How Data Modernization Drives Technological Advancements

In the latest McKinsey Global Survey on AI, 65% of respondents say their organizations regularly use generative AI. While AI adoption is becoming increasingly common across different sectors, the types of technologies being used can vary significantly.

For example, the European Commission’s AI Survey reveals that in 2023, AI tools designed to automate workflows or help with decision-making were among the most popular, with 16.4% of large enterprises using them. Close behind was text mining, which analyzes written language and has an 11.9% adoption rate. Other technologies like machine learning (including deep learning), speech recognition, image recognition, and natural language generation were used by between 14.6% and 7.7% of large businesses.

To get the most out of AI, organizations first need to tackle some foundational data issues. The latest reports show that three-quarters of businesses are ramping up their investments in data lifecycle management to support their generative AI strategies. According to Deloitte, they’re mainly focused on improving data security (54%) and data quality (48%). However, data-related challenges remain a big concern, with 55% of organizations avoiding certain AI use cases due to data sensitivity and privacy concerns.

The same report from Deloitte mentions that only 23% of organizations feel fully prepared to handle the complexities of generative AI, including compliance with new regulations like the European Union’s AI Act, which went into effect in August. The main hurdles include managing risks, regulatory uncertainty, and governance issues. About half of the organizations are working on regulatory forecasts or assessments to address these issues.

We always recommend deciding whether you need to focus on pursuing AI in your operations first. If you determine that it’s the case, get your data ready. It means having strong data management practices in place. Many organizations are making progress in that area, but there’s still work to be done.

Recent surveys reveal that organizations familiar with AI generally feel relatively mature in their data lifecycle management. This might be due to a strong starting point or because 75% have increased their investments in data management due to generative AI.
Despite this, data-related issues constitute a significant roadblock. Deloitte’s State of Generative AI in Enterprises Report for Q3 shows that, for instance, 55% of organizations are avoiding certain AI use cases because of concerns.

The top challenges in adopting AI include lacking necessary skills and poor data quality. To be effective, AI needs clean, well-managed data. To ensure their data is ready for AI, Chief Data Officers should focus on making it findable, accessible, interoperable, and reusable—known as “FAIR” principles. Yet, only 35% of CDOs feel they have the resources to meet these requirements.

Generative AI demands established data governance practices covering quality, privacy, security, and transparency, especially for data from outside sources like public domains or third parties.

For example, proper documentation and labeling are becoming more crucial, and organizations may need to rethink their data storage strategies, whether cloud-based or on-premises. Advanced users might even start working with synthetic data.

Regulatory and ethical challenges

AI systems can inherit human biases and errors, so proper governance practices are essential for addressing these issues and ensuring that algorithms are regularly reviewed and updated to prevent harmful outcomes. Transparency in AI decision-making is also crucial. We can ensure fairness and accountability by understanding how AI makes its choices. Governance, therefore, isn’t just about meeting compliance requirements; it’s about maintaining ethical standards over time.

DataOps and MLOps are integral to achieving successful AI adoption. DataOps addresses the foundational data management issues, while MLOps assumes responsibility for AI models operating effectively.

DataOps principles are usually applied to tackle common data challenges, like data silos and quality concerns, as well as ensuring the high quality of the data feeding into AI systems. They help manage the data flow from collection through integration and cleaning, ensuring the data is accurate and reliable.

MLOps, on the other hand, addresses the management of AI models after data has been prepared. It focuses on deploying, monitoring, and continuously updating machine learning models, which is directly related to the need for ongoing governance and oversight mentioned before. Just as DataOps prepares data, MLOps ensures that the AI models built from that data perform well and remain aligned with business needs.

Modernizing data infrastructure is a significant but necessary investment. As indicated in MIT Technology Review, 54% of organizations have either modernized multiple elements of their data estate in the past two years or are actively in the process of doing so. Another 23% plan to undertake modernization within the next two years. This is especially true for larger organizations, especially those with revenues over $10 billion, which are more likely to have begun this process compared to smaller ones.

Cost remains a substantial barrier, with 40% of respondents citing high expenses as a significant impediment to modernization. Regulatory compliance and security concerns also hinder modernization efforts, particularly in highly regulated financial services and healthcare industries.

But, despite these challenges, the benefits of data modernization are significant and, most importantly, widely recognized. Improved decision-making is the top goal of modernizing data strategies, cited by 46% of executives. The other main advantages of data modernization are that it boosts AI development and streamlines connectivity with advanced AI models. For example, the modernization process makes using techniques like retrieval-augmented generation (RAG) possible, improving model accuracy by enabling efficient access to relevant data. Modernizing data simplifies database migration and code conversion for smooth integration with AI tools and frameworks.

We have reached the point where all that is left is to prepare your organization for the AI-driven future. Here is how to prepare your data processes for modernization

• Break down barriers between isolated data repositories to ensure straightforward access and integration.
• Ensure all data, whether from internal or external sources, is integrated, cleansed, and validated.
• Design your architecture to handle both structured and unstructured data from multiple sources and formats.
• Establish a robust data governance framework to maintain data quality and compliance.
• Develop an agile and scalable data architecture that can handle current data volumes and adapt to future growth.
• Invest in modern data management technologies that improve integration, quality control, and governance.
• Start by cataloging and understanding where all your data assets are and how they are protected.
• Enable self-service analytics and empower users by making data accessible to the right people at the right time.
• Explore platforms that unify data across hybrid and multi-cloud environments. Consider solutions like data lakes and federated querying to integrate enterprise data with external sources.
• Clearly define roles and responsibilities for data quality and governance among senior leadership.
• Regularly review and update your data management strategies to keep pace with new technologies and evolving business needs.

Navigating the complexities of data modernization requires more than advanced technology—it demands the support of a strategic partner who understands your unique needs and goals. That’s where we come in. Our extensive expertise and proven track record allow us to guide you through every step of your data modernization process.

Contact Scalo. Let us help you modernize your data.