Unlocking the next level of demand forecasting: why solid data hygiene is your key to success

Building reliable futures: the vital role of data hygiene

Imagine trying to build a house without a solid foundation – it would be destined for failure. Similarly, forecasting without proper data hygiene is like building on shaky ground. One of the widely used metaphors is ‘shit-in = shit-out’, which quite literally points at the fact that entering bad quality data as an input to your model, will result in bad results.  

Data hygiene refers to the process of ensuring that data is accurate, consistent, and up to date. It involves cleaning, organizing, and validating data to eliminate errors and inconsistencies. Without this foundation, your forecasts are susceptible to inaccuracy, leading to misguided decisions and missed opportunities. The consequences of poor data hygiene are not to be underestimated. Inaccurate data can lead to flawed forecasts, resulting in suboptimal business strategies, overestimation or underestimation of demand, inefficient resource allocation, and possibly frustrated employees (for example sales colleagues might be unhappy with underestimations and therefore lower inventory). Ultimately, this can lead to financial losses, customer dissatisfaction, and damage to your brand’s reputation. 

How data hygiene enhances forecasting accuracy: 

1. Improved Decision-Making: Clean and reliable data allows decision-makers to trust the insights derived from forecasting models. This, in turn, empowers them to make more informed and strategic decisions that align with the organization’s goals.
2. Enhanced Operational Efficiency: Better data quality results in more accurate forecasts, which in turn enable better resource planning, optimizing inventory levels, and reducing the likelihood of stockouts or overstock situations. This efficiency extends to various business processes, contributing to a more streamlined and cost-effective operation.
3. Customer Satisfaction: Understanding customer behavior is at the core of successful forecasting. Clean data ensures a more accurate understanding of customer preferences and trends, allowing businesses to tailor safety stock levels in such a way that customer service level improves drastically.
4. Adaptability to Market Changes: The business landscape is ever-evolving. With accurate data, forecasting models can better adapt to changes in market conditions, consumer behavior, and external factors. This adaptability is crucial for staying ahead of the competition and seizing emerging opportunities.

Investing in data hygiene for precision demand forecasting

A proven method to implement effective data hygiene practices is to ensure that your employees receive proper training, and everyone has an incentive to adhere to standardized validation steps. Also, major steps can be made when investing in a tool that is capable of automating repetitive data extraction and manipulation steps. 

In the era of data-driven decision-making, solid data hygiene is the key to unlocking the next level of forecasting success and a crucial step to implement both basic statistical models and driver-based forecasting. Businesses that prioritize the cleanliness and accuracy of their data are better equipped to navigate uncertainties, adapt to market changes, and make strategic decisions that drive long-term success. As you embark on your forecasting journey, remember that the quality of your predictions is only as good as the quality of your data. Invest in data hygiene today to secure a more prosperous tomorrow.  

Curious about the status of your data hygiene and the potential it offers for forecast optimization? At EyeOn, we’ve developed the Fast Forecast Scan: a quick tool which provides you with rapid insights into the demand characteristics and forecastability of your business. As a first step we perform a thorough deep dive in your data and provide actionable data quality insights. With improved data quality, the Fast Forecast Scan provides you, within a few days, with data-backed insights on the highest possible forecast accuracy that can be reached and identifies the main opportunities for improvement.