Use of Machine Learning for Data Adoption and Sanitization

The ever-expanding channels in this digital era of cutting-edge information technology and data science present both challenges and opportunities. Data about clients is simple to obtain and can be used to create tools for decision-making that are useful to a business. The likelihood of a firm succeeding increase if the data collected is reliable and useful. However, there are numerous data entry points from the business, clients, distributors, and many other sorts of business partners, which raises the likelihood of redundant and/or repetitive data as well as, in some circumstances, inaccurate or incomplete data. Master data management becomes essential for operating a successful firm in such circumstances. Therefore, specific tactics or approaches are required to find anomalies in the acquired data. Strategic decisions may not be very successful if there are no methods to stop faulty data entry or remove duplicate and redundant data from the database.

Experts in data management have traditionally worked to improve data analysis and reporting platforms while ignoring data quality. User experience or pre-established business standards are the foundations of conventional data quality control techniques. In addition to taking a lot of time, this activity has limitations on how well the specified rules will work, is inaccurate, and is prone to human mistakes. The relationship between machine learning and master data management (MDM) is currently a hot issue in the MDM industry. Machine learning and artificial intelligence capabilities are being incorporated into MDM systems to enhance data correctness, manageability, and consistency, among other things.

Machine Learning for Data Sanitization

The following are some ways that machine learning might enhance the quality of data:

● Data identification and capture automatically -  Machine learning can gather the data without human intervention. Various algorithms may be developed to extract the relevant essential figures and their features from various datasets. As a result, it may be possible to collect the data subset that will aid in forecasting the necessary KPI(s) holistically for the decision outcome. Nevertheless, ETL technologies are still used for post-identification and physical data assimilation.
● Decide which records are duplicates (data cleaning) -  Data duplication might result in unnecessary records and poor data quality. In an organization’s database, duplicate records can be removed and accurate golden keys can be maintained using machine learning.
● Identify anomalies -  A minor human error can significantly impact the usefulness and caliber of data. Tuple imprecision and repetition can be eliminated using a machine learning-enabled system. Machine learning-based anomaly implementations can help enhance the quality of data.
● Include third-party data - The total quality of the data for the decision systems can be considerably improved by third-party organizations (for instance, clients, suppliers, and governmental bodies) by providing complete and detailed data that can aid in accurate decision-making. Machine learning may create connections between the data and offer recommendations for what to retrieve from a certain set of data.

The appropriate algorithms and queries are essential for businesses to use their big data effectively. There are other algorithms available to deal with the points above, however, we believe the two listed below are the most suitable ones:

● Random forest - It is a supervised, adaptable machine learning algorithm that generates a random forest of programming logic. It uses randomization to create many decision trees in an uncorrelated forest and then combines them to produce a class output, which is the mean prediction of each individual tree or the mode of the classes.
● Support vector machine (SVM) algorithm -  This approach for supervised machine learning can be applied to both regression and classification. SVM’s main objective is to categorize unknown data.

There will be chances for businesses to make better use of resources, anticipate problems with data quality, and provide fixes to ultimately enhance data management procedures and systems. The primary benefit of machine learning is that it drastically speeds up data cleaning tasks, which formerly took weeks or months to complete and can now be finished in a matter of hours or days. Volume, which was an issue with traditional data processes, is actually a benefit of machine learning systems since they get better with increasing amounts of data. The system may learn for itself which data points are necessary and which ones can be removed using machine learning. Such an examination can aid in the process of redesign and eventual simplification.