A data management system is typically built on a data management platform and includes various components and processes such as a database management system, a data integration tool, data warehouses and lakes, and analytics.
Here we’ll cover the 3 main components in detail and then discuss additional features.
1) Data management platform (DMP) refers to software designed to collect, organize, and analyze large amounts of data from multiple sources. The platform acts as a central hub for managing and activating data, enabling your organization to gain insights into customer behavior, optimize marketing campaigns, and improve overall business performance.
DMPs typically integrate with a variety of data sources, such as CRM systems, website analytics tools, and third-party data providers. The platform collects and aggregates this data, allowing your marketing team to segment and target audiences based on specific attributes and behaviors. DMPs can also help you comply with data privacy regulations by providing tools for managing user consent and data access.
In addition to data collection and segmentation, DMPs offer various tools for data activation, such as audience targeting, personalized content delivery, and programmatic advertising. This allows you to reach your target audiences with more relevant and engaging messages, leading to better engagement, higher conversion rates, and improved ROI.
2) Database management system (DBMS) refers to a software system that allows you to create, access, and manage databases. A database is a collection of related data that is stored in an organized way to allow for efficient retrieval and manipulation.
A DBMS provides a way to define the structure of a database, create and modify tables, and specify relationships between tables. It also provides mechanisms for adding, modifying, and deleting data from a database, as well as for searching and retrieving data.
Some of the key features of a DBMS include data integrity and security, which ensure that the data in a database is accurate and protected from unauthorized access. A DBMS also provides tools for backup and recovery, so that data can be restored in the event of a hardware failure or other disaster.
There are many different types of DBMS, including relational, object-oriented, and document-oriented systems. Relational databases are the most common type and use tables to store data, with each table consisting of rows and columns. Object-oriented databases store data as objects, which can contain data and behavior, while document-oriented databases store data as documents, such as JSON or XML files.
3) Data integration tools are software solutions designed to combine data from multiple sources into a unified view. The tool enables you to extract, transform, and load (ETL) data from disparate sources, such as databases, files, and applications, and consolidate it into a single location for analysis and reporting.
Data integration tools provide a range of features for managing the ETL process, including data mapping, data cleansing, data transformation, and data quality assurance. These features enable users to standardize data across sources, ensure data accuracy and consistency, and transform data into a format that can be easily analyzed and used for decision-making.
Data integration tools may also provide additional functionality, such as data profiling, data enrichment, and data governance, to support management and compliance requirements. They may also integrate with other data management solutions, such as data warehouses, data lakes, and BI platforms, to provide comprehensive enterprise data management.
4) Additional components of your system may include data warehouses, data lakes, data fabric, data mesh, IPaaS, and augmented data management.
- Data warehouse: a system which aggregates large volumes of data from multiple sources into a single repository of highly structured and unified historical data.
- Data lake: a centralized repository that holds all of your organization's structured and unstructured data. It employs a flat architecture which allows you to store raw data at any scale without the need to structure it first.
- Data analytics: the use of processes and technology to combine and examine datasets, identify meaningful patterns, correlations, and trends in them, and most importantly, extract valuable insights.
- Data fabric: a machine-enabled data integration architecture that utilizes metadata assets to unify, integrate, and govern disparate data environments.
- Data mesh: a data architecture where data is owned and managed by the teams that use it. A data mesh decentralizes data ownership and provides a self-serve data platform and federated computational governance.
- Integration platform as a service (iPaaS): a cloud-based software solution that allows you to integrate data and applications from different sources and environments. iPaaS provides pre-built connectors and APIs for popular enterprise applications, data transformation and mapping tools, workflow automation and orchestration capabilities, monitoring and alerting tools, and governance and compliance tools. iPaaS enables you to streamline business processes, reduce infrastructure costs, and improve efficiency.
- Augmented data management uses machine learning and AI technologies to automate and enhance traditional management tasks such as data integration, data quality management, data governance, and data security. Through machine learning algorithms, machines can identify and classify data, monitor and manage data quality, detect and prevent security breaches, and make recommendations for data-driven decision-making. By automating routine tasks, augmented management allows you to focus on more strategic tasks that require human decision-making and expertise.
- Streaming data refers to data which is continuously flowing from a source system to a target. It is usually generated simultaneously and at high speed by many data sources, which can include applications, IoT sensors, log files, and servers. A streaming data architecture allows you to consume, store, enrich, and analyze this flowing data in real-time as it is generated.