How AI Can Help Businesses in Data Management

For the company to be able to benefit from data, data management is essential.

However, managing data is a labor-intensive task that requires cleaning, extracting, integrating, cataloguing, labeling, and organizing data as well as defining and carrying out the numerous data-related tasks that frequently cause frustration among both data scientists and staff members whose job titles do not include “data.”

Globally, it is predicted that by 2025, individuals would produce 463 exabytes (one exabyte is equal to one billion gigabytes) of data per day. To put it in perspective, there were around 44 zettabytes of data (one zettabyte is equal to one trillion gigabytes) available at the beginning of 2020. It would be impossible for businesses to go through this data on their own. However, because Artificial Intelligence (AI)  models can operate much more quickly than humans and don’t need breaks, it is now conceivable.

Thousands of applications for artificial intelligence have been effective, but better data management is one of the less obvious and dramatic ones. In five typical domains of data management, we observe significant contributions from AI:

Classification: Obtaining, extracting, and organising data from papers, pictures, handwriting, and other media.

Cataloging: Helping to locate data.

Quality: Reducing data errors.

Security:  the act of protecting data from malicious users and ensuring that it is utilized in line with all applicable rules, regulations, and practices.

Data integration: Including list merging, this process aids in creating “master lists” of data.

The Impact of AI on Business Data

• Improve Operational Efficiencies

Businesses frequently struggle to maintain effective database systems. In addition to negatively affecting performance, queries that overtax the system, use excessive resources, or interfere with currently running operations also need manual resources to fix. AI may assist by automating the administration of queries based on their probable resource consumption, offering a more stable and dependable system that can prioritize queries, and minimizing manual database governance and monitoring.

• Raise the Accuracy and Performance of Queries  

The overall accuracy of – or confidence in – the query result may be significantly increased by using AI-enabled database querying. Enterprises may reduce the time it takes to create insight and enhance business choices by running queries more effectively.

• Empowers business analysts

It has been difficult to “democratize” technology so that a wider variety of individuals may use it to make analytics-driven decisions. This has been one of the main obstacles in performing analytics. The output of machine learning models may be put in the hands of domain experts and corporate decision-makers by accelerating the development of AI-based apps.

• Structuring Unstructured data

Unstructured data’s qualitative character is transformed into quantitative data via machine learning tools like sentiment analysis, natural language processing, and text analysis. The content from customer reviews and social media postings are indexed by these models, which offer insights into the various kinds of feedback a company is receiving.

After launching a new feature, this kind of organized data is useful. Businesses can use qualitative information from surveys or reviews to determine how many users liked or hated the new feature. They can then explore a more focused area of the unstructured data to ascertain what needs to be fixed.

To Sum Up

Although there are currently certain data management systems that have the advantage of inbuilt AI capabilities or functionality that supports AI-related activities, the idea of a completely AI-enabled database is still in its infancy, with just a few manufacturers working on this sort of endeavor. Data and analytics software will be able to forecast, automate, and optimize, all of which will reduce time to value, as AI use rises.

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