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Can You Apply Machine Learning to Data Cleansing?

Data cleansing is an integral part of the data preparation process that ensures data is consistent and accurate to facilitate precision data analysis for making predictions and informing an organization’s business strategy. While traditional data cleansing methods rely heavily on manual intervention and rule-based systems, machine learning offers transformative capabilities in automating and enhancing this critical process.

It has been estimated that poor quality data costs organizations an average of $12.9 million annually. In addition to the immediate impact on revenue, on a longer-term basis, poor quality leads to poor or ineffective decisions being made, which can further impact the viability or stability of the business.

What is data cleansing?

Data cleansing, also referred to as data cleaning, is the process of detecting, correcting, or removing errors and inconsistencies in data to enhance its quality. It usually includes the following tasks within the process:

• Identifying and removing duplicates
• Handling missing values
• Standardization
• Normalization
• Correcting Errors
• Verification
• Enrichment
• Validation
• Text Cleansing

When combined, each of these activities ensures that the dataset is more suitable for analysis and for use in algorithms. This is why data scientists spend most of their time preparing and cleaning data. Once cleansed, the data provides more accurate and reliable results and avoids erroneous model predictions with unclean data.

Using machine learning with data cleansing

Machine Learning (ML) is one of the big buzzwords of today. It is a variation of AI, albeit with an important distinction. It is generally acknowledged that there are two strands of Artificial Intelligence, Generalised AI and Applied AI.

Applied AI includes the tech behind self-driving cars and trading programs. Machine Learning sits in the Generalised AI field. This covers the types of device or system that can, in theory, handle any task as it learns via the data, ultimately aiming to be able to replicate or even improve upon human cognitive abilities.

After all, without good data, almost everything else in a technology setup struggles, from integrations, to analytics, to allowing for informed decisions to be made. With these two subjects on the table, the next question understandably is:

‘Surely we can use machine learning to speed up and improve the data cleansing process?’

It’s a good question that needs to be answered. We’ll start with the advantages of using Machine Learning for data cleansing.

What are the advantages of using Machine Learning for data cleansing?

Increased speed & efficiency

Machine Learning significantly accelerates the data cleansing process compared to traditional manual methods. This provides the organization with both a strategic and economic advantage due to the timely manner in which insights can be gleaned from data compared to those continuing with manual data cleansing processes.

Continuous evolution

While the overall capabilities of Machine Learning are becoming increasingly impressive, this is especially the case when we look at data cleansing. It is a field undergoing rapid advancement that will continue for the foreseeable future. The ongoing research and development that is taking place means that a range of industries will benefit from more sophisticated and effective tools in both the short- and long term.

Scalability for Big Data

In the era of Big Data, where data volume, velocity and variety are increasing, manual data cleansing is becoming increasingly unrealistic and infeasible. Machine Learning algorithms have been specially designed to handle extensive data sets to deliver cleanliness and integrity, even as the data scales exponentially.

Consistency and Reproducibility

Once a data cleansing Machine Learning model has been trained and refined, it can be consistently applied to new data sets. This ensures uniformity of data quality, which is scarce in manual cleansing due to the variability inherent in manual processes carried out by various individuals.

Adaptive Learning

One of the critical features of Machine Learning is its ability to learn and adapt. As increased amounts of varied data are fed into the model, the greater its capacity to refine and improve its understanding, enhancing its cleansing capabilities over time.

Reduced Human Error

Data cleansing is prone to oversight and inconsistencies, as with other manual processes. Once set up, Machine Learning can minimize these instances and increase data accuracy.

Customization & Flexibility

Advancements in modern Machine Learning mean that algorithms can be specially tailored to specific data cleansing tasks or domains to ensure that specificities of particular datasets or industries are addressed effectively, including supply chain management.

As supply chains generate vast amounts of complex data, ranging from supplier performance metrics to inventory levels and demand forecasts, each area of the supply chain poses a risk of having certain data inconsistencies or errors. When customized for supply chain management, Machine Learning models can identify and address these irregularities.

Machine Learning ensures that organizations can make better informed decisions, reduce costs through optimized operations, remain agile and better respond to changes within the market.

Machine Learning’s Impact on Supplier Data Cleansing

This level of customization is essential for organizations to manage and optimize their supply chains effectively as they become increasingly global and complex with multifaceted operations.

This means it will include numerical data, textual content, time series data and geospatial information. This level of data heterogeneity requires sophisticated supplier master data management cleansing tools that can handle the complexity of this vast amount of diverse data.

The Future of Data Cleansing in Supply Chain Management

The emergence of new technologies will continue redefining how organizations process and utilize supply chain data. Increased utilization of the Internet of Things (IoT) means that growing numbers of sensors and real-time data are being generated that will substantially increase the volume and complexity of data. This increase highlights the necessity for organizations to invest in sophisticated data cleansing software that utilizes AI.

Machine Learning is more than just having the functionality to quickly process vast data sets and identify anomalies and inconsistencies that traditional methods may overlook. It provides the opportunity to predict trends accurately and have a competitive edge over other organizations.

In addition, there is increased awareness and realization of blockchain’s benefits to the supply chain management sector. Blockchain provides acclaimed data validation capabilities while increasing levels of supply chain transparency. It allows recording transactions throughout the supply chain to capture, validate and share data between businesses interlinked within it. This provides organizations access to a single source of truth where supplier data is consolidated and seamlessly exchanged.

The technology is considered advanced enough to complement and take advantage of IoT and Machine Learning offerings to provide supply chains with extensive security. As further developments begin to emerge over the upcoming months and years, what is becoming increasingly clear is that those organizations that are yet to even utilize data cleansing as part of their master data management strategy are going to become an increasing minority and inevitably get left behind in increasingly competitive global markets.

_{Article Updated February 2024}