Go Fig vs. Custom Data Pipelines

The Comparison at a Glance

Factor	Custom Data Pipelines	Go Fig
Time to value	3-6 months to build	2-4 weeks
Ongoing maintenance	Significant (20-40% of build effort)	Managed service
Technical expertise	Requires data engineers	Business-team friendly
Flexibility	Maximum customization	Pre-built for finance use cases
Cost structure	Build + maintain + infrastructure	Predictable subscription
Knowledge risk	Concentrated in individuals	Managed by vendor

What Are Custom Data Pipelines?

Custom data pipeline architectures typically combine several tools:

• Ingestion: Fivetran, Airbyte, Stitch, or custom scripts
• Storage: Snowflake, BigQuery, Redshift, or Databricks
• Transformation: dbt, Dataform, or custom SQL
• Orchestration: Airflow, Dagster, Prefect, or cloud-native tools
• Visualization: Tableau, Looker, Power BI, or custom dashboards

This “modern data stack” can be powerful—but comes with significant overhead.

When Custom Pipelines Make Sense

Building custom makes sense when:

• You have data engineers: Dedicated team to build and maintain pipelines
• Highly custom requirements: Unusual data sources or transformations
• Data is a core competency: Data infrastructure is strategic to your business
• Scale demands it: Processing massive volumes with specific performance needs
• You’re already invested: Existing infrastructure that can be extended

The Hidden Costs of Custom Pipelines

Organizations often underestimate the total cost of ownership:

Initial build (30% of effort):

• Design data models
• Build and test integrations
• Create transformation logic
• Set up monitoring and alerting

Ongoing maintenance (40% of effort):

• Fix broken pipelines when source systems change
• Scale infrastructure as data grows
• Update dbt models for new requirements
• Handle edge cases and data quality issues

Knowledge management (30% of effort):

• Document pipelines and models
• Train new team members
• Manage key-person dependencies
• Handle turnover in data team

A common pattern: the engineer who built the pipelines leaves, and institutional knowledge goes with them.

How Go Fig Differs

Go Fig is a managed data centralization service specifically designed for finance teams:

Pre-built for finance: Connectors and transformations designed for ERPs, financial systems, and business applications.

White-glove implementation: We build the integrations and data models for you.

Managed service: We monitor, maintain, and update pipelines as source systems change.

Business-friendly output: Data delivered to Excel and dashboards, not just a data warehouse.

No engineering required: Finance teams can use Go Fig without SQL or Python skills.

The Build vs. Buy Decision

Build custom pipelines if:

• Data engineering is a core competency you want to develop
• You have unusual requirements that off-the-shelf solutions can’t handle
• Long-term cost optimization is more important than time-to-value
• You have budget for 2-3 dedicated data engineers

Choose Go Fig if:

• You need value quickly (weeks, not months)
• Data engineering isn’t your core business
• You don’t have (or want) dedicated data engineers
• Finance use cases are your primary driver
• Predictable costs are important
• You want someone else to handle maintenance

Hybrid Approaches

Some organizations combine both:

• Go Fig for finance-specific data centralization and Excel sync
• Custom pipelines for product analytics, data science, or other technical use cases

This allows finance to move fast with a managed solution while engineering builds specialized infrastructure for other needs.

Total Cost of Ownership

Custom pipeline stack (example):

• Fivetran: $2,000-$10,000/month
• Snowflake: $3,000-$20,000/month
• dbt Cloud: $1,000-$5,000/month
• Data engineer(s): $150,000-$200,000/year each
• Opportunity cost of build time

Go Fig:

• Predictable subscription
• Implementation included
• Maintenance included
• No additional headcount required

For most mid-market finance teams, Go Fig delivers equivalent (or better) outcomes at a fraction of the total cost.