Data is the fuel of artificial intelligence. Without data of sufficient quantity and quality, even the most sophisticated algorithms aren't able to deliver relevant results. This is even truer when you're looking to build predictive indicators or obtain prescriptive information for your business.
Data Quantity
It takes a substantial volume of data to train successful AI models. As shown by a study by Google¹, Deep Learning models continue to improve as they are provided with more training data, without plateauing. Every additional piece of data counts, highlighting the importance of data collection and centralization within the enterprise, even for SMEs (see previous article in this series: The Crucial Role of Infrastructure in AI. However, it is essential to prioritize data quality over quantity.
Data Quality
Numerous studies have shown that incomplete, erroneous or poorly structured data will mislead AI, and that there is a direct correlation between data quality and the success of AI projects. Data quality is therefore even more important than data quantity: high-quality, well-labeled datasets can lead to successful AI models, even with a reduced number of data points. Conversely, low-quality data requires more sophisticated AI models to make sense of disorganized sets.
FAANG companies (Facebook, Amazon, Apple, Netflix and Google) are an example of successful AI implementation, largely thanks to the control and trust they place in high-quality internal datasets. These companies are using AI to personalize user experience and improve business strategies, demonstrating the transformative power of AI when combined with high-quality data.
The focus on data quality for any type of business is reflected in Ataccama's observations on the relationship between data quality and AI success² and IBM Research, which highlights the importance of data preparation in AI and how improving data quality leads to more accurate models and better decision-making³.
A significant proportion of time spent on AI projects is therefore devoted to data preparation and quality management. Companies are increasingly recognizing the need to invest in robust data quality measures to ensure the reliability and accuracy of AI systems.
These investments may seem heavy and reserved for large companies, but this is not necessarily the case. With thoughtful data governance and the right tools, producing and preserving quality data is not out of reach for SMEs.
Data Governance
To make the best use of corporate data, it is crucial to implement good data governance practices:
- Guide users through data entry with glossaries and business rules
- Automate data consistency and integrity checks
- Implement data review and correction processes
- Appoint "data stewards" responsible for quality in their areas of responsibility
Data governance tools help industrialize these best practices. They detect anomalies in real time and escalate them to data stewards for action. They also provide a complete audit trail to track modifications.
Conclusion
Data quality isn't just a technical necessity; it's an imperative for the successful deployment of AI, and a clear strategic advantage for managers who can then have greater confidence in the results produced by AI. It is by prioritizing data quality that AI's potential can be unleashed and become a driver of innovation, efficiency and productivity.
Prochaines étapes
Find out in our next article why companies need to prepare for the AI revolution now, especially SMEs. And don't hesitate to assess your AI maturity with our online scorecard!
Our experts can help you implement effective data governance, supported by the right tools.
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[1] : Study "Revisiting Unreasonable Effectiveness of Data in Deep Learning Era", Google, 2017 (https://arxiv.org/abs/1707.02968)
[2] : Ataccama on the importance of data quality in AI: https://www.ataccama.com/blog/why-data-quality-crucial-for-successful-ai-implementations.
[3] : Overview of the Data Quality for AI (DQAI) framework from https://research.ibm.com/projects/data-quality-in-ai
Data Quantity and Quality: The Two Pillars of AI Projects