Data Preprocessing: what is it and why is important

One of the most frequent phrases you’ll find in articles about modern technologies is how we’re living in the age of data. Such a statement is so common because it’s true – we have access to more data than ever before. And we’re using it in a lot of ways! From analyzing and understanding customer behaviors to collecting insights for software QA companies, organizations of all kinds are using large datasets on a daily basis.

Yet, even for its growing importance in several key areas, there’s a particular process that a lot of businesses seem to be ignoring. In fact, when training data scientists and engineers, a lot of courses neglect this critical step. We’re talking about data preprocessing, a fundamental stage to prepare the data in order to get more out of it.

What is Data Preprocessing

A simple definition could be that data preprocessing is a data mining technique to turn the raw data gathered from diverse sources into cleaner information that’s more suitable for work. In other words, it’s a preliminary step that takes all of the available information to organize it, sort it, and merge it.

Let’s explain that a little further. Data science techniques try to extract information from chunks of data. These databases can get incredibly massive and usually contain data of all sorts, from comments left on social media to numbers coming from analytic dashboards. That vast amount of information is heterogenous by nature, which means that they don’t share the same structure – that’s if they have a structure to begin with.

Raw data can have missing or inconsistent values as well as present a lot of redundant information. The most common problems you can find with raw data can be divided into 3 groups:

• Missing data: you can also see this as inaccurate data since the information that isn’t there creates gaps that might be relevant to the final analysis. Missing data often appears when there’s a problem in the collection phase, such as a glitch that caused a system’s downtime, mistakes in data entry, or issues with biometrics use, among others.
• Noisy data: this group encompasses erroneous data and outliers that you can find in the data set but that is just meaningless information. Here you can see noise made of human mistakes, rare exceptions, mislabels, and other issues during data gathering.
• Inconsistent data: inconsistencies happen when you keep files with similar data in different formats and files. Duplicates in different formats, mistakes in codes of names, or the absence of data constraints often lead to inconsistent data, that introduces deviations that you have to deal with before analysis.

If you didn’t take care of those issues, the final output would be plagued with faulty insights. That’s especially true for more sensitive analysis that can be more affected by small mistakes, like when it’s used in new fields where minimal variations in raw data can lead to wrong assumptions.

Why You Need Data Preprocessing

By now, you’ve surely realized why your data preprocessing is so important. Since mistakes, redundancies, missing values, and inconsistencies all compromise the integrity of the set, you need to fix all those issues for a more accurate outcome. Imagine you are training a Machine Learning algorithm to deal with your customers’ purchases with a faulty dataset. Chances are that the system will develop biases and deviations that will produce a poor user experience.

Thus, before using that data for the purpose you want, you need it to be as organized and “clean” as possible. There are several ways to do so, depending on what kind of problem you’re tackling. Ideally, you’d use all of the following techniques to get a better data set.

Data Cleaning

Your sets will surely have missing and noisy data. That’s because the data gathering process isn’t perfect, so you’ll have many irrelevant and missing parts here and there. The method you should use to take care of this issue is called data cleaning.

We can divide this into two groups. The first one includes methods to fight missing data. Here, you can choose to ignore the part of the data set that has the missing values (called a tuple). This is only feasible if you’re working with a big dataset that has multiple missing values in the same tuple.

In other cases, the best approach you can take is filling the missing values. How? By inputting the values manually or using computational processes that assign values through attribute mean or by calculating the most probable value. The manual approach is far more accurate, but it can take a significant amount of time, so it’s up to you to decide which is the best way to go here.

The second data cleaning method deals with noisy data. Getting rid of meaningless data that can’t be interpreted by the systems is key to smooth the whole process. Here you can choose among 3 distinct techniques:

• Binning: you can use this on sorted data. Its goal is to smooth the data sets by dividing them into segments of the same size to handle them individually. Depending on how they are formed, you can replace the noise by using means or by defining boundary values to do that replacement.
• Regression: regression analysis comprises a group of statistical processes to estimate the relationship between dependent and independent variables. If you just have one independent variable, you can use a linear regression function, while you can use a multiple regression function if you have 2 or more independent variables.
• Clustering: as its name implies, you group similar data into different clusters for greater organization. All data that can’t be grouped into these clusters (the so-called outliers) are left out and don’t go into consideration.

Data Transformation

After handling the issues mentioned above, data preprocessing moves on to the transformation stage. In it, you transform the data into appropriate conformations for the analysis. This can be done through several techniques, including:

• Normalization: scaling the data values in a predefined range.
• Attribute selection: using the given attributes, you create new ones to further organize the data sets and help the ulterior data analysis.
• Discretization: here you replace the raw values of numeric attributes with interval or conceptual levels.
• Concept Hierarchy Generation: finally, you take the levels you built before and take them to higher levels (for instance, taking values to more general categories)

Data Reduction

Sifting through massive datasets can be a time-consuming task, even for automated systems. That’s why the data reduction stage is so important – because it limits the data sets to the most important information, thus increasing storage efficiency while reducing the money and time costs associated with working with such sets.

Data reduction is a complex process that involves several steps, including:

• Data Cube Aggregation: data cubes are multidimensional arrays of values that result from data organization. To get there, you can use aggregation operations that derive a single value for a group of values (such as the average daily temperature in a given region).
• Attribute Subset Selection: selecting attributes means that only the most relevant will be used and the rest will be discarded. To select subsets, you can define a minimum threshold that all attributes have to reach to be taken into consideration. Every attribute that’s under that threshold is automatically discarded.
• Numerosity Reduction: in order for you to get a more manageable dataset, you can use numerosity reduction, a data reduction technique that replaces the original data with a smaller representation of that data.
• Dimensionality Reduction: Finally, you can wrap the set up by using data encoding mechanisms to further reduce its size. As with all compressing methods, you can go for a lossy or lossless option, depending on your specific needs and whether you want to retrieve the original information in its entirety or can afford the loss of certain parts.

By tackling all of these stages and combining these techniques, you can rest assured that the data sets you’ll use for work will be consistent and in better shape than if you didn’t take the trouble to do so. The suggestion is that you always go through a data preprocessing stage so you have stronger and more accurate results.

It’s important noticing that this suggestion becomes almost a mandate if you’re training an algorithm with the resulting datasets. That’s because preprocessing will remove many of the potential problems that can lead to faulty or erroneous assumptions.

As you can see, data preprocessing is a very important first step for anyone dealing with data sets. That’s because it leads to better data sets, that are cleaner and are more manageable, a must for any business trying to get valuable information from the data it gathers.

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