AI and ML in the World of Data Management

In modern times, technology has become more rampant, both in offices and homes. Artificial Intelligence (AI) and machine learning (ML) are experiencing rapid growth in today's world. It will soon become a common experience for everyone to interact with an AI daily. Artificial intelligence and machine learning have become the new normal and are typically used interchangeably. Finding solutions to certain problems has been the focus of most AI and ML research. In artificial intelligence applications, machines are deployed to handle tasks usually done by humans. ML is the usage and development of a computer capable of learning and adapting without following explicit instructions.

Data Quality Management

Data quality implies the relevance of the information in use. A piece of information not well-suited for a situation cannot be used to make informed decisions. Data quality is affected by a host of factors, such as relevance, completeness, accuracy, reliability and timeliness. The quality of any data or its significance is governed by one or more of these factors.

Large data volumes owned by companies are often difficult to control. Also, the regular data management exercises are inefficient as they lack scalability and cannot handle the increasing volumes of data. Businesses, hence, re-strategize on efficient data management practices. The great news is that AI and ML, with their progress so far, can help companies in data management.

Data Transformation in Machine Learning and Artificial Intelligence

Automatic Data Capture

What is automatic data capture? Automatic Identification and Data Capture (AIDC) refers to a broad category of technologies used in collecting information from persons, objects, images, or sounds without manual data entry.

Aside from making data predictions, artificial intelligence aids in improving the quality of data by data entry automation through executing smart capture. This enables all the relevant information to be captured, leaving no lapses in the system

Know Duplicate Records

Double data entries can cause old records that produce poor data quality. Artificial intelligence aids the elimination of duplicate data, ensuring the precision and accuracy of a company's data. Identifying and eliminating duplicate entries in a large organization's repository is challenging without using complicated systems. A big company can solve this problem by using smart systems that can identify and eliminate duplicate keys.

Identify Anomalies

Minimal human errors can heavily affect data utility and quality in a CRM. Using an AI-enabled system eliminates such errors. Also, companies can improve the quality of their data by deploying machine learning-based anomalies.

Compare and Verify Data

Making rules to compare data gathered from different sources consumes a lot of time and could be tiring. With the increasing numbers, keeping up with the birth rates over time becomes more difficult. ML models can be taught to understand the rules and forecast matches for fresh data.

The Cost of Bad Data

In large corporations or businesses, bad data can be very costly. It's also essential not to forget that decisions made with data errors can sometimes be highly damaging for companies. ML algorithms can detect some of these anomalies before they become detrimental. Financial institutions utilize them to recognize forged or fake transactions. It is reported that machine learning models can cause $12 billion in savings for banks and card issuers.


Many businesses seek fast analytics with premium-quality insights to provide real-time merits based on fast decisions. Companies prioritize this as it gives them leverage. To achieve this, there is an opportunity for companies to apply AI and machine learning techniques in analyzing and managing data, as this would give them the edge they need in making real-time decisions that would foster business growth. The fast-paced development of AI technology and machine learning would allow their applications in various sectors to improve efficiency, reduce errors resulting in heavy losses, and minimize the workload on human workers.

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