Data-Driven Compliance: Overcoming Analytics Roadblocks 🚧🏎️

In the data-driven battleground of modern compliance, those who master analytics gain an insurmountable advantage—yet most organizations remain trapped behind roadblocks of their own making. We brought together experts from Cisco Systems and Honeywell to discuss the implementation of data analytics within ethics and compliance programs. The conversation explored the challenges organizations face when establishing data analytics capabilities, strategies for building effective cross-functional relationships, methods for demonstrating value to the business, and the potential impact of emerging technologies like AI on compliance analytics.

This episode of The Ethicsverse presented a comprehensive exploration of data analytics implementation within ethics and compliance functions, featuring expertise from practitioners at Cisco Systems and Honeywell. The discourse examined various methodological approaches to overcoming structural impediments in analytics adoption, emphasizing the criticality of data integrity, strategic cross-functional alliance building, and incremental value demonstration. Participants articulated a framework for analytics maturation—progressing from descriptive to prescriptive capabilities—while highlighting practical techniques for navigating resource constraints and technical complexities. The session illuminated strategies for aligning compliance analytics with business objectives to enhance stakeholder engagement and provided concrete examples of successful analytics implementations that delivered measurable organizational impact. The conversation concluded with forward-looking perspectives on artificial intelligence’s transformative potential for compliance data management and analysis.

Meet The Ethics Experts:

• Hayley Tozeski, Senior Counsel, Anticorruption Compliance Program, Cisco
• Jeffrey Schwartz, Ethics & Compliance Attorney, Formerly Honeywell, Booz Allen, HP/Hewlett Packard Enterprise
• Nathan Opheim, Director, Legal Compliance – Software Licensing | Digital Fraud Prevention | Data & Analytics, Cisco
• Matt Kelly, CEO & Editor, Radical Compliance
• Nick Gallo, Chief Servant & Co-CEO, Ethico

Strategic Alignment with Business Operations

• Successful compliance analytics programs must be deliberately aligned with existing business operations rather than functioning as disconnected oversight mechanisms.
• Compliance professionals should examine how analytics is implemented elsewhere in the organization and leverage those established models instead of creating isolated approaches.
• By framing analytics initiatives in terms of business value and operational improvement rather than just risk mitigation, compliance teams can gain stronger support from business units who might otherwise view analytics as an additional burden or obstacle to their primary objectives.

Organizational Structure and Talent Acquisition

• The organizational placement of analytics capabilities significantly impacts program effectiveness, with some organizations finding success by embedding dedicated analytics professionals directly within compliance functions.
• This proximity between technical expertise and subject matter experts facilitates faster execution and more relevant insights than traditional IT-driven approaches.
• Organizations that struggle to secure dedicated analytics headcount should consider creative alternatives such as leveraging vendor partnerships, recruiting temporary assistance from other business units, engaging academic institutions, or starting with accessible tools like Excel before gradually building more sophisticated capabilities.

The Analytics Maturity Progression

• Data analytics capabilities typically evolve through four progressive levels: descriptive analytics (understanding what happened), diagnostic analytics (determining why it happened), predictive analytics (forecasting what might happen), and prescriptive analytics (recommending actions to optimize outcomes).
• Organizations should recognize their current maturity level and develop a roadmap for advancement rather than attempting to immediately implement advanced capabilities.
• By starting with basic descriptive analysis of existing data and demonstrating value, compliance teams can build support for more sophisticated analytics initiatives while developing the foundation needed for future growth.

Relationship Building with System Owners

• Multinational organizations must navigate the tension between maintaining consistent global compliance standards while adapting to local regulatory requirements and cultural expectations across various jurisdictions.
• Adopting a “most stringent law” approach often provides practical guidance when facing conflicting requirements, allowing the organization to set a baseline standard that likely satisfies all jurisdictions while making specific adaptations where absolutely necessary.
• Regular communication between global and local compliance functions helps identify potential conflicts early and develop solutions that maintain the integrity of the compliance program while respecting local legal obligations.

Communicating Effectively with Business Leaders

• Developing robust relationships with IT leadership and system owners is critical for sustainable analytics programs, as these stakeholders control access to essential data sources and can provide early notification of system changes.
• Compliance teams should proactively engage with master data management programs, which serve as the backbone for many analytics initiatives and represent an excellent starting point for understanding available data.
• By positioning compliance analytics as complementary to IT objectives rather than competing priorities, organizations can foster collaborative approaches that benefit both functions and ensure analytics initiatives remain viable through technological change.

Creating Value Through Targeted Use Cases

• Analytics initiatives should be anchored to specific, high-value use cases rather than pursuing analytics for its own sake.
• Whether examining third-party risk profiles, testing policy effectiveness, optimizing human resource allocation, or targeting training investments, analytics should address concrete business challenges with measurable outcomes.
• By framing analytics in terms of business process optimization and cost reduction rather than purely compliance objectives, teams can generate enthusiasm and support that fuels continued investment in analytics capabilities.

Collaborative Data Governance

• Effective data analytics requires collaborative governance structures that bring together system owners, business process leaders, and risk owners to maintain alignment as systems and processes evolve.
• Organizations should establish formal mechanisms for ongoing communication about data needs, system changes, and analytics outputs to prevent unexpected disruptions to analytics capabilities.
• The governance approach should shift from viewing systems as time-limited projects to treating them as strategic assets requiring continuous management and oversight to ensure sustained value delivery.

Measuring Program Effectiveness

• Data analytics provides powerful capabilities for measuring the effectiveness of compliance initiatives and validating policy decisions, enabling evidence-based program management.
• By establishing baseline metrics before implementing program changes and then measuring the same indicators afterward, compliance teams can demonstrate the impact of their initiatives and identify areas requiring further attention.
• This approach transforms compliance from a cost center into a strategic function capable of demonstrating quantifiable business value through concrete, data-driven insights.

Incremental Implementation Strategy

• Successful analytics implementation typically follows an incremental approach that starts with modest, achievable objectives and builds toward more sophisticated capabilities.
• By focusing initially on “quick wins” that demonstrate value while requiring minimal investment, compliance teams can build credibility and support for more ambitious initiatives.
• This progressive strategy allows organizations to develop expertise, refine processes, and expand data access while delivering continuous value, rather than pursuing comprehensive solutions that may fail to deliver timely results.

Artificial Intelligence as Transformative Force

• Artificial intelligence represents a significant opportunity to accelerate and enhance compliance analytics capabilities by addressing common obstacles like messy data environments and resource constraints.
• AI tools can help organizations clean and harmonize disparate data sources, identify patterns and anomalies that human analysts might miss, and automate routine analytical tasks to improve efficiency.
• As compliance programs mature, AI-powered analytics can provide increasingly sophisticated capabilities including anomaly detection and predictive risk identification, fundamentally transforming how organizations approach compliance monitoring and management.

Closing Summary

The webinar emphasized that successful data analytics in ethics and compliance requires more than technical expertise—it demands strategic alignment with business objectives, strong cross-functional relationships, and an incremental approach to capability building. By focusing on data integrity, demonstrating tangible value through targeted use cases, and establishing effective governance structures, organizations can overcome common roadblocks to analytics implementation. As artificial intelligence continues to evolve, it offers promising opportunities to enhance analytics capabilities and drive further innovation in compliance monitoring and risk management. The journey toward analytics maturity may be challenging, but the potential benefits in terms of program effectiveness, resource optimization, and risk mitigation make it a worthwhile investment for forward-thinking compliance functions.