Shifting Towards a Data Culture: AI-Ready Data

Sanjeev Aggarwal

Data Architect

In March 2024, Harvard Business Review published an article titled Is Your Company’s Data Ready for Gen AI? (here)- a question that remains just as relevant today. A year later, discussions around AI-ready data continue, with Gartner recently highlighting that 63% of organisations either lack or are uncertain about having the right data practices for AI, read the article here. They further predict that by 2026, 60% of AI projects will be abandoned due to the absence of a solid AI-ready data foundation. Despite the increasing adoption of AI across industries, the fundamental issue remains unchanged—over 80% of AI projects are still expected to fall short of business expectations.

Despite significant investments, many AI initiatives struggle due to unclear objectives, poor data management, and an inability to scale effectively. Gartner and IDC report that up to 80% of AI projects never progress beyond the pilot phase, highlighting a widespread issue in AI implementation strategies.

A major challenge frequently highlighted by Gartner along with industry experts, articles, and thought leaders is that the data feeding AI models is often just not good enough. It lacks the necessary quality and context—poorly organised, potentially inaccurate, and sometimes altered or processed to the extent that it no longer reflects its original meaning. Without proper management, data loses its integrity, making it unreliable for AI-driven insights.

The financial sector faces mounting challenges in managing this data effectively. Enterprises operate thousands of systems, process millions of data points, and McKinsey report that organisations contend with data debt consuming up to 40% of IT budgets. Poor data management leads to 20-30% operational inefficiencies, increasing costs, impacting financial stability, and eroding competitiveness. The situation is further intensified by ineffective solutions that duplicate data, escalate risks, and contribute to AI project failures.

AI adoption requires more than advanced models—it demands an AI-ready data foundation. Gartner predicts AI-ready data will be the biggest area for investment over the next 2-3 years. However, achieving AI-readiness is not just a technological shift; it requires a fundamental transformation in organisational data culture. To ensure success, organisations must focus on four key pillars:

1. Breaking Down Silos: Unified and Scalable Data

AI-ready data must be unified and accessible across the organisation. Many enterprises struggle with disparate data silos, preventing AI from leveraging a comprehensive view of information. While organisations continue to adopt new technologies, many still rely on traditional approaches to data management, expecting different results. Simply changing the technology without evolving the underlying approach does little to drive meaningful progress or see value in return. To achieve real gains, businesses must rethink their data strategies, focusing on integration, accessibility, and contextual accuracy. Compliment and enhance the current strategy.

Connecting silos ensures a single, consistent dataset that supports real-time decision-making. Scalability is crucial data must be structured to grow alongside AI initiatives and integrate seamlessly with evolving business needs. Without a fundamental shift in how data is managed and connected, organisations will continue to struggle with inefficiencies and underwhelming AI outcomes. Many enterprises struggle with disparate data silos, preventing AI from leveraging a comprehensive view of information.

2. Building Confidence: Data Quality and Agile Governance

AI systems rely on clean, accurate data. Poor-quality data feeds directly into AI models, producing unreliable results. Without proper data hygiene and governance, organisations risk reinforcing biases and inefficiencies in their AI-driven insights. Data governance must be agile and adaptable, ensuring continuous data validation, standardisation, and compliance without stifling innovation.

3. Democratising Data: Enabling Insights and Innovation

AI-ready data is not just for technical teams, it must be accessible to business leaders, analysts, and employees across functions. A strong data culture empowers teams with self-service access, contextual understanding, and real-time insights. When organisations make high-quality data readily available, they enhance decision-making, drive innovation, and increase AI adoption across departments.

4. Speaking the Same Language: Context-Driven Data Connectivity

Ensuring AI models understand data correctly requires clear semantic meaning and shared definitions across the organisation. AI-ready data connects the dots, ensuring that SMEs across domains align on definitions and data structures. Establishing a common data language improves collaboration, reduces inconsistencies, and enhances AI-driven insights by providing context that models can reliably interpret.

The Case for Proper Data Management

AI models are only as good as the data they consume. Poor-quality data leads to unreliable outputs, and this is just as true for GenAI solutions as it is for broader AI systems. Without strong data management (quality, context and structure) and well-structured information architecture, organisations risk making decisions based on flawed insights.

The concept of 'Garbage In, Garbage Out' is particularly relevant in AI. High-quality data is fundamental to maximising AI’s potential. Whether it's GenAI or traditional AI, good data fuels better results, while poor data leads to inefficiencies, unreliable insights, and wasted investment.

Data quality challenges have persisted for decades in large enterprises, these issues have remained constant. However, GenAI brings a renewed urgency to addressing them. Poor data management leads to inaccurate results and wasted investments, making it imperative for organisations to prioritise proper data structuring.

Achieving AI-ready data requires committing to effective information management. At a baseline, this means enforcing structured storage, standardising context, removing redundant data, going beyond just metadata and committing to maintaining accurate data content. These foundational practices ensure data is not only accessible but also meaningful for AI-driven decision-making.

AI-Readiness Starts with a Strong Data Culture

AI-readiness is not just about technology—it’s about the people, processes, and culture that drive data-driven decision-making. Enterprises that embrace AI-ready data principles will position themselves for competitive advantage, while those that continue to rely on fragmented, low-quality data will struggle to realise AI’s full potential. As AI transforms the financial sector, organisations must act now to ensure their data is primed for innovation, efficiency, and long-term success.

How deltamap can Accelerate AI-Ready Data

deltamap provides a transformative approach to data observability and management, focusing on the actual data content rather than just metadata. Many organisations struggle with fragmented, inconsistent, and poorly governed data, which ultimately hinders AI adoption and effectiveness. deltamap addresses these challenges by enabling real-time data monitoring, behaviour tracking, and enhanced information flow to ensure AI models are powered by high-quality, context-rich data.

Unlike conventional solutions that focus on cataloguing metadata, deltamap actively analyses how data moves, transforms, and interacts within an organisation’s ecosystem. This visibility allows businesses to detect inconsistencies, validate data accuracy, and optimise information flows, ensuring that AI models function on a solid foundation of trusted data. By structuring and enriching enterprise data dynamically, deltamap helps break down silos, align cross-functional teams, and reinforce governance without stifling agility.

Through its ability to observe data behaviour, deltamap enables organisations to extract deep insights and generate high-quality NLP datasets that enhance AI applications. By integrating with existing infrastructures, it facilitates seamless data management, ensuring AI initiatives are not just experimental but scalable and production ready. With deltamap, organisations can shift towards a truly AI-ready data culture, unlocking innovation and maximising the value of their AI investments.

Key Takeaways

• AI-ready data is fundamental for organisations aiming to maximise AI’s potential.

  
  

• Achieving AI-readiness requires a shift from traditional data management approaches to a more dynamic, content-driven strategy.

  
  

• Data silos, poor data hygiene, and a lack of governance continue to be the primary obstacles to successful AI adoption.

  
  

• Organisations must focus on scalable, well-governed data frameworks that promote accessibility, quality, and semantic consistency.

  
  

• deltamap enhances AI-readiness by providing real-time data observability, monitoring data behaviour, and improving information flows to ensure AI models are trained on reliable, high-quality data.

  
  

• Unlike conventional tools that focus on metadata, deltamap analyses actual data content, enabling organisations to create structured, contextual, and trusted datasets for AI.

  
  

• Enterprises leveraging deltamap can break down data silos, streamline governance, and accelerate AI adoption, gaining a significant competitive edge in an increasingly AI-driven landscape.