“Data is a precious thing and will last longer than the systems themselves.”
Tim Berners-Lee
Data is everywhere, it has become so ubiquitous that even by just going about our daily lives we generate massive amounts of data. Data isn’t just customer records, it covers everything from government data sources such as the Central Statistics Office to commercial marketing analytics. You could for example use Internet Analytics data to determine how old a website visitors device is thereby make predictions on how likely they are to upgrade that device and then show targeted advertisements directly to that user.
In order to trust this wealth of data it requires effective management, storage, processing & security. It takes a lot of work to turn data into something usable and bad data management practices can generate duplicate records, incorrect information, wasted time, storage space issues and can cost Money! Lots and Lots of money! Recently for example IBM have discovered that bad data costs $3.1 trillion annually in the United States alone. Additionally in a GDPR environment, effective data management is not a “nice to have” but a critical requirement.
Data management is such a broad field that trying to boil it down to a single definition is not simple. In DAMA, we define data management as “the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles.”
Therefore data management is a cross organisational discipline that covers the collecting, keeping, and using data securely, efficiently, and cost-effectively. Fundamentally, it ensures that an enterprises entire body of data is accurate & consistent, readily accessible & properly secured. It lays the groundwork for data analysis, ensuring that any conclusions derived are accurate.
Data Management can be but not limited to 11 key ‘Knowledge Areas’
- Data governance, which is the planning, monitoring and enforcement over the management of data assets. This commonly includes improving availability, usability, consistency, integrity, and security of data managed by an organization.
- Data architecture identifies the data needs of the enterprise, which leads to the design and maintenance of a master blueprint to meet those needs.
- Data modeling and design, is the process of discovering, analyzing, and scoping data requirements and representing them concisely in a data model.
- Data storage and operations, which is concerned with the effective design, implementation and support of stored data to maximise its value.
- Data security, which encompasses executing security policies and procedures to provide proper access and auditing of data assets.
- Data integration and interoperability, which includes managing the movement and consolidation of data within and between applications and organisations.
- Document and content management, which includes all forms of unstructured data and the planning, implementation and control through it lifecycle and to make it accessible to, and integrated with, structured databases.
- Reference and master data, is enabling and managing shared data to provide authoritative sources of quality data by utilising standards and data models.
- Data warehousing and business intelligence, which involves the management and control of data for analytics and business decision making.
- Metadata management involves the elements of creating, collecting, organizing, and managing metadata to provide more understanding of the data across the organisation.
- Data quality applies quality management techniques to data in order for it to be fit for consumption and meet the needs of the data consumers
All of these ‘Knowledge Areas’ need to be planned and implemented in strategic sequence to create a comprehensive data management model. A Data Management Maturity Assessment can discover and evaluate critical data management activities across the organisation. It can establish a sustainable enterprise-wide data management program in support of the organisations operational and strategic goals.
It is easy to see why data management is important, poor data management can lead to regulatory breaches, poor quality analytics and making cross data source integration & interoperability almost impossible.
DAMA Ireland are working to bring some education options to our data community Ireland starting with a Fundamentals of Data Management course which will be suitable for all levels of Data Management expertise. If interested in training please contact training@damaireland.org