In data analysis, normalisation is the process of transforming a dataset into a similar scale or range for the purpose of comparison and analysis. This is significant because comparing and analysing datasets that contain variables with varying units of measurement or scales can be challenging.
In most cases, normalising data entails adjusting it so that its new values are uniformly distributed around zero, with a standard deviation of one. This practise, known as standardisation, is frequently used to bring consistency to disparate datasets. Data can also be normalised via min-max scaling, which changes the information so that it lies within a predetermined range, often between 0 and 1.
Patterns, trends, and relationships between the dataset’s many variables can be more readily discerned after they have been normalised. As an added bonus, it can help analytical methods that are sensitive to data size perform better. Here’s one scenario in which normalising the data would be useful:
Suppose you have a dataset that details purchases made by customers, down to the individual level, such as the amount spent and the age of the purchaser. When calculating the customer’s age, years are used instead of dollars. Without normalisation, it can be tricky to establish direct comparisons between these variables because they are on different scales.
You might, for instance, wonder if there is a correlation between purchase price and age. Without normalisation, the range of the purchase amount variable is likely to be much larger than the age variable, which could distort the study.
Using data normalisation, you may make the purchase price and age variables comparable to one another. By way of illustration, standardisation could be used to make both variables have a mean and standard deviation of zero. This would make contrasting the two variables and looking for correlations between them much simpler.
Data normalisation, in a nutshell, helps to get rid of scale disparities and get all the data on the same scale so that it can be compared and analysed more easily.
There are a variety of ways in which a product manager can benefit from data normalisation:
Improved capacity for making choices In order to make better judgements, product managers might benefit from analysing normalised data. Putting data through a normalisation process ensures that all variables are measured on the same scale, making it simpler to see correlations and trends. With this information at hand, product managers will be able to fine-tune their pricing, promotion, and production methods.
Product managers frequently employ statistical models to examine data and predict consumer behaviour. This enhancement to the models’ predictive power is a welcome development. By normalising the data, the precision of these models can be increased. If data is normalised, the influence of outliers is mitigated and the data is distributed more normally, both of which improve the reliability of predictions.
Normalization can help product managers make more effective visualisations, which is crucial because data visualisation is an integral aspect of data analysis. When data is normalised, all of the variables are put on the same scale, making it simpler to construct visualisations that do justice to the data and draw attention to the most important trends and patterns.
When data is normalised, flaws and inconsistencies are discovered and fixed, resulting in higher-quality data. In order to ensure that the data being examined is correct and relevant, normalisation can be used to assist remove irrelevant or redundant data.
As a whole, data normalisation may be highly useful for product managers since it allows for better decision-making, the development of more reliable statistical models, the production of more informative visualisations, and an overall improvement in the quality of the data being analysed.
