One of the biggest challenges in conducting people analytics research of the Qatar National Bank is working with unstructured data. With the growing number of operations, unsorted data is one of the main types of data that analysts work with. The majority of information is unstructured because it cannot be arranged according to the existing pre-sets and models. Such type of data may include texts, emails, media content such as audio, photos and videos, as well as web pages. The wide variety of formats prevents traditional data analytics software from mapping unstructured data into traditional categories. Such data can also be referred to as non-relational. Hence, there is a wide variety of software that addresses such needs.
The software that would fit the needs of the people analytics for Qatar National Bank is MongoDB. It is a free cloud-based software that allows housing, managing and storing unstructured data. In addition, the people analysis can be conducted by analyzing texts and emails for regulatory compliance. Large datasets such as the ones stored by Qatar National Bank can be used for tracking the internal processes within the company to learn about employees’ job satisfaction level and conduct behavior analysis as well. This can be done by analysing the conversations in corporate messenger and e-mails, which is a significant source of unstructured data. MongoDB offers both AI-powered and human-generated data analysis (Jose & Abraham, 2020). This is convenient as it leaves options for meeting various people analysis objectives. For instance, QNB can collect data about employee satisfaction related to new projects, or changes in the organizational structure or policies introduced in the company. People analysis would help increase talent retention, and improve the workplace by receiving indirect feedback stored as an unstructured data.
In conclusion, MongoDB software is a preferred program for the people analysis of Qatar National Bank. This is because it provides sufficient and practical tools for the analysis of unstructured data. As a result, different objectives of the research can be met by applying several strategies.
Reference
Jose, B., & Abraham, S. (2020). Performance analysis of NoSQL and relational databases with MongoDB and MySQL. Materials today: PROCEEDINGS, 24, 2036-2043.