Why the Conventional Data Service Model Falls Short—and What to Do Differently

In modern organizations, there is an ocean of data—yet people remain thirsty. Business users struggle to get the right data at the right time, while data teams are overwhelmed by a large number of requests.

This post explains an intrinsic defect of the conventional data service model, and proposes a new service model that can close the gap.

The conventional data service model was built for a time when data needs were limited, well-defined, and slow-moving. Data flowed from application systems into downstream data sources, and business users submitted requests to technical teams, who delivered reports and dashboards as products.

This transaction-based model once worked well. However, today, it has become an inevitable bottleneck.

As data moves to the front end of business operations—and demand accelerates—organizations increasingly recognize the need for tighter integration between operational and technical capabilities. It is high time to change the data service model to adapt to the evolution of the business operations.

A different Service Model: Domain Experts

Imagine a talent pool embedded within business domains that combines:

• deep understanding of domain workflows,

• hands-on knowledge of in-application data and analytics capabilities,

• and strong data analysis and technical skills.

Let’s call these people Domain Experts (DEs). Their mission is threefold:

1. Optimize in-application data and analytics, including AI-driven capabilities, to improve operational workflows;

2. Partner with the central data team to identify gaps where in-house solutions are truly needed;

3. Enable and coach business users, raising data literacy and confidence so self-service can actually succeed.

This talent pool operates under the leadership of the CDO’s office, systematically breaking down the wall between business and technical teams. When established, the impact is transformative:

• Business users don’t just get access to data—they get solutions that reflect the best available technology and domain knowledge.

• Data teams don’t just fulfill requests—they deliver optimal, future-ready solutions aligned with both vendor platforms and internal data assets.

How the New Model Works

Under this service model:

• Domain Experts first maximize the use of vendor-provided, in-app analytics.

• In-house data development occurs only when real gaps are identified.

• Business users are supported through continuous coaching and partnership, enabling true, sustainable self-service.

The result is a shift from reactive delivery to proactive enablement. Organizations are capable of making data an integral part of their operations and achieving great business outcomes alongside the fast-moving technology with vigor and agility. 

Why This Matters Now

Data generated within application platforms has become the crown jewels of modern software systems. Organizations are investing heavily in embedded analytics and AI capabilities. Without a data service model designed to leverage this reality, those investments fail to achieve their full ROI.

At the same time, the traditional separation between business and technical teams continues to create friction: business users lack access and confidence, while data teams are overwhelmed with requests.

A new service model is no longer optional—it is overdue.

Executive Takeaway

1. The traditional, transaction-based data service model no longer meets the needs of modern businesses.

2. Domain Experts bridge the gap between business operations and data capabilities.

3. A modern data service model prioritizes in-application analytics, targeted in-house development, and effective self-service enablement.

If this perspective resonates with the challenges you’re navigating, I invite you to explore the Laurel Consulting website or reach out for a focused strategy conversation.