Data management is the process of absorbing, storing, organising, and managing an organization’s data. Effective data management is a critical component of implementing IT systems that operate business applications and offer analytical information to enable corporate executives, business managers, and other end users to drive operational decision-making and strategic planning. The data management process ensures that data in business systems is correct, available, and accessible. The majority of the needed work is performed by IT and data management teams, while business users are involved in some aspects of the process. This thorough reference explains what it is and offers information on the various fields it encompasses.
History and Evolution
The early blooming of data management was primarily driven by IT experts who concentrated on tackling the problem of garbage in, garbage out in the earliest computers after discovering that the machines drew incorrect conclusions due to erroneous or inadequate data. Beginning in the 1960s, industry groups and professional organisations pushed optimal data management practises, particularly in terms of professional training and data quality criteria. That decade also saw the introduction of mainframe-based hierarchical databases.
The data warehouse concept was created in the late 1980s, and early adopters started using them in the mid-1990s. Relational software was the dominating technology in the early 2000s, with a virtual monopoly on database deployments. Organizations now have a wider range of data management options because of the emergence of big data and NoSQL alternatives.
Benefits of data management
By increasing operational performance and allowing improved decision-making, a well-executed data management strategy may help firms acquire potential competitive advantages over their business rivals. Organizations with well-managed data may also become more flexible, allowing them to more rapidly detect market trends and seize new business possibilities.
Effective data management may also assist businesses in avoiding data breaches, data privacy issues, and regulatory compliance difficulties that might harm their brand, add unanticipated expenses, and put them in legal trouble. Finally, the most significant benefit that a sound data management strategy can give is improved company performance.
Importance of data management
Data is increasingly being viewed as a corporate asset that can be utilised to make better business choices, enhance marketing efforts, streamline operations, and save expenses, all with the objective of boosting revenue and profits. However, a lack of appropriate data management may leave businesses with incompatible data silos, inconsistent data sets, and data quality issues, limiting their capacity to operate business intelligence (BI) and analytics applications — or, worse, leading to erroneous conclusions. As organisations are subjected to a growing number of regulatory compliance obligations, including data privacy and protection legislation such as GDPR and the California Consumer Privacy Act, data management has become increasingly important. Furthermore, organisations are gathering ever-increasing amounts of data and a broader range of data kinds, both of which are trademarks of the big data platforms that many have implemented. Without proper data management, such settings may become cumbersome and difficult to traverse.
Tasks and duties in data management
The data management process necessitates a wide range of activities, responsibilities, and abilities. Individual workers in tiny firms with less resources may take on several responsibilities. Data management professionals, in general, include data architects, data modellers, database administrators (DBAs), database developers, data quality analysts and engineers, data integration developers, data governance managers, data stewards, and data engineers, who collaborate with analytics teams to build data pipelines and prepare data for analysis.
Data scientists and other data analysts may also undertake certain data management activities on their own, particularly in large data systems containing raw data that must be filtered and processed for specific applications. Similarly, application developers frequently assist in the deployment and management of big data environments, which necessitate the acquisition of new skills in comparison to relational database systems. As a result, businesses may need to acquire new employees or retrain established DBAs in order to fulfil their big data management requirements.
