Data management is the process of organising, storing, and manipulating data to ensure its accuracy, dependability, and accessibility. A data management system can help businesses streamline processes, reduce errors, and ensure that data is used effectively. This eventually leads to increased productivity, improved decision-making skills, and a competitive advantage in the market.
There are various types of data management systems and ways to manage them. One example is relational database management systems (RDBMS), which contain data definitions so that programmes and retrieval systems can refer to data items by name rather than having to describe the structure and location of the data each time.
In this article, we’ll talk more about Data Management Systems, including what they are and how they can help businesses grow.
What is a Data Management System?
A Data Management System is fundamentally a solution for efficiently handling, organising, and securing an organisation’s data across its lifecycle. It refers to a set of processes, policies, architecture, and tools used to collect, store, retrieve, and manage data.
Data Management Systems are critical to ensuring data integrity, consistency, and availability for decision-making. They play an important role in allowing businesses to make informed decisions based on accurate and current information.
Implementing a Data Management System can also boost operational efficiency, reduce data redundancy, and streamline data processes within a company. Businesses that centralise data management can improve team collaboration and ensure compliance with data regulatory requirements.
To understand more about data management systems, you need to know about two things. The first are the components of a data management system. The second are the four major types of data management systems.
Components of Data Management System
A strong Data Management System is made up of several components, each of which serves a specific role in ensuring smooth data operations. According to SAP’s business technology platform, there are at least two major components of a data management system: database warehouses and database lakes.
1. Database Warehouse
Digital storage systems that integrate and standardise massive volumes of data from various sources are known as data warehouses (DWs).
Its goal is to help businesses gain insight from their data and make informed decisions by feeding it into business intelligence (BI), reporting, and analytics while also meeting regulatory requirements. As an organization’s “one true source” for all of its data, data warehouses consolidate both current and historical records.
2. Data Lakes
A data lake is a central data repository that assists in addressing data silo issues. Importantly, a data lake retains massive amounts of raw data in its native, or original, format. The format can be structured, unstructured, or semi-structured. Data lakes, particularly those in the cloud, are inexpensive, easily scalable, and frequently used with machine learning analytics.
Aside from these two components, security is an important aspect of data management systems. Check out our article, Small Business Cyber Security Tips for 2024 to get started implementing a cyber security system in your data management.
What is the Difference Between Data Warehouse and Data Lakes?
Big data can be stored in both data warehouses and data lakes, but they are not the same thing at all.
A data warehouse stores data that has already been formatted for a certain purpose. A data lake, on the other hand, stores data that has not been processed yet and for which no purpose has been set.
Data lakes and data warehouses often work well together. For instance, if you need to answer a business question with raw data stored in a lake, you can extract it, clean it up, change it, and then use it for analysis in a data warehouse.
You can choose the best storage solution by looking at the amount of data you have, how well the database works, and how much the storage costs.
Before you begin implementing data management systems in your business, you should have a solid framework to follow. To learn more, see our article “Data Management Framework: Definition, Importance, and Steps“, which delves deeply into the subject.
What Are the 4 Major Types of Data Management Systems?
The 4 major types of Data Management Systems are relational database management systems (RDBMS), object-oriented database management systems (OODMBS), in-memory databases, and columnar databases. Each type of system has its own strengths and weaknesses, so it’s important to consider your specific needs and goals when selecting a data management system.
Relational database management system (RDBMS):
RDBMS is a type of database management system that stores data definitions so that programmes and retrieval systems can use names to find data items instead of having to describe where they are stored and how they are structured every time. This system keeps track of connections between data items, making them easier to find and stop duplicates.
These connections are based on the relational model. Simple information about an item, like its name and features, is saved only once and connected to pricing tables and lines for customer orders.
Object-oriented database management system (OODBMS)
Object-oriented database management systems are a different way to define and store data. Object-oriented programming system (OOPS) developers make and use these types of data management systems for work. Object-oriented databases store data as objects. Because it’s not in tables like in an RDBMS, OODMS data are self-contained and self-described entities.
In-memory database
In-memory databases (IMDBs) store data in a computer’s main memory (RAM) rather than on a disc drive. This data management system method allows data to be transferred more quickly than from a disk-based system. Programs that require quick responses frequently use in-memory databases. Thanks to IMDB, a report that used to take days to compile is now accessible and can be analysed in minutes, if not seconds.
Columnar database
A columnar database organized groups of related data (a “column” of information) for easy access. It is used in modern in-memory business applications, as well as many standalone data warehouse applications, where retrieval speed is critical (for a limited range of data).
Conclusion
To sum up, companies that want to succeed in today’s data-driven market must have a solid Data Management System in place. Finding the correct DMS can make all the difference, whether you’re trying to manage big data, ensure the integrity of transactional data, gain insights from large datasets, or take advantage of cloud computing’s flexibility.
Security is an important aspect of any data management system. For that reason, data security should be a major concern for any company. Introducing Nexa Lab security hardening services.
We provide a wide range of cyber security services, including vulnerability assessments, application security enhancements, incident response planning, custom security strategies, access control and authentication, and security awareness training.
Nexa Lab was founded and established in Australia, with over 30 years of experience in the MSP and IT industries. With a commitment to cybersecurity, we prioritise protecting Australian businesses’ digital assets and sensitive data.
