To make optimal business decisions, companies need to be able to accurately predict future trends. The better a company forecasts trends that are relevant to them, the better its business decisions will be. And one of the most important trends for any business is the demand for its products and services.
Accurately forecasting the demand for its products will enable a business to optimize its inventory, prepare its budget, develop a pricing strategy, and predict its revenues. Given this, demand forecasting has become an increasingly important process for any business.
Demand forecasting is essentially the process by which a company tries to predict the demand for its product in the future, mainly by using historical demand data. This process is where a lot of crucial assumptions are made, such as expected total revenue, expenditures, cash flow, and profit margins.
Let’s take a deeper look at what demand forecasting is and why it’s useful. We’ll follow that up with six tips for preparing your data for demand forecasting. This hands-on knowledge of data preparation is one of the most important—and time-consuming—parts of the demand forecasting process.
The operation of any company is highly dependent on the demand for its products and services. Without a demand for their products, most companies would quickly fail. It’s no wonder that demand forecasting is a key process in many businesses.
In order for companies to make optimal decisions, they have to be able to understand what the demand for their products has been in the past and then accurately forecast what it will probably look like in the near future. An accurate forecast empowers a company for smart, efficient financial planning, like budgeting, pricing products, and how much inventory the company will have to store.
According to Forbes, data preparation accounts for 80 percent of the work of data scientists. This is usually the case with all tasks involving data, including building demand forecasting models. Although the percentage differs across different reports and surveys, most of them agree that data preparation takes the most significant part of a demand forecasting project, or any project involving data.
That certainly holds true for demand forecasting. Your company forecasts can only be as good as the data used to create them. So let’s make sure you’re properly preparing your data for demand forecasting.
Before you can prepare data for anything, you need to first acquire it and then choose which data to use.
Many companies already collect data from their day-to-day operations. But you might also want to obtain data from external sources. The future demand for your product is not dependent on your company alone—it’s also affected by external factors, such as economic, financial, and social trends. External data allows you to account for these factors in your model as well.
In some cases, the data will be readily available. For example, future macroeconomic movements can significantly affect the demand for your product, so your demand forecasting model can incorporate expectations about the GDP growth rate and the inflation rate. Relevant institutions such as the IMF provide this data at a glance in any format you want.
In other cases, you may have to perform web scraping to create a data set. The majority of data on the internet is not readily available, but that doesn’t mean that it can’t be useful. It just means that there are additional steps to obtaining it. For example, you can scrape Yelp reviews for information about your company or your competitors, which you can then include in the demand forecasting model.
After you choose the sources, you need to consolidate your data. Data consolidation essentially means combining all the data coming from different sources and storing it in a single destination.
To make this step easier, you can use business intelligence tools, which can significantly speed up the process. For example, Ikigai’s DeepMatch method can take the various data sets as inputs (all you need to do is simply import your data into DeepMatch), learn the relationships between the features, and stitch the different data sets together. It does all of this in minutes, eliminating most of the manual work of these tasks.
Even with your data all in one place, it’s likely to be in different formats, and far from uniform. This may include data points being in different units, different scales, different data types, financial amounts in different currencies, etc. Therefore, you need to choose a common format and convert all the data into it, to ensure the transparency and overall quality of your data. This process of making sure you’re comparing apples to apples and not apples to oranges is called *data standardization*.
To begin creating standards for your data, first, define the exact format that all the data must be converted to. For example, all your prices may need to be reflected in the same currency, certain columns should be consistent as phone numbers or emails. This all must be decided on and expressed, even down to details such as the capitalization and punctuation of the values.
Once you’ve defined the data format, you can proceed to transform all of the data into that single format.
Keep in mind that data standardization in this context shouldn’t be confused with the same term in statistics and machine learning! In machine learning and statistics, data standardization refers to the process of scaling the data to have a mean of zero and a standard deviation of one. Although this is a process that you may want to perform when preparing data for demand forecasting, that’s not what the term data standardization means in this article.
Even after you’ve standardized your data, that doesn’t ensure that it’s correct. In fact, it can still be inaccurate, low quality, or faulty in many ways. Of course, using data like that in a demand forecasting model will negatively affect the model’s performance. Data validation ensures your data is in fact accurate.
Data validation is a process that checks data for inaccuracies and inconsistencies. The data is run through a series of checks, such as checking the data types and formats, whether the data is in a certain range (for example, data about age can’t be negative), whether fields that should be unique actually are, and whether there are missing values.
If you find inconsistencies or inaccuracies during data validation, you obviously should address them right away. *Data cleansing* can take a variety of forms, like imputing missing values, encoding categorical variables, transforming numerical values, and dealing with outliers.
Most likely, your data sets will have copies at multiple locations, which can sometimes mean that changes in one data set won’t be reflected in the other copies. This can obviously lead to inconsistency; to avoid this, your data needs to be synchronized.
Real-time is the process of ensuring consistency between different data sets. First, you need to identify when an instance of the data is updated, then perform the same changes in all other instances of the data, either in real-time or on a set schedule.
Finally, you have data that’s consolidated, standardized, validated, synchronized, and ready for modeling. But even at this point, the data is still not useful on its own.
The only way that the data can be useful and create an impact is when it’s operationalized, that is, built into demand forecasting models that are then deployed into production. This is one of the most complex steps in the demand forecasting process.
Model deployment is essentially the process through which the demand forecasting model is put into production and can be utilized to forecast the future demand for a product or service. This is the only way for a model to have practical value.
Once the model is operational, the need for monitoring and maintaining it arises. Monitoring the model’s performance is key, since if left on its own the model’s performance may gradually worsen over time. This is due to the concept of data drift, which happens when the distribution of new data differs from the distribution of the data the model was initially trained on. To prevent data drift, the model should be regularly maintained and updated.
Even when your data is being used in production to make business decisions, that doesn’t mean you’re completely done with data preparation.
Demand forecasting is an iterative process. After you’ve operationalized your model, you may notice new ways to prepare data that you haven’t thought of before. Rethinking your data preparation process is beneficial.
But you don’t have to go back to data preparation just to change existing data. You can also add new data to your demand forecasting process. In fact, you should be looking for new sources of relevant data continually. Models are only as good as the data they’re trained on, and more data usually improves forecasting model performance.
Fortunately, there are platforms that make the aforementioned data preparation steps extremely straightforward. Ikigai, for example, has a demand planning solution that allows you to easily import the chosen data sources into the platform in minutes. It eases the steps of data standardization, validation, synchronization, and operationalization. Finally, it enables you to connect to any data source in no time and to seamlessly add more sources as you go.
These six tips—choosing your data sources and consolidating your data, standardizing the data, validating the data, synchronizing it, operationalizing it, and continually exploring other data sources—should set you up for success in your demand forecasting.
If the checklist seems daunting, keep in mind that there are tools that can help, like [Ikigai](https://www.ikigailabs.io/). Ikigai is an operational BI platform that turns actionable insights into insightful actions. It helps analysts and operations teams automate data-intensive business, finance, analytics, and supply chain operations.
Ikigai helps you prepare your data in minutes instead of hours, lets you choose between an end-to-end solution for demand forecasting or building your own custom app with low-code tools, and provides built-in AI-powered predictive capabilities.
To check out Ikigai for yourself, book a demo.