“Success is where preparation and opportunity meet,” racing legend Bobby Unser once said. His wisdom rings especially true for business leaders navigating AI adoption today.
Organizations need a precise strategy to harness AI’s potential through data modernization. Many companies still store valuable information in legacy silos, limiting their ability to extract meaningful insights. A well-planned migration to cloud-based systems can transform scattered data points into powerful business drivers.
As organizations accelerate AI implementation, those with modernized data infrastructure may gain significant advantages over competitors using outdated systems.
As business leaders navigate AI adoption today, asking these 10 questions may help their organizations seize this opportunity:
1. How will you define and implement your data strategy? A comprehensive data strategy starts by aligning business objectives with specific approaches for customer, product, operational, and financial domains. Begin with a clear budget and ROI framework. Link success metrics directly to business outcomes, such as faster decision making and operational efficiency. Your implementation timeline should factor in dependencies while outlining clear plans for data monetization, sharing, and retention.
2. Which data should you modernize and move to the cloud? Begin with a detailed inventory of your data assets to identify those that drive decisions and enable innovation. These priority datasets may belong in a cloud-based data lakehouse, where structured and unstructured data work together securely. Consider data freshness, usage patterns, and integration needs when prioritizing migration, focusing on datasets that combine to unlock new insights or enable automation.
3. How will you ensure quality throughout the data lifecycle? Deploy automated quality checks at every stage from ingestion to analytics, measuring accuracy, completeness, consistency, timeliness, validity, and uniqueness. Early detection through automated profiling can help prevent quality issues from spreading, while scorecards track improvements and link directly to business outcomes.
4. What governance framework will protect and guide your data usage? Build a governance framework that integrates data ownership, access rights, and ethical use through comprehensive data catalogs. Ensure compliance with the European Union’s General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and industry-specific regulations. Document metadata, lineage, and business context while establishing clear protocols for various sensitivity levels. Embed data stewardship within business units, balancing regulatory compliance with innovation needs.
5. How will you secure your data assets? Layer security protections through end-to-end encryption, zero trust frameworks, and continuous monitoring systems. Strengthen this foundation with comprehensive risk management, including threat modeling and vulnerability assessments. Regular security audits and incident response exercises help ensure your safeguards remain effective.
6. How will you transform raw data into actionable insights? Create standardized workflows for data preparation that encompass cleaning, enrichment, and validation processes. Track data lineage and transformations through robust metadata management while building automated pipelines that handle all data types consistently. This systematic approach helps ensure reliable analysis while maintaining data quality.
7. What analytics capabilities will you need? Design your analytics framework to support a progression from descriptive insights (what happened) to prescriptive insights (what should happen). Build a platform capable of handling both traditional business intelligence and advanced AI workloads to help ensure it can scale as your analytical needs grow more sophisticated.
8. Who will lead your data transformation and cultural evolution? Foster a data-driven culture through organizational change management and continuous upskilling programs. Structure your team to blend technical and business expertise, with data engineers managing infrastructure, data scientists developing models, and domain experts providing context. Your Chief Data and Analytics Officer can orchestrate this ecosystem while establishing clear career paths and fostering cross-team collaboration. Success requires developing internal expertise through training and strategic hiring while actively addressing resistance to change through clear communication and demonstrated value.
9. How will you manage expanding data volumes? Balance performance and cost through intelligent data management strategies that automatically tier storage based on access patterns. Implement smart archiving policies that can maintain accessibility while controlling costs and help you build scalable architectures that efficiently handle both real-time and batch processing.
10. How will you integrate existing and new systems? Approach infrastructure modernization systematically by creating detailed migration plans that account for dependencies and risks. Consider hybrid approaches that preserve critical legacy systems while adopting modern platforms gradually. Focus on building an integration strategy that serves both immediate needs and future innovation.
The path to AI excellence requires careful planning and execution. Organizations that thoughtfully address these questions can position themselves to lead in the AI era. Those that take time to build robust data foundations may find themselves well-equipped to innovate and compete effectively.
Partner with Unisys to develop your strategy and accelerate AI success. Our expertise in data modernization helps organizations navigate the complexities of cloud migration while maximizing the value of data assets. Learn more at unisys.com/ai.
Source : Harvard Business