Data Management
Data Governance, Data Quality, Data Security: All important terms and concepts for successful data management.
What is data managment?
In today's data-driven world, effective data management is crucial for companies of all sizes and industries. It's about viewing data as a valuable resource and managing it so that it can be used to make informed decisions, innovate, and improve efficiency.
Data management components
Data Governance
- Policies and procedures: Define rules for using, storing, and accessing data within the company.
- Responsibilities: Defining roles and responsibilities for implementing data governance policies.
- Control and compliance: Ensuring compliance with data protection regulations and internal policies.
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Data Quality
- Correctness: Data must be free from errors and contradictions.
- Consistency: Data from various sources must be available in a uniform format.
- Completeness: All relevant data must be available.
- Timeliness: Data must be up to date
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Data Security
- Confidentiality: Protecting data from unauthorized disclosure.
- Integrity: Protecting data from unauthorized alteration or destruction.
- Availability: Ensuring access to data for authorized users.
- Risk management: Identification and assessment of security risks and implementation of appropriate protective measures.
Data Integration
- Consolidation: Combining data from various sources in a central system.
- Transformation: Converting data into a uniform format and standard.
- Harmonization: Standardization of data structures and definitions.
- Deployment: Provide integrated data for analysis and reporting
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Data warehousing
- Storage: Central storage of large amounts of data for analysis purposes.
- Organization: Structuring data in a relational or multidimensional model.
- Access: Provision of data for data mining tools and analytics applications.
- Reporting: Create reports and dashboards to visualize data analyses.
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Data Analytics
- Collection: Collecting data from internal and external sources.
- Cleanup: Prepare data for analysis by correcting errors and inconsistencies.
- Analysis: Application of statistical and data science methods to gain insights from data.
- Visualization: Presentation of analysis results in the form of charts, graphics and dashboards.
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Data Architecture
- Definition: Design of the technical infrastructure for the storage, processing and use of data.
- Technologies: Selection of suitable hardware and software systems for data management requirements
- Integration: Ensuring compatibility between different data systems and applications.
- Scalability: Adapting the infrastructure to growing amounts of data and usage requirements.
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Metadata management
- Cataloging: Collection and storage of information about data, such as origin, format, meaning.
- Organization: Structuring metadata in a taxonomy or ontology model.
- Search and access: Enables metadata to be searched and retrieved to support data governance and analysis tasks.
- Improving data usage: Increasing the efficiency and effectiveness of data use by providing contextual information.
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Master Data Management
- Master data: Definition of a central version of important company data (e.g. customers, products, locations).
- Consistency: Ensuring the consistency and consistency of master data across all systems
- Quality: Improving the quality of master data through data cleansing and validation.
- Governance: Establishment of guidelines and procedures for managing master data.
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Data Privacy
- Data protection laws: Compliance with data protection laws such as GDPR and GDPR to protect personal data.
- Data security: Implementation of appropriate technical and organizational measures to protect personal data.
- Transparency: Informing data subjects about the collection, storage and use of their personal data.
- Rights of data subjects: Ensuring the rights of data subjects, e.g. to access, correct and delete their personal data.
Data Lineage
- Proof of origin: Tracking the origin of data, such as data systems, processes, sources
- Transformations: Tracking transformations made to data, such as aggregation, anonymization
- Usage tracking: Monitoring the use of data in various applications and reports.
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Benefits of effective data management
- Improved decision making: Data-based decisions lead to better results and reduce the risk of incorrect decisions.
- Increased efficiency: Optimize processes, reduce costs and increase productivity.
- Increased innovation: Development of new products and services based on data and insights.
- Improved customer satisfaction: Personalized offers, better understanding of customer needs and increasing customer satisfaction.
- Lower risk: Reducing risks through data-based risk management and regulatory compliance.
- Improved collaboration: Enabling effective collaboration between different departments and teams by sharing data and insights.
Data Management Challenges
- Data flood: The amount of data is growing exponentially, making it difficult to manage and analyze.
- Data security: Protecting data from cyber attacks, data breaches, and unauthorized access.
- Data quality: Ensuring that data is accurate, consistent, complete, current, and relevant.
- Data integration: Combining data from various sources in a uniform format and with uniform standards.
- Shortage of skilled workers: There is a shortage of qualified data experts with the necessary skills and expertise.
- Legacy systems: Legacy systems can make it difficult to integrate data and use modern data management tools.
- Organizational culture: Establishing a data-friendly culture in which data is regarded as a valuable resource and is used responsibly.
Best practices for data management
- Define a clear data strategy: Determine how data should be used in the company, which goals should be achieved with data management and what resources are available for this purpose.
- Establish a Data Governance-Structure: Define roles and responsibilities for data management, establish policies and procedures, and enforce data governance policies.
- Invest in data tools and technologies: Use the right tools to collect, clean, integrate, store, analyze, and visualize your data.
- Foster a data-friendly culture: Debt employees when handling data, raise awareness of the importance of data and create an environment in which data is actively used.
- Measure data management success: Monitor key performance indicators (KPIs) to measure the success of your data management initiatives and make adjustments as needed.
- Stay up to date: Learn about new trends, technologies, and best practices in data management and adjust your strategy and tools accordingly.
We are happy to support the aspects of data management with our services Data Audit and expertise from Data Strategy, Data Organization , Data Governance and Data culture.
Note: Our team benefited from the support of AI technologies while creating and maintaining this glossary.
Data management is like building a house.
You can't just start with the roof without a solid foundation.
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