In today’s fast-paced and dynamic business environment, the ability to make accurate predictions and informed decisions is more critical than ever. Best-in-class demand forecasting, a key component of strategic planning, requires the right quality and balance on four pillars: data, tools, process, and people skills. In earlier blogs, we’ve discussed what to do when you experience declining forecastability and introducing statistical models in your demand planning process. In this blog post, we’ll delve into the importance of solid data hygiene in unlocking the next level of demand forecasting and achieving sustained success in your business endeavors. In our next blog, we will discuss the other pillars in more detail.
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:
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.
The Fast Scan quantifies your forecasting improvement potential, by industry benchmarking your forecast and providing insights into the maximum forecast accuracy that can be reached. All in just a few days.See the Fast Scan in action