Why Data Integrity Is Crucial in the Incoming Age of Automation

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Data cleansing is pivotal for trustworthy data in decision-making. Automation and machine learning enhance efficiency and accuracy, replacing laborious manual processes. Traditional approaches are time-consuming and prone to errors, especially with large datasets. Machine learning excels at detecting subtle anomalies, which at least helps with boosting data quality.

The significance of data cleansing in today’s data-driven world cannot be underestimated. It identifies and rectifies errors, inconsistencies, and inaccuracies in datasets to ensure their accuracy, completeness, and reliability.

And in a world where a small ‘inconsequential’ error can lead to exponential consequences, data validity is essential.

Imagine a situation where you are about to make a crucial business decision that could shape your company’s future. However, the data you rely on is heavily affected by errors, duplicates, and missing values. Such inaccuracies in data can result in flawed analyses and incorrect decisions.

Two small examples before we jump into it -— there was the time the UK accidentally stopped counting and tracking up to 16,000 Covid cases after using an older Excel format limited to 64,000 rows.

Or the simple — but hugely frustrating for those this affects — a case where people with the surname Null become invisible to databases.

Or making predictions about staffing levels, stock checks, or expansion plans without good data at your disposal — if something is wrong with the stock count, then too much or too little product turns up at the door.

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Data quality is no small matter in pretty much every avenue of life. And if you will offload it to the machines, you need extreme, if not absolute, confidence in the data.

Until the recent past, data cleansing was a labor-intensive and manual task. However, with the advent of automation and machine learning, this process has become faster, more efficient, and more advanced. Automation and machine learning technologies have data cleansing to an era of enhanced data quality.

Traditional data cleansing mechanisms relied on manual labor to identify and correct spelling mistakes, missing values, duplicates, inconsistent formatting, and outliers. However, this manual approach has limitations.

It is time-consuming, subjective, and prone to errors, especially with large datasets.

As data volumes grow exponentially, the manual approach becomes impractical and costly.

Think of a team of data analysts who, with great attention, browses piles of spreadsheets in search of the errors that are difficult to find while working under strict deadlines. It is a difficult task with the potential for error due to the fatigue of humans.

Here comes automation as the key player in modern data cleansing. Automation simplifies tasks such as error identification and correction, making data cleansing faster and more efficient. It is like having an efficient assistant who can analyze vast data.

Meanwhile, machine learning algorithms, the driving force behind this operation, learn from historical data and detect anomalies and inconsistencies that even the most expert and vigilant human analysts could miss. They act as the investigators in data cleansing, uncovering hidden errors and outliers.

Again, imagine an automated data profiling tool that can scan your entire dataset within minutes, detecting errors and inconsistencies with pinpoint accuracy. It appears similar to having a team of highly perceptive experts working tirelessly to ensure the perfection of your data. Of course, everyone would desire that!

How Machine Learning Empowers Data Cleansing?

Machine learning, powered by advanced algorithms, automates the detection and correction of errors by recognizing patterns and making predictions based on data. These algorithms are trained on historical data, learning to distinguish clean data from anomalies.

Machine learning excels in identifying anomalies and outliers, which are crucial for data cleansing. One may think anomalies are data points that deviate from the usual behavior, potentially representing errors or rare events. Machine learning algorithms identify and flag these anomalies using clustering or classification techniques.

The machine learning algorithm is more like uncovering the most subtle anomalies in data than finding a hidden gem in a treasure. It is like having an all-time alert guard to ensure the integrity of my data.

The power of machine learning extends further, with supervised learning algorithms creating models to classify data points as normal or abnormal. Unsupervised learning techniques reveal hidden patterns and anomalies without predefined labels, making them indispensable when anomalies are unknown. This ability to detect anomalies and outliers enhances data quality and reliability.

Commercially Available Services for Data Cleansing

Several companies offer comprehensive services for data cleansing, utilizing the capabilities of automation and machine learning to ensure data accuracy and reliability:

Harte Hanks: With access to an extensive database of over 573 million B2B and B2C customers, Harte Hanks specializes in identifying inaccuracies, deduplicating records, and achieving data clarity at scale. Many esteemed companies, like Abbott, Sony, GSK, and Unilever, are trusted partners for data cleansing.

Data8: Data8 caters to diverse client needs by providing flexible data cleansing solutions through Batch API, Data8 Pull/Push, and File-Based Exchange. Their data independence allows access to various data sources, boosting reliability.

Emerging Startups: The data industry is experiencing the emergence of innovative startups in the field of data cleansing, like Trajektory, Sweephy, causaLens, uProc, and Intrava. Each startup offers unique solutions to automate and improve the data cleansing process.

Ethical Considerations Related to Automated Data Cleansing

As automation and machine learning become essential components of data cleansing, ethical considerations come to the forefront:

Fairness: It is crucial to prevent the propagation of biases in ML models. Techniques like bias audits and debiasing algorithms are necessary to ensure fairness.

Transparency: Explainable AI (XAI) methods, such as model interpretability tools, aid in understanding algorithmic decisions.

Human oversight: Despite automation, human oversight remains vital to address algorithmic biases and ethical breaches. Therefore, establishing ethical guidelines and frameworks is essential to govern automated data cleansing.

The Bottom Line

The future of data cleansing is closely intertwined with automation and machine learning. These technologies continuously evolve, promising more efficient and accurate data cleansing processes. Businesses can benefit from reduced manual efforts, enhanced data quality, and better-informed decision-making.

In conclusion, automation and machine learning are transformative forces that offer a brighter, data-driven future for organizations embracing these innovations.

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Assad Abbas
Tenured Associate Professor
Assad Abbas
Tenured Associate Professor

Dr Assad Abbas received his PhD from North Dakota State University (NDSU), USA. He is a tenured Associate Professor in the Department of Computer Science at COMSATS University Islamabad (CUI), Islamabad campus, Pakistan. Dr. Abbas has been associated with COMSATS since 2004. His research interests are mainly but not limited to smart health, big data analytics, recommender systems, patent analytics and social network analysis. His research has been published in several prestigious journals, including IEEE Transactions on Cybernetics, IEEE Transactions on Cloud Computing, IEEE Transactions on Dependable and Secure Computing, IEEE Systems Journal, IEEE Journal of Biomedical and Health Informatics,…