Effective AI strategies depend on a strong data foundation
AI has incredible potential, but it can’t run on fumes. It needs data, the right kind of data, structured and managed properly. The trick is managing and refining it so that AI can actually deliver real results.
We’re seeing data volumes skyrocket. Over the last five years, global data volume has doubled. Unfortunately, most companies can’t use even half of what they collect, about 68% of enterprise data sits untapped, gathering digital dust. The problem? It’s complicated, messy, and largely unstructured. Roughly 80-90% of this data comes in forms that machines can’t easily read, emails, images, videos, PDFs. At the same time, companies are expected to process and deliver this data at lightning speeds. Some use cases require response times of less than 10 milliseconds, which is ten times faster than the blink of an eye.
If your data ecosystem can’t keep up, AI projects stall out. To win with AI, companies must build data systems that can handle increasing data volumes, diverse formats, and real-time delivery without breaking down.
Core principles of great data management
Managing data is like designing a factory. If every tool and process isn’t working together, the whole operation slows down. In order to avoid that, there are three critical principles to focus on: self-service, automation, and scalability.
Self-service means giving teams access to the data they need without constant back-and-forth with IT. Imagine a system where your data is at your fingertips, easily searchable and ready to go. This removes friction and helps people to move fast, exactly what innovation requires.
Automation takes this further. Instead of relying on manual processes, core data tasks, like cleaning, governance, and monitoring, should be baked into the system. Automation eliminates human error and helps your team to focus on building the future, instead of fixing the past.
Scalability is where it all comes together. Your data infrastructure should expand with your ambitions. If your system can’t grow as your data grows, you’ll hit a wall. Scalable systems make sure you can handle tomorrow’s challenges without breaking today’s processes.
Producing high-quality data requires better onboarding and governance
Not all data is useful, and bad data leads to bad decisions. That’s why data producers, the people and systems responsible for generating and organizing data, are so key. Their job is to make sure data enters the system in the right format, at the right time, and in the right place. This starts with onboarding, which involves integrating data from various sources into a unified ecosystem.
A self-service portal can simplify this process, offering a centralized control hub for storage, access permissions, versioning, and business data catalogs. The goal is to reduce complexity and make it easier for producers to maintain quality and consistency.
Governance is the backbone of high-quality data production. Depending on the organization’s needs, governance can be centralized (with a single platform enforcing rules) or federated (with local teams managing their own systems under a shared framework). The right approach varies, but hybrid models, a mix of centralized control and localized flexibility, are gaining popularity for their ability to balance consistency with adaptability.
“The bottom line: good governance is what enables reliable, high-quality data to fuel AI innovation at scale.”
Data consumers need simplified access to reliable data
The real magic happens when data consumers, data scientists, engineers, and analysts, can access and experiment with data without being slowed down by technical hurdles. For them, the challenge isn’t gathering data; it’s making sense of it quickly and reliably.
Centralized compute within a data lake is one solution. A data lake stores raw data in its native format until needed. Think of it as a giant pool where data of all kinds, structured, unstructured, and semi-structured, can live together. When using a single storage layer, you can reduce the complexity of managing multiple systems and avoid data sprawl.
To make this system more efficient, companies should implement a zone strategy:
- Raw zone: For unprocessed data in its original form, useful for exploration and experimentation.
- Curated zone: For cleaned, organized data that meets high standards for governance and quality.
This approach lets consumers work in flexible environments without compromising on security or accuracy. It’s the best of both worlds: freedom to innovate, backed by a solid foundation of reliable data services.
Simplicity and trustworthiness are key to AI-driven innovation
Complexity kills momentum. The best AI strategies prioritize simplicity, both in how data is managed and how it’s consumed. If your team is spending too much time searching for or cleaning data, you’re losing ground. Simplifying these processes reduces friction and boosts trust in the system.
Trustworthiness is key. Teams need to know the data they’re using is accurate, consistent, and available when they need it. That trust supports rapid experimentation without fear of breaking things. Over time, a culture of trust and simplicity drives exponential innovation, allowing teams to focus on the next breakthrough rather than getting bogged down by the last problem.
Scalable and enforceable data ecosystems give companies a competitive edge, especially in fast-moving spaces like AI. Prioritizing data quality and access means you’re setting up your business for long-term success.
Key takeaways
- Comprehensive data foundations: In order to drive AI innovation, make sure your data is both accessible and reliable. Leaders should invest in data management systems that handle high volumes, diverse formats, and real-time demands to unlock actionable insights.
- Self-service and automation: Support teams with tools that enable direct data access and automated processes. This reduces manual intervention, cuts errors, and accelerates the pace of innovation across your organization.
- Streamlined governance: Adopt clear data governance protocols that suit your business, whether centralized, federated, or hybrid. Such models ensure consistent, high-quality data onboarding and organization, making your data trustworthy for AI initiatives.
- Simplified data consumption: Create systems that offer secure, straightforward access to data for rapid experimentation. Strategies like centralizing compute within a data lake and using distinct zones for raw versus curated data enable agile, informed decision-making.
