Data management is a strategy used by organizations to make data secure, efficient, and available for any relevant business purposes.
Data management refers to both processes and technology. Processes are usually defined by the organization’s data governance framework, and each of these processes is implemented with the relevant software tools.
What is a Data Management Strategy?
Data management strategy involves all aspects of a company’s data infrastructure. This includes elements such as:
Data must be acquired from reputable sources, such as production databases or trusted third parties. Data lineage – metadata that describes the full history of data – is essential for tracking the origin of information, especially where data may have passed through multiple servers.
Managers must oversee the creation of user roles and ensure that each user receives appropriate read access and write access. Access is an organizational issue that requires co-ordination with each department to ensure that everyone has the permissions they need to do their job.
When acquiring new data or moving existing data, it may need to pass through an integration layer. Usually, this involves creating a master schema and then manipulating the data to fit. This transformation must be done in a way that always maintains data integrity while meeting the requirements of the destination repository.
Big Data sets require a useful metadata schema, so that analytics experts can perform quick data exploration. The data management strategy should have a clear process for gathering and indexing relevant metadata, and that this metadata is available when needed.
There are many compliance requirements that affect data management policy. Laws like GDPR and CCPA dictate the handling of personal information, while auditing rules mean that some financial information must be kept on file for a specified time. Data management policy should reflect all regulatory requirements and ensure that the organization stays on the right side of the law.
Most organizations are now highly dependent on analytics to power their decision-making. The data management strategy must support the efforts of the analytics time and ensure that the available data is timely, relevant, and complete. They must also ensure that data is held in the right kind of repository – for example, a data mart for departmental analytics.
Security is the first and last thing in a data management policy. At no point should any user or system expose sensitive data or allow unauthorized data access. The data manager is responsible for bringing security issues to light, and also for organizing regular audits and tests.
Data often needs to be moved to a storage repository, such as a data warehouse or data lake. The data management strategy will recommend preferred solutions so that the organization has a unified approach to long-term data storage.
Data management has a cost, both financially and environmentally. Organizations should regularly review their data management strategy to ask if the current approach is cost-effective and sustainable. This means that decision-makers need to keep themselves informed about new, alternative data solutions.
Data volumes can increase rapidly. For example, Internet of Things (IoT) and website analytics can both generate enormous quantities of data that need to be stored somewhere. As a business grows, these volumes can start to increase exponentially. A data management strategy should plan to scale up easily when required.
Implementing this strategy is the job of the data manager. A data manager can be an individual with a position such as Chief Data Officer, or a team of data experts. Increasingly, data management is automated, which means that policy is defined at a higher level, and then implemented with a data management system.
What is a Data Management System?
In practice, data management is too complex for manual implementation. Most organizations rely on a data management system to carry out most of the tasks listed above, especially:
- Data imports
- Data transformations
- Data archiving
- Security management
- Access management
The two most common solutions are manual data management and low-code data management.
Manual Data Management
The data team creates a bespoke data management system from the ground up. Manual data management involves tasks such as:
- Documenting the entire data management structure
- Creating models and schemas for data transformations
- Designing the architecture of the ultimate repository, such as a data warehouse
- Building data pipelines to move data from sources to the target, typically with batch scripts and Chron jobs
- Integrating additional sources into the pipeline, using API calls where possible
- Applying transformations to data before loading to the repository, via a staging layer which is hosted on a standalone database
- Making data available to authorized user roles in a timely manner
- Monitoring, maintaining and upgrading the data management system
- Responding to security issues
This kind of system can be built by a team of software engineers and data engineers. It is extremely resource-intensive and requires indefinite support from the engineering team. However, it may be a suitable solution in complex environments.
Low-Code Data Management
Low-code data management platforms are intended for organizations that don’t need a bespoke solution. Instead, these platforms allow companies to implement a data management strategy without an intense investment of resources.
Typical low-code solutions may have features such as:
- Drag-and-drop interface for architecture design
- Automated data pipeline
- Built-in integrations, allowing the platform to connect to data sources, data repositories, and analytics tools
- A dashboard to display analytics results
- Cloud hosting, which includes live support and security monitoring
An ETL platform such as Xplenty can serve as a data processing platform in most business use cases.
Low-code data management systems also address the issue of scalability. These platforms are generally cloud-hosted, so organizations don’t have to provide additional processing power as their data needs grow.