Scalable Data Ecosystem for SMEs

Written by Dr. Andreas Martens

In discussions with senior managers at Small and Mid-Size Enterprises, the topic of scalable data ecosystem for SMEs frequently arises. These companies often face the challenge of designing an effective data ecosystem—limited resources coupled with an overwhelming array of technology options complicate the process. Moreover, many existing solutions are tied to specific vendors, which can restrict future flexibility and scalability.

Definition: a data ecosystem integrates technologies, processes, policies, and people to collect, prepare, store, analyze, and share data. This creates an environment where business processes and product development can be automated and optimized.

What SMEs need is a comprehensive guide for building simple, scalable data ecosystem for SMEs without being locked into a specific technology provider. The solution should focus on essential elements, be achievable within a manageable budget, and lay the groundwork for future expansion without limitations.

Fundamental principle as success factors

How can we move closer—both conceptually and technologically—to meet these requirements??

The idea is to adhere to fundamental principles that will serve as the foundation for the solution. This approach can (but does not have to) be considered within the context of the Data Mesh methodology.

1. Keeping the Big Picture in Mind, Focusing on Added Value The idea is to adhere to fundamental principles that will serve as the foundation for the solution. This approach can (but does not have to) be considered within the context of the Data Mesh methodology.
2. Information Ownership by Business Teams Business teams must understand that they are responsible for creating and processing the information in operational systems. Although this is often already the case, it should be explicitly reinforced—utilizing the domain-oriented data ownership principles from the Data Mesh approach.
3. Keep it simple Data should remain independent of specific IT systems and technologies. To ensure portability and ease of use across various scenarios, data should be stored in simple, widely accepted formats such as CSV, Parquet, or Delta files.
4. Ensuring Data Quality During Data Capture The entire system depends on maintaining a high level of data quality. Business teams must have confidence in the insights derived from the data, which places their responsibility for data quality at the forefront. Technically, this means adopting a “data quality by design” approach to ensure high-quality data right at the point of capture.

These four fundamental principles should suffice to achieve initial tangible successes and serve as the basis for the framework outlined below.

How can we move closer to fulfilling these requirements, both conceptually and technologically? The idea is the following framework, which is intended to outline the framework of the solution. But first a few sentences about the purpose of a scalable data ecosystem for SMEs.

Automation as a goal

Modern data ecosystems and their underlying frameworks are not an end in themselves. The objective is not merely to upgrade an existing data landscape but to demonstrate how immediate benefits can be derived from it. Specifically, the goal is to show how this approach can help you reach a certain level of automation — such as Level 1, Business Assistance.

Level of Automation: This term describes the extent to which logic is embedded to automate routine as well as highly individualized and complex processes within a company. More details on the various levels will follow in subsequent articles, but here is a brief overview for context:

Level 1: at this level, the data ecosystem acts as an assistant to your business teams by handling specific tasks. This may include generating reports, answering business queries, or proactively notifying you when predefined thresholds are exceeded (similar to a lane-keeping assist system). However, business teams retain full responsibility for interpreting the data, determining the appropriate actions, and integrating these insights into business processes.

The Framework

Here is a straightforward framework for SMEs to address their data needs, design a vendor-agnostic data ecosystem, and implement a fit-for-purpose architecture that adapts to evolving business requirements:

Create Transparency Across Departments and Systems

Start by mapping out which data is managed in which systems and departments across your organization (for example, using Confluence or the company-wide wiki). While individual departments can operate autonomously following Data Mesh principles, transparency is essential. Engage your business teams and ensure they take ownership of the data in their source systems and their portion of the data ecosystem. These teams are the key stakeholders who will benefit the most from the solution.

Set Up Simple Mechanisms to Consolidate Data

Establish straightforward methods for continuously extracting data from your operational systems and consolidating it into a simple data lake. This might involve processing CSV files—either manually or through automated processes—on a regular basis. Start with key business objects such as customer data, orders, products, etc., and store these files on platforms like SharePoint, cloud storage (S3, Azure Blob, etc.), or local servers. The beauty of this approach lies in its flexibility: you can easily migrate your data to another platform when needed (for example, via Apache Iceberg to Snowflake or Databricks), ensuring vendor neutrality.

Create Your First Valuable Data Product

Use the extracted data to develop initial data marts and valuable data products, such as calculated KPIs for company performance or sales forecasts. These results can then be visualized in dashboards or reports. Common tools like Power BI, Google Data Studio, or Jupyter Notebook are ideal for this purpose. This method separates business logic from specific visualization tools, offering business teams a consolidated view across multiple systems—far more comprehensive than what a single CRM report could provide.

Drive Data Quality Accountability in Business Teams

We naturally should place some responsibility on business units to ensure data quality right from the point of capture. Data quality is critical—without trust in the data, there can be no trust in the reports. Support this effort by implementing additional restrictions in source systems or Excel files and conducting periodic data quality checks (using Python or SQL queries) led by the business teams.

This framework is inspired by the self-service data platform concept within the Data Mesh paradigm, emphasizing those elements particularly relevant for SMEs in the early stages. It remains streamlined while providing the basis for future expansion in line with Data Mesh principles.

Self-serve data platform: A self-serve data platform enables the creation of new data products without requiring specialized expertise. It is the technical core of Data Mesh, allowing data domain teams to take ownership of their data without unnecessary bottlenecks.

Why is this a Great Solution?

This approach is an excellent starting point for a forward-thinking, pragmatic data ecosystem. It represents an MVP (Minimum Viable Product) that already delivers tangible value to business teams. Such a data ecosystem can support tasks such as reporting, answering business queries, and—with minor enhancements—proactively notifying users when predefined thresholds are exceeded.

The costs for both development and operation remain manageable, as only a few components are necessary: automated data extraction and the preparation of consolidated data for visualization. This can be implemented within 2–4 weeks—a highly pragmatic option.

Moreover, vendor independence is maintained. Your data is stored in portable formats (e.g., CSV or Parquet), making it easy to transition to another technical environment if performance requirements increase, vendor costs become too high, or additional functionalities are needed. You remain the sole owner of your data.

This solution is a fully scalable data ecosystem for SMEs—both in terms of additional data and data sources and in technical performance. Furthermore, this MVP can be continuously and incrementally enhanced with new features to achieve higher levels of process automation. It serves as a flagship project that clearly demonstrates benefits and future potential.

Conclusion and the ultimate Goal

This approach keeps all options open. In the next steps, you can automate entire processes by reintegrating the generated insights and KPIs into process control. Over time, you can empower entire business teams, fully automate numerous processes, and lay a solid data foundation for your company.Step by step, you move closer to the ultimate goal: Level 5 – Connected / Full Autonomous Business, where all your business processes are fully automated and driven by AI agents that manage and optimize them autonomously.