Tear Down Data Walls, Build Up Portfolio Returns

The modern data stack has revolutionized how companies collect, store, and analyze data. However, many private equity portfolio companies are discovering that simply implementing modern data tools isn't enough to drive meaningful value creation. The real challenge lies in breaking down the organizational and technical silos that prevent companies from fully leveraging their data assets. While firms may balk at the expense of upgrading their data architecture, the real question they should ask is “what is the cost of NOT breaking down data silos?”

The Evolution of Data Architecture in Portfolio Companies

To understand the current challenges, we should examine how data architecture has evolved in typical portfolio companies. Many begin with disparate legacy systems accumulated through years of operations and acquisitions. The introduction of the modern data stack—typically including tools like Fivetran for data integration, Snowflake for warehousing, and Looker for visualization—promised to solve these fragmentation issues. However, this technological transformation often preserves or even reinforces existing organizational silos.

Consider a mid-market healthcare insurance provider with a heavy investment in modern data infrastructure. They found themselves with pristine data pipelines feeding into sophisticated visualization tools, yet still struggling to derive actionable insights. The problem wasn't technical—it was structural. Different departments maintained separate data definitions, used inconsistent metrics, and rarely shared insights across functional boundaries. Clinical personnel were unable to make real time decisions about care management because of disparate data systems and data silos.

By centralizing the distinct systems of record, clinical personnel now had timely access to the full breadth of trusted data and could make informed decisions about best care practices. Furthermore, this enabled tracking and monitoring of quality metrics, making automation of patient follow-ups possible. Most importantly, the organization saved nearly $4M annually in operational efficiencies, while increasing their quality of care ratings.

The Hidden Cost of Data Silos

Data silos can create multiple layers of value destruction in portfolio companies. For example, an industrial manufacturing company’s data was disconnected and didn’t share the same definitions across its business units' IT systems in manufacturing, sales, and customer service. This fragmented and inconsistent data led to inaccurate production, inventory, and sales forecasts–resulting in profit margin losses as well as negative customer reviews. Despite having a state-of-the-art data warehouse, the lack of shared definitions and cross-functional data governance meant that each team maintained its own "version of the truth."

Negative impacts can extend beyond operational inefficiency. An e-commerce company found that their customer service, marketing, and product teams each maintained separate customer interaction data. Each silo cherished the usability of their software stack, yet instead of breaking down silos, their technology had built stronger walls between them.

This fragmentation prevented them from developing a unified view of the customer journey, limiting their ability to improve customer experience and drive retention. The cost of this fragmentation became apparent when they discovered they were spending marketing dollars–such as sending new customer discount coupons via email to existing customers–while those same customers were simultaneously raising product concerns through customer support channels.

Breaking Down Silos: A Comprehensive Approach

Successfully breaking down data silos requires a multi-faceted approach that goes beyond technical solutions to standardize data governance, align the organization, and integrate processes.

Data Governance and Standardization

A business services company formed a cross-functional data governance group to establish common definitions for key business metrics. Introducing uniform data definitions ensured that financial metrics and KPIs were consistently defined across the company. This seemingly simple step revealed misaligned incentives and GTM strategies given different customer churn rate calculations.

Organizational Alignment

Organizational realignment can enhance data quality and reduce bottlenecks, leading to more timely and data-driven decision making. For example, a digital media company created cross-functional teams aligned with business domains such as content recommendation and user engagement rather than functional technical teams like data engineering and analytics. The domain teams treated their data as a product to be used by other teams within the company. These domain teams were composed of both business stakeholders and technical specialists. Focusing on cross-functional teams enables more comprehensive understanding of key priorities.

Process Integration

An online food delivery company implemented a "data mesh" architecture that decentralized data ownership while maintaining centralized governance. This approach allowed different business units to maintain autonomy over their data while ensuring interoperability and consistency. The company broke down data silos and enabled real-time analytics. The result was a reduction in time-to-insight for cross-functional analytics projects and an increase in the reuse of data assets across departments. This paradigm shift in data architecture has also prepared the company to introduce AI tools to support rapid growth and complex data analysis needs.

Implementation Strategy

Breaking down data silos requires careful attention to several key factors:

• Change management becomes crucial, as established patterns of data ownership and usage must evolve. Successful implementations often start with pilot projects that demonstrate quick wins and build momentum for broader transformation.

• Technical architecture must support rather than hinder organizational goals. While the modern data stack provides powerful tools, its implementation should reflect the desired future state of data sharing and collaboration.

• Incentive structures need careful consideration. Teams should be rewarded for sharing and collaborating around data assets rather than maintaining control over their individual domains.

Conclusion

While the modern data stack provides powerful technical capabilities, true value creation within portfolio companies requires breaking down the silos that prevent effective data utilization. Forrester Research found that knowledge workers waste an average of 12 hours a week just "chasing data." Success requires a holistic approach that combines technical architecture, organizational design, and cultural change. Private equity firms that encourage and support their portfolio companies to navigate this transformation will be well-positioned to drive superior returns through data-driven value creation. Equally important, what is the cost of not adapting?

NextAccess Authors: Scott Kosch and Valerie VanDerzee

NextAccess is an advisory firm of experienced operators with deep experience running top-performing organizations and delivering exceptional results. We help executive teams and investors build stronger, more valuable companies through a powerful mix of operational expertise, strategic insight, and data-driven solutions.

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