This is Part 3 of a multi-part series about Data Preparation for AI-driven Analytics.
We can agree that it's been said enough that data quality is important. And we don’t need to explain to you why feeding your AI model poor-quality training data, and then validating it with more poor-quality data, is a bad idea. What matters is knowing how to spot common data issues, how to fix them, and how to prevent them from happening again.
Preparing data for AI teaching and validation processes requires addressing several common issues to ensure data quality and reliability
In Part 1 and Part 2 of our Data Preparation Essentials series, we covered why clean, transformed, and enriched data is critical for AI success—and how proper training, testing, and refinement lead to better model accuracy. In this post, we’ll look at the most common data quality challenges and share practical steps to address them.
Data fields that are empty (NULL), missing or undefined, leading to gaps in analysis
Duplicated data records that can skew results or inflate counts
Values stored in the incorrect data types, causing errors in processing
Unusually high or low values that may distort trends or averages
Variations in how data is presented and formatted, reducing data reliability
Whether you build or borrow your datasets, validation is necessary
We’ll also provide some helpful code samples for both SQL and Python to help get you started. When data quality is a priority, AI-driven analytics perform better and deliver more reliable insights.
Identify missing values to ensure unbiased analyses, accurate models, and maximum statistical power
Missing values, often represented as NULLs, are a common issue in datasets. They can result from entry errors, incomplete data collection, or system failures.
If a large portion of a dataset contains missing values, it can skew the results of statistical tests or machine learning algorithms, leading to misleading conclusions. Additionally, missing values complicate data preprocessing, often requiring extra steps to address them properly.
Where to start:
Assess the extent and pattern of missingnes s using visualizations (e.g., heatmaps, missing value matrices) and summary statistics to understand how much data is missing and whether it follows any patterns.
Choose an appropriate handling strategy , depending on the context, to remove, impute, or flag values
Document your approach to handling missing data to ensure transparency and reproducibility in analysis or modeling.
The simplest approach is to run a query to identify where there are missing (NULL) values. You can then replace each identified NULL with a single value. Depending on the type of field, you can replace strings with specific text, numbers with 0 or another single value, and dates with the current date or the duplicate creation date for a given entry. In some cases, when N/A is a product of a faulty data copy/creation process, you can use mechanisms like JOIN or LOOKUP to retrieve missing values from your source systems.
After handling missing or default values, it’s important to check how common they are in a given column. If a large percentage of the values—say, 30% or more—are missing or set to a default like 0, the column may be too statistically skewed to be useful for AI or analytics. Running a simple query to calculate this percentage can help you decide whether the column is a good candidate for modeling.
Dedupe datasets to ensure accurate counts, achieve unbiased summaries, and avoid overfitting your models
Duplicates are unintended multiple entries of the same data point in a dataset. They can arise from data entry errors, merging datasets without proper checks, or system glitches. Datasets that contain multiple identical records can artificially inflate totals, distort summary statistics, and even lead to overfitting in machine learning models.
Duplicates also pose problems during data integration, where unique identifiers are essential for accurately merging or joining records.
Where to start:
Identify and review duplicates using tools to detect and investigate repeated records.
Remove or consolidate duplicates by dropping exact matches or merging partial ones with grouping and aggregation.
Since the duplicate rows are identical, don’t stress about which one to keep — use MIN, ROW_NUMBER, or drop_duplicates() to your advantage!
Prevent duplicates at the source through validation rules, unique constraints, or deduplication in data pipelines.
Correcting data types can reduce errors in data processing, analysis, and visualization
Incorrect data types occur when data is ingested and stored in a format that does not match its intended use, caused by improper data entry, incorrect data import settings, or lack of data validation. Common examples are dates stored as strings or numerical values stored as text.
These mismatches can cause issues, such as errors when performing calculations or unexpected results during analysis. They can also slow down database operations and data processing, leading to inefficiencies and higher computational costs.
Where to start:
Audit and validate data types using tools or code to ensure each column matches expected formats.
Convert columns to the correct types with functions like astype() or pd.to_datetime() while handling errors.
Standardize data entry and ingestion to prevent incorrect types from entering the dataset in the first place.
Effectively managing outliers can lead to cleaner data and more reliable outcomes
Outliers are data points that differ significantly from the rest of a dataset. They can occur naturally due to normal variability, or they may result from errors in data collection or entry, like measurement mistakes or incorrect input. Even a single outlier can have a large impact. It can distort key metrics like the mean and standard deviation, which may lead to misleading conclusions.
Outliers can complicate key steps in data preparation, such as normalization, scaling, and feature engineering. If left unaddressed, they can distort the range of values, reduce the effectiveness of algorithms like k-means or linear regression, and lead to biased or unstable model performance.
Where to start:
Detect outliers using statistical methods like IQR, Z-scores, or visual tools such as box plots and scatter plots.
Handle outliers based on context by removing, capping, transforming, or treating them as a separate category.
Validate and document your approach to ensure transparency and account for whether outliers are errors or meaningful data.
Create consistent formatting to make aggregation and analysis more accurate
Inconsistent formatting happens when the same type of data is represented in different ways within a dataset. Common examples include mismatched date formats, inconsistent capitalization, or unexpected special characters.
These types of inconsistencies seem trivial but make it harder to analyze or combine data accurately and often lead to errors or extra cleanup work. It can also disrupt data integration processes, where consistent formatting is essential for correctly merging or joining datasets.
Where to start:
Audit your data fields to identify inconsistent formats , so you know exactly what needs to be fixed and where issues exist.
Define and document standard formats for each data type , ensuring consistency across your dataset and setting clear rules for future data entries.
Create and apply transformation rules to standardize values , enabling reliable sorting, filtering, and AI-ready analysis.
Build or borrow your datasets, but always validate
If you’re creating your own dataset, everything discussed in this blog post—handling NULLs, deduplication, correcting data types, managing outliers, and ensuring consistency—applies from start to finish. You're responsible not just for the structure of your data but also for its completeness and integrity.
On the other hand, using a pre-existing dataset can save time and reduce initial effort, especially if it comes from a trusted source or has been curated for similar AI or analytics use cases. That said, whether you build or borrow, validation is essential. Remember to make sure to separate your testing datasets and validation datasets!
Depending on your project, sourcing data may get you moving faster, but public datasets can still contain bias, outdated records, or inconsistent formats. If you're sourcing data externally:
Look for accompanying documentation, licensing details, and data dictionaries that clarify how the data was collected and maintained
Use profiling tools to understand the shape and distribution of the data
Always run checks for missing values, unexpected types, or anomalies before feeding it into an AI model.
Even “clean” data deserves a second look—assume nothing, validate everything.
When it comes to tooling, there’s no single solution, but there are clear patterns. The key is consistency. Pick tools that help you catch problems early, document fixes, and apply the same rules across your data pipeline.
Core data cleansing can be achieved using flexible, low-level options like SQL and Python (especially with libraries like Pandas and NumPy)
Need to validate? Use tools like Great Expectations, Soda, and Pandera to help you define and enforce data quality rules
Store and govern data using modern warehouses like Snowflake, BigQuery, and Delta Lake support schema validation and versioning out of the box
Download code samples for all the steps above here. Samples are provided in both SQL and Python.
Ensuring your data is AI-ready means more than just removing NULLs or fixing the occasional typo. It requires a consistent, structured approach to cleaning and validating your data.
There’s a reason these are considered “common problems”—any organization working with data will run into them sooner or later, and not always because of manual errors. In many cases, these issues arise from changes in upstream systems, unanticipated dependencies, or well-intentioned updates that have unintended consequences. Someone shuts off a system without realizing it feeds other processes. Someone makes a formatting change that breaks your formulas. It happens.
Proactively preparing your data helps reduce friction later in the AI development pipeline:
Audit your datasets regularly
Apply consistent formattin g and business logic rules
Use validation queries and basic data profiling techniques
Standardize inputs and create clear documentation
While having the right monitoring in place helps, the reality is that many problems are still flagged by humans first—someone noticing that “something feels off.” Even with the best preparation, data issues will still happen.
That’s why it’s so important to know where to look and what to look for:
Know where issues are most likely to crop up (e.g., manual entry fields, API feeds, third-party integrations)
Set up anomaly detection or alerts around critical metrics
Build a habit of root cause analysis —don’t just fix symptoms
Use automated checks for common failure points
Data prep might not be the flashiest part of the AI workflow, but it's the foundation on which everything else is built. Your future AI models—and your future self—will thank you.
