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Top Use Cases of Augmented Data Management in 2024

Updated on Jan 2
2 min read
Written by
Gulbahar Karatas
Gulbahar Karatas
Gulbahar Karatas
Gülbahar is an AIMultiple industry analyst focused on web data collection, applications of web data and application security.

She is a frequent user of the products that she researches. For example, she is part of AIMultiple's web data benchmark team that has been annually measuring the performance of top 9 web data infrastructure providers.

She previously worked as a marketer in U.S. Commercial Service.

Gülbahar has a Bachelor's degree in Business Administration and Management.
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Top Use Cases of Augmented Data Management in 2024Top Use Cases of Augmented Data Management in 2024

AIMultiple team adheres to the ethical standards summarized in our research commitments.

As the volume and the variety of data increases, data management becomes more complicated and time-consuming for businesses. Data ingestion and preparation are also becoming more labor-intensive, reducing overall efficiency and productivity. Traditional data management can be inefficient to address these challenges. Augmented data management can provide a solution to changing requirements of data management.

What is augmented data management?

Augmented data management is the application of AI and ML to automate many data management tasks that are done manually, such as:

What are the problems with traditional data management?

Data management is about collecting, processing, storing, and protecting data. As the volume of data increases, traditional methods can fall short to meet the challenges of the data management process because:

  • The traditional approach organizes and manages data in a traditional file environment, storing the same data in different systems, databases, and files. This can lead to data redundancy. If the data is not managed centrally this can also lead to data inconsistencies.
  • As the volume of data increases, the control, organization, and protection of data can get out of control.
  • Backing up traditional datasets is labor-intensive because it is a manual process involving copying, labeling, and re-filtering.
  • Some traditional data management platforms can’t maintain data lineages and relationships, resulting in data being stored in silos. 

Analysis

1. Improved Data Quality

Augmented data management automates data quality checks and improves the manual data cleansing process. It detects data quality issues like patterns, anomalies, etc., and removes any inconsistencies in the data. It allows businesses to make decisions based on reliable data.

According to a recent study by Gartner, implementing AI and ML into the data management process will reduce manual data management tasks by 45 percent through 2022. ADM eliminates the need for human input as it automatically ingests, stores, and organizes data. Technical specialists can focus on more valuable tasks or generate insights rather than clean or organize data.

2. Automated Metadata Management

Metadata provides relevant information about other data, such as file size, the author, and so on. With augmented data management, metadata can be automated. It automatically collects and organizes structured data and unstructured data. It provides an augmented data catalog and analysis for all types of metadata and their relationships.

3. Unified View of Data

In general, traditional data management tools focus on replicating and moving data. Augmented data integration combines all data coming from different data sources to get a unified view of your data.

4. Automated Master Data Model Generation 

Master Data Management provides every employee in an organization with a one-stop shop for business-critical information by creating a master data record. It collects data from systems used by different departments in an organization to create a master record for all departments. Augmented MDM automatically generates a master data model, a single source of truth for disparate data sources.

If you believe that your business may benefit from a data management solution, check our list of data management platforms to find the best vendor for you. 

If you have questions, we would like to help: 

Find the Right Vendors
Gulbahar Karatas
Gülbahar is an AIMultiple industry analyst focused on web data collection, applications of web data and application security. She is a frequent user of the products that she researches. For example, she is part of AIMultiple's web data benchmark team that has been annually measuring the performance of top 9 web data infrastructure providers. She previously worked as a marketer in U.S. Commercial Service. Gülbahar has a Bachelor's degree in Business Administration and Management.

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