In today’s retail landscape, data is both an asset and a challenge. Beyond sheer data volume, most retailers struggle with the complexity of integrating diverse data sources. Supply chains, customer interactions, marketing platforms, and operational systems all generate data — but in different formats, from different locations, and often on incompatible platforms. This fragmentation creates barriers to deriving insights, optimizing operations, and delivering seamless customer experiences.

To navigate this challenge, retailers must prioritize flexible, scalable data integration strategies that allow them to harmonize disparate sources without disrupting existing workflows. A one-size-fits-all approach won’t work — successful retailers are adopting modular, adaptable architectures that can evolve with new technologies, customer engagement channels, and business needs. 

To set the stage for this challenge, Veronika Durgin, the Vice President of Data at Saks, spoke with Fivetran’s Kelly Kohlleffel to discuss how addressing data fragmentation head-on can transform data from a chaotic burden into a competitive advantage that drives strategic decision-making and operational efficiency.

Navigating data source chaos in retail

In the world of luxury retail and e-commerce, data integration is complicated by more than data volume or velocity. While companies like Amazon and Walmart may contend with massive transaction volumes, the true complexity for most retailers lies in data variety — the sheer number of disparate sources feeding into their ecosystems.

Retailers must manage data from supply chains, inbound and outbound logistics, websites, customer interactions, and third-party platforms — each operating on different systems and generating structured and unstructured data. This fragmented landscape, sometimes referred to as "data source chaos," creates a significant obstacle when trying to harmonize, analyze, and derive actionable insights.

Durgin emphasizes that at the core of this challenge is the need to integrate multiple applications and microservices, ensuring they communicate effectively and provide a unified view of business performance and customer behavior. Organizations that rely on internally built software often struggle with interoperability, requiring extensive efforts to enable cross-platform data connectivity.

Successful retail data strategies must prioritize flexible, scalable integration solutions that can adapt to new tools, technologies, and customer engagement channels as they emerge. By addressing data fragmentation head-on, retailers can move beyond data chaos and unlock valuable, cohesive insights that drive better decision-making, operational efficiency, and enhanced customer experiences.

Build vs. buy: Balancing time, cost, and flexibility in data strategy

The build vs. buy debate is a constant conversation in data strategy — one that business leaders and key stakeholders must navigate carefully. Durgin notes that at its core, the decision involves weighing total cost of ownership (TCO), scalability, integration complexity, and long-term sustainability.

When building a custom solution, organizations gain tailored functionality but must invest in engineering teams, tools, ongoing maintenance, and institutional knowledge to keep the system operational. On the other hand, buying a solution offers faster implementation and vendor support. However, the rise of AI and rapid technological acceleration has shifted the focus to time to value. In today’s fast-moving business environment, leaders often need immediate solutions — even if building a custom system might be ideal in the long run.

Introducing the third “B”: Bridging the gap

Beyond building vs. buying, there’s a third approach, bridging — bringing in a temporary solution to quickly address the immediate problem while creating space to build or refine a longer-term solution. This approach allows organizations to:

• Solve urgent business needs quickly
• Evaluate market solutions before deciding where to invest
• Invest in architectures with flexibility, ensuring that components remain replaceable and swappable

Focus engineering on what truly adds value

In today’s rapidly evolving data landscape, organizations must strike a balance between leveraging existing platforms and building custom solutions. With platforms like Snowflake, Databricks, and other modern data infrastructure tools readily available, there’s little reason to reinvent the wheel. Instead, businesses should focus engineering efforts where they create unique, strategic value.

According to Durgin, engineering time is one of the most valuable assets within an organization. Therefore, companies should prioritize custom development efforts on capabilities that are either unsolvable by existing tools or that create a distinct competitive advantage. These may include:

• Industry-specific enhancements – Custom features that serve niche market needs, such as fashion retail analytics or healthcare-specific compliance frameworks.
• New revenue-generating opportunities – Innovations that open new value streams for the business.
• Customer experience improvements – Tailored solutions that enhance user experience and engagement beyond what generic platforms provide.

Outsource the rest: The efficiency mindset

While the instinct to build from scratch is strong — especially among engineering teams — not everything needs to be built in-house. Just as outsourcing household chores can free up time for more valuable activities, outsourcing non-differentiated technical tasks allows teams to focus on what truly moves the needle.

By adopting this mindset, organizations can:

• Accelerate time to value by leveraging pre-built solutions
• Reduce technical debt by avoiding unnecessary custom builds
• Empower engineering teams to innovate where it genuinely matters

The most successful data-driven organizations don’t take an all-or-nothing approach to building solutions. Instead, they layer industry-specific customizations on top of proven, scalable platforms, ensuring they get the best of both worlds—speed, flexibility, and innovation.

By outsourcing commodity tasks and focusing engineering time on high-value differentiators, businesses can drive efficiency while still creating unique competitive advantages — ensuring they stay agile, scalable, and ahead of the curve.

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