As computing technology has evolved, so too has our relationship with data. At first, it was something to be created, as organizations moved from a paper environment to a digital one. Then, it was something to be organized, as the amount of data being created started to outgrow basic filing systems. Then it was something to be shared, to ensure interoperability and consistency across organizations.
Now, it’s all of the above, and more. Data is something that needs to be managed, especially at the enterprise level, and data management is a multi-dimensional undertaking.
So much so, that the Global Enterprise Data Management Market is growing at a CAGR of 8.66 percent from 2013-2018.
Enterprise data management is a relatively cover-all term, referring to solutions that help organizations profile, cleanse, standardize, and de-duplicate data related to finance, operations, marketing, risk management, and vendor relations.
Enterprise data management allows organizations to develop and apply the appropriate policies, procedures, standards, governance, and tools required to create, maintain, and share high-quality data throughout the business process. There are several dimensions of the enterprise data management market, including data stewardship, data standards, data processes, data governance, data quality management, and data architecture.
Data Stewardship
A data steward is responsible for managing data standards and formats, and establishing and enforcing data standards. Enterprise data management enables stewards to improve the quality of data, reduce data redundancy, and improve data management capabilities across companies.
Data Standards
Data standards are framed by the data governance committee to ensure that all data elements of an organization comply with standard terms, definitions, and values. Data standards enable the data stewards to frame validation rules to filter business exceptional data and improve their trustworthiness.
Data Processes
Data processes include standard procedures with respect to the creation, updating, or deletion of data. They also involve migration and archiving of data across the IT landscape in the organization. They help organizations to manage better quality of data across business areas with strategy and roadmap.
Data Governance
A data governance framework focuses on people, process, and technology. It ensures accessibility, availability, quality, consistency, security, and audit-readiness of data. Senior management in data governance forming the steering committee and operational team of the data governance committee include business users, data architects, and data stewards.
Data Quality
Data quality is a key component of enterprise data management enabling companies to address issues in the quality of data and identify exceptions in data elements. Data quality tools help organizations to profile and standardize data, match and merge data, monitor the quality of data, and track issues in data quality. Data quality tools assist in meeting business outcomes.
Data Architecture
Enterprise data architecture comprises of major segments such as data integration, data migration, master data management, metadata management and data warehousing.
TechNavio analysts forecast that the ever-increasing amounts of organizational data, as well as growing need for easy data access, and increased demand for better governance will be significant factors that further contribute to growth in the enterprise data management market over the projected period.
