How Can Data Quality be Increased for ML Models?

Transforming Data Quality with Machine Learning: From Errors to Excellence

Author: Inza Khan

In today’s dynamic technological era, where data accuracy is important, machine learning plays an important role in bolstering Data Quality. High-quality data backs up informed decision-making, while inaccuracies can lead to flawed decisions.

Data Quality segregates accurate insights and flawed outcomes. High-quality data is reliable and precise, and it leads to intelligent decision-making processes. Whereas, low-quality data introduces inaccuracies, undermining the efficacy of decision-making efforts.

Originating as a response to the challenges faced by the industries adopting artificial intelligence in the late 1970s, machine learning emerged as a distinct industry. It came as a dynamic force driving innovation across various sectors. Today, machine learning stands as a testament to adaptability and resilience in the face of technological evolution.

The Three Functions of Machine Learning

Machine learning operates on three fundamental functions: descriptive, predictive, and prescriptive. Descriptive algorithms evaluate past events, predictive algorithms forecast future trends, and prescriptive algorithms recommend actionable steps.

Machine Learning: Beyond Automation

Machine learning encourages automation and automation streamlines processes by replicating human behavior. By learning from experiences, adapting to new situations, and discerning patterns in data, machine learning transcends traditional automation, supporting the increase of intelligent adaptation.

At its core, machine learning operates through algorithms – step-by-step instructions that guide decision-making processes. These algorithms leverage past experiences to make informed decisions, constantly evolving to improve accuracy and reliability. With a plethora of algorithms available, machine learning tailors its approach to suit diverse contexts.

Machine learning begins with data – text, images, or numerical inputs – serving as the raw material for training. The selection and preparation of this data lays the groundwork for a robust machine-learning model. Through iterative training and parameter tuning, machine learning models evolve to make accurate predictions.

Enhancing Data Quality Through Machine Learning

Applying machine learning to data quality involves using algorithms to identify and address issues in datasets, ultimately enhancing the overall quality of the data. Here are some examples that illustrate how machine learning contributes to improving data quality:

Reconciliation:

Reconciliation involves comparing data from trusted sources to ensure accuracy during data migration. Machine learning algorithms leverage historical data and user actions to learn how reconciliation issues were previously resolved.

Example: If discrepancies are found in financial records during data migration, machine learning algorithms can analyze past instances where similar issues were resolved, using logic to efficiently reconcile the data.

Missing Data and Filling Gaps:

Machine learning regression models are used to predict trends and outcomes. They can also be employed to improve data quality by estimating missing data within an organization’s system. ML algorithms can fill in missing data when there is a relationship between data points or historical information. Feedback from humans helps these algorithms improve over time.

Example: In a customer database, if certain records are missing information (e.g., contact details), ML models can predict and fill in the missing data based on patterns observed in the available data.

Similarly, if a sales dataset has missing values for certain products, machine learning can predict these values based on historical sales patterns and user feedback, gradually refining its predictions.

Data Quality Rules:

Machine learning can transform unstructured data into a usable format and generate rules for real-time quality assessment. It excels at detecting unknown issues in complex data.

Example: ML algorithms can analyze incoming data and it automatically generates rules to identify and communicate quality concerns in real-time. This is particularly useful for handling the increasing complexity of data where manual or automated rules may fall short.

In-House Data Cleansing:

Machine learning corrects common errors in manual data entry, such as misspellings, and half-put information thus it leads to improving data standardization.

Example: ML algorithms can identify and correct inaccuracies in names and addresses that traditional spellcheck tools might overlook. Continuous learning from reference data ensures ongoing improvement in data accuracy.

Improving Regulatory Reporting:

Machine learning helps prevent incorrect records from being submitted during regulatory reporting by identifying and removing them before submission. ML algorithms can analyze datasets for compliance issues, ensuring that only accurate records are included in reports submitted to regulatory bodies.

Creating Business Rules:

Decision tree algorithms can use existing business rules and data warehouse information to create or enhance business rules, ensuring alignment with the evolving nature of the data.

How to Improve Data Quality with Unsupervised Machine Learning?

The importance of high-quality data cannot be overstated in machine learning. Data scientists often grapple with challenges in traditional data preparation, where the absence of domain experts, human errors, and the time-consuming nature of data cleaning can hinder the accuracy of results. In this blog, we explore a transformative approach to data quality – Unsupervised Machine Learning.

Traditional Data Preparation Challenges:

1. Need for Domain Expertise: Traditional data preparation relies on domain experts who possess deep knowledge of the subject matter. When preparing data for machine learning, the absence of these experts can lead to challenges in accurately categorizing and labeling data. This is particularly problematic when the criteria for determining the “right” data are not well-defined.
2. Human Errors and Incorrect Understanding: The process of data categorization and labeling involves human judgment, and this introduces the potential for errors. Misinterpretation of the final output expected from a machine learning model, incorrect categorization of data, and general human errors can result in inaccurate outcomes. These errors may propagate through the model, leading to persistent issues.
3. Time-Consuming Data Cleaning: Data scientists invest a significant portion of their time in cleaning and preparing data before training machine learning models. Despite these efforts, achieving ideal data quality remains challenging. Even spending up to 80% of the time on data cleaning, it doesn’t guarantee them the complete elimination of errors and bias, highlighting the complexity of reaching the desired data standard.

Unsupervised Machine Learning:

Supervised learning relies on labeled data, where the machine learning model is trained on input-output pairs. In contrast, unsupervised learning operates without predefined labels. The unsupervised approach is particularly valuable when there is uncertainty about what constitutes the “right” data or when uncovering hidden patterns without a clear predefined measure of accuracy is essential.

Unsupervised learning stands out for its ability to uncover patterns that may be unknown to human experts. It’s particularly effective in scenarios where the end goal is not well-defined or when there might be hidden structures within the data that traditional data preparation methods might overlook.

Unsupervised Machine Learning Approaches:

1. Dimensionality Reduction Algorithm (UMAP)

Purpose: UMAP serves the purpose of reducing the dimensionality of data, making it more manageable and insightful.

Features: The non-linear representation capability of UMAP is crucial, especially when dealing with complex datasets, such as images or high-dimensional data.

Use Case: The efficiency of UMAP is showcased through its ability to quickly process and project large datasets, as seen in the MNIST dataset example.

2. Data Clustering

Purpose: Clustering algorithms like DBSCAN and HDBSCAN organize data into meaningful groups based on similarities.

Applications: These algorithms are powerful tools in market segmentation, recommendation systems, and fraud detection, where identifying patterns within data is crucial.

Performance: DBSCAN’s ability to find data structures and HDBSCAN’s hierarchical approach enhance the accuracy of clustering results.

3. Anomaly Detection Algorithm

Integration: Anomaly detection, often combined with dimensionality reduction and clustering, serves as a multi-faceted approach to identifying and understanding irregularities in datasets.

Use Cases: Examples like spam filtering and fraud detection demonstrate how anomalies, when detected and clustered, can provide valuable insights and predictions.

4. Association Mining Algorithm

Purpose: Association mining is applied to uncover relationships in large datasets, particularly when dealing with non-numeric, categorical data.

Data Handling: Its capability to work with non-numeric data sets it apart, making it suitable for scenarios like market basket analysis or healthcare diagnostics.

Use Cases: From optimizing public services to understanding relationships between symptoms and diseases, association mining proves versatile.

The Financial Toll of Bad Data

Bad data imposes a significant financial toll on companies, with estimates suggesting that businesses lose 15% to 25% of their revenues due to poor data quality. The annual impact on the US economy alone is staggering, estimated at $3.1 trillion by IBM. Beyond financial losses, data scientists spend 80% of their time dealing with data-related tasks, leaving only 20% for actual analysis.

The inaccuracy of data leads to flawed decisions and the consequences of flawed decisions are profound, affecting governments with long-lasting policy implications and jeopardizing customer relationships for commercial enterprises. However, machine learning offers a strategic solution by flagging potential issues before they escalate. In sectors like finance, ML models play a crucial role in identifying forged transactions, potentially saving card issuers and banks a substantial $12 billion, highlighting the pivotal role of technology in mitigating the costs and challenges associated with bad data.

Conclusion

Machine learning significantly boosts data quality, ensuring reliable decision-making across sectors. Through algorithms addressing data issues like discrepancies and missing entries, machine learning improves overall data integrity. Unsupervised Machine Learning offers a practical alternative, particularly beneficial when traditional data preparation faces categorization challenges.

The financial impact of poor data quality emphasizes the role of machine learning in minimizing revenue losses and time investments. By detecting issues early, machine learning proves essential in reducing the costs tied to inaccurate data. Going forward, the collaboration between machine learning and data quality will remain pivotal in shaping effective decision-making and fostering innovation in our data-driven landscape.