What Is Data Management?
With organizations relying on data from multiple sources, it becomes increasingly important to have a consistent set of standards for managing data from collection to usage. Data management refers to the practices that companies follow when collecting, storing, using, and deleting data. An effective data management process includes the establishment of a unified technological infrastructure and a governing framework that outlines an organization’s preferred data-handling protocols. Having the right data management solutions in place can make it simpler and faster to process and validate large quantities of data that they receive and make it easier for companies to extract real-world value from their data-based insights.
Why Is Data Management Important?
Managing data can be a complicated ordeal, especially as companies seek to gain value using data from multiple sources. The goal of most companies’ data management strategy is to establish a consistent set of standards for handling data across an organization that applies to all applications and business operations.
As organizations seek to consume data at a considerably faster pace, having data management solutions empowers organizations to gain data-based insights faster and get deeper insights into a wide range of use cases. These insights can help organizations respond more effectively to a wide range of scenarios, such as identifying customers at the highest risk of churn or determining the best products to offer to a customer based on their specific needs. Having strong data management practices and tools in place helps organizations to quickly convert data into actionable plans.
Data management tools can be used to help organizations understand which users and administrators have been handling data. This, in turn, can help organizations remain compliant with existing laws, such as the EU’s Global Data Protection Regulation, (GDPR) by ensuring that any data used by the organization is kept secure and private, with the right access management procedures in place. Having a strong data management strategy in place can also help companies understand the best practices to employ when storing data using on-premise resources or across multiple clouds.
Data Management + DataRobot
DataRobot added AI Catalog to its platform last year following its acquisition of data collaboration platform Cursor. With AI Catalog, companies gain stronger data management capabilities, such as access to data assets from a governed AI environment and strictly monitored sharing permissions to ensure machine learning applications are kept trustworthy. Users can also search for and connect to data assets from any location, including data lakes, databases, in-cloud, or on-premise resources. AI Catalog provides access to a collaborative environment for enterprise AI that enables companies to search for trusted data assets as they pursue their AI ambitions.
The availability of an AI catalog provides organizations with a critical advantage as they work to become AI-driven. By implementing strong data management solutions, organizations can automate their AI pipelines to more seamlessly collect, organize, and validate their data. Most importantly, this level of automation can also result in organizations gaining actionable insights from their collected data and pinpoint opportunities to improve efficiencies, reduce costs, deliver more personalized services, increase revenues, and find opportunities to expand into valuable new markets.