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MIDS Essay
Business Research Methods (DMS 502)
University of Nairobi
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The present world is information-driven. Most organizations, if not all, rely on data to make strategic decisions. Also, organizational strategic plans are premised upon available data. Undoubtedly, companies are tapping into technological advances. Hence, giving market controllers a run for their money. Notably, companies capitalize on open source software, statistical modeling, and data analytics to achieve and maintain a competitive advantage against their competitors. Multibillion organizations like Airbnb and Uber have capitalized on data to connect prospective customers to their products. We live in a world where every person's touchpoint with any digital appliance or service is monetized and recorded. Therefore, all businesses must go to the drawing board. They must reinvent and transform to adapt their services and products to meet the needs of their tech-savvy customers. Failure to change, the companies will become obsolete. Previously, managers depended on their intuition to make decisions. As a result, they undertook a hypothesis-driven approach to making strategic decisions. The field of data science has completely changed this paradigm. Data science has made business flexible. Also, it has made it more convenient to get services and products. The convenience and flexibility are due to the advancements made in machine learning and pattern recognition methods. Further, the advent and growth of cloud storage have complemented the above improvements. Parallelization of computational abilities has also allowed business managers to manage previously impossible tasks and make data- oriented decisions. My extreme interest in applying for graduate study in Data Science stems from the chance that the course will give me data science knowledge. I believe that it will allow me to learn more about data science through an interactive online platform. Further, I hope to get a chance to develop healthy, sustainable, and close relationships with my classmates and faculty members to make me a better Workday Release Manager. As a Workday Release Manager, I am currently monitoring the features of Workday releases. I also cater the list to
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the requirements of the organization. In Workday Release Management, every element settles under functional areas. These functional areas have several categories with distinct organizational hierarchies. Hence, forecasting the feature that releases and deployed is hard. This is coupled with the ambiguity of configuring the work feature of every team's capacity. In addition, the challenge is caused by the several features containing varied work efforts, downstream impacts, and integrations. Further, the organization does not have adequate data for regression learning since it has a new and small team with limited data. Another challenge in the organization is grouping the business requests and identifying which falls where to provide a timely solution. Classification could help in solving the problem. However, the blocker would be if the said requests are tagged appropriately, so they are filed in specific categories for consistent data. Long-term unsupervised learning seems to be the most appropriate way to measure the organization's releases and requests' high property levels and behavior. However, I also want to learn more about data science's specific differences and details to understand the types of learning that would be efficient in terms of costs and time for the organization. Eventually, if we can collect adequate data and have an additional set of rules for the system, we can automate the release cycles. The automation will save time since there will be no need for meetings to establish the capacity for certain features to be turned on. The adjusted system could be accurate, thus, saving several teams from deliberations on why their team's project becomes a priority over other groups. I want to study Data Science to find a way of helping my company's blockers, among other reasons. I am also interested in further defining the inputs and output metrics required to measure impact across the cross- functional teams accurately. In the future, my goals in utilizing data science include ensuring that our team can establish critical features from data sets while undoing irrelevant features for better
MIDS Essay
Course: Business Research Methods (DMS 502)
University: University of Nairobi
