Ensuring effectiveness of data models in real-time data analytics
Sangwen Keima Apollo
Consuldata and Research Centre, Kenya
: J Comput Eng Inf Technol
Abstract
Data is essential for processing and interpreting information. Organizations gather data to make decisions and create reports. There are several elements that make up an effective data modeling methodology. Each element helps develop a well-modeled database, promoting easy access to reliable data for real-time analytics. Real-time analytics use enormous amounts of data to inform decision-making processes instantly. Interpretation involves using rules to identify patterns within large amounts of data. This process generates actionable insights that change strategies and outcomes immediately. Developers base model design on the business needs they intend to meet. In general, there are two types of models: descriptive and prescriptive. Descriptive models provide a list of rules that interpret raw data. Prescriptive models suggest how existing datasets should be organized and interpreted so they provide a baseline for analysis. Approaches for creating effective data models vary widely according to the elements involved. Modelling is an essential part of real-time analytics; it allows organizations to interpret and make use of valuable data in real time. Effective modeling methodology involves several effective modeling approaches that suit various needs. Modeling approaches must also adhere to standards while creating models that suit various needs- such as security or accessibility requirements. A solid understanding of how models function helps analysts interpret and make use of valuable data in real time. This research explores the different challenges in ensuring effectiveness of data models in real-time analytics, and the possible solutions. Recent Publications 1. Schwartz, M., & Carroll, A. B. (2019). Corporate social responsibility: A three domain approach. Business Ethics Quarterly, 13(4), 503–530. 2. Smith, H. J., Dinev, T., & Xu, H. (2018). Information privacy research: An inter disciplinary review. MIS Quarterly, 35(4), 989–1016. 3. Solove, D. J. (2018). Taxonomy of privacy. University of Pennsylvania Law Review, 154(3), 477–564.
Biography
Sangwen Keima is the co-founder and chairperson of Consuldata and Research Centre, a Kenyan firm that deals in research and consultancy in data analysis. Similarly, he is the founder of the 97Club Data Science, which is a professional community that brings together data scientists, industry partners, students, and other enthusiasts in the profession to build and promote data science, and to mentor and train young people in data science and machine learning in Kenya. With a background of Actuarial Science, Sangwen has extensive knowledge and interests in data, technology, and business. Over the past years he has been active in promoting the growth of data science in Kenya and in equipping young people with the much-needed skills to build their careers. He is driven by the passion to transform lives and to create a better society through the disruptive solutions of data science, machine learning, and associated technologies.
