Data management is an essential administrative process that involves the acquisition, validation, storage, protection and processing of data to ensure its accessibility, reliability and timeliness for users. It is the practice of collecting, maintaining and using data in a secure, efficient and cost-effective manner. The goal of data management is to help people, organizations and connected things to optimize the use of data within the boundaries of policies and regulations so that they can make decisions and take measures that maximize the benefit to the organization. A robust data management strategy is more important than ever as organizations increasingly rely on intangible assets to create value. Data management is the practice of managing data as a valuable resource to unlock its potential for an organization.
To achieve this, effective data management requires a data strategy and reliable methods to access, integrate, clean, govern, store and prepare data for analysis. In our digital world, data reaches organizations from many sources: operating and transactional systems, scanners, sensors, smart devices, social networks, video and text. However, the value of data is not based on its source, quality or format; it depends on what you do with it. Data management encompasses all disciplines related to data management as a valuable resource. While data processing, data warehousing, data governance and data security are part of data management, the success of any of these components depends on the company's data architecture or set of technologies.
A company's data infrastructure creates a pipeline for the acquisition, processing, storage and access to data by integrating these systems. Data services and APIs bring together data from old systems, data lakes, data warehouses, SQL databases and applications providing a holistic view of business performance. SAS Data Management includes all the capabilities you need to access, integrate, clean, govern and prepare your data for analysis including advanced analytics such as artificial intelligence and machine learning. While specific data needs are unique to each organization's data strategy and data systems; preparing a framework will pave the way to easier and more effective data management solutions. However none of that data is useful if the organization doesn't know what it has; where it is located; and how to use it. New technologies allow for different types of data management repositories to work together eliminating the differences between them.
As organizations create and consume data at an unprecedented rate; data management solutions become essential in making sense of enormous amounts of data. Over the past decade advances in hybrid cloud; artificial intelligence; Internet of Things (IoT) and edge computing have led to an exponential growth in big data creating even greater complexity for companies. Reducing the need for manual data management is a key objective of a new type of technology; the autonomous database. Data is collected from an increasing number and variety of sources such as sensors; smart devices; social networks; video cameras etc. Suggesting transformations in this type of big data requires a discovery engine that can analyze both the content and metadata in order to suggest improvements over time using machine learning.
However big data is also presented in a wider variety of forms than traditional data and is collected at a high speed; processes must also identify incorrect or inconsistent formatting; spelling errors; and other errors that will affect results. Learn how an analytics platform that balances choice and control helps you get the most out of your investments in data technology; talent; analytics etc. In addition; a visual interface provides a better way to interact with the data making processes faster and easier. Addressing these challenges requires a comprehensive set of best practices which provide standardized laws that give individuals control over their personal information and how it is used.
