As the Internet-based media platforms and services are growing, businesses today are collecting large amounts of data than ever before. According to the surveys, it is reported that over 500 terabytes of data is collected every day. This information helps organizations to better understand their existing and prospective customers. Although the information provides great opportunity to comprehend customer preferences and improve the overall customer experience, the majority of the organizations are struggling to manage the sheer volume and complexity of the data, as a result, most of the data remain unused. To overcome these challenges and make effective use of all the data, many organizations today are implementing machine learning and AI technologies.
These technologies have the ability to identify and learn from data patterns and apply statistical analysis to predict the outcomes and make recommendations. Machine learning can simplify the data analysis and provide organizations with a better understanding of their data while allowing them to use it more efficiently and effectively. By providing the holistic view or granular view of their entire data lake, machine learning helps users to draw correlations and find related data sets to further reap meaningful insights.
By leveraging machine learning, enterprises can enhance the level of automated data tagging—allowing them to classify specific data sets with better results, and then share those data items with specific users. This also assists enterprises in staying compliant with regulations such as GDPR.
In addition, machine learning adds value by providing the ability to automatically identify and categorize personally identifiable information (PII). This is done by leveraging metadata and ensuring it remains compliant by incorporating data analytics, and anonymizing the data when necessary. The machine learning tools also have the potential to detect anomalies and proactively thwart the cyber threats.