The practical application of the Bronze-Silver-Gold Model

Many managers recognize it: data is everywhere, but often scattered across Excel files, accounting exports and manual lists. In the early stages of data maturity, this can still provide actionable insights, but as a company grows, the limitations become apparent. Reports become time-consuming, error-prone and offer only retrospective rather than current guidance.

Data maturity - or data maturity - describes an organization's journey from simple, manual reporting to scalable, reliable and automated data solutions. For SMBs, specifically those growing from small to medium and perhaps beyond, this journey is essential to staying agile and making decisions based on facts rather than assumptions.

The purpose of this white paper is to show how data transformation tools such as Alteryx, Knime or Matillion support growing enterprises every step of this growth path. We do this using a recognizable example: building a Profit and Loss dashboard. In doing so, we follow different levels of data maturity and show how to apply a scalable methodology using the Bronze-Silver-Gold model.

The Bronze-Silver-Gold (BZG) model is a proven way to develop data step by step from raw input to high-quality, immediately usable information. This model fits well with the practice of many SME entrepreneurs to build reliable reports without complicated IT processes. Without a clear method, a patchwork of separate files, manual operations and ad hoc solutions quickly emerges. The BZG model provides precisely the structure to make data growth manageable and create sustainable insights.

The Source layer contains the raw data: the raw files and exports as they come directly from a source. This could be Excel sheets from Finance, CSV files from a CRM system, API downloads from a web shop or even log files from machines or sensors. This data is often heterogeneous, incomplete, inconsistent or still contains errors.

The Silver layer is the phase in which source data is cleaned up and harmonized. Here the quality is improved: duplicates disappear, inconsistencies are repaired and different data sources are brought together in a uniform structure. Think, for example, of standardizing product codes, validating addresses or combining customer information from multiple systems.

Finally, the Gold layer delivers the data sets that are immediately ready for use by the business. In this phase, the silver data is matched to the information needs of the organization. The data is reliable, validated and structured so that it can be used in reports, dashboards or predictive analytics without additional processing. For example, this could be a revenue summary by region for Power BI, a 360° customer view for marketing, or a data set that is automatically published daily to a data platform such as Snowflake. This table is then automatically refreshed and serves as a direct source for dashboards in Power BI or Tableau, for example.

The BZG model thus ensures that organizations not only gain insight into their data, but can also deploy it in a structured and scalable way. For enterprises, this means that they can grow in data maturity step by step, without the process becoming unnecessarily complex or subject to unnecessary change.

To make the growth path of data maturity concrete, let's take a recognizable example: building a Profit and Loss Dashboard. Virtually every company periodically reports financial results, but in practice this information is often fragmented and difficult to access. Reports are compiled in Excel based on loose accounting indexes, manual customer lists or additional product data. The result is labor-intensive, error-prone and barely scalable.

With the right tools, this process can be matured step by step. In the first phase - beginning data maturity - the entrepreneur starts with a simple CSV export from accounting, supplemented by Excel files containing, for example, customer and product information. In Alteryx, Knime or Matillion, these files are brought together, cleaned and enriched to produce an overview of sales and costs per customer and product. The output is often still an Excel report, but with a solid improvement in quality.

In the next phase, the data is no longer exported manually, but is accessed directly from an accounting or ERP system, such as Exact, Afas or Salesforce, via an API or connector. This creates not only timeliness, but also reliability and scalability. Transformations in Alteryx become more complex, with validation rules and calculations that directly reflect business logic. The results no longer provide an Excel report, but a dataset that is directly usable in a visualization tool such as Power BI or Tableau.

At the most mature stage, the entire chain is standardized: raw data flows in periodically or even real-time, the logic is centrally defined, and the Gold layer provides a semantic model that can be used uniformly throughout the organization. The Profit and Loss Dashboard thus grows into a strategic steering tool for the entrepreneur.

Step 1 - Initial phase: bringing together separate files

Many entrepreneurs start with data manually exported from various systems. Think of a CSV file from accounting and Excel lists of customers and products. In this Source layer, the data is often heterogeneous, incomplete or inconsistent.
These Bronze files are brought together in the Silver layer, cleaned and enriched, for example by linking customer names to customer IDs or standardizing product categories. The result in the Gold layer is an initial Profit and Loss statement, often still as an Excel report, but reliable and reproducible. The images below show what such a process might roughly look like in, say, Knime.