How to enhance data quality for AI & ML initiatives

Jun 25, 2024 12:23:12 PM

 

Any business you build on top of AI is only as solid as the data behind those algorithms. With the pressure of rushing towards AI gains, it’s easy to get ahead of yourself. Here’s how to make sure the data powering your AI & ML initiatives stays clean.



Why clean data matters for AI

Since you’re here, we probably don’t need to convince you much about the value of high-quality data. It’s the basis of effective operations and sound decision-making – the foundational building blocks of a sustainable competitive advantage.

With the current AI hype, the importance of data quality has skyrocketed. The whole point of AI is to be able to achieve creative outputs in a superhuman way, without the time and physical constraints of human creativity. But you can’t achieve that without clean, well-ordered data. AI in its best form should drive insights and innovation - but if it’s trained on messy data, you won’t be able to trust what comes out of it. Garbage in, garbage out. We speak of AI hallucinations when we receive incorrect or misleading output. These hallucinations quickly become problematic when you make business decisions based on this output.

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Data quality and integrity are common challenges with AI

Is this the case for your data? If so, look for missing data, duplicate entries, formatting inconsistencies, and outdated or expired data. If you have fragmented data architectures, data silos, and incompatible systems, these will be a roadblock to success, and a larger one the more powerful your AI implementation at that.

Not fun. Outcomes include:

  • unreliable AI data models
  • incorrect and biased predictions
  • friction in developing AI/ML processes
  • wasted resources and increased costs
  • limited benefits from AI implementations
  • even poorer data quality, that continues do devolve (yes, it’s a vicious cycle)

 

Improve the data that runs your AI models

There are many ways to ruin data for AI & ML use cases, but there are also a number of remedies that take you forward. Much of it comes down to technological enablers. In this case, we mean the data platform capabilities that are key to ensuring AI model accuracy, performance, and scalability.

Data cleansing

Modern data product platforms can detect and correct errors, complete missing values, remove duplicate data, and standardize data formats for you. These automated features also enable you to detect and manage outlier data that could skew AI model training.

Data profiling and monitoring

Continuous data profiling helps you understand data structure, relationships, and anomalies, while real-time monitoring tools provide immediate alerts on data quality issues for quick corrective actions. Make sure you can also access historical data analysis to track changes in data quality over time and identify trends.

Data integration

Seamless data integration ensures comprehensive and unified datasets by integrating data from diverse sources, while efficient ETL processes aggregate data without compromising integrity. Additionally, data lineage tracking brings valuable transparency and traceability by monitoring the origin and transformation of data used in AI implementations.

Data labeling

For AI and ML use cases, annotation tools provide efficient and accurate data labeling, which is critical for supervised learning tasks. Clear standards ensure consistent and correct labeling by human annotators, while AI-assisted automated labeling enhances labeling speed and accuracy.

Data enrichment

To enhance the dataset’s richness and diversity, you want to incorporate additional relevant data from external sources. Synthetic data generation augments datasets and is especially useful when data is scarce or imbalanced. AI for AI, anyone?

Data governance

Strict data governance policies, role-based access control, and detailed audit trails and compliance reports are a few more ways to improve data quality for AI and ML purposes. These tap into the very core of data integrity, security, and compliance. It’s about avoiding, detecting, and correcting data issues and ensuring that the data used for AI and ML can be trusted.

Invest in a resilient data warehouse

We’ve designed Agile Data Engine to help you build and maintain resilient data warehouses that can handle AI training models without a hitch. Our DataOps platform automates CI/CD pipelines to make managing all the tasks listed above easier, resulting in higher data quality, restored trust, and AI implementations that drive value.

Want to see for yourself?

Send us a message and let's walk through how ADE supports data quality in AI/ML implementations.

 

 


 

What did we miss?

High data quality is a sum of many things, and we definitely haven’t mentioned everything here. What’s the number one thing to pay attention to in your mind?

However you plan to clean up your data, expect the effort to result in more robust AI models and faster time-to-market with increased accuracy and enhanced predictive capabilities.