Introduction:
Augmentation is the growing trend that organizations adopt to automate most data management workflows to free up vital time for their data scientists. Machine learning and artificial intelligence are used to automate manual data management tasks in augmented data management (ADM). Gartner predicts that ADM will free up to 20% of data science teams time by 2023.
As per the report, organizations that dynamically automate, connect, and optimize their data management processes through active metadata, artificial intelligence, Machine Learning, and Data Fabric will spend 30% less time on Data Integration processes by the same time. It helps organizations to make correct decisions quickly and maximize their business processes benefits. Data management encompasses several disciplines, including; metadata management, data quality, master data management, data preparation, data governance, and data integration.
Augmented Data Management aims at:
- Automating manual data management operations.
- Allowing less tech-savvy users to be autonomous while using data.
- Freeing up time for such technical specialists as data scientists and data technologists.
Best practices to ensure successful augmented data management?
Organizations can implement augmented data management to make data easier for real-time analytics. The desired quality and consistency of organizational data are required in order to to make use of it on the go and draw informed insights to help make business decisions instantly.
- ADM processes help organizations harness data across their departments and improve collaboration while undertaking various tasks.
- Augmented data management helps organizations to manage their recurrent operations and activities by assisting users in making proactive decisions within their departments. The immediate use of data arising from these processes also helps eliminate the data silos, thereby significantly reducing business operations costs.
- Augmented data management processes will only be as good as the rules in place.
Before augmenting data management, an organization’s data governance needs to be set in place and strong enough. Users will have automated access to the system and its operations based on their permissions. If those permissions are incorrect, the organization’s data may be accessible to unauthorized people, resulting in a data breach and, as a result, a violation of the law, such as the GDPR.
Conclusion:
Organizations are in a constant need to revamp their data management processes to automate their data circulation processes and further optimize their course of analysis on the data. Armed with the right tools for effective data management, organizations can enhance their products and services by eliminating datas intricacies. Reducing complexities is critical for any organization to strive and achieve its business objectives. Successfully deploying augmented data management practices is the secure way forward to remain competitive and give your competitors a run for their money.
