AI Data Quality Metrics for Machine Learning
The output quality of the systems that leverage machine learning for analytics depends largely on the quality of the input data. For some businesses, the stakes are high as they have expectations of high accuracy from the analytics insights generated by these ML systems.
The necessity of a high-quality dataset for machine learning systems, therefore, cannot be ignored.
Data Quality in Data Science and Machine Learning
Data science and machine learning are two intrinsically different concepts, though they appear similar on the surface. Data science pertains to processes that extract meaning and insight from organizational data.
On the other hand, machine learning activities pertain to using organizational data to improve performances, identify patterns, or generate forecasts and predictions.
AI data quality for both these operations commands how accurate, efficient, and relevant the output insights would be. Providing ML engines with enriched, well-structured data greatly improves the likelihood of generating high-quality, actionable insights at the output.
Some of the data quality key performance indicators businesses use to establish benchmarks are duplicate data rate, data timeliness rate, and accuracy rate.
What Are the Core Modules in YData Quality?
Even though studies highlight that data scientists consider the availability of high-quality data as the toughest challenge they face, organizations find cleaning and tidying up their data a tedious task. To solve this problem, a modern solution called YData Quality is simplifying things for data scientists with its eight core modules.
- The Data Relations module checks for associations between features, assesses feature importance, and identifies high-collinearity features.
- The Bias and Fairness module ensures that the input data remains unbiased and there is no differentiated treatment for sensitive attributes.
- The Data Expectations module checks for data that has particular properties. Leverages these validations to the framework to check data quality.
- The Labeling module enables data quality checks for imbalanced and outlier labels using special engines for categorical/numerical targets.
- The Duplicate module checks for redundancies and duplication in organizational data.
- The Missing module checks for the impact of missing data on the output and gauges its severity.
- The Drift Analysis module helps ensure the stability and applicability of data features and targets as new patterns evolve.
- The Erroneous Data module helps assess data for misguided values or for values without relevance or meaning.
Data Quality and Machine Learning: What’s the Connection?
The popular phrase, “Garbage in, garbage out,” pertains to machine learning algorithms that glean from poor-quality data inputs. In addition to that, the increased volume, velocity, and complexity of data over the years has sparked a consequent increase in data quality incidents as well.
Machine learning has a deep connection with data quality that ultimately influences business decisions and consequent outcomes:
- Filling data gaps
- Assessing data relevance
- Detection of anomalies
- Duplication alerts
- Data validations
Data Quality in Machine Learning: How to Evaluate and Improve?
Reports suggest that it is the data consumer that is impacted most of the time by poor data quality or missed data incidents. It is essential that organizations mobilize solutions that help evaluate data quality and improve it before machine learning algorithms kick in to do their tasks.
Evaluating Data Quality
Data quality assessments can primarily be designed to focus on the following aspects:
- Missing values and their quantity
- Duplicate values
- Identification of outliers, anomalies, variations
- Invalid values, bad-format values, inconsistencies in data
Improving Data Quality
Based on the identified data incidents, data quality improvement can then commence:
- Handling or removal of missing data
- Restricting the sample weight on duplicate values
- Imputing new values for outliers and other data inconsistencies
- Reformatting malformed values to the correct datatypes
Best Practices for Utilizing Data Quality Metrics
Utilize the five key best practices listed below to transform the data habits at your organization:
Data quality needs to be a pan-enterprise exercise. It is essential for all the stakeholders to understand the importance of high-quality data and take accountability for their part. Enterprise buy-in requires participation and management that is hierarchy-agnostic and deployable at every desk.
Identify Data Quality Key Performance Indicators
Lay down your business goals and synchronize the data quality requirements with business targets. Monitoring the metrics is essential to understand data status and health over time in a trackable, measurable, and actionable manner. Identify the KPIs and establish systems to track them.
Data incidents do occur, and these need to be identified and addressed. Investigate all your data quality failures to determine and eliminate the root cause for achieving quality consistency in the data stream. It ensures that errors are arrested and do not continue to occur.
Govern the Data
It is imperative to establish data policies, standards, and metrics that define how data is to be treated at your organization. Data governance guidelines help ensure that organizational data goes through the right processes and roles and is used efficiently towards fulfilling business goals.
Audit the Data
Audits help gauge the effectiveness of your data governance initiative and QA implementation. Data audits reveal data issues like poorly populated fields, data format inconsistencies, duplicated entries, inaccuracies in data, missing data, and outdated entries.
Case Studies: Successful Implementation of Data Quality Metrics
Two case studies aptly showcase how quality data can transform ML operations for businesses:
General Electric (GE)
GE leverages data science for solutions that provide predictive maintenance. The industrial equipment transmits data to hubs which then go into GE solutions to predict the need for maintenance. High-quality and firsthand data fed into GE predictive maintenance modules help provide excellent outcomes:
- Reduction (30%) in unscheduled maintenance by leveraging sensor data from jet engines.
- Increase (15%) in wind turbines' operational efficiency using data-driven predictive analytics.
- Savings to the tune of $50 million in maintenance costs owing to predictive maintenance models.
Spectrum Labs, a leading SaaS business, struggled with content moderation issues posing challenges in scaling. They faced issues with assessing the quality of their datasets – in particular, the accuracy of their moderation labels.
MarkovML implemented a full-scope AI data analytics engine that helped Spectrum Labs address:
- Discrepancies in datasets
- Topic modeling, keyphrase analytics, and clustering analytics using Enhanced Data Understanding (using auto-EDA)
- Labeling inconsistencies using Label Trust Estimate to gauge the quality of labels attributed to data.
MarkovML implementation at Spectrum Labs automated the data quality checks, saving them countless hours each week that were otherwise wasted in manual checks using Python notebooks and Excel spreadsheets.
Data quality measurement involves assessing the reliability, accuracy, validity, and wholeness of your organizational data. Your machine-learning operations rely on high-quality data inputs to generate usable insights.
MarkovML empowers your organization with advanced ML and AI capabilities that enable faster insights on data, workflow automation, data quality measurement, collaborative environments, and much more.