This Is Why Data Analysis is Concerned with Data Reduction

The world is advancing day by day. The amount of data available is more than ever. Conventional data analysis methods are dying, and it’s high time people use more efficient data analysis techniques than ever. Since most businesses nowadays are becoming data-driven, analyzing the data at hand as fast as possible is the key.

However, as we keep seeing massive surges in the amount and dimensions of data available, data analysis starts going slow, no matter how efficient techniques we employ. So, what do we do? The data and its dimensions aren’t going to stop increasing anytime soon. How do we make data analysis smoother and faster? Well, the answer is data reduction!

This article will dive into the depths of how data analysis is concerned with data reduction and how the two concepts are tightly knit to one another. So, let’s start without any further ado!

What is Data Reduction?

When we collect lots of data from multiple sources intending to analyze and draw conclusions from it, we often end up with a huge volume of data at data warehouses, which is not only hard to manage but also pretty exhaustive to process; this is where data reduction comes to the rescue.

Data reduction is the act of reducing the volume of the data available while keeping its integrity intact. Various techniques are employed to make sure the data is fully identical to the original one but uses much lesser volume.

Further in the article, we will explore some data reduction techniques and see in more detail why high-dimensional data is problematic.

Why Data Analysis Is Concerned with Data Reduction

Data analysis involves digging deep into data to find the smallest trends and patterns there are to find. The process is quite exhaustive since every possibility needs to be fully explored to uncover any detail that might be useful.

Data reduction helps reduce the volume of the data available while protecting its integrity. This makes the data easier to manage and analyze, making the data analysis techniques more efficient and cost-effective.

As a result of employing data reduction, analyzing data gets faster, and a lot of resources are saved since you’re analyzing the same data. Given that now it comes with a much smaller volume and reduced dimensions.

Interesting Article to Check Next: The Importance of Data Cleaning in Analytics

Problems with High-Dimensional Data

So, other than being very high in volume, what exactly are the problems with high dimensional data that it becomes hard to manage for us? Let’s see.

Computational Cost

Firstly, having to bear substantial computational costs is one of the biggest drawbacks of having high-dimensional data. While the information it packs is large-scale and might be very useful in some scenarios, this computational overhead is pretty hard to ignore.

For example, suppose you’re a small company and decide to set up a data warehouse and later perform analysis to extract some actionable insights about your customers. In that case, it’ll get quite hard to analyze all the available data if you don’t use data reduction before the analysis pipeline. 

High Correlation

Another major issue of high-dimensional data is the high amount of correlation between variables it comes with. The data is not much randomly distributed, and the introduction of correlation further adds fuel to the fire.

This kills the primary essence of data analysis, and the whole aim is jeopardized. There are often artificial correlations between data that can be easily wiped away using good data reduction techniques

Overfitting Issues

Suffering from overfitting issues is often amongst the biggest worries of ML Engineers. Not only does it make problems in training the models and achieving good accuracies, but the process to get rid of the overfitting is also lengthy.

So, it’s always best to avoid as much overfitting as possible in the first place, so you will need minimal effort to get rid of it in later stages. Since overfitting takes a huge toll on performances, we certainly can’t avoid taking it into account, and dimensionality reduction helps us in this matter.

Hard to Visualize

Think about how easy it is to visualize 2D data and the added difficulty you experience while visualizing when the data jumps to 3D. It gets confusing right after 3d, and it’s only easy to visualize 4d data for people who have a solid mathematical background. Now, can you even imagine how it would be like to visualize 10D data? Well, not so much!

So, as the number of dimensions increases, it gets more and more hard to intuitively visualize a certain data set. It might be possible to do it with the help of some libraries, but it’s certainly very hard for a human brain to do so.

Hence, data reduction reduces the number of dimensions of the dataset while retaining its properties and eventually helps us make better sense of it.

The next point of discussion is what are the benefits of data reduction. Before digging into that, let me add below some of the top related and interesting articles that can add to what you’re learning from this one. If any of the titles picks your interest, please click and open in a new tab, so you can check them out later. Enjoy!

The Benefits of Data Reduction 

Let’s jump on to some of the biggest benefits we get to enjoy from data reduction techniques before diving into data analysis.

Lesser Space Required

Since the data reduction algorithms help transform the data into its equivalent representation with lesser volume, the new data takes up much lesser space than the old one. This might not be much on a small scale, but for big companies, this means saving hundreds of Gigabytes!

Cutting Down on Operational Costs

As I mentioned in the previous point, data reduction helps save a lot of unnecessary space being used by data warehouses, resulting in huge cost savings on the enterprise level. However, this isn’t all. Data reduction also helps save costs in processing power since the reduced amount of data needs lesser power to process.

Faster Data Analysis

Data analysis is a detailed process that increases directly as we increase the amount of data available. So, once data reduction techniques are applied, and there are lesser amounts of data to deal with, data analysis techniques get significantly faster, and companies become more productive in this regard.

4 Useful Data Reduction Techniques

Now that we know why data reduction is so valuable and the problems that arise if we ignore it, let’s briefly go through some of the most used data reduction techniques.

Dimensionality Reduction

Dimensionality reduction is one of the most used data reduction techniques that’s used to deal with high-dimensional data. It picks up the redundant attributes and figures out the essential attributes in a dataset as well. Once this is done, the redundant attributes that did not affect the dataset are removed, and the important attributes are combined into a smaller number of attributes.

Data Cube Aggregation

Data cube aggregation is a way to aggregate related data in the form of a cube. The concept of a simple cube is followed and just like a cube has multiple dimensions, the data is arranged similarly. Consequently, aggregate functions can be applied to the cubes. Instead of individually applying functions on the attributes, you can now apply them all at once, saving considerable time and computational power.

Numerosity Reduction

Numerosity reduction is the process of finding ways to express the same data in alternative forms, which are smaller and take up lesser space. The original data is replaced with newly made-up data, but it’s just a different form of representation, and the data is still the same.

This is an excellent technique since it doesn’t involve any data loss, and everything is being retained, just in a more precise manner. However, the new data is sometimes ‘guessed’ and might not entirely have the same properties as the original data.

Data Compression

Data compression involves using different techniques to encode the data and replace the original data with the new encoded one. The encoded form takes up much lesser space which makes this technique very popular.

There are two major types of compression techniques – lossy and lossless. While the former might lose some data when converted back to the original representation from the encoded form, the latter doesn’t.

Wrap Up

Data analysis is becoming a vital part of any company nowadays because of its role in identifying new market trends, customer patterns, and so on. However, with a rapid increase in data availability nowadays, it gets quite exhaustive and unmanageable to run analysis on such huge amounts of data.

The answer to this problem is data reduction. Not only does data reduction help you save a lot of storage space since it reduces the volume of data while preserving its properties, but it also helps to lower the computational costs since now you have to deal with a much smaller volume of data for data analysis.

So, always make sure you don’t forget data reduction techniques before diving into data analysis. We have discussed some common data reduction techniques as well in the article that you can easily use to get started with.

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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