Data management is the process of creating, collecting, storing, maintaining, securing, archiving, destroying and, most importantly, using data to bring value to a business. Most businesses create and collect an astronomical amount of data so they require quality data management to maintain and use the data in its lifecycle.
Why Is Data Management Important?
Merely accumulating data will not bring value to a business. Without a proper data management process, organizations can’t make use of high-quality data to make data-informed decisions. As a result, an organization will fail to compete in their respective marketplaces.
11 Areas of Data Management Knowledge
- Data Governance
- Data Storage and Operations
- Data Security
- Data Integration and Interoperability
- Document and Content Management
- Reference and Master Data
- Data Warehousing and Business Intelligence
- Data Quality
- Data Architecture
- Data Modeling and Design
Data Management: Challenges and Benefits
Business growth brings a lot of data challenges such as data quality, accessibility, data redundancy, contradictory insights, security, scalability and so on. In other words, in the absence of proper data management, businesses will struggle to grow and scale. Without ready access to valuable, high-quality data, employees will default to making decisions based on instinct or by using low-quality data that doesn’t show the whole picture.
Introducing proper data management tools requires major investments in time and resources from the individual employees, all the way up to the business as a whole. That said, a clear data management strategy helps decision makers within the company access quality data faster and make stronger data-informed decisions that can lead to significant growth within the organization.
How to Develop a Data Management Model
The Data Management Association (DAMA International) has developed the Data Management Body of Knowledge (the DMBoK), which outlines the entire scope of data management. According to DMBoK, data management covers 11 separate knowledge areas. Each area possesses its own nuances and complexities that require specialist management.
1. Data Governance
Data governance is the process of overseeing and planning all the processes that involve data within the entire business. In other words, data governance is at the heart of data management. Data governance ensures the data quality throughout its entire life cycle by considering and developing systems for all the potential processes, people and technical aspects involved in collecting, maintaining and using the data.
2. Data Storage and Operations
Data storage and operations are concerned with managing and deploying the data storage, which is usually by way of cloud-based storage or physical servers. Data storage and operations are likewise concerned with all related data storage operations such as data migration, data recovery and ensuring data availability throughout its entire lifecycle.
3. Data Security
Those involved in data security manage who has access to what data along with ensuring the protection and integrity of the data itself. This is important because quality data security processes safeguard our data from corruption, manipulation, loss and unauthorized access.
4. Data Integration and Interoperability
Most organizations use multiple software and information systems. As a result, businesses often require help unifying these sources, aligning the information and data across them, and ensuring system maintenance.
5. Document and Content Management
Unstructured data are usually stored in a different format compared to structured data (such as raw textual data or images). Specialists must then decide how to make unstructured data accessible to, and integrated with, structured databases. This process requires decisions on how to store, index and access the data.
6. Reference and Master Data
Since a business’s data can come from different sources and functions while being stored in different places, it is important to reduce redundancy and standardize data values, which will consequently improve the data quality.
7. Data Warehousing and Business Intelligence
Data warehousing and business intelligence involve managing, analyzing and reporting on data in order to make data insights available to all key stakeholders. This, in turn, empowers those stakeholders to make data-informed decisions.
This knowledge area focuses on managing and creating a metadata schema (or taxonomy) and deciding how to control, maintain and attribute content with metadata while making it available to the rest of the business.
9. Data Quality
Our data needs to be complete, accurate, reliable, consistent and up-to-date. We ensure data quality by defining what quality data means within the business, as well as making plans to maintain and constantly improve the data.
10. Data Architecture
Developing data architecture involves identifying and designing the overall structure of existing and emerging data entities and data sources within the business along with the relationships between them. Quality data architecture helps companies manage their data as a strategic asset.
11. Data Modeling and Design
Data modeling and design is the process of identifying and designing data entities, their attributes and the relationships between the entities. Data entities are the abstraction of concepts, which will later become the tables in the database, such as customer and product entities.
Data Management Tools and Techniques
While, according to DMBoK there are 11 areas to consider when it comes to data management, you need to understand your business strategy first and prioritize the data management goals accordingly. This technique will then help with finding the most useful tools to achieve your data management goals.
There are many data management tools out there to use depending on your business priorities. Some of the tools are designed to cover multiple areas of data management while others are more niche.
Some of the most famous data management tools are:
- Oracle data management platform, which covers multiple data management solutions including data governance
- IBM data management solutions
- Microsoft, which covers a wide range of services for data management
- Talend, which is especially useful for data integration, data quality and cloud storage
- Tableau, a specifically data analytics and business intelligence tool
- Amazon web services, a cloud storage and data analytics tool
- Google Cloud