In the modern business context, the ability to predict future events based on data and analysis is crucial. Forecasting enables companies to plan strategically, optimize operations, and make informed decisions, thus contributing to long-term success. One of the fundamental elements for making accurate forecasts is the use of historical data.
Historical data represent a valuable archive of past information, including sales, market trends, economic data, and other relevant indicators. These data provide a solid foundation for building forecasting models, allowing companies to identify patterns, trends, and anomalies that could influence future business performance.
The analysis of historical data allows companies to better understand economic cycles, seasonality, and other recurring factors that can have a significant impact on their operations. Using this information, companies can develop more accurate forecasts, reducing uncertainty and enhancing their ability to respond to market challenges.
Role of Historical Data in Forecasting models
Historical data form the basis for building accurate forecasts. Their importance lies in their ability to provide context and perspective based on real past events. Their use involves three stages:
Historical Data Collection
Collecting historical data is the first essential step to making accurate forecasts. Companies gather data on past sales, market trends, customer behaviors, and economic indicators. These data can come from internal sources, such as Enterprise Resource Planning (ERP) systems, or from external sources, such as industry reports and government statistics. The quality and quantity of data collected directly affect forecast accuracy.
Trend Analysis
Analyzing past trends is essential to identify recurring patterns and cycles. For example, a company that sells flu medication might observe increased sales during the winter months. Understanding these trends enables companies to anticipate demand fluctuations and adjust their strategies accordingly.
Forecasting Models
Various forecasting models rely on historical data. Among the most used algorithms are ARIMA, DeepAR, and Prophet. Artificial intelligence and machine learning play a fundamental role in forecasting. The choice of model depends on the nature of the data and the company’s forecasting objectives.
Limitations and Challenges
One of the main challenges in using historical data is its availability. In some cases, data may be incomplete or unavailable, making it difficult to make accurate forecasts. Companies need to invest in data collection and management to overcome these limitations.
Additionally, sudden market changes, such as technological innovations or shifts in consumer preferences, can reduce the effectiveness of forecasts based on historical data. Therefore, it is essential to adapt and update forecasting models to reflect new market realities.
Lastly, historical data may contain biases and errors that can influence forecasts. It is important for companies to identify and correct these distortions to improve forecast accuracy. Data cleansing techniques and statistical analysis can help reduce the impact of these errors. Using the right technology is crucial to overcoming these limitations. Forecasting projects can be implemented with both paid platforms and services or open-source tools. Each company should choose the solution that best suits its needs.
Use Case
Blue BI supported a client in the pharmaceutical sector in developing an advanced system to forecast sales of its products (volume, revenue, and prices) for the next two years. Our team of data scientists used the client’s historical data to train various forecasting models and identify the one with the best performance on test data.
The selected model generates detailed future sales forecasts for each product, providing the client with valuable information for more accurate budget planning and optimal inventory management. To further increase forecast accuracy, we integrated additional data, such as past advertising campaigns and future planning.
Additionally, we structured a continuous update process: each time new historical data is available, the forecasts are recalculated, and the model is periodically updated to maintain high performance.
Conclusion
Companies that invest in data collection and analysis will be better positioned to face market challenges and seize emerging opportunities.
Blue BI helps companies recognize the value of historical data and invest in the technologies and skills needed to use them effectively. Only through a data-driven approach can companies achieve accurate forecasts and make informed decisions. Blue BI has created the BBI x Forecasting solution, which, thanks to a hybrid statistical and ML approach, generates reliable forecasts based on historical data, allowing companies to differentiate themselves in the market and overcome the risk of the analytical divide.
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