Case Study 1: Turning Data Chaos into Value

A Data Product Strategy in Action

Helping a global manufacturer align people, process, and platforms to unlock real business results. Scroll down for the full story.

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The Challenge

This Fortune 100 off-road equipment manufacturer had invested heavily in data platforms, engineering talent, and cloud infrastructure—but still struggled to turn that investment into enterprise-wide value.

With siloed dashboards, redundant pipelines, and conflicting KPIs, teams spent more time wrangling data than using it.

60% of analysts’ time was spent just selecting and cleaning data before they could generate insights.

60%

The Key Pain Points

Siloed teams building duplicate reports and dashboards

No centralized ownership or standards

Stalled cross-functional initiatives

Rising analytics costs (40–50% annually)

Conflicting KPIs and lack of trust in reporting

Heavy analyst effort spent on data prep instead of insights

The Solution: Enterprise Data Product Operating Model

Black Antler partnered with the client to reframe their data foundation and create lasting operational alignment.

Modern Data Engineering Strategy

  • Formalized accountability for pipeline quality

  • Clear interfaces between engineers, analysts, and product owners

Refocused Analytics Org Structure

  • Shifted from siloed teams to domain-driven pods

  • Centralized customer analytics across sales, service, and marketing

Data-as-a-Product Strategy

  • Defined reusable, curated data products

  • Launched cross-functional product charters with business-focused success metrics

Culture + Literacy Initiatives

  • Delivered internal data fluency workshops

  • Established a data product catalog and governance council

Results in 6 Months

  • $1.8M cost savings from reducing redundant reports

  • 30% faster time-to-insight for marketing

  • 70% increase in executive trust (internal NPS)

  • 25% increase in data reuse across departments

Looking Ahead

With strong foundations in place, the company is set to scale its data transformation. The data product catalog will expand into areas like supplier performance and sustainability, supported by clear ownership and standards. Investments in AI and automation will enable real-time forecasting and self-service analytics. A data product council will guide governance and roadmap alignment, while external monetization opportunities are being explored. Finally, data literacy will be embedded into onboarding and leadership development to sustain a strong, data-driven culture.

Let’s partner together

Want to see what this kind of transformation could look like inside your business?

Let’s talk about how to turn your data investments into action.

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Case Study 2: Bridging the Gap with Data Catalyst Roles