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Importance of Automated Data Preprocessing for Machine Learning

February 9, 2024
min read

Data preprocessing pertains to the tasks that help clean up and prepare the raw enterprise data for further perusal. Raw enterprise data comes unstructured and in different formats (such as images, text, videos, etc.), which need to be put into order and structure for machine learning systems to run efficiently.


However, considering the veracity and volume of data that organizations produce on a daily basis, manual data handling can put great strain on manpower. Given that the accuracy of analytics completely depends on good quality data, it is important to consider automated data preprocessing as a potent solution.

Pain Points in Manual Data Preprocessing

Manual data preprocessing poses several inefficiencies that may stunt the machine learning operations for an enterprise looking to scale:

1. Time-Consuming Tasks

Tasks such as identifying missing values, data segmentation and categorization, removing redundant and duplicate data, etc., tend to take up considerable time. Manpower invested in these activities remains unavailable to critical tasking.

2. Human Errors

Lack of focus and distractions introduce errors in data preprocessing tasks. Validation and verification processes may suffer at the hands of human errors. Automated data preprocessing can help remove these risks completely.

3. Efficiency

What humans can achieve in an hour, an auto-programmed, AI-based preprocessing engine can complete within a matter of minutes. Organizations looking to handle their data with high efficiency need to reallocate their resources towards automation.

4. Impact on Data Quality

Manual data preprocessing cannot remain consistent across all datasets and at all times. Variations are bound to occur in data quality, which may degrade the results of the ML engines over time.

Empowering Machine Learning with Automation

Human resources still remains the most critical aspect of an organization’s set of tools. However, it is extremely challenging to scale the workforce in proportion to modern needs pertaining to enterprise data, where speed, accuracy, and efficiency are paramount for business furtherance.


With automated data preprocessing tools, it is possible for enterprises to compile the following five processes into an automated algorithm:

  • Data completion
  • Noise reduction
  • Transformation
  • Data reduction
  • Validation

Automation requires significantly less time to produce high-quality datasets that conform to prescribed standards. The tools allow for the flexibility to adjust data quality and dataset nature according to the requirements of the subsequent ML models.

Not only does automation empower enterprises to accelerate their data operations (before ML engines kick in), but it also allows for the elimination of human errors, data enrichment, and predictability in the machine learning ecosystem.

Transformative Benefits of Automated Data Preprocessing


Automated data preprocessing offers several benefits to enterprises:

1. Consistency in Quality

Automated data preprocessing produces datasets that are consistent in quality. The information in the datasets remains updated at all times without human intervention. The machine learning algorithm works with high-quality data and can thus improve its output with time with consistent quality datasets.

2. Higher Efficiency

Automation tools accelerate the process of data preprocessing significantly as compared to manual tasking. Depending on the scale of preprocessing operations, these tools can significantly increase their output depending on the data volumes that they have been programmed for. Organizations can thus function with higher efficiencies.

3. Cost Optimization

A scale-up in data operations inevitably requires more hands on deck to manage increasing volumes of raw enterprise data. Human resources can prove to be a costlier solution when compared with AI-based automated data preprocessing tools. While the initial cost of implementation may seem high, the long-term cost benefits are significantly higher.

4. Improved Analysis

Machine learning engines are able to produce better analytics working with high-quality, consistent datasets. Removing manual processing bottlenecks ensures that required data is always available to the ML models when required and that it is error-free and accurate.

Tools Driving Automated Data Preprocessing in ML

Today, there are several solutions and implementation methods available to organizations to mobilize automated data preprocessing across their machine-learning workflows. The following five tools are the most effective ways to do it:

1. Pandas

Pandas Python library allows enterprises to work with datasets with functions to analyze, clean, explore, and manipulate their organizational data. Using Pandas makes it extremely simple and streamlined to work with Big Data and generate accurate analyses using statistical theories.

This is the best tool to use for cleaning chaotic datasets.

2. NumPy

NumPy is another Python library that consists of fundamental packages that allow enterprises to perform scientific computing on their data.

It leverages multidimensional array objects, derived objects, and various routines to accelerate operations on data arrays that include mathematical, sorting, logical, shape manipulation, Fourier transforms, and other operations. This advanced tool can help structure complicated data more efficiently.

3. Scikit-learn

Scikit-learn is a machine learning tool in Python that allows enterprises to apply straightforward and efficient tools for predictive data analytics. It is open source and is built on top of NumPy, matplotlib, and SciPy.

Organizations can perform data operations like classification, regression, clustering, dimensionality reduction, model selection, preprocessing, feature extraction, normalization, and more using this tool.

4. TensorFlow Data Validation (TFDV)

For organizations seeking to focus on data exploration and validation, the TensorFlow Data Validation library can provide highly effective features. This scalable tool can perform anomaly detection, missing features, outliers, generation of automated data schema, feature types, and more operations on raw enterprise data.

This platform is highly scalable; if your enterprise requires agile data management, TFDV is an excellent tool.

5. Matplotlib and Seaborn

Matplotlib is a library that data scientists can use for 2D plotting of enterprise data. It helps with the creation of diverse plots using lines, scatter graphs, histograms, or even bar charts. It is a powerful data visualization tool used in data engineering in machine learning.

The tool is a cross-platform utility and has the potential to provide animations and interactive data visualizations as well.

Future Trends

The future for automated processes in data engineering is bright.

1. Large Language Models

Unstructured enterprise data consists of several bits of natural conversation, for example, call center manuscripts. It is possible to leverage large language models akin to GPT-3 that empower organizations to derive better context and insight from such data.

2. On-Device AI

Data preprocessing can be further accelerated by shortening its path to preprocessing models. The tools housed in the cloud can be moved on personal devices to gain quick, pre-processed data in hand.


It is essential to consider the implementation of effective automated data preprocessing tools to imbue efficiency and quality in enterprise data meant for ML operations. In a data-driven business market, it is risky to rely on manual processes where volume, speed, and quality steer the equation of success/failure.

MarkovML empowers businesses with an AI-driven data-centric platform, handing them total control over their organizational data. Data engineering features like Data Analyzers enable enterprises to identify and remove data gaps, pinpoint patterns, measure outliers, and much more for efficient ML modeling.

The platform provides an Intelligent Data Catalog for the compilation of data and insight into a well-architectured space, easing ML workflows while enhancing data efficiencies. 

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