Part of the implement a scalable solutions is to evaluate the common tables and queries consuming to understand some critical points about the data ingestion and take some following actions: > Use the right data partition strategy (functional, horizontal and vertical) to support the data. > Optimization queries based on the temperature data and data modeling. > Optimization storage based on the data format and compresion to optimize the space. > Use a data storage dedicate to support specific activities; for example on discovery action on data warehouse; use exploration datawarehouse. If you will forecast your storage space; you must take in mind how the current insights and future space will require of these techniques to build it.

Analyze Historical Data Growth Trends: Review historical data usage patterns to identify trends over time. Look for seasonal variations, growth spikes, and average growth rates. Use statistical methods to project future growth based on past data, such as linear regression or exponential growth models. Understand Data Types and Lifecycles: Categorize data based on type (structured, unstructured, semi-structured) and its lifecycle (active, inactive, archival). Different data types may have different growth rates and storage requirements, so understanding these nuances can improve forecasting accuracy. Implement Monitoring Tools: Utilize storage monitoring tools and analytics to track current usage and growth in real time.

I will analyse historical data trends, assess future growth projections & collaborate with key stakeholders to forecast storage needs. Leveraging scalable solutions & regular reviews ensures the organization stays ahead of demand.

Accurately forecasting storage needs requires a combination of data analysis, trend monitoring, and scalable solutions. Start by analyzing historical growth patterns and identifying factors driving data growth. Use predictive analytics to estimate future needs while considering factors like new projects, user growth, and compliance requirements. Implement scalable storage solutions, such as cloud-based services, to handle unexpected surges. Regularly review and adjust forecasts using monitoring tools to ensure alignment with actual growth trends.

I would suggest using Exponential Trend Models. These models capture exponential growth or decay where the rate of change is proportional to the current value. The basic formula involves parameters for initial value, growth rate, and an error term to account for random fluctuations. Then i would employ exponential smoothing. Single Exponential Smoothing is useful for data without trends or seasonality, assigning more weight to recent observations. Double Exponential Smoothing (Holt’s Method) on the other hand accounts for linear trends by incorporating a trend component. These methods are effective for forecasting time series data with trends and seasonality by emphasizing recent data points and adapting to changes over time.