data storage, integration and processing so you can choose the appropriate databases and tools
• Data security and privacy: To put measures in place to protect your data from breaches and unauthorized access while ensuring you follow data protection regulations such as GDPR
• Data quality management: To set up processes for data preparation, including profiling, cleansing and monitoring
• Data modeling and business intelligence: To enable data-driven business decisions, often with modern tools featuring machine learning and artificial intelligence, to visualize and interpret your enterprise data and use metadata to understand your data flows

Data security is the outcome of protecting data from unauthorized access, corruption or theft throughout its lifecycle. Types of data security include:

• Access control: Restricting data access based on user roles and permissions ensures the only authorized individuals can view or modify sensitive data. Most organizations use role-based access control (RBAC) or multi-factor authentication (MFA).
• Data backup and recovery: It’s crucial to create copies of data to protect against loss due to hardware failure, cyberattacks or other disasters. Backup solutions can also restore datasets to their original state when an event occurs.
• Data masking: Obscuring specific data within a database protects it from unauthorized access while maintaining usability for data analysis and testing. Data masking is most often used to protect personally identifiable information (PII) or other sensitive data.
• Encryption: This is the standard way to protect data at rest (stored data) and data in transit (data being transmitted). It transforms data into a coded format that requires a decryption key to be read.
• Intrusion Detection and Prevention Systems (IDPS): This is the practice of monitoring network traffic for suspicious activity and taking action to prevent potential breaches. IDPS tools identify and mitigate threats in real time.
• Security Information and Event Management (SIEM): SIEM systems collect and analyze security data from across your organization to give you visibility into security events and the power to be proactive in threat management.