Clustering in Unsupervised Data Analysis
Clustering is a way to organize unsupervised data, using specific categories in order to help better navigate the given information. In the banking industry, clustering is usually necessary when talking about clients. Certain client groups can be formed based on the needs or financial capabilities of clients, providing the bank with certain information at at-a-glance (Zheng et al., 2018). In addition, the use of clustering for unstructured data compensates for the need for human intervention, enhancing the process of working with algorithmically-analyzed data (Delua, 2021). Both criteria were used in my own organization in the past.
Potential Benefits and Contribution
The use of clustering has allowed me to navigate between customers more quickly, as well as get access to necessary banking information quickly. As a result, my professional efficiency has increased, giving me an opportunity to do more work in one day. Furthermore, I think the use of clustering has highlighted the importance of categorization and structure in banking, improving my professional pool of knowledge.
Design Thinking, Data Analysis and Visualisation
One of the problems my organization has faced in its work is customer retention. With the wide availability of banking, the selection for potential partners is increasing, and our company struggles to keep up with the rise in supply. In this constant endeavor, the use of data analysis and visualization has been especially helpful. In addition, big data collection was being used in order to improve company analytics, owing to the massive amounts of information it can provide (McAfee & Brynjolfsson, 2012). We used these techniques to answer two of our company’s needs – understanding customer needs and creating a customer profile. In order to best cater our services to clients, we have created visual representations of the client demographics and their experiences with our bank.
Commercial, Operational, and Sustainability Data in Use
Different types of data are used to support better business practices, realize our position in comparison to the competition, and enhance the bank’s basic services. By using different types of data and continuously collecting information, it becomes possible to get an advantage over other companies (Provost & Fawcett, 2013). In addition, holding social, environmental, and sustainability concerns as a prime priority allowed the organization to secure its own unique identity and values in a diversified market (Bennett, 2021). The process is conducive to promoting positive change in the organization’s structure, including sustainability efforts and workplace efficiency.
Emerging Techniques
New and emergent technologies have been of great service to our organization. In order to enhance our customer service and expand the reach to potential clients, we have adopted such approaches as online banking and machine learning algorithms. Similarly, customer support robots are optimized to learn from their experiences with customers to identify emergent problems correctly. In this climate, combatting potential ethical and privacy concerns became necessary. For our client data and online banking, we use a number of passcode, encryption, and verification measures aimed at ensuring that both people’s data and money do not get into the wrong hands. Following ethical considerations, our own personnel has limited access to customer data or personal profiles, which is used to ensure personal privacy and confidentiality.
Changes and Affect
Personally, it has been difficult to adjust to some of the changes introduced by the organization as a whole. Having to manage improvement and change strategies while keeping customer performance to a sufficient degree. However, I feel that the new technologies and tools introduced to improve organizational processes have been helpful in my stride to providing banking services to individuals.
Reference List
Bennett, J. (2021). Google Cloud BrandVoice: How To Keep Sustainability At The Forefront Of Decision-Making. Forbes. Web.
Delua, J. (2021). Supervised vs. unsupervised learning: What’s the difference? IBM. Web.
McAfee, A. and Brynjolfsson, E. (2012). Big Data: The Management Revolution. Harvard Business Review. Web.
Provost, F. and Fawcett, T. (2013). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. 1st ed. O’reilly.
Zheng, J. et al. (2018). The Clustering for Clients in a Bank Based on Big Data. 2018 4th International Conference on Universal Village (UV). Web.