Data management and data governance are terms used interchangeably, but are totally different techniques. Today, data understanding, storing, accuracy, strategizing, and implementing are essential for business success and development.
Data literacy is also crucial because it teaches about how to derive useful information from the gathered data. Approach data literacy consulting agency EWsolutions for help!
Data management vs. data governance
Data management is viewed as an IT practice. If there is no solid data management created, the entire data landscape will be beyond your reach. The IT team aims to organize and control data resources to ensure it is manageable, accessible, and accurate whenever your employees need them.
The IT teams depend on 360° customized data collecting practices, systems, processes, and an entire group of tools that gather, validate, store, process, organize, maintain and protect. If data is not treated properly then it becomes unusable or corrupt and useless.
Data management incorporates the entire data asset’s lifecycle right from initial data creation to final data retirement. There are multiple categories and disciplines associated with data management.
- Data architecture
- Data Governance & Stewardship
- Data quality management
- Data security management
- Data warehousing
- Metadata management
- BI & Analytics
Data management defines the organization and management of data, while data governance involves strategizing policies and responsibilities for cross-company reach. The data governance aims to offer tangible answers bow a business can identify and prioritize the gathered data’s financial benefits without compromising the data quality. It determines –
- What is the acceptable data?
- Where to collect it?
- How to use it?
- What is the accuracy level?
- Which rules must the data adhere to?
- Who is involved in different data lifecycle stages?
Data governance goes beyond IT and involves the stakeholders to ensure the reliability, integrity, and safety of every data gathered. If every business silo designs a separate data strategy, then the end result will be not comprehensive but chaotic and useless.
A solid data governance strategy comprises an extensive range of practices, theories, and processes. In many data zones like privacy, usability, compliance, integration, and security the policies and liabilities can overlap. However, you get a system that defines the roles and responsibilities of participants and processes.
For example, when to use which process and who will be responsible to take specific actions under definite situations. Data governance determines a holistic way of controlling data assets to ensure the business attains maximum data value.
Data governance is a strategy and not a technology
Data governance takes support from technology via automation, augmentation, and scaling. Data governance initiates as a theory but becomes tangible when you define –
- The business glossary defines the meaning of every data for better clarity.
- Data quality determines integrity and compliance.
- Roles and responsibilities for data maintenance and care.
- Governed data catalog that helps to access and understand data more deeply.
- Metadata that keeps track of data lineage across different sections.
Active participation in data governance allows fixing incorrect data directly. This promotes better data quality as well as trust that the information is accurate, reliable, and strong.