At Cypher 2024, Gaurav Anand, Head of Data and Analytics at Diageo India, delivered a compelling talk on the often-overlooked foundation of analytics success. While the spotlight frequently falls on generative AI and advanced data models, Gaurav highlighted the critical importance of building a robust data foundation to ensure scalability, reliability, and actionable insights.
The Foundation Matters
Drawing an analogy with constructing a house, Gaurav emphasized that while design and interiors often get the glory, a strong foundation is what truly ensures longevity and stability. Similarly, in analytics, organizations need to focus on the core data architecture that supports advanced AI and analytics capabilities.
Gaurav identified two types of organizations:
- Born-Digital Companies: With relatively simpler data landscapes, these organizations focus on managing data velocity and variety.
- Legacy Organizations: Often burdened by complex ecosystems due to mergers, acquisitions, and siloed systems, these companies face significant challenges in integrating data and deriving value.
For both, a strong foundation is non-negotiable.
Why a Strong Data Foundation Is Essential
Gaurav outlined three key reasons why organizations need to prioritize their data foundation:
- Garbage In, Garbage Out (GIGO):
Advanced models are only as good as the data they consume. Poor-quality data leads to irrelevant insights, creating a disconnect between technical outputs and business needs. - Single Source of Truth (SSOT):
Without a unified data source, organizations risk confusion and inefficiency. Meetings can derail when stakeholders question the validity of data due to multiple, conflicting sources. - Scalability and Flexibility:
Analytics solutions should be adaptable. Use cases from one function (e.g., marketing) should be transferable to others (e.g., supply chain) with minimal rework. Building rigid systems hampers future growth and innovation.
Challenges in Building an Analytics Foundation
Gaurav shared practical challenges organizations face in establishing a resilient data architecture:
- Data Silos:
Departments or regions often maintain their own data systems, creating a fragmented landscape. This “cottage industry” of dashboards and analytics prevents organizations from achieving a unified view. - Inconsistent Data Definitions:
Different teams often use varying terminologies for the same data. For instance, a product name in one geography might have a different code or alias elsewhere, leading to misalignment. - Lack of Data Ownership:
A foundational issue is the absence of clear data ownership. While technical teams manage infrastructure, business teams must take responsibility for data quality and completeness. - Data Accuracy and Completeness:
Inaccurate, incomplete, or outdated data undermines analytics efforts. Business alignment is crucial to ensure transformations and calculations reflect operational realities. - Data Literacy:
Without adequate data literacy, business users struggle to understand and adopt analytics solutions, limiting their effectiveness.
Key Solutions for Analytics Ascent
Gaurav proposed actionable strategies to address these challenges and build a resilient analytics foundation:
- Governance Framework:
Establish a governance mechanism with clear roles for data stewards, owners, and translators. Regular cross-functional meetings can resolve inconsistencies, such as differing data definitions, and ensure alignment. - Integration and Automation:
Avoid manual data aggregation by leveraging APIs and automation tools to ensure seamless data flow across systems. Contracts with SaaS providers should include provisions for retrieving data to prevent it from becoming inaccessible. - Balancing Open Source and Closed Systems:
While open-source tools are cost-effective, they may face compliance challenges. Gaurav recommended involving legal and audit teams early to ensure the chosen technology aligns with organizational policies. - Investing in Data Literacy:
Organizations should create data literacy programs to empower business users. This not only improves adoption but also fosters collaboration between technical and business teams. - Securing Leadership Buy-In:
Gaurav underscored the importance of securing sponsorship from top leadership. A CEO’s commitment to analytics as a priority can transform it from a departmental effort to an organizational mandate.
Advanced Analytics and Visualization
Once the foundation is established, advanced tools can accelerate analytics capabilities. Gaurav highlighted modern BI systems and real-time analytics platforms that allow organizations to create visuals, conduct causal analysis, and query data on the fly. These tools, when integrated with a robust foundation, enable faster decision-making and deeper insights.
Preparing for the Future
Gaurav also touched on emerging trends that organizations should prepare for:
- Scaling Generative AI:
While generative AI is gaining traction, its compute-intensive nature requires a well-architected foundation to scale effectively. - Cloud and Hybrid Architectures:
Organizations should leverage the flexibility of cloud systems while addressing data security and integration challenges. - Focus on Talent:
Continuous upskilling is essential as the analytics landscape evolves. Organizations must invest in developing both technical and business-oriented data skills.
Key Takeaway: Data as Nutrition
Gaurav concluded with a powerful analogy: “Data is the nutrition for AI.” Just as junk food impacts health, low-quality data impairs analytics outcomes. Investing in clean, well-structured, and actionable data is essential for long-term success.
As organizations aim for analytics ascent, they must remember that a strong foundation isn’t just a technical requirement—it’s a strategic enabler for innovation and growth.
