Data security
Data security is about giving people the exact access they need, no more, no less. As generative AI becomes a key part of decision-making, protecting your data becomes non-negotiable.
Third-party data breaches are skyrocketing. A recent study showed a 61% rise in breaches over the past year alone, a 49% year-over-year increase and this isn’t only from bad actors hacking your systems. Organizations must also make sure that vendors and partners they work with don’t become weak links. That’s why adopting zero trust principles, where every access request is verified, is key.
The first step is understanding your data. What do you have, where is it stored, and how sensitive is it? Once you’ve got that foundation, you enforce strict access controls based on the principle of “least privilege.” This means users get only the data access necessary for their work, nothing more. Combine this with clear governance policies for AI tools to make sure sensitive information stays protected while maintaining the functionality of AI-driven insights.
For unstructured data: emails, PDFs, chat logs, the challenge is greater but not insurmountable. These formats are harder to secure and process, but tools powered by AI are getting better at managing them. Data breaches can cost millions in lost revenue and reputation, investing in data security is the smartest bet you can make.
Data quality
Generative AI is only as good as the data it learns from. If you feed an AI messy, incomplete, or biased data, you’ll get equally flawed results. That’s why data quality is so important, especially for large language models (LLMs).
Think of unstructured data (documents, chat transcripts, emails) as a raw material. Before it’s useful, it needs to be cleaned, organized, and refined. This process involves steps like entity extraction (identifying key names, dates, or concepts), sentiment analysis (figuring out emotional tone), and bias detection (making sure of fairness). Done well, this means that the outputs from LLMs are accurate, timely, and aligned with your organization’s goals.
Let’s take an example: Retrieval-Augmented Generation (RAG). This AI model combines stored knowledge with real-time queries to generate highly relevant responses. To work, it needs clean and prepared data. Without this, your LLM might serve up irrelevant or, worse, incorrect insights. AI and machine learning technologies have made these tasks faster and more reliable, automating what used to be manual and tedious.
Quality in equals quality out. Cutting corners on data preparation might save time in the short term, but it’ll cost you in poor decision-making and missed opportunities.
Centralizing data
Not everyone in your company is a data scientist, and that’s okay. What’s not okay is making data so hard to access that only specialists can use it. Centralizing your data, and making it easy to access, lets your teams make smarter decisions, faster.
With tools like data fabrics, all your data is connected in one place, no matter where it’s stored. This simplifies access, making it possible for “citizen data scientists” (non-technical employees trained to analyze data) to dive in without help from IT. When your team doesn’t have to wait for data analysts to generate reports, they can focus on creating insights and driving innovation.
APIs are the solution here. An API acts like a bridge. It lets your teams access data, machine learning models, or even pre-built visualizations without needing to know what’s under the hood. Think of it as a “plug-and-play” system for data-driven innovation.
However, centralizing data is also great for security and governance. Even the best data tools need guardrails. Policies must make sure users only access what they need, and sensitive data stays protected. In supporting citizen data scientists while keeping data secure, you get the best of both worlds: faster decision-making and fewer bottlenecks.
Centralized data is a cultural shift. When everyone in your organization has the tools and confidence to act on insights, you unlock a level of agility and creativity that’s hard to match.
Data marketplaces
Data marketplaces are huge for organizations trying to make their data more accessible and actionable. Think of them as internal app stores, but instead of apps, your teams can browse, search, and access datasets tailored to their needs. The result is faster insights, better decisions, and a culture where data drives everything.
For many companies, data is abundant but scattered. Without a centralized system for discovery, employees spend valuable time searching for the right data, or worse, recreating it. Data marketplaces solve this by aggregating data from across the organization into one unified platform, equipped with tools like data catalogs (organized lists of datasets) and data dictionaries (explanations of what each dataset includes). With automation, this process becomes seamless, giving your team instant access without bureaucratic delays.
Industries like manufacturing, logistics, and energy benefit enormously from this approach. These sectors often deal with massive, real-time data streams, think supply chain metrics, production stats, or energy outputs. A well-designed marketplace simplifies integrating these datasets for tasks like predictive analytics or AI model training. Imagine a supply chain manager pulling live data from multiple sources to predict bottlenecks before they occur. That’s the kind of agility data marketplaces give.
But let’s not ignore the importance of governance. Simplifying access doesn’t mean sacrificing security. Your data marketplace needs built-in controls that make sure sensitive data is only available to those who need it, with a clear audit trail for accountability. In balancing ease of access with comprehensive security, data marketplaces support your teams without creating new vulnerabilities.
Data products
Data products are the glue connecting technical and business teams. They’re solutions tailored to specific problems, with clear objectives and measurable value. Treating data assets like products makes sure they’re functional, user-friendly and impactful.
A data product could be an advanced dashboard for sales forecasting, a machine learning model predicting customer behavior, or an AI agent that automates support tickets. Like any product, it needs a defined customer segment (who will use it), a value proposition (what problem it solves), and a roadmap (how it evolves over time). This change in mindset, from managing data to developing products, aligns teams around shared goals.
Data products blur the lines between technical and non-technical roles.
Business teams no longer see data as something abstract or out of reach; they interact with intuitive tools that deliver actionable insights. At the same time, technical teams work with clear priorities, knowing exactly what end-users need. Alignment accelerates innovation and reduces friction.
In order to make this work, you need to manage data products like any other development initiative. Start with a clear vision, what’s the purpose of this product, and what impact will it have? Define KPIs to measure success and continuously iterate based on user feedback. Importantly, make sure governance and quality checks are built into every stage, so your products are reliable and secure.
As generative AI and data-driven decision-making change industries, treating your data like a product is the future. It’s how you move from being reactive to proactive, creating solutions that don’t just support your business but drive it forward. The companies that get this right will dominate their markets, while those that don’t will struggle to keep up.
Key takeaways for decision-makers
- Prioritize data security as a core business strategy: Protecting sensitive information is non-negotiable in AI-driven organizations. Implement zero trust principles, enforce least-privilege access, and strengthen governance policies to mitigate risks from growing third-party data breaches.
- Elevate data quality for generative AI applications: Make sure unstructured data is cleansed, cataloged, and prepared for large language models (LLMs). Techniques like entity extraction and bias detection improve AI outputs, reducing errors and aligning insights with organizational goals.
- Centralize data to support non-technical teams: Deploy data fabrics and user-friendly APIs to make data access smooth for citizen data scientists. This approach accelerates innovation while reducing dependency on technical teams, creating a self-service data culture.
- Develop data products for cross-team collaboration: Treat dashboards, AI agents, and machine learning models as products with clear objectives and user-centric designs. Build comprehensive governance into their development to make sure of security, reliability, and continuous improvement based on user feedback.
