Data management: this short article explains Data management in a practical way. Next to what it is (definition), this article also highlights why it’s important, databases, Data modeling, tricks and tips. Enjoy reading!
Data management: this short article explains Data management in a practical way. Next to what it is (definition), this article also highlights why it’s important, databases, Data modeling, tricks and tips. Enjoy reading!
The definition of Data management is that it’s the process of collecting, storing, organizing and maintaining data produced and collected by an organization. Effective data management is critical to deploying IT systems that enable business applications and deliver analytical insights to support decision-making and strategic planning by leaders.
Data management goes beyond document management, which is purely aimed at storing and managing electronic documents.
Data management comprises a combination of different functions that together aim to ensure that all data in business systems is available as accurately as possible to everyone who should have access to it.
In summary, data management includes:
Data are increasingly seen as a tool that can make a difference. Important decisions are made based on data, for example in managing marketing campaigns, costs and business processes. Often these decisions aim to reduce costs and increase profits.
A lack of effective data management can leave companies facing inconsistent data sets and data quality issues that can lead to erroneous findings and wrong decisions.
Data management has also become more important in recent years as organizations are increasingly subject to strict compliance requirements that are laid down in laws, including data privacy and protection laws. An example of this is the GDPR and the California Consumer Privacy Act. To ensure data is protected and risks are minimized, it’s essential to have a solid security framework in place. A Data Security Posture Management (DSPM) tool helps achieve this by identifying vulnerabilities, ensuring compliance, reducing attack surfaces, and safeguarding sensitive information.
In addition, companies are increasingly capturing large amounts of information, for example through Big Data systems. Without effective management, navigating these environments can become impractical and difficult.
Data management encompasses a series of steps from data processing and storage to managing data and how it is formatted and used in systems. Developing an architecture is the first step in the data management process, especially in large organizations that produce a lot of data. An architecture can be seen as a kind of blueprint for the databases that are used, including technological techniques that are part of them.
Databases are the most commonly used platforms to store data. They contain a large amount of data that is organized in a way that it can be accessed, updated and managed. Databases are used for many things, such as transaction processing systems, customer records, sales orders, etc.
Database management is therefore a core function in data management. Monitoring performance and acceptable response times are some of the most important things when it comes to database management. Other tasks include initial database design, configuration, installation and updates, security policies, privacy, and database recovery.
In addition to managing databases and data, it is also important that the data is made transparent. A large collection of random data does not mean much to most users. Therefore, data modeling is applied. Data modeling uses abstraction to represent the nature of data through visual representation. Its purpose is to illustrate the relationship between data by grouping, organizing, and formatting the data.
The models are built based on the need for them. The business stakeholders, such as management who base decision-making on the models of the data management department, provide feedback that defines the rules and requirements of a model.
The process therefore starts with collecting the feedback on the requirements of the end user. These are translated into data structures to create a database design. A database structure, or roadmap, is a formal diagram that provides insight into exactly what is being designed.
Data modeling uses standardized schemes, signs and techniques. This ensures that there is a common, consistent and predictable way to model data.
Conceptual data models are also known as domain models and they visualize an overall picture of what data is contained in a system. These models are independent of any underlying business applications. For example, it allows sales reps and sales managers to view sales data, expense figures, products and costumers.
These models are less abstract than the conceptual models and provide more details about the concepts and relationships within a domain. It records the structure of data elements and underlying relationships and is independent of a database. The logical data model goes one step further than the conceptual data model by adding more information to it.
Physical data models provide a scheme for how data is physically stored in a database. These models are the least abstract of all and often consist of tables, columns, and the relationships between data from those data objects.
The articles linked to the data management tag include theories, models and techniques used to effectively manage data within organizations. Are you missing a topic related to data management? Let us know in the comments below an article or send an e-mail.
