Data Reduction

Data reduction refers to compacting the storage space required for data and improving its efficiency through summarizing while decreasing data complexity and retaining its inbuilt characteristics. Its main purpose is the optimization of storage space, enhancement of data processing speed, and improvement of data for computational efficiency.

It works by using the method of deduplication, numerical approximation, dimensionality reduction, and attributing subset selection of data. Organizations use it to store and analyze huge volumes of data more efficiently, resulting in heightened operational efficiency and cost savings. Financial companies use it to handle and assess humongous data for risk analysis, portfolio management, financial forecasting, and fraud detection.

• Data reduction entails condensing the storage space needed for data and enhancing its efficiency by summarizing and reducing complexity while preserving its inherent characteristics.
• The primary objective of data reduction is to optimize storage space, improve data processing speed, and enhance business computational efficiency.
• It offers cost-saving benefits for firms by reducing operational, hardware, and maintenance expenses, but it can potentially result in information loss, compromising accuracy in decision-making and financial analysis.
• Data reduction techniques, such as feature selection, feature extraction, sampling, data compression, binning, and data aggregation, enhance storage efficiency, computational speed, and model performance in machine learning and data analysis by decreasing dataset complexity and size without altering its traits.

Data Reduction can be defined as the process of reducing the size or complexity of a dataset while retaining its inherent nature and characteristics. It uses multiple techniques like the feature selection method, feature extraction, sampling techniques, data compression methods, and data binning or discretization to remove noisy data and eradicate redundant data to change it into efficient presentable data.

It works by eliminating and identifying unnecessary data through the abovementioned techniques. Every technique approaches data reduction in its own way for optimization. Its usage has multiple benefits: efficiency improvement in analysis, processing, and data storage, reduced time of processing and computational requirements, enhanced data interpretability, mitigation of dimensionality curse, and model accuracy and performance improvements.

However, it has certain negatives like loss of inherent granularity or information from the dataset, errors may creep into the data set during processing, and reduced data set may adversely impact its representativeness. It has wide applications in data reduction in qualitative research, including:

• Data mining - For improvement in model performance and handling large data sets as well as machine learning
• Data analytics - For simplifying complex datasets to enhance visualization and exploratory analysis
• Database management – In improving processing efficiency and storage optimization
• Signal processing- Improving signal-to-noise ratio and noise reduction

One can use a multitude of techniques for data reduction to decrease the complexity or size of the data set without changing any traits or features of the dataset. Machine learning, data reduction Excel, and data analysis use these techniques to reduce storage requirements, enhance efficiency, and improve model performance. Hence, many data reduction methods have been listed here:

1. Feature Selection Method

It comprises identifying and selecting a subset of appropriate features related to the original data set, plus eliminating redundant or useless data. As a result, data dimensionality decreases, and computational efficiency improves. It deploys using methods like Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination (RFE), statistical tests, information gain, and correlation analysis. 

2. Feature Extraction

It transforms the actual data set to lower-dimensional space by creating new features representing essential features. Moreover, it uses Principal Component Analysis (PCA), which can easily capture maximum variance in the data by identifying linear combinations of features. It uses T-distributed Stochastic Neighbor Embedding (t-SNE) and Independent Component Analysis (ICA). 

3. Sampling Techniques

It selects a nominal sunset from the actual data set for evaluation rather than using the whole data set. It becomes useful when the dataset is huge for processing completely or whenever there is a need to balance out imbalanced classes in datasets.

4. Data Compression Methods

It utilizes various data reduction algorithms like normal & compression algorithms like ZIP, gzip, Huffman coding, and Run-Length Encoding (RLE) to decrease the storage space needed to denote the dataset while retaining the information.

5. Binning Or Discretization

It segregates continuous data into various categories or intervals. As a result, the complexities of the continuous data through categorization or transforming to ordinal forms. Techniques commonly used here include entropy-based binning, equal-frequency binning, and equal-width binning to reduce outliers' impact and manage disoriented data distribution.

6. Data Aggregation

It involves joining many data points into one common representation. Numerical data get summarized using the aggregation function, while categorical data use categorization and grouping data for data reporting and warehousing to summarize huge data into useful insights. 

Moreover, above techniques could be used singly or in combination with other techniques as per the requirements. 

Let us use a few examples to understand data reduction:

Example #1

Suppose a finance blogger, Archie, wants to add historical market into their blog post. But, the huge data set could perplex the blog's reader if included in its original form. Hence, Archie uses the data compression technique to reduce the data through algorithms like ZIP to decrease the file size while maintaining the data integrity. 

Archie also reduced the storage space needed for storing such a stock of historical data. Then, Archie provides a link to the reduced data in the blog post without increasing the blog size and complexity to readers. 

Example #2

Suppose an accountant has been assigned to reduce the data of the annual return of a firm for annual presentation as the data involved is large, so the accountant uses a binning technique for categorizing the data of annual returns. The accountant creates their categories, namely - "Lowest Returns," "Median Returns," and "Highest Returns. 

As a result, the firm's performance gets summarized for the management in an easy-to-understand form. Hence, the management is able to comprehend the annual returns in terms of portfolio trends, providing them with accessible and concise analysis. 

Let us use the table below to know the advantages and disadvantages of data reduction in banking and finance:

This has been a guide to what is Data Reduction. Here, we explain its techniques, examples, and advantages and disadvantages. You can learn more about it from the following articles –

• Data Snooping Bias
• Data Quality Assurance
• Data Cleansing