“Success is where preparation meets opportunity,” horse racing legend Bobby Unser once said. His wisdom is especially applicable to business leaders who are implementing AI today.
Organizations need a precise strategy to harness the potential of AI through data modernization. Many companies still store valuable information in traditional silos, limiting their ability to extract meaningful insights. A well-planned migration to a cloud-based system can turn scattered data points into powerful business drivers.
As organizations accelerate the implementation of AI, companies with modern data infrastructures may gain significant advantages over competitors using outdated systems.
As business leaders approach AI adoption today, asking the following 10 questions may help their organizations seize this opportunity.
1. How do you define and implement a data strategy? A comprehensive data strategy begins by aligning business goals with specific approaches to customer, product, operational, and financial areas. Start with a clear budget and ROI framework. Connect success metrics directly to business outcomes such as faster decision-making and operational efficiency. Implementation timelines should account for dependencies while outlining a clear plan for monetizing, sharing, and retaining data.
2. What data needs to be modernized and moved to the cloud? Start with a detailed inventory of your data assets to identify the data assets that drive decisions and enable innovation. These priority datasets may belong to a cloud-based data lakehouse where structured and unstructured data securely work together. When prioritizing migration, consider data freshness, usage patterns, and integration needs, and focus on datasets that can be combined to unlock new insights or enable automation.
3. How do you ensure quality throughout the data lifecycle? Deploy automated quality checks at every stage from ingestion to analysis to ensure accuracy, completeness, consistency, timeliness, validity, and uniqueness. Measure gender. Early detection through automated profiling helps prevent quality issues from escalating, and scorecards track improvements and link them directly to business outcomes.
4. What governance framework will protect and guide the use of data? Build a governance framework that integrates ownership, access, and ethical use of data through a comprehensive data catalog. Ensure compliance with the European Union’s General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and industry-specific regulations. Establish clear protocols for varying sensitivity levels while documenting metadata, lineage, and business context. Embed data management within business units to balance regulatory compliance and innovation needs.
5. How do you protect your data assets? Increase security with end-to-end encryption, a zero trust framework, and continuous monitoring systems. We strengthen this foundation with comprehensive risk management, including threat modeling and vulnerability assessment. Regular security audits and incident response exercises help keep safety measures effective.
6. How do you turn raw data into actionable insights? Create a standardized workflow for data preparation that includes cleaning, enrichment, and validation processes. Track data lineage and transformation through robust metadata management while building automated pipelines that consistently process all data types. This systematic approach ensures reliable analysis while maintaining data quality.
7. What analytical capabilities do you need? Design your analytical framework to support the transition from descriptive insights (what happened) to prescriptive insights (what should happen) . Build a platform that can handle both traditional business intelligence and advanced AI workloads, allowing you to scale as your analytics needs evolve.
8. Who will lead the data transformation and cultural evolution? Foster a data-driven culture through organizational change management and continuous upskilling programs. Assemble teams that blend technical and business expertise, with data engineers managing the infrastructure, data scientists developing models, and domain experts providing context. Chief data and analytics officers can orchestrate this ecosystem while establishing a clear career path and fostering collaboration across teams. To be successful, you must develop internal expertise through training and strategic recruitment, while proactively addressing resistance to change through clear communication and demonstrated value.
9. How do you manage growing data volumes? Balance performance and cost through an intelligent data management strategy that automatically tiers storage based on access patterns. Implementing smart archiving policies helps maintain accessibility while controlling costs, and helps build scalable architectures that handle both real-time and batch processing efficiently.
10. How will you integrate your existing and new systems? Approach infrastructure modernization systematically by creating a detailed migration plan that takes dependencies and risks into consideration. Consider a hybrid approach that maintains critical legacy systems while gradually introducing modern platforms. Focus on building integrated strategies that address both immediate needs and future innovations.
The path to AI excellence requires careful planning and execution. Organizations that carefully address these questions will be positioned to lead in the AI era. Companies that take the time to build a robust data foundation may find themselves well-equipped to innovate and compete effectively.
Partner with Unisys to develop your strategy and accelerate your AI success. Our data modernization expertise helps organizations navigate the complexities of cloud migration while maximizing the value of their data assets. For more information, visit unisys.com/ai.
