Building a data strategy for the AI era

To unlock data's potential and prepare for AI, businesses must create a well-defined data strategy that aligns a clear vision with AI and business priorities, assesses current capabilities, and establishes a roadmap to transform data into a strategic asset.

Data, much like financial capital, is now a core business asset. Yet, despite accumulating vast amounts of information, countless organizations struggle to extract the necessary insights for driving meaningful results. This issue frequently stems from the absence of a clear, intentional data strategy. Without one, data remains a passive byproduct of operations rather than a powerful engine for driving growth, which can lead to significant costs, both visible and hidden. 

More specifically, industry research shows:  

• Lost revenue: Companies that fail to tie data strategy to business goals often fail to spot key trends, underserved customers, or other insights, leading to lost sales and missed opportunities.

• Operational and productivity drag: Poor data quality leads to flawed analysis, poor decisions, and diminished trust in data systems.

• Underutilized investments: Investing in expensive data systems without a clear purpose can lead to redundant systems, data silos, and expensive analytics projects that deliver minimal value.

• Substantial compliance risk: A lack of strategy usually leads to inadequate governance, increasing the risk of data breach and privacy violations. 

In the context of AI, building an effective data strategy becomes even more crucial, especially given the excitement around generative AI. While these technologies carry immense promise for transformation, an underlying truth exists: AI initiatives are fundamentally dependent on a solid data foundation to succeed. In fact, a recent Deloitte survey reported that 55% of organizations avoid certain gen AI use cases due to data-related concerns. 

Put simply, if your data isn’t ready for AI, neither is your business. Therefore, a well-defined data strategy that aligns processes, technology, and people is now essential for harnessing your data and transforming it into a strategic asset that powers sustainable value. With that in mind, we’ll outline three key steps for creating a targeted, achievable, and actionable data strategy — designed to fuel AI success. 

1. Establish your vision, AI and business priorities

Data leaders today are under immense pressure to translate technical capabilities into value while navigating increasingly complex data ecosystems. An effective data strategy provides a clear roadmap for transforming data into a strategic asset by aligning processes, technology, and people to drive strategic objectives. To do this, you’ll need to start by defining what success looks like, connecting your data efforts to your organization’s AI initiatives and overarching priorities. 

For instance, conducting stakeholder interviews with executives, department leaders, and key subject matter experts can help identify where data-driven insights can make a tangible difference. These insights can help capture business goals, identify pain points, and clarify different data requirements, ensuring your data strategy is grounded in solving real business problems — not just building technology for the sake of it. 

Here, one of the biggest pitfalls we encounter is falling into the extreme — focusing solely on technical foundations or prioritizing rapid business transformation above all else. We have found that technical teams often favor a bottom-up approach, seeking to build foundations first that ensure reliable, accessible, and secure data assets. However, this can delay business outcomes and jeopardize executive buy-in as clear business outcomes can be too distant. In contrast, a purely top-down approach, driven exclusively by leadership aiming to achieve immediate outcomes can lead to technical debt or emphasizing quick wins at the expense of long-term success.

Google Cloud recommend a combined approach: establishing a clear, compelling vision for using data to transform your business while simultaneously working to provide solid foundations, efficient data operations, and drive organization-wide utilization. This vision acts as a guiding principle for all subsequent work, helping you avoid both aimless system building and pursuing goals without the data to back them up. 

2. Assess where your organization is right now

To develop a coherent strategy that gets you to your goals, you need to know where you’re starting. This requires taking an honest look at your current data foundations capabilities, key performance indicators (KPIs), and investment. These insights can then be used to inform a more comprehensive, effective data strategy. 

Capabilities:

• Data strategy & culture
• Collection and process
• Storage and analysis
• Insights activation and AI use cases
• Data governance and security

KPIs:

• Lost Productivity due to data related challenges
• Lost time due to unplanned downtime or a notable degradation in the performance
• Delay in time-to-market (months) for new products due to lack of data integration

Investments:

• Spend on data platform as a % of total IT spend 
• Spend on data platform as a % of revenue
• Allocation of spend across different layers of the data and analytics stack

A recent survey of more than 400 customers globally revealed that organizations across industries are facing challenges in key data capabilities, including data literacy, orchestration and automation of pipelines, real-time analytics, the development of data products and monetization, and data governance. If left unaddressed, these gaps can hinder your progress in maximizing the returns you realize from your AI investments. 

When working with our customers to build and deliver their data strategies, we recommend they self-assess their current state and data-readiness for AI using our Data and AI Strategy Assessment. This tool, used during our sales process, helps discover your organization's readiness for AI-powered transformation by:

• Benchmarking your current state across core data foundation capabilities, including how you collect, store, secure, and access data. 
• Examining the ways you activate and govern data, as well as your company’s data culture and mindset. 
• Evaluating several critical KPIs related to data and AI and your existing level of investment to not only understand your current constraints, but also reveal any areas of opportunity. 

3. Define a clear roadmap

Once you understand your business priorities and current data capabilities, the final step is to build your data roadmap. Think of a data roadmap as a strategic plan that outlines how you’ll move from where you are to where you want to be in the future, transforming data into a key driver for achieving your end goals. This is not purely a technical exercise but rather about establishing how you can deliver organizational intelligence — getting the right information to the right people at the right time. 

Google Cloud has developed a framework to help data leaders articulate how technical investments connect directly to business outcomes across what Google Cloud call the Intelligence Value Chain — where data becomes a driving force behind entirely new ways of creating and capturing business value. Rather than discussing abstract capabilities, this framework enables data leaders to show a clear path to progress — from foundational data trust to operationalizing intelligence to empowering users. 

The Intelligence Value Chain entails building an AI-powered enterprise platform that transforms data into actionable intelligence.

The Intelligence Value Chain includes four interconnected layers: 

Building trust in data. Creating a unified data foundation to establish reliable, accessible, and secure data assets that your entire organization can trust and confidently use. 

Making data intelligent. Organizing and refining data to fuel initiatives with intelligent data operations, including standardized processing pipelines, knowledge organization systems, business logic implementation, and accessible data services.

Putting data into everyone’s hands. Providing the tools that make data and AI capabilities accessible, and valuable to people across your organization to drive faster, more informed decision-making, boost productivity, and increase satisfaction. 

Transforming through collective intelligence. Utilizing data and AI to transform how your organization creates and delivers value, enabling new revenue streams, operational models, and customer experiences. 

Instead of focusing exclusively on foundation-building or business innovation, the Intelligence Value Chain recognizes that data roadmaps are iterative, not a straight line. For example, you might create new dashboards for your marketing teams (Layer 3) and then discover inconsistencies in customer segmentation. This requires revisiting your data sources to refine your data models then updating your data pipelines (Layer 2) and then ensuring the reports reflect the improved data (Layer 3). In other words, data roadmaps aren’t necessarily about reaching a final destination but rather a coordinated, continuous journey that must balance immediate needs with long-term vision.

As a result, it’s essential to maintain your vision to help guide every decision you make in your roadmap while simultaneously advancing all areas of your data strategy. By balancing immediate value with long-term capability building, you can ensure your roadmap is practical, sustainable, and truly drives the business outcomes you desire.

To learn more about establishing a data strategy in your organization, please contact our sales team.