Big Ag Must Prioritize Data in Digital Transformation

Brian Regan, Senior Vice President, Americas Delivery at Syniti, emphasizes that digital transformation promises substantial operational efficiencies for agriculture, but these advancements rely heavily on one crucial factor: high-quality, accurate data.

As AI and generative AI increasingly anchor digital transformation initiatives in agriculture, it becomes clear that any technological advancement or implementation hinges fundamentally on the reliability of data. With precision agriculture, supply chains, and customer relationships increasingly reliant on digital tools, prioritizing data quality isn’t optional.

Why Data-First Matters

Historically, agricultural enterprises have been quick to adopt technology to enhance efficiency, whether through Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), or Product Lifecycle Management (PLM) systems. These investments are aimed mostly at achieving scale, reducing costs, and simplifying complex processes. Many of these efforts have fallen short due to one overlooked critical factor: data quality.

Take, for example, a commodity firm selling soybeans. The difference between retaining a customer or losing them to a competitor often boils down to transportation efficiency. Yet, achieving such efficiency demands impeccable data, from costs associated with production and transport to detailed traceability records. As businesses expand, integrating accurate data from various operational locations becomes both essential and challenging. If the underlying data is flawed, these expensive digital change initiatives inevitably falter, undermining their purpose and profitability.

The Hard Reality of Prioritize Data

Why hasn’t a data-first strategy always been the norm? Simply put, it’s because managing data is complex and inherently cross-functional. Accurate, high-quality data isn’t created in silos; it demands cooperation across departments from procurement and logistics to marketing and sales. Historically, the agricultural industry could tolerate suboptimal data management. Organizations could maintain operations despite unreliable data, running warehouses and supply chains adequately, even if not optimally.

But the landscape has shifted dramatically. Today, poor-quality data is no longer just inconvenient; it can severely damage competitiveness. Regulatory standards are tightening, customer demands for transparency and traceability are increasing, and competition is fiercer than ever. Poor data quality now translates directly into higher costs, reduced efficiency, and lost opportunities. For agricultural enterprises, this means competitors gaining market share due to superior operational efficiencies derived from better-managed data.

Change with a Data-First Mindset

Successfully adopting a data-first strategy requires a structured approach to change management. Organizations must clearly understand their business goals and the specific role data will play in achieving them. Digital transformations often begin with enthusiasm fueled by vendor promises and success stories. However, organizations must temper enthusiasm with realism about the effort required to reach those same results. Clear-eyed assessments of data readiness and rigorous data management processes must accompany these ambitious digital initiatives.

Three Major Advantages of Being Data-First

Prioritizing data quality in digital transformation delivers three distinct advantages:

1. Enhanced Business Performance
   Accurate data directly supports superior decision-making. Whether the goal is gaining comprehensive customer insights, optimizing inventory, or managing supplier relationships effectively, having correct and complete data significantly boosts the potential for business success.
   
2. Cost Efficiency
   Implementing major digital transformation initiatives typically involves significant expenditures, often reaching millions or billions of dollars. Yet, poor data quality is frequently the primary cause of delays, leading to significant budget overruns. Organizations can substantially reduce project timelines and costs by establishing robust data foundations early, thus avoiding delays and associated expenses.
   
3. Risk Mitigation
   Inaccurate data enables poor decision-making, creating operational and strategic risks. Adopting a data-first strategy reduces these risks, providing decisions are driven by trustworthy insights. The financial and reputational benefits of decrease mistakes through data accuracy cannot be overstated.
   

Commitment to Data Quality

Data-first strategy is about that accurate data is the bedrock of successful digital change. Investing heavily in digital technology without prioritizing data management is like building a house on shaky ground. Poor-quality data creates ongoing challenges and undermines the effectiveness of even the most advanced systems, limiting long-term business performance.

Adopting a data-first approach requires commitment, patience, and organizational alignment. It’s not simply about purchasing new technology or implementing new software it’s about a culture that values data accuracy as a cornerstone of decision-making and strategic planning.

Conclusion

While transitioning to a data-first approach may seem daunting, the alternative, making critical business decisions based on wrong or incomplete data, is far more damaging. Agricultural enterprises stand to benefit by prioritizing data management as a strategic priority. Doing so ensures that digital transformations yield meaningful, lasting benefits, from optimized supply chains and enhanced customer relationships to improved operational efficiencies and profitability.