Reliable AI Models: 10 Data Reliability Checks Every Analyst Should Perform in 2023

  • Ndz Anthony
  • September 28, 2023

From the fictional AI character JARVIS in the Iron Man series to real-life AI models powering businesses, artificial intelligence has woven itself into the fabric of our everyday lives. But at the heart of these AI models lies a crucial component – data. The quality and reliability of this data can make the difference between an AI model that drives growth and one that falls flat.

As data analysts, we are the custodians of this data. Our role is to ensure that the data feeding into AI models is not just abundant, but accurate, consistent, and reliable. But what does data reliability mean in the context of AI? How can we ensure it, and why is it so vital?

In this post we get into the concept of data reliability, discuss its importance for AI models, and share the top 10 data reliability checks that should be in every data analyst’s tool box.

A Layman’s Approach to AI Models

Artificial Intelligence (AI) models have become an integral part of our everyday lives, powering everything from our social media feeds to our online shopping recommendations. But what exactly are these models? At their core, AI models are algorithms and software that:

  • Process information,
  • Learn from data,
  • And make decisions based on that learning.

They take in information from the world around them, process it, and use it to make decisions or take actions without being explicitly programmed to do so.

AI models can be built in many ways. Traditional models, like those simulating how an airplane flies, are built by people using knowledge of physics and engineering.

However, machine learning models are shaped by data – they learn from experience. You can think of a model as a function that accepts data as an input and produces an output. For example, a model might be given a harness size and estimate boot size.

There are many types of models, some simple and some complex. Simpler models are often the most reliable and easy to understand, while complex models can potentially perform impressive feats. The kind of model you should choose depends on your goal.

In the context of AI, data reliability is crucial. The quality and reliability of the data feeding into AI models can make the difference.

What is Data Reliability

Data reliability in AI refers to the assurance that the data used in model training is accurate, consistent, and will be available at the time of prediction. This is crucial because unreliable data can lead to overestimation of the model’s utility when run in a production environment, causing suboptimal model selection. “Unclean” data will also often lead to what we call data leakage in AI models.

There are two primary sources of data leakage in AI models:

  • Feature leakage and
  • Training example leakage.

Feature Leakage

Feature leakage/Target leakage occurs when the model is trained using data that will not be available during prediction. This could be a duplicate label, a proxy for the label, or the label itself. For example, including a ‘MonthlySalary’ column when predicting ‘YearlySalary’ or ‘MinutesLate’ when predicting ‘IsLate’ can result in leakage.

Training Example Leakage

Training example leakage/ Train-test contamination happens when there is improper sharing of information between rows of data. This could be due to premature featurization, duplicate rows between train/validation/test, non-i.d data, time leakage, or group leakage.

For instance, in time-dependent datasets, the system’s structure evolves over time, introducing systematic differences between the training and validation sets.

How to Make Data Reliable?

To ensure data reliability, it’s crucial to detect and eliminate leakage. This can be done by carefully selecting the features used for training the model, ensuring proper data splitting, and using techniques like cross-validation to validate the model’s performance.

And that’s why In the next section, we’ve got some practical data reliability checks that every data analyst should know.

Top 10 Data Reliability Checks for 2023

Data reliability is a continuous journey, not a destination. And the importance of ensuring data reliability continues to grow. Here are the top 10 checks that should be part of every data analyst’s toolkit:

  1. Quality Assessment: Before using data for any AI model, assess the four dimensions of data quality: accuracy, completeness, consistency, and timeliness. For example, in a customer behavior analysis, the data should accurately reflect customer actions, be devoid of missing values, maintain consistency across all records, and be updated at regular intervals.
  2. Validation: Data should conform to predefined criteria or rules. Consider a customer feedback scenario, ratings should fall within the acceptable range (1-5) and comments should be screened for inappropriate content.
  3. Verification: Cross-check your data with other reliable sources. If you’re analyzing sales data, verify it against financial reports to ensure accuracy.
  4. Profiling: Profiling involves understanding the structure, content, and quality of data. Profiling sales data might involve identifying patterns like seasonal trends, outliers like unusually large sales, and inconsistencies like sudden drops in sales.
  5. Governance: A robust data governance strategy ensures the availability, usability, integrity, and security of the data. Establish clear policies on who can access the data, how it should be used, and how to ensure its integrity and security.
  6. Lineage Tracking: Trace your data’s journey from its origin to its current state. In a customer data scenario, tracking the data collection process, transformations, and its usage in AI models is crucial.
  7. Reconciliation: Comparing data from different sources helps identify discrepancies. Discrepancies between sales data from the CRM and the financial system can be identified and resolved through reconciliation.
  8. Security: Aim to protect your data from unauthorized access, corruption, or theft. Implement strong access controls, use encryption, and regularly backup your data to ensure its security.
  9. Privacy: Comply with relevant data privacy laws and regulations. If you’re collecting customer data, ensure you have their consent, only collect data that’s necessary, and protect their personal information.
  10. Integration: Harmonizing data from different sources into a unified view enhances data reliability. In a global sales data scenario, ensuring harmonized and consistent data provides a complete and reliable view of global sales.

Implementing these checks in data handling processes ensures data reliability, leading to more accurate and reliable AI models.

Finally, the quality of your data also depends heavily on how it’s prepared and the tools you use for this process. There are many technologies available to help data analysts prepare their data, and the choice of tool can significantly impact the reliability and usefulness of your AI models.

Personally, I’ve found Datameer to be an invaluable tool in my data preparation toolkit. It offers robust features for data profiling, governance, and integration, making it a go-to resource for ensuring my data is ready for use in AI models. However, the choice of tool ultimately depends on your specific needs and comfort level.