A strategic look into how data evolves from operational tool to innovation engine—shaping AI, leadership, and business transformation from the inside out.
Hi Andrew, welcome to AITP. Could you tell us about your background, your journey to becoming CEO of Data Axle, and how it led you to author a book?
Of course. Thank you for the opportunity. I have been fortunate to build a career spanning over three decades, during which time I’ve held leadership roles across agency, marketing services, enterprise software, and SaaS industries. I’ve led startups, driven successful turnarounds, and managed billion-dollar global businesses. My operational experience extends to raising capital in private and public markets and steering mergers and acquisitions at every stage of a company’s growth. These experiences have honed my ability to transform challenges into opportunities and unlock the true potential of organizations.
As CEO of Data Axle, I’m working to build upon the company’s legacy of delivering innovative, data-driven solutions that empower businesses to succeed while further developing our industry-leading client solutions and providing exceptional services. What drew me to Data Axle was its unparalleled reputation in data quality, data management and its ability to help organizations transform complex data into actionable insights and desired business outcomes.
We serve a diverse range of clients, from Fortune 100 companies to small businesses and nonprofits. Our focus is on creating scalable, personalized solutions that drive meaningful connections between companies and people. This is my passion, and I’m proud to have witnessed so many client successes throughout the years. My journey has always been about pushing boundaries and exploring how data and technology can create value for businesses and their customers alike.
My decision to write Igniting Customer Connections was inspired by a desire to act as something of a guidebook for marketers looking to improve their performance by providing practical advice that they could apply to their work. The book explores how emotional engagement and meaningful connections can elevate marketing outcomes. It was an opportunity to share and synthesize the strategies and principles I’ve developed over the years—principles that are even more relevant today. By blending the latest technology (LLMs, GenAI, martech, etc.) with a deep understanding of customer needs, I believe businesses can achieve extraordinary results, and I’m proud to be a part of that transformation.
With 30 years of experience as a data strategist, what would you say are the main changes in the role of data concerning driving innovation?
The role of data has undergone a remarkable evolution, transforming from a passive tool to a dynamic catalyst for innovation. In the past, data was primarily used to report on historical performance—a static record of what had already occurred. Today, technological advancements, particularly AI and machine learning, have turned data into a predictive and prescriptive force. What was once perhaps too time-consuming, or even impossible is now within reach. Data drives not only decisions but also the creation of entirely new opportunities and business models.
Consider how automation and AI have enabled organizations to do more with less. Tasks that traditionally required significant manual effort can now be completed in seconds, allowing businesses to redirect resources toward creativity and innovation. For example, personalization at scale and in real-time, once a pipe dream, is now a reality. Businesses can deliver hyper-relevant, dynamic experiences to millions of customers simultaneously, fostering deeper engagement and loyalty.
Looking ahead, the role of data will only grow more sophisticated. Emerging technologies, such as generative AI, are poised to unlock entirely new avenues for innovation. From designing custom products in real time to automating complex workflows, the possibilities are endless. At its core, data’s role in innovation lies in its ability to bridge the gap between imagination and implementation. It empowers organizations to think bigger, act faster, and create solutions that not only meet current needs but also anticipate future ones. The future is about using data not just to optimize but to invent—to turn bold ideas into tangible experiences.
Only 3% of organizations consider their data management highly mature despite the recognition of data’s importance. What do you think are the key barriers preventing organizations from achieving data maturity
The barriers often stem from a combination of cultural, technical, and structural challenges. First, many organizations lack a unified vision for data—with some businesses not knowing how to activate their data to leverage it as the strategic asset it is. One of the biggest challenges I see is the persistence of legacy systems and outdated processes that can’t keep pace with modern data demands and technologies. These systems create silos that prevent seamless access to and integration of data. Breaking down these silos requires adopting advanced integration platforms and cloud-based solutions that enable a unified, scalable approach to data management.
Another hurdle is the lack of a cohesive vision. Data maturity requires more than just tools and technology; it requires leadership that prioritizes data as a strategic asset. Organizations need to align their data strategy with their overall business objectives, ensuring that data initiatives are not just isolated IT projects but central to decision-making and innovation efforts. Building this alignment involves fostering collaboration between data teams and business units to create shared accountability.
Finally, achieving data maturity hinges on cultivating a culture of data literacy and curiosity. Many organizations face a skills gap when it comes to leveraging data effectively. To overcome this, I advise businesses to invest in upskilling their workforce and democratizing data access so employees at every level can use data to drive impact. By addressing these barriers, organizations can elevate their data maturity and unlock the full value of their data to fuel innovation and growth.
What specific strategies can organizations implement to improve data quality and streamline integration for AI-driven initiatives?
The success of AI-driven initiatives starts with high-quality data. Without accurate, consistent, and complete data, even the most advanced AI models will struggle to deliver meaningful insights. Organizations benefit from establishing clear data governance frameworks that enforce standardization across all sources. Defining and maintaining consistent formats, taxonomies, and validation rules ensures that AI models are trained on reliable, high-integrity data to produce the best results.
Automating data cleaning processes—such as de-duplication, anomaly detection, and missing value imputation—further enhances quality while reducing manual effort. Real-time monitoring and continuous validation help maintain data integrity as new information flows into the system.
To streamline integration, organizations should leverage modern data platforms, such as cloud-based data warehouses and lakes, that enable seamless access and consolidation of data from multiple sources. API-driven architectures can enhance interoperability, making it easier for AI models to access and process diverse datasets.
Organizations should also have a robust experimentation approach that allows for the continuous testing of new data sets on their ability to impact business performance.
Beyond technology, collaboration is key. Encouraging cross-functional teams—including data engineers, analysts, and business leaders—to collaborate ensures that data initiatives align with strategic goals. Organizations will be poised to unlock more precise predictions, stronger decision-making capabilities, and ultimately, more impactful business outcomes.
In the case of businesses that want to modernize their data infrastructure, what are the key steps they must take beforehand to ensure their data is hygienic and complete for AI applications?
Preparation is critical when modernizing data infrastructure. The first step is to conduct a thorough data audit to assess the current state of data, including its sources, quality, and accessibility. Next, businesses need to define specific objectives for their AI initiatives, which will help determine data requirements. Addressing gaps in the data is crucial for ensuring it will solve problems, not create them.
Implementing governance frameworks will establish accountability, security, and compliance, which are essential for AI readiness. Finally, testing the scalability of the infrastructure is vital to ensure it can handle large-scale AI workloads, including real-time processing.
With 88% of decision-makers planning on boosting investment into GenAI, what specific data-related challenges should they address first to tap into the generative AI’s full potential?
To unlock the full potential of generative AI, organizations must address several critical challenges. One of the first priorities is ensuring data diversity. Generative models thrive on diverse, representative datasets that reflect the complexity of their intended use cases. Organizations must also work to identify and eliminate biases within their datasets to prevent skewed outputs. Scalability is another challenge—businesses need to build systems capable of managing the computational demands of generative AI. Additionally, privacy compliance is essential to avoid legal risks, which means ensuring data usage adheres to regulations like GDPR and CCPA. Lastly, maintaining transparency around data sources and explainability of AI/ML models is crucial for building trust in AI outputs.
When it comes to implementing AI initiatives, how do you think businesses can change their mindset from treating data as a commodity to viewing it as a strategic asset?
Shifting the perception of data from a byproduct of operations to a core driver of innovation requires a fundamental cultural transformation. Businesses that successfully make this transition embed data into every aspect of decision-making, rather than treating it as an isolated resource or a technical concern.
One of the most effective ways to cultivate this mindset is by demonstrating the tangible impact of data on business outcomes. Leaders can highlight real-world examples where data-driven insights have led to breakthrough innovations, operational efficiencies, or competitive advantages. Showcasing how predictive analytics, automation, and personalization create measurable value helps reinforce data’s role as a strategic differentiator.
Empowering employees across all functions to engage with data also accelerates this shift. Rather than limiting data access to specialized teams, organizations benefit from making insights available and actionable for departments such as marketing, sales, and customer service. When people at all levels see how data can inform their daily work—whether through customer behavior insights, market trends, or process optimizations—they begin to view it as a powerful tool rather than just an IT asset.
Additionally, rethinking key performance indicators (KPIs) can reinforce this mindset shift. Organizations that integrate data-driven goals into performance metrics ensure that data is not just collected but actively used to drive strategic decisions. When business leaders assess success based on data-driven insights—rather than intuition or historical precedent—data naturally becomes a central part of the company’s strategic vision.
Lastly, investing in training and fostering a culture of continuous learning ensures that employees have the skills and confidence to leverage data effectively. The goal is to create an environment where curiosity and data-driven experimentation are encouraged. When teams are given the right tools and knowledge, they’re more likely to identify new opportunities and contribute to innovation, making data an integral part of long-term success.
As organizations grow their AI initiatives, what role does data governance and security play in ensuring that AI-driven solutions are both effective and compliant with regulations?
Data governance and security are the backbone of responsible AI adoption, ensuring that AI systems operate on reliable, ethical, and compliant data. Without proper governance, organizations risk training AI models on old, inaccurate, biased, or incomplete data, leading to flawed insights and unreliable automation. Well-structured governance frameworks establish data integrity by enforcing accuracy, consistency, and accessibility across all sources.
Security is equally critical. As AI applications process vast amounts of sensitive data, organizations must implement strong access controls, encryption, and audit mechanisms to protect against breaches. Compliance with regulations like GDPR and CCPA isn’t just about avoiding penalties—it builds trust with customers and stakeholders.
Another key factor is transparency. Organizations benefit from maintaining clear documentation on how AI models use data, ensuring accountability and enabling better troubleshooting. Regular audits and continuous monitoring help organizations adapt governance strategies as AI capabilities evolve.
By treating governance and security as enablers rather than constraints, businesses can scale AI confidently, knowing their systems are both high-performing and aligned with ethical and legal standards.
How do organizations measure ROI on investment in improving data infrastructure while gearing up for significant investments in AI, and what are the metrics that organizations should focus on to determine success?
Measuring ROI on data infrastructure investments requires a balance of efficiency gains, business impact, and AI readiness. One key indicator is the speed at which data can be transformed into actionable insights. Faster data processing and reduced latency in decision-making signal a well-optimized infrastructure.
Cost savings from automation and improved operational efficiency also provide measurable ROI. AI-driven data infrastructure reduces manual effort in data management, streamlining workflows and lowering expenses. Additionally, the scalability of AI applications—how easily they adapt to increasing data volumes without performance loss—can signal the success of infrastructure investments.
From a revenue perspective, organizations can track improvements in customer acquisition, retention, and personalization effectiveness. Enhanced data infrastructure enables more precise targeting and customer engagement, directly impacting profitability. Compliance and risk mitigation are also critical. Reduced regulatory violations, fewer security breaches, and improved data governance lower long-term financial risk.
Ultimately, success is measured by how seamlessly AI initiatives integrate with business goals. If AI-driven decisions lead to smarter product recommendations, better fraud detection, or improved supply chain efficiency, the investment in data infrastructure has delivered real value.
What would your advice be to data and analytics leaders bridging the gap between the current state and the need for Generative AI activation?
Focus on creating a culture of growth and collaboration. Start by identifying specific, high-impact use cases for generative AI that align with your organization’s goals. Then, bring together cross-functional teams to pilot these initiatives, ensuring alignment and a shared sense of ownership.
Investing in your people is just as important as investing in technology. Provide accessible training opportunities and resources that empower staff to grow their skills and deepen their understanding of AI. This isn’t just about technical knowledge—it’s about fostering confidence and curiosity. When employees feel supported and equipped to engage with new technologies, they’re more likely to contribute fresh ideas and drive meaningful outcomes.
Finally, adopt a phased approach. Begin with small, targeted projects that allow teams to experiment, learn, and refine their strategies. Celebrate early successes and share learnings across the organization. By focusing on people, collaboration, and continuous learning, leaders can activate generative AI in a way that’s both impactful and sustainable.
Andrew Frawley
CEO of Data Axle
Andrew (Andy) Frawley, with over 30 years of operational experience, including 25 years in senior leadership, has excelled in diverse industries such as agency, marketing services, software, and professional services. As a seasoned leader, he specializes in SaaS, Digital Marketing, CRM, Big Data, and Marketing Automation. As the CEO of Data Axle, Andy is dedicated to further developing industry-leading client solutions and delivering world-class services to Data Axle clients.
A published author of “Igniting Customer Connections” (2014), Andy is a sought-after speaker on various business and technical topics related to Digital Marketing, Product Innovation, Agency Innovation, Customer Analytics, Big Data, and Customer Value Management. During his distinguished career, he has advised many clients, ranging from small digital businesses to the largest global marketing organizations. Andy’s breakthrough thinking and methodologies, including ROE2, Cliquity, Continuous Customer Management, and Value in Play, have guided organizations in delivering tangible ROI from their customer and marketing investments.
Prior to joining Data Axle, Andy amassed a wealth of leadership experience, steering transformative initiatives at several organizations. In the 1990s, Andy founded Exchange Applications, one of the industry’s first MarTech platforms, going public in 1998. During his tenure as President and CEO of ClickSquared Inc., he repositioned the company to an enterprise SaaS solution to automate multi-channel marketing, dramatically increased the customer base, and achieved record growth, which led to it being named the fastest-growing company in New England several times from 2005 to 2008. Over seven years at Epsilon, Andy ascended from leading the digital division to President of Marketing Technology and, ultimately, CEO. Under his stewardship, Epsilon launched innovative digital products and transitioned from a product-centric to a customer-centric model, achieving significant revenue growth. He also oversaw the integration of two large agencies, Aspen Marketing and Hyper Marketing, into Epsilon. At V12, he strategically repositioned the company into the CDP market, unveiled an industry-leading Intent data product, and orchestrated a successful sale to Porch (NASDAQ: PRCH) in January 2021.
With an MBA from Babson College and a B.S. in Finance from the University of Maine, Andy currently serves on the boards of CURO (NYSE: CURO), Alterian, and Marketing Evolution, contributing his strategic insights to committees on strategy, audit, and compensation. Beyond his corporate engagements, he is passionately involved in various nonprofit organizations. Andy resides with his wife, Juli, in Kennebunkport, Maine, and Naples, Florida.