In this digital era of advanced information technology and data science, the ever-expanding channels bring both opportunities and challenges. It is easy to acquire customers’ data which can help to design effective decision-making tools for an organization. If the data acquired is relevant and accurate, it increases the chances of a business becoming successful. However, there are many data entry points, both from the company, customers, suppliers, and many other types of business partners, that increase the chances of repetitive and/or redundant data and possibly inaccurate or incomplete data in certain cases. In such cases, master data management becomes key for running a successful business. Hence, to detect anomalies within the acquired data, there needs to be specific strategies or approaches. If there are no strategies to prevent inaccurate data entry or cleanse redundant and duplicate data from the database, strategic decisions undertaken may not be effective at all.
Traditionally, data management experts were involved in refining data analysis and reporting platforms while overlooking data quality. Traditional data quality control mechanisms are based on user experience or predefined business rules. Apart from being a time-consuming exercise, it also limits the performance of the defined rules, has low accuracy, and is prone to human errors. How machine learning and master data management (MDM) can work together is a hot topic right now in the MDM realm. MDM platforms are incorporating artificial intelligence and machine learning capabilities to improve accuracy, consistency, and the manageability of data, among others.
Machine learning can improve the quality of data through the following ways:
Automatic data identification and capture – Machine learning can grab the data without manual intervention. Various algorithms may be devised to identify the pertinent key figures and their characteristics from several datasets. Therefore, the data subset that will help in predicting the desired KPI(s) holistically for the decision outcome may be obtained. However, post-identification, physical data assimilation remains in the purview of ETL tools.
Identify duplicate records (Data cleansing) – Duplicate entries of data can lead to superfluous records that result in poor data quality. Machine learning can be used to eliminate duplicate records in an organization’s database and keep precise golden keys in the database.
Detect anomalies – A small human error can drastically affect the utility and the quality of data. A machine learning-enabled system can remove imprecisions and repetitions of tuples. Data quality can also be improved through the implementation of machine learning-based anomaly.
Third-party data inclusion – Third-party organizations (for example customers, vendors) and governmental units can significantly enhance the overall quality of data for the decision systems by presenting better and more complete data which can contribute to precise decision-making. Machine learning can make suggestions on what to fetch from a particular set of data and build connections within the data.
It is imperative for companies to have the right algorithms and queries to operate on their big data. There are many algorithms available to address the above points out of which we found the below two as the most appropriate ones:
Random forest – It is a supervised and flexible machine learning algorithm which creates a forest of programing logic and makes it random. It utilizes randomness to establish several decision trees in an uncorrelated forest and combines them to achieve a more stable and accurate prediction by outputting the class - that is the mode of the classes or mean prediction of individual trees.
Support vector machine (SVM) algorithm – It is a supervised machine learning algorithm that can be used for both classification and regression. The primary goal of SVM is to classify unseen data.
In organizations, there will be opportunities for using resources more efficiently, predict data quality issues, and prescribe solutions to ultimately improve data management systems and processes. The biggest strength of machine learning is that it considerably expedites data cleaning activities, and what that would have typically takes weeks or months can now be completed within hours or days. Also, volume, which was a disadvantage with manual data operations, is actually an advantage in machine learning programs, as they keep improving when inputted with more and more data volume. Using machine learning, the system can self-teach on the data points required and the ones that can be eliminated. Such an analysis can help revamp the process and, eventually, make it simpler.
Most businesses look for faster analytics with high-quality insights to deliver real-time benefits based on quick decisions. They consider this as one of the top priorities and the means of competitive advantage. To enable this, there is an opportunity for organizations to finetune and enhance current data quality approach using ML techniques. Many leading data quality solution providers have ventured out into the ML territory in anticipation of increasing the effectiveness of their solutions. Thus, it has the potential of becoming a game changer for the businesses in pursuit of improved data quality. Machine learning can complement the contemporary rule-based solutions, and synergistically and gradually lead to process evolution around data quality.