NextQuestion

Fiber Network analytics set up from scratch

Cost per Mile, Cost per Passing, Active Base and other core fiber KPI implementation using modern tools

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AI-native tools

dbt and Cursor based data pipelines

10-14 weeks

Typical end-to-end timeline to go from no data warehouse to a set of core KPI trackers

Software Used

Modern technology stack

Common Issues

Where KPI monitoring fails before it begins

Data silos between accounting, Sitetracker, VETRO, and implementation systems

Conflicting metric definitions for core fiber KPIs like cost-per-mile and install costs

Stale KPI dashboards without dependable pipeline refresh cycles

Limited governance around centralized fiber datasets and permissions

Method

Strategy -> Build -> Track

A focused engagement for fiber networks to align management and shareholders on KPIs, build AI-accelerated data pipelines, and launch dashboards used in weekly operating decisions.

Strategy

Review available systems and datasets, then align management and shareholders on KPI definitions.

Build

Implement AI-assisted ingestion and transformation pipelines with the right stack for your data reality.

Track

Launch dashboard sets, iterate data pipelines, and stabilize reporting for ongoing decisions.

Typical delivery window for three fiber dashboard sets: 10-14 weeks.

Differentiation

Why this differs from traditional data consultancies

Most data consultancies optimize pipelines first and business logic second.

NextQuestion starts with business issues and KPI intent, then engineers the data layer to match.

The result is faster implementation and reporting that leadership can act on.

Case Studies

Execution examples

Fiber Network Provider

Fiber Infrastructure

The business had fragmented KPI definitions, missing field inputs, and disconnected systems that made cost and execution tracking unreliable.

Approach

Ran a strategic KPI alignment process between shareholder and management teams.
Audited Sitetracker, VETRO, accounting, and delivery data sources to identify missing metrics for board-level reporting.
Built AI-assisted data engineering pipelines on Snowflake and launched Tableau dashboards for end-user consumption.
Iterated company processes after dashboard rollout to improve data completeness and KPI reliability.

Results

Consistent KPI language across management and shareholder discussions
Faster visibility into build performance and project economics
Reliable dashboard-led operating cadence for fiber rollout decisions

About Me

Founder-led, operator-minded analytics transformation

Up to 3x faster than traditional data consultancies

Implementation velocity

Adomas Malaiska is a hands-on builder of AI and data engineering pipelines with a strategy consulting background. NextQuestion combines business context, data architecture, and current AI tooling to deliver faster, more decision-relevant analytics programs.

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If you are running a fiber network and need faster, more trusted KPI visibility, this is the right first step.

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