Are you concerned about data quality? If so, you should be concerned about data normalization. Data normalization consists of transforming data without distorting it, so that it corresponds to a predefined and constrained set of values to improve its efficiency.
Discover the importance of this technique, which has become indispensable for data-driven companies.
As with any company that turns to data to improve its productivity and efficiency, or the relevance of its offer or its approach to its market, data representativeness is crucial. Your challenge is to maximize the intelligence derived from your data. To achieve this, you need to do everything in your power to limit the distortion of information. This is the vocation of data normalization.
Data normalization is commonly used in statistics, data science, and machine learning to scale the values of different variables within the same interval. The main company objectives in normalization are to make data comparable with each other and to make them more easily interpretable by analysis and modeling algorithms.
Why is data normalization important for companies?
In many cases, data can have very different scales, i.e. some variables may have much larger or smaller values than others. This can pose problems for certain statistical techniques or machine learning algorithms, as they can be sensitive to the scale of the data. Normalization solves this problem by adjusting variable values to lie within a specified interval, often between 0 and 1, or around the mean with a given standard deviation.
What are the benefits associated with data normalization?
Data normalization improves the quality, performance, and interpretability of statistical analyses and machine learning models by eliminating problems associated with variable scaling and enabling fairer comparisons between different data characteristics. In practice, this translates into concrete benefits:
Maximum comparability: Normalized data are scaled to the same level, enabling easier comparison and interpretation between different variables.
Optimized machine learning: Normalization facilitates faster convergence of machine learning algorithms by reducing the scale of variables, helping to achieve more reliable and consolidated results more quickly.
Enhanced model stability: Normalization reduces the impact of extreme values (outliers), making models more stable and resistant to data variations.
Improved interpretability: Data normalization facilitates the interpretation of coefficients, making analysis more comprehensible.
What methods are used to normalize data?
There are several methods of data normalization, but two stand out from the crowd starting with the Min-Max Scaling method. It is based on the principle of scaling the values of a variable so that they fall within a specified interval, usually between 0 and 1. This technique is particularly useful when you want to retain the linear relationship between the original values.
Another method, called Z-Score normalization, is a more standardization-oriented technique. It transforms the values of a variable so that they have a mean of 0 and a standard deviation of 1. Unlike Min-Max normalization, standardization does not impose a specific upper or lower limit on the transformed values. This technique is recommended when variables have very different scales, as it allows data to be centered around zero and scaled with respect to standard deviation.
Other methods may also be considered for data normalization, but these are more marginal. Decimal Scaling and Unit Vector Scaling are two examples.
Decimal normalization involves dividing each value of a variable by a power of 10, depending on the number of significant digits. This moves the decimal point to the left, placing the most significant digit to the left of the decimal. This technique adjusts the values to lie within a smaller interval, thus simplifying calculations.
Unit vector normalization is used in machine learning. It consists in dividing each value of a data vector by the Euclidean norm of the vector, thus transforming the vector into a unit vector (of length 1). This technique is often used in algorithms that calculate distances or similarities between vectors.
What’s the difference between data normalization and data standardization?
Data normalization and data standardization address the same issue of data representativeness but from different perspectives. Although they are both data scaling techniques, they differ in the way they transform variable values.
Data standardization transforms the values of a variable so that they have a mean of 0 and a standard deviation of 1. Unlike normalization, standardization does not set a specific range for the transformed values. Standardization is useful when variables have very different scales and allows data to be centered around zero and scaled with respect to standard deviation, which can facilitate the interpretation of coefficients in some models. Depending on the nature of your data and the lessons you wish to learn from it, you may need to use either data normalization or data standardization.
