Challenges in Data Preparation for AI/ML

With Artificial Intelligence (AI) and Machine Learning (ML) solutions, effective data preparation stands as a critical foundation. The process of collecting, cleaning, and categorizing data, although challenging, is vital for successful AI/ML project outcomes. Businesses and organizations face significant hurdles in ensuring that their data is ready for the sophisticated algorithms that drive AI and ML innovations.

The lack of proper data preparation and governance is one of the biggest bottlenecks. A Database Trends and Applications report found that 57% of organizations discover data quality issues while implementing next-generation data products, and 49% discover them in database mergers, migrations, and consolidations. These data preparation challenges are upstream from the solution and will impact all AI/ML projects.

Data Quality

Omissions, duplicates, missing values, and inconsistency impact model performance. You can end up with suboptimal accuracy and reliability of your AI/ML insights, resulting in user complaints, poor adoption, and incorrect decision-making.

Data Integration

The data required to surface and evolve critical insights is scattered throughout multiple systems, databases, and formats. An MIT Technology Review Insights report found that 81% of organizations operate 10 or more data and AI systems, and 28% use more than 20. That’s not counting all the data hidden in your organization’s SaaS applications, Excel spreadsheets, and other locations (both expected and unexpected). Integrating this data is a complex and time-consuming process, especially when you’re dealing with inconsistent formats and structures.

Data Cleansing and Preprocessing

Raw data requires preprocessing and cleansing to evaluate outliers, handle missing values, normalization, and address other data quality issues. Your organization needs the right resources with specialized data engineering skills and domain knowledge.

Data Volume and Scalability

AI/ML solutions require high data volumes to deliver meaningful and useful insights. Poorly designed infrastructure won’t have the scalability to handle growing data efficiently, nor will it be able to do so within reasonable timeframes. Additionally, the wrong architecture for your data management can also result in cost overruns.

Data Governance and Privacy

Is your data governance strategy suitable for AI/ML solutions? Privacy protection and compliance challenges are prevalent, especially with sensitive and personal data.

Data Bias and Representativeness

Bias in your training data can lead to biased outcomes. Without representative and diverse data, your AI/ML solutions make skewed predictions and inaccurate insights. These can often be hard to spot and can expose your solution to both legal and ethical scrutiny.

Data Security and Protection

Are you ready to safeguard large data volumes against unauthorized access, breaches, and cyber threats? You need robust security measures in place throughout the data lifecycle, in transit and at rest. This allows the right people to access the data at the correct times.

Data Accessibility and Availability

How easy is it to access and acquire the relevant data that the AI/ML solution requires? Siloed, inaccessible, or incompatible formats hinder data collection for these projects. Further, these disparate data sources require a strategic vision to bring them together in a meaningful way.

Now that you know the data challenges associated with AI/ML, learn how to create a robust data foundation. Get our white paper “Is Your Data Ready for AI/ML” to get more out of your pilots and proof of values.