Garbage in, garbage out: This is one of the oldest sayings in computer science. It was first used in a 1957 newspaper article about the US Army’s work with early computers—yes, it’s older than the Internet itself.
Almost 70 years later, this idea – that flawed input produces inaccurate output – is more relevant than ever. After all, AI models have to train on vast amounts of data, and the accuracy of their output hinges on the quality of the said training data.
If you don’t want to be among the 74% of companies who struggle to achieve and scale value from their AI projects, data quality should be on your mind from day one. Here’s why – and what it means in practice.
3 Reasons to Care About Data Quality in AI Development
Simply put, without ensuring data quality, you won’t be able to get an accurate, scalable AI solution. Here are some of the key ways how data quality can impact AI development:
- Model accuracy. Besides losses from making decisions based on flawed data, subpar model accuracy can also land you in hot water with regulators, damage your reputation, and undermine customer and investor trust.
- Risk of biased output. If your AI model deals with high-impact decisions like loan or insurance claim approval and recruitment, you’ll risk reproducing harmful algorithmic bias by not paying attention to data quality.
- Training efficiency. High-quality, properly cleaned data ensures the training process goes as smoothly as possible. Poor data quality, in turn, may require more computational resources for training and model optimization.
3 Data Quality Challenges to Address
Data is a primary concern when adopting both analytical and generative AI. Let’s break down the three main challenges you or your AI developer may have to overcome.
Data Provisioning
First and foremost, you need to collect a sufficient amount of data to comprise the training datasets. That may prove challenging if you lack quality first- and zero-party data.
In this case, you’ll have to collect data using web scraping or buy it from third-party vendors. Synthetic data, i.e., AI-generated data, is also a viable option in some projects. However, it has limitations because it doesn’t always accurately reflect real-world scenarios.
Data Consistency
All the training data has to follow the same standards across multiple parameters, from data formats and record fields to the level of detail (granularity). Discrepancies within the dataset may lead to incorrect pattern recognition and reduced model accuracy.
Data Labeling
If you’re planning to use supervised or semi-supervised machine learning, the inputs in your datasets have to be accompanied by labels that represent the desired outputs. Adding those labels is usually a time-consuming process that takes hundreds of hours of work.
How to Ensure Data Quality in AI Development
While data quality is seen as important almost universally (its significance is recognized by 89% of CIOs), only 22% have a data quality program in place. Yet, you need this program because ensuring data quality is a cross-functional undertaking that should be aligned with your long-term goals.
Define Data Quality Standards
Before you start working on your data quality strategy, establish the data quality standards across these six dimensions:
- Accuracy (no errors)
- Completeness (no missing values or gaps in records)
- Timeliness and currency (no outdated or irrelevant data)
- Consistency (no inconsistencies in data formats)
- Uniqueness (no duplicate records)
- Data granularity and relevance (the right level of detail)
Establish Data Governance Processes and Roles
A data governance framework isn’t just a set of standards. It also includes the how (processes) and who (roles) of ensuring the data quality at scale. Processes should encompass data quality management across the whole lifecycle, including standardized practices for:
- Acquisition (collecting data)
- Profiling (analyzing data for quality issues)
- Cleansing (fixing the identified quality issues)
- Transformation (converting data to align it with established quality standards)
- Monitoring (keeping track of data quality metrics)
As for the roles, ensuring data quality typically requires the involvement of:
- Data stewards
- Data quality analysts
- Master data management (MDM) analysts
- Data analysts
- Solution architects
- Data engineers
Final Thoughts
Data quality isn’t something to take lightly in AI development. However, ensuring it requires advanced expertise in both AI and data science, especially if you don’t have a data quality management strategy in place yet.
Need an AI development partner that takes data quality seriously? Consider S-PRO, an AI and data science company that prioritizes data quality at every step of the way, as proven by its 50+ projects.
