Critical data management automation

Data management automation refers to the use of automated tools and technologies to handle and streamline data management tasks to reduce the need for manual intervention. It involves automating repetitive and time-consuming processes, such as data collection, cleansing, transformation, storage and movement.

With automation, you can ensure your data is processed efficiently, accurately and consistently. It helps improve data quality, governance and compliance by establishing automated workflows, monitoring systems and error-checking mechanisms. Automation can ensure that data coming from various sources gets validated, standardized and transformed before being moved to the appropriate data storage system.

Today, data management automation moves data securely, quickly and accurately across all types of IT environments, including on-premises, hybrid and cloud systems. Many organizations are starting to incorporate machine learning and artificial intelligence to drive predictive data science and facilitate easier data analysis.

Read more about the power of data management automation.

Each type of data management focuses on a specific aspect of handling data throughout its lifecycle. These types include:

• Data governance: Establishing policies and procedures for managing data to maintain compliance and protect data quality, security and privacy
• Data storage management: The management of physical and cloud storage systems, including databases, to store data efficiently and securely in data lakes, data warehouses or other target systems
• Data integration: Combining data from different sources using tools like extract, transform, load (ETL) and APIs to maintain consistency across an organization
• Data security management: Ensuring the privacy and security of data with encryption, access control, data protection compliance and more
• Data quality management: Keeping data accurate, consistent and reliable with processes such as data cleansing, validation and enrichment
• Master Data Management (MDM): Creating a single source of truth for important business data across all functions
• Metadata management: Tracking data lineage, origin and context to make it easier to manage and retrieve data
• Data Lifecycle Management (DLM): Managing data from its creation to its archival or deletion by handling retention, usage and disposal over time

Read more about the importance of data management and movement and ensuring data accessibility.

Data management is the process of collecting, storing, organizing, protecting and using data in a way that meets an organization’s operational and strategic needs. It encompasses a broad set of practices and technologies aimed at ensuring data is available, reliable, secure and easily accessible to authorized users.

Effective data management enables organizations to make informed decisions based on accurate and timely data. Data analytics can’t drive business value without the core components of data management being aligned:

• Data collection
• Data storage
• Data organization
• Data security
• Data quality
• Data accessibility

Read more about how automated data management drives a competitive advantage with customer experience (CX).