Breaking Down Data Silos: 5 Steps to Integrate Commerce Data

As an executive at a multi-brand commerce organization, you're no stranger to the challenges posed by data silos. With each brand operating its own e-commerce platform, marketing channels, and data sources, gaining a comprehensive view of your customers, operations, and performance metrics can be incredibly difficult.

Fragmented data doesn't just create operational inefficiencies—it can actively hinder your ability to make informed decisions and provide seamless customer experiences. According to a recent Gartner report, companies that effectively leverage data and analytics are twice as likely to beat their competitors regarding factors like new product launches and customer retention.

The solution lies in breaking down those data silos and integrating your disparate sources into a centralized, unified platform. This holistic view empowers you with deeper insights, streamlined operations, and the agility to seize new opportunities. Here's a step-by-step guide to help you get started on that critical data integration journey.

Step 1: Audit Your Data Sources

Before effectively integrating data, you must understand the full scope of sources across your organization. Conduct a comprehensive audit to identify all the platforms and systems housing data relevant to your business, spanning:

• E-commerce platforms and online stores
• Marketing channels (paid ads, email, social media, etc.)
• Customer data platforms and CRMs  
• Analytics tools for web, app, and in-store activity
• Enterprise resource planning (ERP) software
• Supply chain and inventory management systems

Don't just look at the core brand systems - dig deeper into departments like finance, operations, and customer service to uncover other data repositories. According to McKinsey research, upwards of 70% of enterprise data gets stranded in departmental silos.

Step 2: Prioritize Key Data Sources

With your full audit completed, prioritize the order of the data sources you should integrate based on business impact and value. A few factors to consider:

• Revenue contribution: Start with the sources that house data directly tied to revenue streams and profitability metrics.
• Customer impact: Focus on customer data sources that influence marketing campaigns, service interactions, and experience personalization.  
• Operational importance: Prioritize systems that track supply chain, inventory, fulfillment, and other core operational data.
• Growth potential: Identify sources of housing data that reveal new market, channel, or product opportunities.

Conduct stakeholder interviews to further understand which data is most crucial to different teams and what decisions it informs.  

Step 3: Define Your Integration Approach

With priority sources mapped, you'll need a plan for integrating that data into a centralized platform or warehouse. There are two primary approaches:

• Build a custom solution in-house using integration tools and data engineers.
• Leverage a pre-built data integration platform designed specifically for commerce/retail use cases.

The DIY path gives you complete control but requires significant technical expertise and ongoing maintenance. A specialized platform offers rapid deployment, extensive integrations, and data models tailored to the insights retailers need.

According to a Deloitte study, a lack of data integration skills and resources is one of the top barriers to effective analytics for consumer products companies. Whichever route you choose, be sure you have the proper talent and tools in place for long-term scalability.

Step 4: Establish Agile Data Governance Protocols

Maintaining data integrity is paramount as you blend datasets across brands, geographies, and departments. Establish agile data governance protocols that provide guard rails without inhibiting data flow and insights. Strike a balance between governance and empowering self-service data access. An effective governance model will cover:

• Standardized data definitions, calculations, and nomenclatures across the organization to ensure consistency
• Automated processes for data cleansing, deduplication, transformation, and normalization
• Role-based data access controls with approval workflows
• Policies for data masking and anonymization to protect sensitive/PII data
• Disaster recovery and backup procedures to safeguard data
• Compliance with data privacy regulations like GDPR, CCPA, and others

Don't take a rigid, top-down approach to governance. Get stakeholders from IT, legal, marketing, finance, and operations involved in defining agile policies and processes. The goal is to strike a balance between ensuring data quality and security while still enabling self-service data exploration.
Leverage integrated data platforms with robust, configurable governance capabilities built-in. Look for features like:

• Centralized policy management
• Auditing and data lineage tracking
• Automated data quality monitoring
• Approval workflows for data model changes
• Role-based access with single sign-on

Strong yet agile data governance breeds trust in the insights that surfaced from your unified data sources. According to a Forrester survey, over 60% of data and analytics decision-makers cite governance as a critical priority.

By adopting an agile data governance approach, you can accelerate the flow of trusted data while giving business teams safe means to access and analyze the integrated insights they need.

Step 5: Operationalize Data-Driven Decision-Making

Even with an integrated data platform in place, extracting value requires operationalizing insights through processes, training, and cultural change. Some best practices include:

• Establishing KPIs and success metrics tied to the integrated data
• Building custom dashboards tailored to different teams/roles
• Updating reporting cadences and automated data distribution
• Investing in data literacy training for non-technical teams
• Creating processes for collaborating on insights across brands/departments
• Tracking how integrated data influences key decisions over time

More importantly, leadership must model and reinforce data-driven behaviors throughout the organization. A study by the MIT Sloan School School of Management found that companies with data-driven decision-making achieved 4% higher productivity and 6% higher profits.

Break Down Silos, Unlock New Potential

While integrating disparate data sources across a multi-brand organization is no small undertaking, the potential rewards are significant. From optimizing customer acquisition costs to driving supply chain efficiencies, the insights surfaced by a unified commerce data platform can fuel growth and resilience.

Most importantly, a holistic view of your customers empowers you to deliver cohesive, personalized experiences that build lasting brand affinity and loyalty. With heightened data visibility, you can break free of organizational constraints and operate as one high-performing, customer-obsessed business.

So get started on that integration journey today - the future of your business depends on bridging those silos.