Your organization is ready for GenAI. But is your data?
Gartner* survey discovered only 4% of organizations are prepared—use our quick checklist to evaluate your GenAI readiness.
Where are you on the GenAI data readiness scale?
Actian’s GenAI data readiness checklist was developed to help organizations determine organizational readiness for GenAI adoption. These are the foundational questions you must ask and answer before investing in any AI initiatives.
Have the right conversations with the right stakeholders
See how each team—business users, data scientists, IT developers, data engineers, data stewards, and business analysts—must collaborate to define use cases, prep data, ensure quality, comply with regulations, and more.
Define data quality standards and policies
Manage data volume, optimize machine learning for use cases, and ensure the accuracy and quality of all data. Prevent poor-quality data from entering upstream and assess risks associated with poor quality.
Plan for data quality lifecycle management
Plan for the evolving nature of use cases and data sets, ensuring flexibility. Extend data quality to pipelines, conduct readiness tests, assess the cost of skipping steps, and track data quality over time.
Create a balanced strategy
Consider the pros and cons of adopting GenAI too quickly or slowly, weighing risks and costs of reworking workflows, falling behind competitors, and automating decisions based on faulty data. See the importance of data readiness.
The best leaders invest in data readiness to put AI to work
Three strategies to ace the data readiness checklist.
Focus on data quality and cleanliness
Poor data quality can lead to biased outputs, inaccurate predictions, and ultimately, loss of trust in AI systems. To address this, implement robust data cleaning pipelines that can handle issues such as missing values, outliers, and inconsistencies.
Prioritize data governance and lineage
A robust data governance framework ensures that data is accurate, consistent, and used responsibly throughout its lifecycle. It should define roles and responsibilities, establish policies for data usage, and ensure compliance with relevant regulations.
Verify accuracy of training data
A rigorous process for verifying and validating the accuracy of your training data includes not only checking for factual correctness but also ensuring that the data is representative and free from historical biases. Implement a multi-layered approach to data accuracy.
"Actian is a critical part of our infrastructure. Without it, we couldn’t do the processing and automation needed for our banking operations."
Learn about Actian data quality and governance solutions
AI-ready data is ongoing and continuous. Don’t let poor data quality or lack of confidence be the weak links in your AI strategy. Actian experts can help you establish the right data foundation to unlock the full potential of your data for GenAI applications.
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* Source: Gartner, We Shape AI, AI Shapes Us: 2023 IT Symposium/Xpo Keynote Insights, by Mary Mesaglio, Don Scheibenreif, Hung LeHong, Rita Sallam, 16 October 2023. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its a iliates in the U.S. and internationally and is used herein with permission. All rights reserved.