April 12, 2022
Is your company seeking ways to prevent financial losses caused by poor data quality?
According to research in the MIT Sloan Management Review companies lose between 15% and 25% of their revenues because of poor data quality. Additionally, poor data quality costs organisations approximately $15 million annually.
Here's the problem:
Businesses face an overwhelming amount of data but remain severely short of useful insights. Data scientists dedicate approximately 80% of their working time to identify and prepare data which reduces their analysis time to 20%.
But there's good news...
Recent advancements in machine learning are reshaping data quality management by automating repetitive tasks and delivering unmatched precision.
This article will detail how machine learning transforms data quality management and explain its significance for business operations.
Data quality has evolved from an IT issue to become essential business strategy impacting financial outcomes.
Let me break this down for you...
Any decision derived from data which lacks accuracy or completeness becomes unreliable and flawed. Think about it:
And these mistakes add up quickly.
The MIT Sloan Management Review study found that businesses experience revenue losses between 15% and 25% because of substandard data quality. When a business generates $100 million in revenue poor data quality costs that organisation between $15 million and $25 million in lost revenue.
Here's what's happening behind the scenes:
Data quality problems create disturbances throughout every part of your company.
The most damaging part? Organisations often fail to recognise their data quality issues until they face significant problems.
A recent survey showed that 61% of participants see AI and machine learning as their primary data management focus for the year, demonstrating the increasing significance of these technologies for data quality management.
Traditional data quality management depends primarily on human-operated processes and predefined rule-based systems. Current systems manage data quality at a basic level but fail to cope with the immense amount and complexity of modern data sets.
This is where machine learning comes in.
Through advanced analytical capabilities machine learning systems process extensive data sets to detect patterns and generate predictions beyond human or conventional system capacity.
The strength of machine learning for data quality lies in its ability to learn from data experience and improve over time.
Machine learning functions as your round-the-clock data quality assistant which continuously improves at its job.
Traditional data quality approaches function reactively because fixes occur only after problems arise.
Machine learning enables businesses to adopt a proactive strategy that identifies and resolves issues before they affect company operations.
Data quality management requires this shift since the cost of resolving problems becomes exponentially higher the longer they stay undetected.
The application of AI and machine learning in data quality management brings groundbreaking changes through automated process handling which detects real-time anomalies and adapts dynamically to emerging data patterns. The gap between belief and action stands clear as 80% of executives foresee AI transforming their organisations yet only 6% have moved to production implementation.
We will explore the exact methods through which machine learning transforms data quality management.
A major difficulty in data quality management lies in understanding the full extent of your data assets.
Machine learning algorithms have the ability to perform automatic profiling of your data while detecting various data types and value distributions alongside relationships between data components and potential sensitive information.
Machine learning algorithms provide a full overview of your data landscape while eliminating manual inspection requirements.
Traditional data profiling methods operate slowly and deliver only surface-level analysis. ML profiling analyses all records to detect multi-column patterns and highlights unusual patterns needing further investigation.
Data cleansing stands out as the most time-intensive segment within data quality management.
Machine learning revolutionises data cleansing by automatically detecting and fixing typos and standardising data formats across various sources while managing outliers and proposing accurate estimates for missing values.
The best part? The software improves its performance with each correction and feedback it receives over time.
Storing multiple copies of records creates confusion about which version is accurate and wastes storage space.
Machine learning-based deduplication techniques surpass exact matching methods to recognise records that represent the same entity even when presented in different formats and to distinguish between partial duplicates with important shared information and near-duplicates that could be separate valid entities.
The advanced matching method achieves significantly fewer false positives and negatives when compared with traditional rule-based methods.
The tremendous amount of data moving through contemporary organisations makes manual inspection of all information for anomalies unfeasible.
Machine learning demonstrates exceptional capability in anomaly detection through the establishment of normal patterns across multiple dimensions and identification of unusual values or behaviours while adapting to new data patterns and ranking anomalies by their potential business impact.
The most significant capability of machine learning is its ability to forecast the potential locations and timing of data quality problems.
ML analyses historical patterns to detect vulnerable data sources for quality issues while predicting error-prone transformations and forecasting data drift to recommend preventive measures before issues develop.
The ability to predict data quality issues allows organisations to adopt proactive data quality management which can save millions through the prevention of costly errors and problems.
The future of data quality management lies in machine learning capabilities. You can utilise its capabilities to turn a data quality issue with high costs into an organisational strategic benefit.
Businesses face revenue losses of 15% to 25% because of inadequate data quality. By implementing ML-powered big data engineering services, you can reclaim that lost revenue while gaining competitive advantages through superior data insights.
Although the transformation journey presents significant difficulties the returns are substantial through cost savings operational efficiency and business insights. Businesses that adopt this transformation process will gain the necessary advantages to flourish within today's data-centric commercial environment.
Have you prepared to change your data quality management practices? The new technology exists and its potential advantages cannot be overlooked.
