What is Data Management?

Data is quintessential for businesses and when left unused becomes trash. Every hour tons and tons of data is created and consumed. And it becomes difficult to manage and extract the essence from those data. To make it easy for businesses to handle large amounts of data, data management tools come to the rescue. These tools process the data and help companies use it to make informed decisions.

Key Components of Data Management

Data management may sound very simple. However, it encompasses a wide range of processes and practices to make sure that data is accurate, secure, and accessible. Let’s look at some of the key components of Data Management:

1. Data Governance

Data governance is a part of data management that is concerned with the overall management of availability, integrity, security, and usability. It ensures that data management complies with the policies, regulations, and best practices.

Key Elements of Data Governance:

• List out the rules for handling and using data
• Distribute responsibility and ensure compliance with rules such as GDPR, HIPAA, etc.
• And to make decisions related to the management and usage of data

2. Data Quality

Data quality is an important project management tool as it looks after the quality of data. This means it ensures that the data is relevant, accurate, complete, and reliable for the specific purpose.

Key Elements of Data Quality:

• Data quality ensures the correctness, relevancy, and accuracy of data collected. 
• It ensures consistency of Data distribution and uniformity across systems. 
• Make sure it is the latest and up-to-date version of the data
• Detect and rectify errors and inaccuracies in datasets

3. Data Integration

Data is available in various silos, separated from each other. Combining data from these different sources is important in order to make it usable and relevant. And come Data integration for rescue. It is a process of combining data from various sources to offer a unified view. This cohesive data can now be analyzed and used for various purposes.

Key Elements of Data Integration:

• Collect and combine data from different sources
• Make it easily accessible to current data
• Centralize the storage
• Transport data from one system or format to other

4. Data Security

As cyber crimes are on the rise, securing these data becomes an important part of data management, so it also helps in cyber security. Furthermore, data security makes sure that the data is protected from unauthorized access, corruption, or theft.

Key Elements of Data Security:

• Encrypt data
• Implement role-based access to data
• Hide sensitive data
• Make it easily restorable after any mishaps
• Comply with legal standards of data security such as GDPR, HIPAA, etc.

5. Master Data Management (MDM)

Master data management is responsible for maintaining consistency and uniformity across the organizational data.

Key Elements of Master Data Management:

• Create a master database for different data records
• Detecting and removing duplicate data entries
• Assigning responsibility for data management

Best Data Management Tools

Managing data manually will require many people and a high level of diligence to ensure consistency, accuracy, and security. It had led to piles of files with dust settled on them, inefficiencies, inaccuracy, and weak security. 

Moreover, its accessibility was also limited, which hindered insightful decision-making. But now, the field of data science has modernized with data management tools. From high quality and improved efficiency to robust security and centralized accessibility, the tools have made the management of volumes of data simple with just a few clicks.

But you must wonder which tool you should use. Here is a list of some of the best data management tools that you can use to simplify the handling of data:

1. Informatica

Informatica is an all-in-one enterprise data management tool that supports data governance, data quality as well as master data management. It can help enterprises to extract, transform, and load (ETL) data from various different sources.

Key Features:

• Supports ETL from various different data sources and systems.
• Also supports hybrid data integration for cloud and on-premise data.
• Moreover, it offers end-to-end data governance for data cataloging and metadata management.
• It also looks after the automation of data lineage, reporting, and quality checks.

2. Power BI

Power BI is a business analytics tool powered by Microsoft that provides data visualization and business intelligence capabilities. It helps in creating interactive dashboards and reports from multiple data sources.

Key Features:

• Real-time data visualization and interactive reports.
• Connects with various data sources, including SQL, Excel, cloud-based, and on-premise sources.
• Built-in AI capabilities for advanced data analysis.
• Integration with Microsoft Teams and Excel for seamless sharing.
• Dashboards and reports are accessible on all devices including mobile.
• Extensive range of connectors for cloud-based and on-premise databases.

3. Tableau

Tableau is a leading data visualization and business intelligence tool. It enables users to create dynamic, interactive visualizations from complex datasets in a user-friendly way.

Key Features:

• Easy-to-use interface for creating visualizations.
• Offers forecasting, trend lines, and statistical analysis.
• Combines data from multiple sources without extensive scripting.
• Supports live data connections and real-time analysis.
• Connects to a wide range of data sources, including spreadsheets, SQL databases, and cloud-based systems.
• Allows sharing of dashboards within teams and across departments.

4. Collibra

Collibra is a data governance and data cataloging platform that ensures data quality and compliance across organizations. It provides a central platform for managing metadata, data lineage, and compliance.

Key Features:

• Comprehensive governance framework to ensure data accuracy, privacy, and security.
• Offers a searchable data catalog with metadata, data lineage, and relationships.
• Helps organizations comply with data regulations such as GDPR.
• Automates data stewardship tasks and governance workflows.
• Encourages collaboration across data teams with a user-friendly interface.
• Tracks the flow of data across systems and processes.

5. Profisee

Profisee is a master data management (MDM) solution that focuses on simplifying the management of master data across organizations. It integrates well with other enterprise data systems to ensure data consistency.

Key Features:

• Centralized platform for managing master data across applications.
• Designed for enterprises of any size, with flexible deployment options.
• Seamless integration with systems such as CRM, ERP, and other data sources.
• Ensures data quality by standardizing, matching, and deduplicating data.
• Offers both cloud-based and on-premise deployments.
• Provides tools for managing data stewardship processes.

6. Ataccama

Ataccama is an AI-powered data management and governance platform that combines data quality, data cataloging, and master data management in one solution. It offers automation for data discovery, data quality improvement, and governance.

Key Features:

• AI powered automation in data discovery, cleansing, and quality checks.
• Ensures consistent data governance with robust policy management.
• Centralized platform for managing and governing master data.
• Self-Service Data Quality: Allows business users to manage and improve data quality.
• Supports real-time data quality checks and processing.
• Connects with a range of data sources, both structured and unstructured.

7. Talend

Talend is an open-source data integration and management platform that provides a wide range of tools for ETL, data quality, and master data management. It is known for its extensive support of open-source technologies.

Key Features:

• Supports cloud, on-premise, and hybrid data integration with a wide range of connectors.
• Efficiently extracts, transforms, and loads data from multiple sources.
• Provides data profiling, cleansing, and deduplication tools to ensure high data quality.
• Offers both open-source and enterprise-grade versions for flexibility.
• Native support for cloud and big data technologies like Hadoop and Spark.
• Ensures compliance and governance with integrated data cataloging and lineage tracking.

The Final Line: 

These tools provide a range of features that cater to different aspects of data management, including integration, governance, visualization, and quality assurance. Depending on the specific needs of a business or organization, one or more of these tools can be combined to create a robust data management strategy.

Also Read Our Blog: Cyber Security: Types, Services, Safety Tips and Tools