Storage
Mobility
Protection
Analysis and control
TOPICShare
More about Data Management
Subscribe to our blog
Thanks for subscribing to the blog.
July 26, 2021
Topics: Cloud Data Sense Elementary5 minute read
What is Data Management?
The term “data management” refers to a wide range of practices and methodologies employed for the purpose of helping organizations better leverage their data.
Typically, data management practices make use of a combination of processes, including:
- Data collection—integrating data sources in an organized manner, to enable the organization to derive value from the data.
- Data access—implementing strategies that enable organizations to create, update, and access data across various data tiers, including archives.
- Data storage—building storage strategies that leverage as many data storage types as needed, including multi cloud, private and public clouds, and on-premise storage.
- Data availability—implementing backup and disaster and recovery plans that ensure business continuity during various disaster scenarios.
- Data security and privacy—enforcing security and privacy policies on a continual basis.
The main goal of data management is to ensure that data remains a secure, private, and accessible asset, generating actionable insights for the organization.
In this article, you will learn:
- Data Management vs Data Governance
- What is Data Management Software?
- Features of Data Management Software
- Data Management Best Practices
Data Management vs Data Governance
The term “data governance” refers to the process of creating data lifecycle strategies, enforced across the organization. The purpose is to create baselines and standards that ensure high quality data. This often requires defining standards for data integrity, usability, and security.
While data management and data governance may seem identical, the two perform different functions. Essentially, data management processes are responsible for implementing the objectives defined by data governance strategies.
What is Data Management Software?
Data management software integrates with organizational data sources, and performs actions such as extracting, integrating, cleaning, warehousing, transforming, and visualizing data.
Data management systems must integrate data sources without risk to data integrity, and display data in an accessible manner.
To ensure accessibility and usability, the software should be able to answer various types of queries, answering the questions of different stakeholders.
Features of Data Management Software
Data management software helps organizations leverage their data. Whether the system manages a small database or massive pools of data, it needs to be able to support a minimum of key processes, including:
- Data quality—the software helps you identify missing information. This feature helps decrease data redundancy.
- Data control—the software provides visibility into how data is used and modified. You can set up alerts that notify you when data modifications occur.
- Data security—the software provides features that extend security, such as encryption, access controls, and tokenization.
- Risk management—the software provides controls for minimizing data risks across the entire data lifecycle.
- Workflow automation—the software provides automation capabilities, which enable you to automate repetitive tasks and optimize the process.
Data Management Best Practices
There are several practices you can adopt when implementing data management across the organization. You can reduce data redundancy, focus on data quality rather than quantity, prioritize data protection and security, and set up monitoring and alerts for maintaining visibility.
Reduce Duplicate Data
There are many reasons to purposely duplicate data. For backup purposes, for example, as well as for creating disaster and recovery copies of data, and for version control. There are also cases when similar data is created, due to repeated processes. However, not all of this data is needed.
Not all point in time copies of data are needed for the purpose of restoring previous versions. Setting up a manual or automated process that audits data regularly and removes duplicates can help you better manage your data, and reduce the cost of unnecessary use of storage. This also keeps your data clean and ready for analysis and queries.
Focus on Data Quality
Maintaining a high level of quality is critical to ensure your data is usable, and relevant. Organisations do not need to retain all the data they generate. In many cases, an organization may create an expensive “data swamp” that collects low quality or irrelevant data, which cannot really be used.
Another way to ensure data quality is to continuously validate data accuracy. Keeping old data is useful for business analytics, but before retaining it, make sure it is accurate, relevant, and actually suitable for ongoing analysis. The same goes for real time data generated by production systems.
Prioritize Data Protection and Security
Your data management strategy should be continuously updated to meet data security and privacy standards, as set by regulatory entities where your organization does business. Here are key data protection measures to keep your data secure:
- Access control—these controls enable you to specify privileges for each type of user. The goal is to prevent the abuse of credentials.
- Encryption—turns your data into meaningless code, which can only be deciphered by keys you control. The goal is to ensure that important data cannot be used even if accessed by unauthorized individuals.
- Physical security—use techniques such as hardening to help secure data stored on devices, and ensure you have strong security measures in your physical facility.
Setup Monitoring and Alerts
Monitoring processes and systems help you gain visibility into your data repositories. Set up monitoring processes based on metrics that provide specific and actionable insights into important patterns and events affecting your data.
The more data you have, the more difficult it becomes to maintain visibility. To extend your reach and control, you can leverage automated data classification systems. Prefer monitoring tools that use behavioral analysis, generating alerts only if behavior deviates from the norm, to minimize false positives.