Top five data modernization strategies for business success

It’s a no-brainer that legacy systems can be the main reason an organization slows down. But what the majority of leaders are asking today is: how do we move beyond just updating to the latest tech? What, how and when can we see our data turning into insights that contribute to business goals?

Our research shows that 46% of technology leaders point to better decision-making as the top objective of modernization. The message is clear: this isn’t about chasing the latest tools. It’s about building the footing to make smarter, faster and more reliable decisions at scale.

We’ve outlined five practical modernization strategies to help organizations cut through structural barriers, modernize with purpose and turn data strategy into measurable business outcomes.

Strategy 1: Align data strategy with business goals

Many data modernization initiatives stall because they lack a direct line to business outcomes. In fact, only 39% of organizations say their data strategy is fully aligned with company objectives. To close that gap, here are three steps to align data strategy with business goals:

• Define measurable outcomes. Connect every data initiative to a business lever, whether it’s revenue growth, cost optimization, improved compliance or customer experience. Clear targets make it possible to track real impact, not just technical progress.
• Bring in business stakeholders early. Treat senior leaders and domain experts as co-owners of the strategy. Their involvement ensures priorities reflect business realities and keeps investment focused.
• Create a feedback loop. Review strategy at regular intervals and adjust based on shifts in the market, regulation or customer needs. This turns data strategy into a living framework, not a static document.

When data strategy is visibly tied to business goals, it drives accountability, secures executive sponsorship and reduces the risk of wasted investment.

Strategy 2: Adopt an incremental build strategy

Organizations’ growing adoption of AI over the past decade has heightened attention on the state of their data. With only 27% of executives reporting a data strategy older than two years, most are still early in their journey. This is the moment to adopt an incremental build strategy. Start small. Skip multi-year data platform builds that drain budgets and patience. Instead, focus on fast, visible gains that reinforce stakeholder confidence.

• Avoid long runway projects. Big foundational efforts can stall before delivering tangible outcomes and every stalled project risks stakeholder fatigue.
• Identify and deliver 'proof of value';  small but critical solutions to long-standing gaps in your businesses analytical capabilities.
• Celebrate early wins. Share what’s working: faster data delivery, improved decision speed or clearer business insights. Keep momentum by regularly showcasing impact to executives and key sponsors.
• Track the right signals. Tie metrics to business outcomes:
  • Leading indicators such as data-pipeline uptime, throughput or onboarding speed.
  • Lagging indicators like cost savings, time-to-insight reduction or revenue impact.

Strategy 3: Improve data quality and timeliness

According to recent research,  41% of executives say poor quality is their biggest frustration with data, while 33% cite late delivery as a major concern. It’s hard to make confident decisions if the numbers can’t be trusted or arrive too late. Reliable, timely data is what builds confidence across the business. 

• Strengthen governance. Put clear owners in place for your most important datasets and agree on simple, enforceable standards. Governance isn’t paperwork, it’s what keeps everyone working from the same version of the truth.
• Automate quality checks. Integrate checks into data pipelines and build processes   to catch errors early. Automated monitoring, testing and anomaly detection take the burden off teams and keep pipelines running smoothly.
• Promote stewardship. Make sure teams know how to use metadata, understand their responsibilities and see data quality as part of their role. When leaders set the example, stewardship becomes part of the culture.

Improving quality and timeliness isn’t about one tool or one project. It takes structure, automation and a shared commitment across the organization to make data something people can trust.

Strategy 4: Move to the cloud and manage costs wisely

For many organizations, developing AI use cases is now a top priority. In fact, 40% of leaders rank it as their second most important goal for modernization. Cloud plays a central role here, giving teams access to the infrastructure needed to train and run AI models. It also promises scalability and powerful analytics but costs can spiral if left unchecked. Without a clear plan, what should be an accelerator can quickly become a drag on long-term value.

• Migrate with purpose. Prioritize moving core workloads that drive business impact, and track both usage and outcomes as you go.
• Implement FinOps. Treat cost governance as a business discipline. Make sure every dollar of cloud spend maps to a business priority, so leaders see impact, not just invoices.
• Optimize performance. Use autoscaling, refactor inefficient workloads and modernize infrastructure to make the most of what you’re paying for.
• Focus on ROI. Don’t just count costs, measure how cloud modernization delivers faster insights, better customer experiences or reduced overhead.

Cloud modernization delivers the most when cost transparency is built in and every investment is judged by the outcomes it produces.

Strategy 5: Build a data-driven culture

Tools do not drive decisions. People do. A strong data culture ensures employees understand, trust and act on data across the enterprise.

• Invest in literacy. Provide training that helps teams at all levels work confidently with data and analytics.
• Assign ownership. Create data management and stewardship roles with clear accountability.
• Encourage collaboration. Support cross-team projects and promote knowledge sharing across enterprises.
• Celebrate success. Highlight stories where data modernization led to better outcomes and reinforce the value of data-driven thinking.
  

The path to business transformation isn’t about patching isolated data issues. It’s about building a data modernization strategy that connects vision with execution. When strategy aligns with business goals, when practices strengthen quality, governance and cloud adoption and when a data-driven culture takes hold, organizations gain the confidence and speed to compete.

For leaders, the question is no longer whether to modernize but how quickly these five strategies can be put into action. Start by assessing your current data landscape against them and use that as the foundation for a modernization roadmap that delivers measurable impact.

Partner with us to modernize your data estate with purpose, precision and secure data management and turn your data into a growth engine for the enterprise.