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Data management is the practice of collecting, keeping, and using data securely, efficiently, and cost-effectively. The goal of data management is to help people, organizations, and connected things optimize the use of data within the bounds of policy and regulation so that they can make decisions and take actions that maximize the benefit to the organization. A robust data management strategy is becoming more important than ever as organizations increasingly rely on intangible assets to create value.
Managing digital data in an organization involves a broad range of tasks, policies, procedures, and practices. The work of data management has a wide scope, covering factors such as how to
A formal data management strategy addresses the activity of users and administrators, the capabilities of data management technologies, the demands of regulatory requirements, and the needs of the organization to obtain value from its data.
Today’s organizations need a data management solution that provides an efficient way to manage data across a diverse but unified data tier. Data management systems are built on data management platforms and can include databases, data lakes and warehouses, big data management systems, data analytics, and more.
All these components work together as a “data utility” to deliver the data management capabilities an organization needs for its apps, and the analytics and algorithms that use the data originated by those apps. Although current tools help database administrators (DBAs) automate many of the traditional management tasks, manual intervention is still often required because of the size and complexity of most database deployments. Whenever manual intervention is required, the chance for errors increases. Reducing the need for manual data management is a key objective of a new data management technology, the autonomous database.
A data management platform is the foundational system for collecting and analyzing large volumes of data across an organization. Commercial data platforms typically include software tools for management, developed by the database vendor or by third-party vendors. These data management solutions help IT teams and DBAs perform typical tasks such as
The increasingly popular cloud data platforms allow businesses to scale up or down quickly and cost-effectively. Some are available as a service, allowing organizations to save even more.
Based in the cloud, an autonomous database uses artificial intelligence (AI) and machine learning to automate many data management tasks performed by DBAs, including managing database backups, security, and performance tuning.
Also called a self-driving database, an autonomous database offers significant benefits for data management, including
The increasingly popular cloud data platforms allow businesses to scale up or down quickly and cost-effectively. Some are available as a service, allowing organizations to save even more.
In some ways, big data is just what it sounds like—lots and lots of data. But big data also comes in a wider variety of forms than traditional data, and it’s collected at a high rate of speed. Think of all the data that comes in every day, or every minute, from a social media source such as Facebook. The amount, variety, and speed of that data are what make it so valuable to businesses, but they also make it very complex to manage.
As more and more data is collected from sources as disparate as video cameras, social media, audio recordings, and Internet of Things (IoT) devices, big data management systems have emerged. These systems specialize in three general areas.
Companies are using big data to improve and accelerate product development, predictive maintenance, the customer experience, security, operational efficiency, and much more. As big data gets bigger, so will the opportunities.
Most of the challenges in data management today stem from the faster pace of business and the increasing proliferation of data. The ever-expanding variety, velocity, and volume of data available to organizations is pushing them to seek more-effective management tools to keep up. Some of the top challenges organizations face include the following:
Addressing data management challenges requires a comprehensive, well-thought-out set of best practices. Although specific best practices vary depending on the type of data involved and the industry, the following best practices address the major data management challenges organizations face today:
With data’s new role as business capital, organizations are discovering what digital startups and disruptors already know: Data is a valuable asset for identifying trends, making decisions, and taking action before competitors. The new position of data in the value chain is leading organizations to actively seek better ways to derive value from this new capital.
Within companies, the data management responsibilities of the DBA are also evolving, reducing the number of mundane tasks so that DBAs can concentrate on more strategic issues and provide critical data management support in cloud environments involving key initiatives such as data modeling and data security.