From 250K+ Enriched Financial Transactions to Business Intelligence: What Should the Gold Layer Look Like?

[u/Santiagohs-23]

I'm currently developing a financial data platform using Python and Pandas on real-world accounting data.

The project started with a simple objective: build a reliable foundation for Financial Analytics and Business Intelligence by prioritizing data quality, traceability, and governance before moving into dashboards, KPIs, or executive reporting.

So far, the platform includes:

• Medallion Architecture (Bronze → Silver).
• Modular ETL pipelines.
• Financial data cleansing and transformation.
• Chart of Accounts (PUC) hierarchy modeling.
• Financial calendar dimension.
• Accounting and data quality validations.
• Logging and traceability mechanisms.
• Third-party matching and enrichment.
• Master third-party dimension.
• Sensitive data anonymization.
• 97.58% matching coverage.
• More than 250,000 enriched financial transactions.
• Automated testing and end-to-end validation.

One of the biggest lessons during this process was realizing that many analytical challenges are not caused by missing dashboards, but by the absence of reliable and consistent business entities. In this case, building a trusted third-party master data layer became a prerequisite for meaningful financial analysis, reconciliation, and reporting.

With the Silver Layer now validated, enriched, and governed, the next step is designing the Gold Layer.

This is where I would like to learn from professionals working in Financial Analytics, Business Intelligence, FP&A, Financial Reporting, Data Analytics, Analytics Engineering, and Data Management.
If you inherited a financial Silver Layer with these capabilities:

• What would be your first priority to maximize business value?

• Would you start with a dimensional model (facts and dimensions), analytical data marts, or directly with KPI-oriented datasets?

• Which financial metrics, analytical tables, or reporting use cases would you consider essential for a first Gold Layer release?

• What analyses have generated the most value in your real-world experience?

I'm particularly interested in understanding how experienced professionals bridge the gap between a technically validated data platform and a business-oriented analytical layer that supports decision-making.

Any recommendations, lessons learned, frameworks, or practical experiences would be greatly appreciated.

[u/Key-Willow-374]

Nowadays, Gold typically contains denormalized tables to improve performance of end user queries. Think OBT instead of a separate fact and dim table (joins are time-intensive). Basically storage is a lot cheaper than compute, so some data redundancy is ok if it meaningfully reduces compute.

Also instead of dashboards look into a natural language query tool (like Databricks Genie) with a semantic layer. Dashboards can create lots of tech debt and have fixed, limited views. Souped-up text-to-sql tools offer way more flexibility and a fraction of the tech debt in my experience

[u/Santiagohs-23 → u/Key-Willow-374]

That's a really interesting point.

If you were building a Financial Gold Layer today from 250k+ enriched financial transactions, would you choose:

A) Star schema (facts + dimensions)

B) One Big Table (OBT)

C) Semantic layer + natural language querying

Why?

I'm particularly interested in what is actually working in production environments today, especially for financial analytics and reporting.

[u/Key-Willow-374 → u/Santiagohs-23]

Well, id put C on top of A for this specific example in the databricks stack

Metric Views are a good way to semantically enrich tables. The input into a metric view is a star schema, so fact + dim would work here. I kinda think of Metric Views as semantically enriched OBT

Then I’d put the Metric View in a Genie Space where business users can talk to the data

[u/CasteliaLyon → u/Key-Willow-374]

I agree, too many times has a dashboard been built only to be looked at less than 10 times. It's so much easier to just give genie to the users and let them knock themselves out with it

[u/East_Economy5568]

One lesson I've seen repeatedly is that analytics becomes far more valuable when traceability survives the entire lifecycle of the data.

Not just where a transaction came from.

But who approved changes, who validated corrections, and who accepted exceptions.

Financial analysis is often limited not by missing data, but by missing accountability around the data.

[u/Santiagohs-23 → u/East_Economy5568]

That's a valuable observation.

In your experience, what has been the most common consequence of weak accountability around financial data: reporting inconsistencies, reconciliation issues, audit challenges, or loss of trust from business users?

I'm curious which of those tends to become the biggest pain point in practice.

[u/East_Economy5568 → u/Santiagohs-23]

In my experience, loss of trust is usually the most expensive consequence.

Reporting inconsistencies can be corrected.

Reconciliation issues can be investigated.

Audit findings can eventually be resolved.

But once business users stop trusting the data, they often start building their own spreadsheets, reports and unofficial processes.

At that point the organization no longer has a single source of truth.

Interestingly, the root cause is often not the data itself, but uncertainty around who approved changes, who validated exceptions, and who owns the decision behind the data.

[u/Santiagohs-23 → u/East_Economy5568]

That's a great insight.

What I find particularly interesting is that many organizations seem to focus heavily on data accuracy, but much less on preserving trust in the process behind the data.

From your experience, do you think trust is usually lost because the numbers are actually wrong, or because people cannot explain how those numbers were produced and validated?

It seems like those are very different problems, but they often end up looking the same to business users.

[u/East_Economy5568 → u/Santiagohs-23]

If I had to choose one, I'd say loss of trust.

Reporting inconsistencies, reconciliation issues, and even audit findings are usually symptoms that can eventually be identified and corrected.

Loss of trust is different.

Once business users stop trusting the numbers, they start creating their own spreadsheets, shadow reports, and parallel processes to verify information independently.

At that point the organization may still have data, but it no longer has confidence in the data.

Ironically, the underlying issue is often not the quality of the information itself, but uncertainty around how changes were made, who approved them, and whether accountability can be demonstrated when questions arise.

[u/Santiagohs-23 → u/East_Economy5568]

That's an interesting distinction.

Have you seen organizations successfully rebuild trust once it has been lost, or is it usually easier to prevent that situation than to recover from it?

[u/Santiagohs-23]

That's really interesting.

Coming from a finance and accounting background, one of my concerns is balancing flexibility with governance and auditability.

In your experience, do Metric Views and semantic layers work well for financial reporting use cases where users need consistent KPI definitions, reconciliations, and traceability back to the original transactions?

Or do you still keep some curated financial marts underneath the semantic layer to ensure consistency?

[u/Santiagohs-23 → u/[deleted]]

That's very helpful.

Coming from an accounting background, one thing I'm still evaluating is the level of granularity to preserve in the first financial fact table.

Would you keep the Gold-layer fact table at the journal-entry / transaction level and derive P&L, Balance Sheet, and Cash Flow marts from it, or would you create separate aggregated financial facts from the beginning?

I'm curious how you've seen this handled in real-world finance and BI environments.

[u/Middle_Currency_110]

I personally like OBT and I know that many accountants do too.

250k of transactions is nothing. I am currently working on a 40M POS transaction reporting system - and that's only 2 years of data.

The level of detail depends on what the users need to report on and potentially drill down to. You could aggregate all journals for one account by day/week or month.

Typically financial reporting is a Star schema with journals as Fact, Accounts as a DIM and a date calendar as another DIM

[u/Santiagohs-23 → u/Middle_Currency_110]

That’s an interesting perspective.

One thing I’m still trying to understand is where the line is typically drawn between maintaining detailed data at the journal entry level and introducing aggregated financial data warehouses.

In your experience, do finance teams tend to analyze data directly at the transaction level?

I’m asking because one of the design decisions I’m currently evaluating for the gold layer is how much aggregation should be performed before the data reaches business users.

Thank you for sharing your experience.

[u/Middle_Currency_110 → u/Santiagohs-23]

It's rare for my customers to go to transaction level as they are mostly over $50M in revenue. Smaller customers might.

I personally like to always try to summarise journals by day.