What is Data Management?

What is Data Management?

Data management is collecting, organizing, protecting, and storing the data created by an organization. The managed data is readily available in time of need, and the data analysis drives the company’s operational decision-making and strategic planning by the authorities.

The world has been gradually shifting toward digitalization over the past few years. While this transformation makes it a lot easier to access important data from anywhere, the new system comes with its problems. Billions of users generate massive loads of data every day. Without proper management, the data will keep piling up until it becomes a headache even for the modern, powerful machines.

Therefore, all successful modern businesses, organizations, and corporations understand the importance of proper data management. Numerous software applications and tools are available that automate the management process and provide a simple and effective output file. Moreover, data scientists are always looking for even better techniques to keep up with the ever-growing pile of data in the world.

Data Management Systems and Components

The basic concept behind data management is to transform raw data into a meaningful numerical output. The data management systems are based on various platforms further run by different processes and components to help you derive results. Here are a few data management systems and their components that most organizations commonly employ:

• Database management system (DBMS)

The DBMS is a special software package programmed to define, store, manipulate, manage, and retrieve the data from a database. It is a systematic package that defines a set of rules before manipulating a single data entry. A DBMS generally consists of the following components:

• Relational database management system (RDBMS)

A relational database management system contains data definitions for manipulation and retrieval of the data. The system recognizes various data entries by assigning them names rather than accessing the data based on the location and structure of the entry. Furthermore, the system also maintains relationships between the different entries to avoid duplication and system errors. Usually, the RDBMS is used by the grocery stores, warehouses, and other places requiring inventory management and customer billing in a single program.

• Object-oriented database management system (OODBMS)

An OODBMS is very different from RDBMS and other systems. As the name suggests, this DBMS stores and manipulates the data as objects as defined by object-oriented programming (OOP). For instance, the RDBMS stores data as conventional rows and columns, whereas OODBMS stores data as objects. Database management is important for businesses, programmers, and developers. The OODBMS approach helps correlate the database with the code as both are written in the same syntax.

• In-memory database (IMDB)

Computer technology has greatly evolved since the first model. Modern computers are smarter and much faster than ever before. So, it would be unwise not to utilize lightning-fast memory speeds for database management. An in-memory database or IMDB is based on exactly this principle. The data is stored on the random access memory (RAM) rather than the HDD or SSD. So, the data processing and retrieval speeds have become unmatched. For example, the data that took several days to process can now be processed, retrieved, manipulated, or stored in minutes.

• Master data management (MDM)

Master data management or MDM is the equivalent of universal truth in the database. It helps the organization to maintain consistency in the smaller databases by providing a master reference database. The MDM usually includes important data that must never be corrupted, erased, manipulated, or duplicated for safety. An MDM can consist of customer data, product data, asset and financial data, and company profiles data. In short, an MDM system prevents potential errors due to the inconsistency of data in various smaller databases. The three key pillars of effective MDM include data quality management, data governance, and data consolidation.

• Cloud Data Management

Cloud is the most reliable and accessible solution to data management to date. The data is stored on a cloud rather than on a physical hard drive, and the cloud is connected to a secured server with several levels of encryption. The cloud data management system ensures data collection, monitoring, manipulation, and processing within cloud storage. Moreover, you can access the database anywhere, regardless of the laptop, PC, or smartphone, as long as it is connected to high-speed internet. 

Types of Data Management

Data management systems have allowed numerous organizations around the globe to save time and money. The modern management systems can process large databases and provide quick results if you know which type of data management suits you the best. Here are the 7 basic types of data management that focus on different specialties within the field: 

•     Data catalogs: A data catalog works on metadata rather than the whole database. It creates a complete picture of the data along with a summary of locations, changes, and quality of data so that it is easy to retrieve when needed.

•     ETLs (Extract, Transform, Load): ETLs (Extract, Transform, Load) is an algorithm that extracts the data from one system, transforms it into a suitable format, syntax, or language, and loads it into the organization’s data warehouse.

•     Data pipelines: Data pipelines work like virtual pipes interconnected throughout the network. These pipelines help automatically transfer important data between the connected systems. Thus, all the systems connected to a data pipeline will remain fully synced.

•     Data preparation: Data preparation is the pre-processing stage in which the raw data is prepared for the upcoming stages through transformation into the correct shape, format, language, and syntax. The data preparation helps in database management by preventing potential problems when combining the datasets.

•     Data governance: Data governance is a framework that defines processes, standards, and policies that help with data management and maintains the data’s structural and functional integrity. Furthermore, the governance also protects the data through advanced security encryptions.

•     Data architecture: Data architecture refers to how the data is stored within the database. For instance, the RDBMS stores the data in row/column structure, while the OODBMS stores the data as objects. Thus, it’s a formal approach to managing and creating the data flow.

•     Data modeling: Data modeling is a logging system that keeps track of the data flow throughout an organization. This tracking data is documented simultaneously so that the supervisors can keep a check on the database. Furthermore, data modeling is also an important component of most machine learning and deep learning programs.

•     Data warehouse: Data warehouse is an all-in-one location for both the new and old databases. It combines various data sources and connects with several locations to offer a clean route for data analysis and business insights.

•     Data security: Data security is the defender of the database regardless of its type. The system prevents the files from corruption by keeping the records clean. Moreover, it also protects the database by preventing unauthorized access.

Why is data management important?

The world is gradually shifting towards digitalization in all fields. Digital data storage allows organizations to collect, process, transform and retrieve important data faster. Furthermore, the internet automatically connects various databases, forming a complete user profile. Companies can then use these profiles to access the individual personality of each user and personalize the experience accordingly. In other words, it won’t be wrong to say that database management is the king of the present and will pave the way for future development. 

The biggest examples of how efficient data management can transform your business are Google and Facebook. Google launches at least two new apps every year, and the data from all these apps are stored on various small databases. Through proper DBMS, Google has allowed its users to take full control of their social and physical life. Similarly, Facebook’s latest project, the Metaverse or Meta, has made headlines several times. The main reason for this is that Facebook actively manages and optimizes its DBMS such that the whole world is accessible from a single application.

In other words, active data management is the first step toward effective data analysis for business insights and other purposes. The modern DBMS offers data visibility, security, reliability, scalability, and the comforts of automation in a single software package. Therefore, all successful businesses and organizations must adopt effective data management systems to keep up with the changing global trends.

Conclusion

The digitalization of different fields requires active data management systems to avoid data loss and resource consumption due to data piling. In this regard, data scientists have developed different systems and components that help collect, transform, manipulate, and analyze data. Moreover, an organization can further benefit from the latest systems by integrating ML and NLP branches of artificial intelligence in their DBMS. In short, active data management has become a necessity for all modern businesses and organizations.