Forecasting has always been at the heart of predictive analytics, but as datasets expand and decision cycles shorten, organizations are turning to more scalable and adaptive systems. In a recent publication in the International Journal of Research Publication and Reviews, finance, risk and technology expert Courage Oko-Odion discussed how integrating forecasting models with advanced database management systems (DBMS) is redefining how businesses anticipate trends and make data-driven decisions.
Oko-Odion noted that forecasting models ranging from traditional ARIMA and regression approaches to more advanced AI-driven frameworks like Long Short-Term Memory (LSTM) networks are essential for converting historical data into actionable insights. However, as he explained, the real challenge lies in connecting these algorithms to dynamic databases capable of processing massive data streams in real time.
He pointed out that relational databases such as MySQL and PostgreSQL, and non-relational systems like MongoDB and Cassandra, are central to this process. Each offers trade-offs depending on factors like data volume, query complexity, and real-time responsiveness. According to his analysis, scalable database architectures enable seamless interaction between forecasting algorithms and live queries, ensuring that predictive systems maintain both speed and accuracy.
Oko-Odion’s paper also emphasized the growing influence of machine learning (ML) and deep learning (DL) in refining predictive models. Algorithms such as Decision Trees and Random Forests, he explained, are already helping logistics firms predict demand and optimize warehouse operations. Yet, he cautioned that as AI becomes more embedded in forecasting, the issue of model bias and interpretability must be addressed.
“Forecasting systems trained on incomplete or biased datasets can unintentionally reinforce inequities,” he wrote, citing examples where demand forecasts underestimate needs in underserved regions. He highlighted hybrid approaches combining ARIMA’s ability to detect linear trends with LSTM’s strength in modeling nonlinear dependencies as key to improving both fairness and accuracy in predictive analytics.
In another of his recent works, Leveraging Technology in Internal Audit Processes for Streamlined Management and Risk Oversight, published in the International Journal of Science and Research Archive, Oko-Odion explored how emerging technologies such as AI, data analytics, robotic process automation (RPA), and blockchain are reshaping corporate governance. His analysis underscored how automation can reduce audit cycle times, enhance transparency, and enable predictive risk detection while also warning of challenges around integration costs, workforce skills, and data privacy.
These themes mirror the broader conversation taking place at the policy level. The United States government, for example, has recognized the strategic importance of AI through initiatives like the National Artificial Intelligence Initiative Act and the National AI Research Resource (NAIRR), which emphasize responsible innovation, risk assessment, and governance. Experts like Oko-Odion whose work bridges AI, data science, and internal control systems are contributing to this evolving dialogue about how AI can be used safely, ethically, and effectively in high-stakes decision environments.
About Courage Oko-Odion
Courage Oko-Odion is a distinguished professional in AI Governance, Risk Assessment and Control, with extensive experience at leading global firms such as Deloitte. His expertise lies at the convergence of artificial intelligence, predictive analytics, regulatory compliance and risk management, where he investigates how emerging technologies can strengthen transparency, accountability, and operational resilience within complex organizations. Oko-Odion’s scholarly and professional contributions encompass peer-reviewed research and industry publications that advance data-driven governance frameworks, bridging the gap between technological innovation and enterprise risk oversight in the digital era.
