Insufficient data quality: Is the solution machine learning?

Data quality can be a major challenge for organizations, as it can affect the accuracy and reliability of analytics results, as well as slow down efficiency. Machine learning provides a suitable solution to this challenge, as its use improves data quality and consistency, leading to more accurate and reliable analyses.

One example is the use of machine learning in image processing: when images are of poor quality, AI can improve them by detecting patterns in the data and filling in missing details.

In addition, AI helps monitor and improve data quality. For example, machine learning models can help automatically detect and correct erroneous data. The model can learn from existing data and make predictions about which data are likely to be in error. These data can then be corrected automatically or manually.

To improve the quality of data, your organization also needs to ensure that your data collection and storage processes are accurate and reliable. Artificial intelligence can also be helpful here.

AI can play a valuable role in master data harmonization by assisting with duplicate identification, validation and completion, and mapping data to the correct categories.

Machine learning models identify duplicates in large data sets. The model can analyze various features, such as names, addresses, phone numbers, etc., to identify similar records and merge them into a single record.

Machine learning can be used to fill in missing data or automatically correct incorrectly formatted data.

If different data sets use different categories, this can lead to inconsistencies and difficulties in the analysis. AI can help solve these problems by using patterns and characteristics to assign the correct categories for each data set.