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.
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.