Harnessing Big Data: Strategic Insights for IT Management

Big Data analytics has become an essential tool for IT management, enabling data-driven decision-making in various areas, such as resource allocation and strategic planning. This research examines the use of ARIMA (Auto Regressive Integrated Moving Average) models to improve decision-making in IT management. ARIMA is a popular time-series forecasting method that provides predictive skills, allowing businesses to foresee future patterns and base decisions on historical data analysis. ARIMA models are beneficial in strategic planning by predicting market trends, service demand, and IT resource utilization, which helps firms make proactive resource allocation decisions and maximize operational efficiency. Additionally, ARIMA aids predictive maintenance techniques by forecasting equipment failures and maintenance needs, enabling businesses to reduce downtime and interruptions in critical IT systems. For resource allocation, ARIMA simplifies IT budget optimization by predicting spending needs and identifying potential cost-saving areas. Through accurate forecasts of future budgetary requirements, ARIMA facilitates smart financial resource allocation, investment prioritization, and efficient cost containment, all while optimizing value delivery. Furthermore, ARIMA supports risk management initiatives by evaluating and predicting risks associated with IT projects, operations, and investments. Analyzing historical data and identifying potential risks and vulnerabilities, ARIMA enables firms to mitigate risks, limit adverse effects on business operations, and enhance decision-making processes. Integrating ARIMA into data-driven decision-making processes for strategic planning and resource allocation in IT management has great potential to improve organizational efficiency, agility, competitiveness, and effectiveness. Implemented using Python, the proposed approach has an MSE of 1.25, making it more efficient than current techniques like exponential smoothing and moving average.

@article{Siddiqui2024,
  title     = {Harnessing Big Data: Strategic Insights for IT Management},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Asfar H Siddiqui and Swetha V P and Harish Chowdhary and R.V.V. Krishna and Elangovan Muniyandy and Lakshmana Phaneendra Maguluri},
  doi       = {10.14569/IJACSA.2024.0150790},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150790}
}