Leveraging Data Analytics to Drive Organizational Strategy: A Guide for Senior Leaders

At IIA, we believe that the key to unlocking real ROI in analytics doesn’t just lie in technology. It’s about the underlying analytical foundations: people, data quality, organizing model, etc. The insights shared at our recent Analytics Leadership Consortium (ALC) Summit reaffirmed this vision, underscoring a need for investment in analytics foundations as businesses struggle with increasingly intricate challenges despite ever-advancing technology that some believed would be the savior from long-held data quality debt. Below is a collection of our top insights from the Columbus ALC Summit.

People First: The Engine of Analytics Success in the AI Era

Despite the rapid evolution of AI and automation, human expertise remains irreplaceable. Companies often prioritize tools, but without skilled analysts and strong leadership, even the most advanced technology will fail to deliver results without strong people underpinning it. Organizations that foster a culture of analytical excellence by hiring, training, and retaining top talent gain a significant competitive edge.

A recurring theme at the ALC Summit was the importance of upskilling analysts to think strategically and must frame discussions around business priorities. While technical skills are essential, the ability to communicate insights effectively and align analytics with business goals is what sets high-performing teams apart. Leaders who prioritize continuous learning create an environment where innovation thrives.

Expanding Data Sources: The Black Box Challenge

As organizations integrate data from an increasingly broad range of sources—internal systems, third-party providers, social media, IoT devices, and more—the challenge of maintaining high-quality data intensifies. The origin of this data often lacks transparency, leading to potential risks:

• Data inconsistency – Variability in structure, definitions, and formats.
• Unknown biases – Hidden biases within third-party datasets.
• Security and compliance risks – Unclear provenance of data leading to regulatory concerns.

Why Data Quality Matters More Than Ever

Poor data quality can distort analytics, mislead AI models, and ultimately result in flawed strategic decisions. Organizations must invest in:

• Automated data validation and cleansing
• Rigorous vendor and source assessments
• Continuous monitoring and auditing of data streams

The Role of Data Analytics Leaders in AI Strategy

Data analytics leaders are uniquely positioned to bridge the gap between AI capabilities and business strategy. They play a crucial role in ensuring that AI adoption aligns with organizational goals and responsible data practices.

Key Actions for Analytics Leaders:

• Educate the organization – Provide training at all levels on AI opportunities, risks, and appropriate use cases.
• Advocate for data hygiene and governance – Promote best practices for maintaining clean, secure, and well-managed data.
• Enable AI best practices – Lead pilot programs, test cases, and controlled experiments. Serve as both educator and evangelical for the potential of AI with the right foundation.
• Recommend the right tools for the job – Ensure AI solutions are fit-for-purpose rather than chasing hype.
• Link usage to strategy and business results – Challenge business requests. Assess how data and AI will be used and whether the request is appropriate and aligned with current metrics and strategic priorities.
• Quantify usage costs – Tie AI investments to strategic objectives and measurable business value.
• Prioritize problem-solving over technology adoption – AI is a tool, not the solution itself. Focus on addressing business challenges first.

"AI is not the answer—it’s a tool to get to the answer." -Data Analytics Leader, IIA Summit, Columbus 2025

The Link Between Data Fundamentals and Business Outcomes

Many organizations chase AI and advanced analytics without first establishing strong data foundations. Analysts must draw a direct line between good data practices and the quality of strategic decision-making:

• Clean, well-governed data leads to trustworthy AI outputs – If data quality is poor, AI-generated insights will be flawed.
• Consistent data formats enable faster decision-making – Misaligned definitions of key metrics across departments create confusion and inefficiency.
• A shared understanding of business-critical data improves collaboration – Finance, marketing, and operations teams must align on data definitions and usage. The savvy data analytics professional can lead that process and reconciliation.

Emphasizing these connections helps executives appreciate why investments in data governance, hygiene, and literacy are essential.

Moving Forward

Analytics leaders must move beyond the technology-first mindset and recognize that sustainable success comes from investing in people and data quality first. At IIA, we help organizations navigate these challenges through our Research and Advisory Network and our Analytics Leadership Consortium, bringing top minds together to share insights and strategies that drive real results.

If you’re looking to elevate your analytics function and unlock greater ROI, the key isn’t just in more tools—it’s in building a strong foundation of talent, organizational alignment, and data trust. Let IIA guide you along the way.