Is your data ready for AI? 20 questions to ask
Subscribe via Email
Subscribe to our blog to get insights sent directly to your inbox.
Having AI-ready data is no doubt one of the biggest barriers to AI adoption. More than half (63%) of organizations lack or are uncertain about having the necessary data management practices for AI, according to Gartner.
Many companies experience a bit of a reality check when they discover their data is disorganized, scattered, and in need of Marie Kondo stepping in to ask: "Can generative AI spark joy by helping us fix a specific business challenge…and do we have the data ready to fuel that spark?”
Unlike traditional AI, which often demands collecting country-sized datasets for training from scratch, today's large language models (LLMs) offer a significant head start: multiple foundational models already exist. This is a huge advantage, especially for problems rooted in natural language and related processes. Keep in mind that LLMs won't simply replace all traditional AI approaches; their strengths lie in specific use cases.
This, of course, doesn't mean your data can suddenly become the equivalent of a dusty floppy disk stored away in the attic. Instead, the focus shifts toward understanding and using the data you have.
To do this, you need to figure out what data is relevant and available to fuel your AI solution and uncover where that data is stored, how it is processed, and what data governance is in place.
Simple ways to get your data AI-ready
In order to get your data AI-ready, it’s important to structure it in a way that will get you reliable results.
The previously mentioned Gartner study of 1,203 data management leaders in July 2024 underscores the danger of overlooking the distinct data requirements of AI compared to traditional data management, warning that such oversights will jeopardize AI initiatives. Gartner forecasts that through 2026, 60% of AI projects lacking AI-ready data will be abandoned.
To figure out if your data is ready for AI, here are 20 questions you need to ask, organized by four distinct categories.
Category 1: Data basics
1. What data do you have?
The first step in getting your data AI-ready is to understand what data you have available. Prioritize which datasets you think might be the most helpful to you based on their relevance, quality, and accessibility.
2. Where are you gathering data from?
It’s important to know where your data is coming from so you can be aware of reliability, potential biases, and any legal or ethical constraints on its use in AI models.
Public data may be easily accessible but less specific, while private data can be highly relevant but requires careful governance. The APIs from which you collect data may change, which can lead to misunderstandings or errors if not handled correctly.
3. How discoverable is your data?
Creating a well-documented and searchable database helps make sure your AI projects don’t get delayed or become incomplete due to missing information.
4. How much data do you have?
By assessing your data quantity, you can determine the appropriate infrastructure for training and deploying AI models and identify suitable algorithms.
5. Is your data labeled? If so, what is the process for this?
Labeled data is the foundation for many AI algorithms, and understanding the existence and quality of labels, along with the labeling process, is an important step to consider.
6. What are your latency requirements for data processing?
The required speed of data processing will dictate the architectural choices and technologies needed for AI applications that require timely insights or actions.
Category 2: Data quality and trustworthiness
7. Is your data immutable?
Immutability can simplify debugging and protect the integrity of the data you’re using for training AI models, leading to more reliable results.
8. Is your data processing reproducible?
Reproducible data processing ensures that the same data inputs will always yield the same outputs, which is important for the reliability of AI pipelines and model training.
9. Is reporting/monitoring in place for your data pipelines?
Monitoring data pipelines allows for proactive identification and resolution of data quality issues or failures that could negatively impact AI model training and performance.
10. Are your data pipelines tested? If so, how?
Testing data pipelines assures that the data delivered to AI models is accurate, complete, and in the expected format, preventing errors and improving model reliability.
11. How is your training data collected?
The method of data collection and the separation of training and testing sets are important for building AI models that generalize well to unseen data and avoid overfitting or bias.
It’s important to know where your data is coming from so you can be aware of reliability, potential biases, and any legal or ethical constraints on its use in AI models.
Category 3: Data governance
12. Which data platforms is your company using?
The choice of data infrastructure impacts data accessibility, processing capabilities, and scalability for AI workloads. By understanding the rationale behind the architecture, you can be more confident in its ability to support AI needs.
13. What access controls have you implemented?
Granular access controls are important so that only authorized personnel can access specific data, safeguarding privacy and preventing misuse.
14. Is your data stored securely?
Protecting sensitive data is important, especially in AI models that could inadvertently expose patterns or insights.
15. Is your data infrastructure compliant with relevant regulatory frameworks?
Compliance with regulations is non-negotiable when dealing with sensitive data and helps to guarantee that AI development and deployment adhere to legal and ethical standards.
16. Do you have clear owners of datasets and data pipelines?
Defined ownership helps to make sure a specific team or team member is responsible for your data's quality, maintenance, and accessibility.
Category 4: Data experience
17. Have you considered the cost of processing data?
Efficient data processing is necessary for managing the costs associated with AI, especially as data volumes and model complexity increase.
18. Do you use data for machine learning and statistics?
Prior experience with ML and statistics suggests a foundational understanding of data-driven processes that can be used for more advanced AI initiatives.
19. How are models deployed, as a web service, embedded in an application, or some other way?
This impacts the model's scalability and integration complexity, and as a consequence, its ability to provide results.
20. What are the end products produced with your data?
Knowing how data is currently used can highlight areas where AI could provide significant improvements or create entirely new data-driven products and services.
The prospect of AI innovation might seem daunting, but by breaking it down into small steps, you can realize tangible value. Start by identifying a specific business problem that needs to be solved.
Four phases of AI data readiness
To make AI innovation more accessible, let’s break it down into manageable phases of AI data readiness that provide value at each point
Phase 1: Asking the right data questions
Explore your current data to identify potential uses, starting with a specific business problem, such as:
- categorizing customer feedback
- building an internal knowledge base
- summarizing customer interactions.
Analyze your data to find relevant information and patterns.
Phase 2: Building the data model
Tailor AI solutions using existing resources like pre-trained models, open-source tools, and frameworks. Consider fine-tuning for specific tasks, using Retrieval-Augmented Generation (RAG) to connect to internal knowledge, or agentic capabilities for autonomous task execution.
Phase 3: Evaluating data quality and accuracy
Build an evaluation framework along with the model development. This serves a similar purpose as a test dataset for classical ML. Implement a continuous feedback loop to monitor model behavior and identify errors. Utilize human-in-the-loop evaluation with subject matter experts to create evaluation datasets, assess accuracy, and build trust by identifying potential inaccuracies.
Phase 4: Planning for production and growth
Consider aspects for production readiness, including:
- data consistency
- security, privacy
- ownership
- storage
- pipelines
- integration with existing platforms
- monitoring
- scalability
- long-term maintainability of the AI system.
Address these questions proactively for a smoother transition from PoC to a functional data product.
Small steps in AI data readiness lead to significant value
In the next three years, 92% of companies expect to invest more in AI. However, despite this widespread investment, only a tiny fraction, 1% of leaders, consider their AI deployment mature, meaning it’s fully integrated and driving significant business outcomes.
The prospect of AI innovation might seem daunting, but by breaking it down into small steps, you can realize tangible value. Start by identifying a specific business problem that needs to be solved, and use these 20 questions as your guide to make your data ready for a successful AI implementation.
Don’t let the fear of the unknown hold you back; intelligent automation is more accessible than ever.
Maria Knorps is a Principal Consultant: Data Engineer at Modus Create, uniquely blending academic credentials in mathematics and numerical modeling with extensive industry experience in software development and data science. She leads diverse projects, excels in technical execution and fosters collaborative environments as a squad lead. Maria actively contributes to the open-source community, shares her expertise through conference speaking, and engages with GenAI and software development initiatives, consistently delivering innovative, high-quality solutions.
Related Posts
Discover more insights from our blog.
