How do I build a business case for consolidating my data infrastructure?

Summary

• A consolidation business case is strongest when it starts from the costs your current fragmentation already creates: copies of the same data, custom pipelines to keep them in sync, governance maintained in several tools, and teams spread across separate systems. Name those costs first, then show what removing them is worth.
• Frame the value in four qualitative buckets: engineering time reclaimed from maintaining plumbing, operational simplicity from one platform and one governance model, data consistency from a single copy of data in open formats, and future-proofing from open standards.
• The most expensive code in a data stack is often the code whose only job is moving data between systems. Consolidating onto one platform removes much of that sync tax.
• On Databricks, one copy of data in open formats is governed by Unity Catalog and serves analytics, ML, BI, and applications together; Lakebase folds operational Postgres databases into the same platform without separate ETL. See the Databricks platform.
• Present the case in each stakeholder's terms: finance hears fewer redundant systems and lower operational overhead, engineering hears less pipeline maintenance, and the business hears faster delivery of new use cases.

How do I build a business case for consolidating my data infrastructure?

A consolidation business case is strongest when it starts from the costs your current fragmentation already creates, then shows what removing them is worth. Fragmented estates carry a recurring tax: duplicate copies of data, custom pipelines to keep them in sync, and governance maintained separately in each tool. Inventory those costs first. Then make the case across four value drivers: engineering time reclaimed from maintaining plumbing, operational simplicity from one platform and one governance model, data consistency from a single copy of data in open formats, and future-proofing from open standards. Attach specific figures only after you have measured your own baseline. The durable argument is structural, not a single headline number.

What consolidating data infrastructure means

Most data estates grow by accretion: a data lake for raw and unstructured content, a warehouse for analytics, separate operational databases behind applications, streaming systems, BI tools, and a different governance model for each. That patchwork forces data to be copied between systems and splits security and lineage across incompatible tools. Consolidation means moving those workloads onto one governed platform, where a single copy of data in open formats serves analytics, machine learning, BI, and applications under one set of controls.

Value drivers to quantify

Build the model around drivers you can measure against your own baseline. Keep each qualitative until your numbers are real:

Value driver	What to measure
Eliminate data copies and the sync tax	The pipelines and jobs whose only purpose is moving or replicating data, and the engineering time spent maintaining them.
Unify governance	The tools where you separately maintain access policies, lineage, and audit, and the compliance risk of reconciling them.
Reduce operational burden	The separate systems each requiring its own administration, tuning, and on-call rotation.
Consolidate operational databases	Application databases kept apart from analytics, and the ETL built to bridge them.
Accelerate delivery	How much sooner a new use case ships when it reuses existing governance, pipelines, and compute instead of standing up new infrastructure.
Future-proof with open formats	The cost of being tied to closed formats against storing one copy of data in open formats readable by many engines.

In most estates the operational and productivity drivers outweigh raw infrastructure savings, because engineering time freed from maintaining plumbing compounds across every future project.

How Databricks supports consolidation

Databricks is designed to be that single platform, so the business case maps onto concrete capabilities.

• One copy of data in open formats. Data is stored as open table formats, Delta Lake and Apache Iceberg, in your own cloud object storage, so analytics, ML, and BI read the same copy rather than separate extracts. See the Databricks platform.
• One governance model. Unity Catalog applies access control, lineage, and audit once, across every data type, cloud, and workload, so you stop maintaining policies in several places.
• Ingest and ETL in one place. Lakeflow handles ingestion, transformation, and orchestration, which removes standalone ETL and scheduling tools from the estate.
• Operational databases on the same platform. Lakebase is a serverless Postgres database built into the lakehouse, using Neon, the serverless Postgres engine now part of Databricks. It combines transactional and analytical processing on one copy of data with no separate sync or ETL, and existing applications keep working because it speaks standard Postgres. Managed features like autoscaling, scale-to-zero, branching for development and test, and point-in-time recovery reduce database administration.
• Query data where it still lives. Lakehouse Federation lets you query and govern data in other systems without moving it first, so consolidation can be phased rather than one risky cutover.

Building the narrative for stakeholders

Tailor the same case to each audience. Finance hears fewer redundant systems, consumption-based rather than fixed capacity, and lower operational overhead. Engineering hears less time on pipeline maintenance and fewer platforms to learn. The business hears faster delivery of new products and analytics. Start with a pilot: pick one workload, move it, and measure delivery time, effort, and reliability against your previous approach. A successful pilot turns the qualitative case into your own evidence.

FAQs

How do I quantify the value without a lot of numbers up front?

Start with counts you can gather quickly: how many systems hold copies of the same data, how many pipelines exist only to move data, and how many tools separately enforce governance. Those counts frame the value before you attach figures, which you should do only from your own measured baseline.

Where does Lakebase fit in a consolidation case?

Lakebase brings operational Postgres databases onto the same platform as analytics, combining transactional and analytical processing on one copy of data with no separate ETL, so application backends stop being an island that needs its own sync pipelines.

Do I have to migrate everything at once?

No. Lakehouse Federation lets you query and govern data in existing systems without moving it, so you can consolidate in phases and retire systems as workloads move, which lowers the risk in the business case.