The power of AI algorithms is well known, but perhaps sometimes overvalued. What truly fuels their intelligence is AI-ready data.
At Kezzler, we understand that the true power of AI isn’t just in sophisticated algorithms; it lies in the quality of the data fed into AI applications. For AI to unlock its full potential, your data needs to be “AI-ready.” In this blog post, we explain what this means, why it is important, and how companies should approach this.
What does AI-ready data mean?
Simply put, AI-ready data is data that is meticulously prepared, organized, and structured for optimal use in AI applications. It’s more than just having a lot of data; it’s about ensuring the data:
- Is accurate and complete: Free from errors, inconsistencies, missing values, and duplicates.
- Is clean and structured: Processed to remove anomalies and formatted in a way that AI models can easily digest, with clear schemas and relationships.
- Is relevant and contextual: Up-to-date and directly pertinent to the specific AI use case or use cases (irrelevant data can introduce noise and reduce model performance), with sufficient context and metadata to help both humans and AI models understand its meaning, origin, and how it can be used.
- Is accessible and governed: Stored in a way that allows easy access for AI systems, with robust security and clear governance policies to ensure integrity and compliance.
Why is AI-ready data important?
AI-ready data is the bedrock upon which successful AI initiatives are built, delivering a multitude of benefits:
- Enhanced accuracy and reliability: AI models trained on high-quality, AI-ready data produce more precise predictions and reliable insights, leading to better decision-making and preventing skewed or untrustworthy results.
- Accelerated AI development: By reducing the significant amount of time typically spent on data preparation, AI-ready data allows for faster model training and deployment, accelerating time to value.
- Unlocking business value: AI models thrive on rich, clean data. With AI-ready data, organizations can derive actionable insights, streamline operations, create new revenue streams, and gain a competitive edge.
- Ensuring trust and compliance: In a world where AI-driven decisions impact customers and operations, AI-ready data, coupled with strong governance and security, is crucial for maintaining trust, protecting sensitive information, and adhering to regulatory standards.
The importance of AI-ready data cannot be overstated. Think of it as providing high-quality fuel for a high-performance engine. Without the right fuel, even the best engine won’t perform optimally.
How to ensure AI-ready data?
Ensuring AI-ready data is a continuous journey that spans business strategy, data governance, technical execution, and the crucial element of fostering a data-driven culture across the organization:
- Clearly define AI goals and data needs: Clearly define what problems you want AI to solve and what specific data is needed to address them. This includes defining the types of data needed at what granularity level (e.g., master data, transactional data, event data, or a combination), at what frequency, and from what sources they can be captured.
- Conduct a comprehensive data audit: Assess your current data landscape to understand its strengths, weaknesses, and readiness for AI. Document and evaluate all existing sources and look for data quality gaps.
- Build a strong data governance framework: Implement clear policies for data ownership, standardization, security, and privacy to maintain data integrity and compliance, and ensure ethical AI use. Implement systems to document your data assets (metadata) and create searchable catalogues.
- Cleanse, prepare, and structure data: The end goal should be to have data “born AI-ready”, with all data sources delivering readily prepared data to AI applications through a central repository. Realistically, most companies, especially those with a lot of legacy infrastructure, need to spend resources on transforming data into structured, consistent formats suitable for AI. To reduce the manual burden, AI-powered tools can be employed for tasks such as anomaly detection, automated error correction, and smart ingestion of missing values.
- Ensure a continuous flow of fresh, reliable data: Build robust, automated pipelines to ingest data into a central repository. Implement systems to track and manage changes to datasets and integrate automated checks at various stages of the pipeline to identify and flag inconsistencies, errors, or anomalies in real-time. Ensure sufficient storage and processing capacity to scale to your needs.
- Monitor and refine continuously: Continuously monitor data quality and track key metrics with alerts for deviations. Foster a data-driven culture and emphasize everyone’s role in contributing to AI success. Educate employees on the importance of data quality and establish feedback loops to drive continuous improvement. Regularly align with business needs, refine data preparation processes, and update data assets to maintain relevance.
At Kezzler, we believe that by systematically addressing these areas, companies can transform their raw data into a reliable, high-quality asset that effectively fuels their AI initiatives, leading to better insights and business outcomes. We also expect to see more widespread use of standards, such as GS1’s events-based traceability standard EPCIS, to ensure that data is “born AI-ready”. Ensuring this is not just a technical task; it’s a strategic imperative for any organization looking to thrive in an AI-powered future.
For more insights into how Kezzler can help you with data integrity and traceability, explore our Resources page.
You might also find this interesting
- Our blog on “Why transformation events are the future of traceability”
- Our blog on “GS1 2D barcodes: the future of data sharing”
- A deeper dive into EPCIS with our solution brief “The power of GS1’s EPCIS 2.0 standard”