Data integrity refers to reliable and accurate data with consistency and context for confident decisions and improved business agility.
What is Data Integrity?
Although the definition of data integrity changes with its context, for the ML world, it refers to trusted data with consistency, maximum accuracy, and context that results in faster and confident decision making.
ML models face data integrity issues due to several reasons including,
Sometimes, production models struggle while predicting, as feature input is unavailable at inference time. At other times, prod models observe a lot more missing data than the training data. Such situations occur due to coding errors that allow accepting null value inputs for an optional field and induce missing data inconsistencies.
Feature input is crossing expected bounds. Different range violation examples include numerical input variables with typos such as age, incorrect country name entered.
Data pipeline points to an older version of the table due to unresolved version conflict.
Changes in feature code
A few decisions lead to inconsistencies and senseless feature codes that generate garbage. For example, the model was trained to offer a 40% promo discount, but the marketing campaign revised the offer and availed 100% promo discount resulting in meaningless outcomes for dependent feature code.
Variations in data types of inputs. It is observed when data wrangling operations misalign column order.
Why Data Integrity Matters?
In the modern organizational context, data integrity is prevalent and ensures:
- Meaningful insights
- Enhanced analytics
- Better decision making
- Improved business agility
ML algorithms learn from data. When this data contains inaccuracies and bias, the model starts behaving unfairly. Moreover, these models will propagate and exaggerate these inaccuracies resulting in potentially damaging outcomes that negatively impact the end user.
A lack of data integrity leads to garbage in, garbage out, or GIGO. If ML models are trained on inaccurate data, it will result in faulty predictions. And as these ML projects scale, the 'garbage in' issues can cause large-scale 'garbage out'.
Ensuring Data Integrity using AI Observability
Getting data integrity right is a key to successful and business-critical AI initiatives. One of the best methods to ensure data integrity is using an ML monitoring tool/platform that helps detect data inconsistencies before they hit model performance.
The Censius AI Observability Platform is one such platform that helps in data quality monitoring and ensures data integrity consistently. Our monitors help recognize missing values, data range issues, and type mismatch issues instantly for your ML projects.
Such an end-to-end data integrity monitoring solution makes it a breeze for ML engineers to confidently ship high performing ML models to production.
Why Data Integrity is key to ML Monitoring
The success of AI comes down to the integrity of the data