AI readiness: How to improve your data management strategy for AI
Learn how to enhance your data management strategy and build data infrastructure for AI. Plus get insights on data governance for AI and cloud data management.
Feb 24, 2025 • 5 Minute Read
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Anything you do with AI is only as good as the data you give it. If you want to see success with your AI initiatives, you need a solid data management strategy.
So what exactly does that involve, and how many organizations actually have the data infrastructure AI requires?
We surveyed 600 tech executives and leaders from various industries to learn more about their AI readiness across data and four other key pillars:
In this article, we focus on the Data pillar. Regardless of where you’re at in your journey, you can use these insights to strengthen your organization’s data readiness for AI.
Building the data infrastructure for AI: What every leader should know
A data strategy covers the tools, technologies, architecture, and resources your organization needs to leverage your data to achieve business goals and make informed decisions. It covers what data you use, how, and why throughout the entire data lifecycle.
When AI enters the picture, you need to update your data management strategy and infrastructure to support it. A modern data infrastructure for AI should improve the performance of your AI models and help you achieve organizational goals with:
- Data architecture that supports diverse data types and sources
- Low-latency storage
- Data pipelines with real-time data ingestion
- Clean, high-quality data for training
- APIs to leverage for data exchange
- The ability to scale with changing AI requirements
- Data governance, privacy, and security
The intersection of data and AI: Why data management is critical
Data is what makes AI possible. Without data to learn from, AI can’t generate predictions, make informed decisions, or generate any type of response. The better the data is—the more organized, accurate, and clean it is—the better the AI output.
In the rush to adopt AI, though, more than half of organizations (52%) are still in the process of upgrading their data infrastructure to support AI use.
The faster they can modernize their strategy, the better. Data infrastructure that isn’t ready to support AI will limit the technology’s effectiveness and overall ROI.
How to upgrade your data infrastructure for AI use
62% of leaders say their organization has a data management strategy that’s mature enough to fully handle the demands that AI will bring next year.
But what about next year, or the year after that? The AI and data landscape is constantly evolving. Whether you’re planning to build or buy AI solutions, you need a data strategy that can keep up with the changes. Here’s what you can do to modernize your data infrastructure and ensure you’re ready for AI.
Assess your current data landscape
Your data and AI strategy should be closely aligned with organizational objectives. What are you trying to achieve with AI? How can AI help organizations improve operations? What use cases are you exploring? What data will you need to achieve that?
Once you know the answers to those questions, conduct a data inventory. A data audit ensures your AI systems have everything they need to deliver. This inventory should uncover:
- What data you have: Do you have unstructured data in the form of emails, images, and audio files? Do you have structured data in databases?
- Where data lives: Is your data stored in databases, data lakes, or the cloud?
- What data you don’t have: Are you missing any data you’ll need to enrich your AI output? Are there any data siloes you need to break down across teams or departments?
Build a data platform to standardize data management
AI solutions pull from different data sources which may contain repetitive data or inconsistencies. Create a data platform to standardize data management for AI with a single source of truth.
A data platform (or data stack) is a technology solution that helps teams manage data across its entire lifecycle. This includes data collection, storage, preparation, analysis, transformation, governance, and observability. Data platforms reduce data silos and the need for multiple data pipelines, enabling creativity, enhanced analytics, and improved problem-solving in the process.
But data standardization goes beyond pipelines and platforms. It also includes standardizing the language people use to talk about data and AI within your organization. Build data literacy and create a shared vocabulary for more effective communication.
Start building data literacy in your organization with these courses:
Prepare your data for AI adoption
If you give AI poor data, don’t be surprised when you get poor results back. Ensure your data is ready for AI by:
- Removing or correcting inaccurate data
- Deleting duplicate data
- Adding missing data
- Identifying and removing bias
- Standardizing data formats
- Adding metadata
As time passes and models drift, reassess your data to ensure it's still accurate, clean, and bias free.
Learn more about assessing data readiness for generative AI in this course.
Manage data governance, security, and privacy
Data governance ensures you use data responsibly, mitigate risk, and stay compliant with regulations like the GDPR. To enhance your organization’s data governance for AI:
- Keep records of your data and how you’re using it, including data lineage and model development, to maintain transparency and trust
- Create data governance policies with clear responsibilities to foster accountability and compliance with data and AI regulations
- Define users and implement access control to maintain data privacy and security
Assess your cloud and cybersecurity readiness for AI investments
As you prepare your data for AI, shore up your cloud and security strategies while you’re at it. Why? AI and data touch nearly all aspects of technology.
For example, you can choose between cloud and on-premises for AI infrastructure. Yet less than half (46%) of organizations are confident their use of cloud services can handle AI for the next year. If you choose to go with cloud or a hybrid approach, ensure your cloud infrastructure can scale with and accommodate the processing power AI requires.
Luckily, organizations feel more prepared on the security side: 69% believe they have the cybersecurity and privacy best practices needed to handle AI. Working with AI opens the door to potential threats like data poisoning and manipulation, as well as data security and privacy concerns. Ensure you have formal processes to address these AI cybersecurity risks.
Learn how to assess your infrastructure readiness for generative AI.
Build data skills throughout your organization
Like with any technology, your use of AI is only as good as the people who know how to use it. Equip your teams with the data skills they need for AI and the MLOps practices to accelerate and simplify the machine learning process.
Get them started with these courses:
Explore more resources to boost your AI readiness
AI success relies on more than AI technology. It also requires a strong foundation in data, cloud computing, and security. Learn more about each pillar of AI Readiness and how to take your organization to the next level:
- How to enhance your AI strategy development
- How to bridge the AI skills gap with upskilling for AI
- How to mitigate risk with AI governance
- How to create an AI investment strategy to maximize ROI
Check out these content pieces for more: