What is Data Management?

Key Takeaways 

• Data is essential for digital transformation, automation, AI, and informed decision-making. 

• Companies work with structured, semi-structured, and unstructured data and must process each type differently. 

• The benefits of data management include automation, error reduction in data processing, fast response times, and minimizing the risk of data loss. 

• Data management challenges include high initial resource investment, lack of standards, and data migration into modern data architectures. 

Data Management explained

Data management refers to systematically handling data within an organization or institution. It includes planning, organizing, controlling, and monitoring all processes required for data collection, storage, processing, analysis, and utilization.

The importance of Data Management in the modern world

Increasing automation, which simplifies our lives in both the business world (e.g., AI-driven process automation) and private settings (e.g., robotic vacuum cleaners), is only possible by processing vast amounts of data. Organizations must be able to store and analyze the generated data to extract value from it. In particular, artificial intelligence (AI) can accelerate big data analysis and make it accessible even to non-experts. However, the key foundation for this is efficient and consistent data management. 

The foundation: Three types of data  

Before data can generate value, it must be prepared. This is often the most labor-intensive step for data experts, as high-quality analysis results depend on it. Typically, organizations work with the following three types of data: 

 1. Structured data 

Data in a tabular format is usually considered structured because each column contains a specific data category, making comparisons and processing easier. 

2. Semi-structured data 

Sensors and machines often generate semi-structured data. While the data follows a certain structure, where the same categories appear in the same place in each dataset, it must be reformatted for analysis. 

 3. Unstructured data 

Data in text or audio files is considered unstructured because relevant information must first be identified and extracted. Only once converted into a structured format can it be analyzed. 

Core concepts of data architecture 

Organizations must choose an appropriate data architecture based on their data types. Typically, data architectures are classified into centralized and decentralized models. 

The Data Warehouse is a well-established centralized architecture for storing structured data, whereas the Data Lake serves as a central repository for unstructured data, often containing raw data. The Data Lakehouse represents a modern hybrid model that combines the flexible storage of a Data Lake with the structured approach of a Data Warehouse. 

Data Fabric is an intelligent and interconnected data ecosystem that integrates and centrally manages data from multiple sources. It provides a unified data platform across different storage locations, including on-premises, cloud, and multi-cloud environments. 

In contrast, Data Mesh follows a decentralized approach, distributing data ownership across teams or business units. This model leverages “Data Products”, which are made available through standardized interfaces such as application programming interfaces (APIs). For real-time data processing, a decentralized Event-Driven Architecture (EDA) is typically used. 

Objectives of Data Management 

The primary goal of data management is to enable well informed business decisions based on high-quality data. This leads to optimized business processes, cost savings, and ultimately, a competitive advantage. 

Benefits of Data Management:

Challenges of Data Management 

Establishing an effective data management system requires significant effort and resources but is essential for competitiveness in today’s data-driven world. To maximize the value of data and implement a successful data strategy, the following challenges must be addressed. 

Challenges in Data Management: 

Types and subdomains of Data Management

An effective data management strategy requires an understanding of its various subdomains: 

    Data Modeling 

Data Modeling refers to the creation of data structures used within an organization. It involves developing both logical and physical models to ensure consistent data storage and clearly define relationships between data points and structures. Well-designed data modeling facilitates efficient data access and utilization. 

    Data Integration 

Data Integration refers to the process of combining information from different databases into a unified structure. This is particularly crucial for organizations that need to consolidate data from various systems to gain a holistic view of their business processes. A central concept in data integration is ETL (extract, transform, load), which governs data flows between systems. 

    Data Analysis 

Data Analysis encompasses methods for examining and evaluating raw data to identify patterns and derive valuable insights. Techniques such as statistical analysis, machine learning, and AI-driven analytics play a key role. The ability to effectively analyze data and visualize findings is essential for data-driven decision-making. 

     Data Governance 

Data Governance includes all policies, standards, and processes ensuring responsible data usage within an organization. This framework governs how data is stored, processed, and deleted while maintaining compliance with regulations. Key elements include data quality, access control, and regulatory compliance, ensuring data is strategically managed as a valuable resource. 

    Master Data Management (MDM) 

Master Data Management ensures the consistency and quality of key business data, such as customer, product, and supplier information. A structured MDM approach ensures these datasets remain uniform and reliable across all systems and business units. 

     Data Quality Management 

Data Quality Management focuses on ensuring that data is accurate, up-to-date, and consistent. This involves continuous monitoring, error detection, data cleansing, and quality assurance processes. High-quality data is fundamental for meaningful analysis and efficient business operations. 

     Metadata Management 

Metadata Management deals with organizing and maintaining information about data, such as descriptions of data sources, structures, and relationships. Proper metadata management ensures data availability, quality, and traceability, allowing organizations to efficiently catalog and access their datasets. 

    Data Stewardship 

Data Stewardship refers to the operational responsibility for data management and data quality. Data Stewards ensure compliance with data policies and maintain accurate datasets. Acting as a bridge between IT and business units, they facilitate optimal data utilization across the organization. 

     Data Archiving 

Data Archiving involves long-term storage and management of data that is no longer actively used but must be retained for regulatory or business purposes. Key aspects include storage strategies, access controls, and deletion policies to ensure archived data remains accessible when needed. 

     Data Security & Privacy 

Data Security and Privacy focus on protecting sensitive information from unauthorized access, loss, or misuse. This includes technical measures such as encryption and access controls, as well as organizational policies to ensure compliance with data protection regulations like GDPR. 

    Big Data Management 

Big Data Management addresses the organization, governance, and analysis of large and complex datasets. Scalable storage solutions, cloud platforms, and advanced analytics tools enable organizations to derive valuable insights and leverage big data for strategic decision-making.

Data Management Tools and Software 

Selecting the right data management technology is essential to fully leverage the benefits of effective data handling. Below, we introduce different categories of data management tools and their applications. 

Categories of Data Management Tools

 1. Open Source Tools  

Open source data management tools are freely available and customizable solutions covering various aspects of data management, such as data integration, storage, and analysis. One prominent example is Apache Hadoop, an open-source framework for distributed storage and processing of large datasets. Due to its scalability and flexibility, it is widely used in big data applications.  

2. Enterprise Solutions 

Enterprise data management solutions are designed for business environments, offering comprehensive functionalities such as data backup, storage, and utilization. A prime example is SAP Data Services, a data integration platform particularly suited for companies using SAP systems. This tool enhances data quality and facilitates the consolidation of heterogeneous data sources, ensuring a consistent and reliable data foundation. 

3. Cloud Platforms 

Cloud-based data management platforms eliminate the need for costly hardware investments by allowing organizations to pay only for the resources they use. These platforms provide a scalable alternative to enterprise solutions, offering fast deployment, global accessibility, and seamless integration with other cloud services. For instance, Google BigQuery is a serverless, highly scalable data warehouse solution that integrates seamlessly with Google Cloud services and is optimized for fast and efficient processing of large datasets. 

Criteria for Selecting the Right Tools 

A decision for one of the categories described above or a tool should be based on the individual requirements of the organization. Also when selecting tools, it is essential to consider both current and future requirements to ensure long-term efficiency and scalability. 

The following guide can help make the best decision:

• Do you need high flexibility and have a limited budget?

In this case open source tools are the best solution. 

• Do you require stability and have high demands for an integrated platform?

In this case enterprise solutions are the right choice. 

• Do you need easy implementation and experience fluctuating data volumes?

In this case cloud platforms offer the best solution. 

Importance of Data Management for Businesses 

Organizations collect vast amounts of data, however, large amounts of data often remain unused or are not optimally leveraged due to being gathered across different departments. A unified data management strategy provides the foundation for data-driven decision-making, ensuring a holistic rather than isolated approach to data utilization. This also facilitates the integration of AI-driven process optimization, market analysis, and other key use cases. 

EFS Consulting works with its clients to develop a tailored data management strategy, supporting them in tool selection, technical implementation, and the establishment of governance frameworks with practical guidelines and clearly defined roles. This approach takes into account not only the existing IT landscape but also the specific business objectives of the organization. 

Best Practice: EFS Recommendations for implementing and optimizing Data Management 

When starting with data management, it is advisable to first clearly define objectives and determine what should be achieved with the data. It makes sense to define KPIs to measure progress and success. Creating data catalogues helps to gain an overview of existing data assets, identify data silos, and systematically break them down. Based on these objectives and the available data, a data strategy is developed, responsibilities are assigned, and standardized processes are introduced. Right from the start, data governance must be considered, and compliance requirements must be taken into account to establish clear rules for data protection and access rights. 

When optimizing existing data management, AI can help classify data and detect anomalies, improving data quality. Automating data pipelines can reduce manual errors and streamline processes. Suitable KPIs are then used to track data quality and utilization. 

Finally, employees need to develop data literacy and be empowered to use data independently (e.g., with Power BI). This can be achieved through targeted training programs. 

Conclusion 

Efficient data management is not a one-time project but a continuous process that must be regularly adapted to new conditions and technologies. Optimize your data management together with EFS Consulting to unlock the full potential of your data.