Data Management is the practice of collecting, maintaining, and using data in a secure, efficient, and cost-effective manner. It is the process of ingesting, storing, organizing, and maintaining data created and collected by an organization. Data management is essential for implementing IT systems that run business applications and provide analytical information to help drive operational decision-making and strategic planning. Effective data management requires a reliable data strategy and methods to access, integrate, clean, govern, store and prepare data for analysis.
Data reaches organizations from many sources such as operating and transactional systems, scanners, sensors, smart devices, social media, video and text. The value of the data depends on what you do with it. Data management is the practice of managing data as a valuable resource to unlock its potential for an organization. It involves collecting, organizing, and accessing data to support productivity, efficiency, and decision-making.
A robust data management strategy and a modern data management system are essential for all companies regardless of size or industry. Data management enables more efficient access to data analytics that provide the information needed to improve business operations and identify opportunities for improvement. It also helps establish a better framework for accessing the wide swaths of data generated by each company. Companies can make more informed decisions and improve their ability to deliver valuable products and services to their customers with effective data management.
Data quality must be unassailable for data analysis efforts to bear fruit. Data lakes store groups of big data for use in predictive modeling, machine learning, and other advanced analytics applications. A well-designed data governance program is a fundamental component of effective data management strategies, especially in organizations with distributed data environments that include a diverse set of systems. Data management software accelerates and simplifies complex coding tasks by visualizing them, working from easy-to-use templates, managing compliance considerations, and more. It brings out the full picture of an organization's data in a single pane of glass.
Relational technology still has the largest share but the increase in big data and NoSQL alternatives have provided organizations with a broader set of data management options. Data that is outdated, unreliable, incomplete or not fit for its intended purpose will not be trusted causing problems across the organization. These data management best practices can improve your organization's relationship with the data it collects and stores making it easily accessible for use in improving business processes as well as ensuring that collection and use complies with laws and regulations and with current security. Machine learning needs very large and diverse datasets to “learn” identify complex patterns solve problems and keep its models and algorithms up to date and functioning effectively. You can't have a big data model without a data management strategy trying to do so would be like saying that your cluttered desktop is a perfectly organized chaos in which you can find anything over time you'll lose something important. In short, data management not only conforms to a big data model but it is the umbrella under which all big data falls. Business executives and users must be involved to ensure that their data needs are met and that data quality issues are not perpetuated.
Data scientists and other analysts typically do their own work preparing data for specific analytical uses. Industry-leading data integration and management platforms such as Talend provide a unified way to move and manage all data operations from code creation to cold file storage.
