7 Key Features of Data Management Systems

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data management systems

Data Management is the practice of gathering, organizing, securing, and storing data for an organization so it can be analyzed for business decisions. Data Management helps minimize potential errors by setting processes and policies around its usage and building confidence in the data used for decision-making throughout the company. Businesses implement Data Management systems to ensure that data is trusted.

A Data Management system is the cornerstone of many successful business operations. These systems help businesses organize and store data so that it is readily accessible when required. The data can be anything from customer records to inventory lists to shipping details and more. 

This article covers some examples of different types of Data Management systems you may be using in your organization, as well as the key features to consider when implementing a Data Management system. 

How Does a Data Management System Operate?

A Data Management system is a software application that organizes and stores data in a database. Businesses use Data Management systems to help them manage their information. When a business has a robust Data Management system, employees can easily find the information they need to do their jobs. A Data Management system can include an assortment of different services. These include data integration, data analytics, artificial intelligence, a data visualization platform, data security, and more. 

A typical Data Management system may comprise databases, data lakes and stores, big data management systems, and data analytics platforms. Other related disciplines of Data Management include Data Modeling, which maps relationships among data elements and the way that data flows across systems; data integration, which integrates data from various sources to enable operational and analytical uses; Data Governance, which sets policies and procedures for making sure that data is uniform across the organization; and Data Quality management, which seeks to correct errors and inconsistencies in raw data.

IT personnel and data managers ensure the systems they deploy are appropriate for their intended purposes and that they provide data-processing capabilities and analytics insights required for an organization’s business operations. Moreover, business applications need technological solutions for maintaining, protecting, managing, and processing the data stored in databases. Databases function collectively as the data utility, providing the Data Governance capabilities an organization needs for the proper functioning of all business applications.

The Role of Data Management Teams in Enterprises

Data Management teams also help to determine roles and responsibilities to ensure data access is provided in an appropriate way; this is especially important for maintaining data confidentiality. Enterprises usually use specialized data teams with deep expertise in Data Quality, Data Science, metadata management, privacy, and security, to reinforce those responsibilities daily. Application developers are typically the ones who are helping to deploy and run large data environments, and this requires new skills in general when compared with relational database systems. 

Different Types of Data Management Systems 

There are a few different types of Data Management systems that businesses implement. This section highlights some common types of Data Management systems used in organizations.


Databases are the most common platforms used for holding enterprise data; they hold a collection of data organized in such a way that they are accessible, updated, and managed. Data can be accessed, modified, managed, controlled, and organized to carry out a variety of data-processing operations. Independently from the data integration technology used, data is typically filtered, combined, or aggregated during data processing to fulfill requirements for its intended purpose, which may vary from business analytics dashboards to predictive machine learning algorithms.

The database manager is a central software component in the DBMS solution, which performs core functions related to data storage and retrieval. Data warehouse management provides and oversees the physical or cloud-based infrastructure used for managing raw data and data analytics activities.

Big Data Management Systems

New types of databases and tools are required for managing big data, which is usually vast amounts of structured, unstructured, and semi-structured data. By visualizing complex coding tasks, working with UI-friendly templates, managing compliance considerations, and much more, the Data Management software accelerates and simplifies the complicated process of big data management. 

Marketing Automation Systems

The marketing automation systems are built around centralized data storage systems, with which other tools can integrate to pull data and optimize content, messaging, social media, mobile marketing, and other campaign requirements, such as site personalization, mobile websites, social media ads, account-based marketing, data enrichment, and several others. Commercial data platforms typically comprise management software tools developed either by a database provider or by third-party vendors.

Customer Relationship Management Systems (CRM Systems)

Customer relationship management systems are databases that generally contain all customer data sources, including personal details, sales opportunities, sales conversion data, revenue data, new offers, and subscription renewals, among many others. This platform is a primary interface for sales team representatives to store all accounts, leads, contacts, cases, and all other customer-facing data. CRM systems also let businesses track and record customer interactions, such as purchases, customer feedback, and more. They facilitate communication between businesses and their customers by storing customer data, such as names and email addresses, in one place. This enables companies to respond to their customers more efficiently. 

Return on Investment (ROI) Systems

One of the biggest benefits of implementing a good Data Management system is improved ROI. When businesses track their ROI with a robust Data Management system, they can more accurately track the effectiveness of their marketing strategies. This can help businesses better understand which marketing tactics produce the best ROI. This can be especially helpful when businesses want to make strategic decisions about their marketing budget. When businesses know their return on investment, it becomes easier for them to determine their profitability. This can help businesses determine which products and services are most profitable and make adjustments where necessary. 

E-Commerce Platform Systems

When businesses use an e-commerce platform, they can manage their online presence, inventory, and orders. This system is often used in conjunction with other Data Management systems. It is a great choice for companies that sell products online. 

Businesses that use an e-commerce platform can efficiently manage their customer information, inventory, and orders. This can help companies save time and improve efficiency in processing online orders, tracking shipments, and more. When companies use a Data Management system to manage their e-commerce platform, it is easier to integrate other software programs into the platform. 

7 Key Features of a Good Data Management System‍

In the digital world, Data Management is a key part of any organization’s digital strategy. The right Data Management system can help your business achieve its objectives and meet its needs. As the DMS brings order to all the raw information you collect from surveys, databases, websites, or social media, selecting an appropriate solution is crucial for the success of any business. 

With so many different Data Management system products on the market, how do you know which one best suits your business? For starters, here are seven key features of a good Data Management system: 

  • Data integration and cleansing: Your Data Management system should have the ability to integrate and combine data from diverse sources. This will help you bring order to data that may already be stored in your servers, databases, or legacy systems and save time and money. It should also have the ability to cleanse data. This is important to ensure your data is accurate and consistent and can be used for a variety of business purposes, such as reporting or analytics. The data cleaning capability of a Data Management system provides the confidence that the data you are storing is accurate, consistent, and reliable. 
  • Security and data retention: As part of your organization’s digital strategy, it is likely that you will be storing a lot of data, either as input or output. This can include everything from customer data and campaign results to product information and marketing collateral. A good Data Management system will offer robust security features to protect your data from unauthorized access, misuse, and tampering, as well as disaster-recovery capabilities so that you can recover lost data in case of a disaster. It should also have rules and regulations in place for how long data will be retained, to help you remain compliant with government regulations and industry standards. Data security is a serious concern and one that should not be overlooked. A good Data Management system, therefore, will have security features and technologies in place that protect your data from both internal and external threats. 
  • Automated data analytics: Your Data Management system should include automated data analytics (or data visualization) so that you can easily view your data in a variety of ways. This will help you identify trends and patterns and make more informed decisions based on your data. If you are storing data at the source, you can streamline data analytics and visualization by integrating your Data Management system with your analytics platform. This will allow you to view your data in one central place, so you don’t have to switch between multiple systems. Whether you’re using a Data Management system as a data source or destination, an analytics module will help you make sense of your data. Data visualization tools can help you spot trends, identify important data, and make better-informed decisions. 
  • User-friendly interface and experience: The user experience (UX) of your Data Management system is very important. If your users find it difficult to navigate, they are unlikely to use it, which defeats the purpose of having a Data Management system in the first place. A good Data Management system will have a user-friendly interface and offer a positive experience for your end users, helping them quickly and easily find the information they need. Your Data Management system should have a modern, intuitive design and offer a personalized experience. This will help employees work more efficiently and reduce the time they need to spend searching for data. It should also offer self-service capabilities so that employees can find the information they need without any help from the busy IT department. This will help employees save time and reduce the overall frustration of using the system. 
  • Real-time data capabilities: A good Data Management system will have real-time capabilities that allow you to manage data as it’s created. This will help you spot issues and resolve them more quickly and also help reduce the risk of error. It is important that you have a centralized place where all data is stored, enabling you to easily monitor it, review it, and respond to it as needed. If you collect data from a variety of sources, your Data Management system should have real-time capabilities that integrate with all of them and offer a single view of that data. This will help you identify data issues, take corrective action, and resolve them quickly. Your Data Management system should also be able to identify data issues and not allow them to enter the system. This will help you spot issues and resolve them before they become bigger problems. 
  • Robust reporting features: Your Data Management system should offer robust reporting capabilities. This will help you generate reports that are accurate, timely, and easily accessible by everyone who needs them. It will also allow you to access the data you need, whether that’s at the source or within your Data Management system. Data visualization tools can help you quickly spot trends, identify important data, and make better-informed decisions. A good DMS will offer a variety of standard reports, as well as custom reports that allow you to filter and view your data in many different ways, including by date range, source of data, user, or department. Your DMS should also be able to integrate with your existing reporting tools so that you can view your data across all systems in one place. This will help you quickly identify trends and patterns, and make better-informed decisions based on your data. 
  • Data storage capability: Your data is only useful if it’s accessible when you need it, which is why a robust data storage system is key. It is also important that your data is stored in a safe and secure environment that protects it from internal and external threats. A good Data Management system will have a variety of storage options, so you can choose the one that best meets your needs. Ideally, it will also have a built-in disaster-recovery solution. This will help you protect your data from security threats. It will also offer a secure solution for storing your critical data and protecting it from cyber-attacks. 


Data is an important part of any organization’s digital strategy. It is a key resource that you must use to your advantage by leveraging digital transformation. As the Data Management system automates and streamlines the processes involved in managing data throughout its lifecycle, it is important to consider the system’s key features, such as data integration and cleansing, security and data retention, automated data analytics, user-friendly interface and experience, real-time data capabilities, robust reporting features, and data storage capability.

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