Data Preparation Process- The First Step to Accurate Forecasting
Businesses in the modern era are growing increasingly dependent on data analytics.
There is a clear reason for this without this quantifiable company information, businesses are unable to accurately forecast future challenges, potential sales, profits, and losses.
Predictive modeling and data preparation, therefore, are becoming more and more important in this number-crunching age.
What is Data Preparation?
There is a general misconception that raw data relating to a business' costs, sales and profits are all immediately ready to be used for analysis without further preparation. However, inconsistencies in measurements such as time periods can dramatically skew results.
Preparing data is, in its most basic form, the collating, and cleansing of information from several different sources. This involves restructuring and organizing numerical figures so that it is ready to be analyzed for visualization or forecasting.
The general data preparation steps are as follows-
While collecting and merging quantitative information from various internal and external sources can be time-consuming, data cleansing has never been more crucial for forecasting accuracy and business growth.
Why is Data Preparation Important?
Preparing data is essential for precise analysis, insight, and planning. Without this information, demand forecasts may be financially misleading or inconsistent, and crucial gaps could be overlooked during the analysis process.
Oftentimes, due to human errors, data is presented with inaccuracies or missing values. When this information is stored in separate databases or files under various formats, it must first be verified, adjusted, and then combined to be ready for analysis.
This process can also save time and effort in the long run as editing information later down the line can be time-consuming and more costly as it would add additional labor costs to rectify previously made errors.
6 Steps to Cleansed Data
Unsurprisingly, cleansing data can be a pain-staking process. According to the New York Times, preparation to ensure the original data is usable can take up to 80% of the overall time spent on the data analysis. This can be a major barrier for businesses wanting to get quick and accurate results.
However, with modern advances in technology, this has now become a largely automated task. Depending on the existing data and the software tools available, preparation processes can differ.
However, businesses undertaking this process manually can follow 6 general steps-
1. Data collection The first step involves actively pulling information from all available sources such as clouds and data lakes. This step aims to create the largest possible pool of information.
2. Active preparation This is when data analysts must begin to refine and cleanse the quantitative information they collect. This means that they must meticulously look for errors and missing values in the raw data and toss out any bad information that could corrupt results.
3. Information loading At this stage, the cleansed data will be uploaded to a database and transformed so that it is usable.
4. Processing Here, the data will be subject to further processing using algorithms so that it is easy to digest and interpret across various systems and channels. The details of this step will vary depending on the business and its industry.
5. Interpretation At this stage, the processed quality data is pulled into digestible formats such as text, visual media, or graphs.
6. Final storage This last step will be ensuring that all data is stored clearly and concisely in an accessible unit, such as a file or USB for future use.
There are drawbacks to applying such methods on a manual basis, as this can take considerable time and may be subject to human errors.
Therefore, many businesses choose to automate data transformation entirely by using software, which can retrieve, process, and store all information without any risks of misinterpretation.
Businesses that use cloud-based forecasting software, which connects directly to POS systems, are even able to pull data directly from the source. This also enables them to push said data through the above steps automatically, making it immediately ready for consumption as and when needed.
Tips to Keep in Mind
Automated data preparation tools will only work in practice if the initial information is relevant in the first place.
Here are two tips to keep in mind during the preparation process-
1. Don't account for stock-out periods in the data set. These are unlikely to help in building accurate expectations. It's recommendable to use average numbers to fill gaps wherever possible.
2. Make sure to iron out wild spikes and anomalies. Large peaks and valleys in data may skew preparation averages. For example, companies may experience one or two unusual seasonal spikes, which should ideally be removed for consistency. Be sure to adjust the data and business expectations accordingly to account for this seasonality.