Data management software is any software that helps organizations control and maintain their data. This kind of software may have features related to data governance, data integration, data analytics, and other aspects of data management.
Because of data sprawl, most companies no longer rely on a single data management software platform to handle everything. Instead, data management software complements other tools within the data infrastructure.
The Elements of Data Management
When a company has lots of physical inventory, it needs good stock management policies to help keep everything organized. Similarly, when an organization has large amounts of data, it needs robust data management policies.
Data management comprises many strands, including:
Data governance: Governance is about high-level policy that sets the standard for the rest of the organization. The data governance team lays out the rules for data management, and they implement those rules in every department.
Data architecture: Architecture is about the design of the entire network, including the data pipelines between systems. Data management ensures that the architecture is secure and meets requirements.
Data Security: Security is at the heart of good data management. Managers ensure that every system and process is fully watertight.
Master data management (MDM): Organizations sometimes use MDM to create a golden copy of certain data types. MDM usually references people, products, or locations. For instance, a hospital might use MDM to create a definitive patient record.
Metadata management: Metadata is data about data, and it is especially important for unstructured repositories like data lakes. Good metadata management makes it easier to control and analyze these massive structures.
Data integration and storage: Data management can also involve bringing multiple data sources together in a unified format. The organization may then move this data to a warehouse for long-term storage.
Data quality management: Data managers have to implement quality assurance checks on data. Quality issues may point to a problem elsewhere in the data infrastructure. Data managers may also actively work to improve quality through data cleansing and data enrichment.
Data analytics: Analytics is often the end-point of a data management process. The management team may work with analysts to ensure data relevance and availability.
Data management is a massive operation. A dedicated management team might oversee it, or an organization might choose to split management responsibilities between other leaders.
The same is true of data management software. Some organizations may use a single platform that performs many of the above tasks. But often, they will rely on a suite of tools to handle each aspect of data management.
What are the Features of Data Management Software?
Perhaps the most basic form of data management software is a Relational Database Management System (RDBMS), such as MySQL or Microsoft SQL Server. These platforms offer many tools beyond the core relational database, and developers can use these tools to perform a certain level of data management.
But in an enterprise environment, organizations need more powerful tools. Common examples of data management software include:
Regulatory compliance is a huge concern for data management. Organizations need to watch out for potential breaches, such as unlawfully moving data to another territory, which is something that can easily happen with cloud-native services.
Built-in compliance tools can help automate this aspect of data management. Using these tools, data managers can establish their rules, and then the system will automatically flag any breaches. This can cut down the time spent on compliance tasks, plus it helps to avoid privacy and security issues.
Robotic Process Automation (RPA) makes it easier than ever to automate complex tasks. This allows data managers to pass repetitive, resource-intensive jobs to an algorithm that always gets things right.
An increasingly popular example of this is metadata management. Through machine learning, these tools can create metadata without human intervention. For instance, if you store a picture of a Lamborghini in your data lake, an automated metadata system can interpret the picture and tag it as “sports car.”
Data Analytics and Business Intelligence (BI)
Many data analytics and BI tools already have extensive data management tools built into the design. Good management is essential at this stage, as data quality has a massive effect on analytics. Any insights gathered from poorly managed data will have no value.
These platforms are getting better at working with unstructured data repositories, such as data lakes. A data lake doesn’t require as much upfront management, as incoming data doesn’t pass through a cleansing or integration process. But these systems still need some management to prevent them from turning into data swamps.
A data warehouse is a highly structured repository. It can only hold relational database tables, so an organization will typically try to clean and integrate any incoming data. Data within a warehouse will make a good golden copy candidate for master data management.
Data warehouses are perhaps the embodiment of a good data management system. If the organization has laid the correct groundwork, it can make the warehouse the center of the data infrastructure. Everything stored in the warehouse is clean, consistent, and reliable.
Data pipelines connect all the above systems. These pipelines often run on an automated Extract, Transform, Load (ETL) process that can integrate with databases, warehouses, and other enterprise systems. Data can move through a pipeline at any point in its life cycle.
One of the most useful things about a data pipeline is that it offers visibility into current processes. Data managers can monitor the pipeline’s logs to see how data moves through the network, and they can monitor any transformation processes. Visibility is the key to good data management.