How to Establish a Single Source of Truth?
Many companies collect data but struggle with fragmentation, inconsistent reports, and inefficient processes. The result? Data is scattered everywhere, leading to decisions based on conflicting figures. How can your organization ensure it operates with one reliable Single Source of Truth (SSOT)?
Why is a Single Source of Truth Essential?
Businesses consist of people, processes, and systems—three key components that keep an organization running. While data is captured in systems daily, it often remains siloed across different departments and tools.
The Consequences of Fragmented Data
🚨 Inconsistent Reports – Different departments rely on different numbers.
⏳ Manual Work – Significant time is wasted collecting, merging, and verifying data.
🔎 Limited Insights – Data is not fully utilized for strategic decision-making.
A Single Source of Truth eliminates these challenges, ensuring everyone works with one unified version of the truth.
How Do You Centralize Data from Operational Systems?
To extract and make data usable for analysis, a structured process with three key steps is required:
- Ingestion (Extract & Load) – Data is retrieved from source systems and stored centrally.
- Transformation (Transform) – Data is cleaned, combined, and enriched with business logic.
- Distribution (Serve) – Data is prepared for use in reports, dashboards, and analytics.
This process, managed by data engineering, forms the foundation of an SSOT.
How Mature is Your Organization in Data Management?
Not every company is at the same stage in becoming data-driven. There are three main maturity levels:
Level 1 – Basic (Initial Stage)
Data is primarily managed manually, often in Excel.
Little to no automation is in place.
The focus is on data collection rather than analysis or optimization.
Challenge: How to gain control over data without adding complexity?
Solution: Start with a simple database or a no-code solution like TimeXtender.
Level 2 – Growth (Scaling Stage)
The organization integrates multiple data sources and establishes a foundational data architecture.
Dashboards and automated reports are in use.
The challenge is data integration and management—ensuring seamless collaboration between systems and data sources.
Challenge: How to make data accessible and useful across the organization?
Solution: A Best-of-Breed approach with tools like Snowflake for storage and dbt for transformation.
Level 3 – Advanced (Innovation Stage)
Data is actively used for predictive analytics.
Machine learning and AI optimize business processes.
A Lakehouse architecture and real-time data processing are in place.
Challenge: How to leverage data as a competitive advantage?
Solution: A pro-code approach using platforms like Databricks and Microsoft Fabric for maximum flexibility and scalability.
đź’ˇ Where does your organization stand, and what is the next step?
Technology Choices: Selecting the Right Data Architecture
Choosing the right data architecture depends on data volume, technical expertise, and existing IT infrastructure.
Option 1: All-in-One Platform (e.g., TimeXtender)
Quick implementation with minimal technical expertise.
Ideal for businesses looking for a fast setup without a large data engineering team.
⚠️ Less flexibility and potential vendor lock-in.
Option 2: Best-of-Breed Architecture
Maximum flexibility by selecting the best tools for each component.
Combines solutions like Airbyte, Azure Data Factory, and Snowflake.
⚠️ Requires greater technical expertise and ongoing maintenance.
Option 3: Lakehouse Architecture
Ideal for organizations handling large data volumes and advanced analytics.
Data is stored in a data lake, making it cost-effective for large datasets.
⚠️ Requires a team skilled in Python and SQL.
No-Code, Low-Code & Pro-Code: How Much Control Do You Need?
A crucial decision in data engineering is the level of control and technical expertise required.
No-Code: Quick deployment without technical knowledge.
Suitable for companies without data engineers.
Tools like TimeXtender and Airbyte simplify data integration.
Low-Code: A balance between ease of use and customization.
Ideal for businesses with some data expertise.
Solutions like Azure Data Factory, dbt, and Dagster offer versatility without deep programming skills.
Pro-Code: Full control for technical teams.
For organizations with a strong data engineering team.
Platforms like Databricks, Snowflake, and Microsoft Fabric provide maximum flexibility and scalability.
What Are the Benefits of a Single Source of Truth?
- Faster, more informed decision-making – Everyone works with the same reliable data.
- Automated processes – Reducing errors and increasing efficiency.
- Unlocking data value – Enabling predictive insights and strategic decision-making.
- Building a scalable data solution – Growing with the organization.
By choosing the right architecture and technology, any organization can transition to an SSOT.
Conclusion: How to Make the Right Choice?
The ideal data architecture depends on:
🔹 Data volume – Are you dealing with gigabytes or petabytes?
🔹 Technical expertise – Do you have a team of data engineers or primarily business users?
🔹 Existing IT infrastructure – Are you working with Microsoft Azure, Google Cloud, or AWS?
🔹 Required flexibility – Do you need a quick solution or maximum control?
There is no one-size-fits-all solution, but asking the right questions will help align your data strategy with business goals.
Next Steps: What Can You Do Now?
- Take the Data Maturity Scan to determine your organization’s level of data maturity: Click here.
- Join the Data Automation Pitstop on May 15, 2025, to explore key aspects of data and automation strategy: Register here.
- Contact Alexander Mik to discuss a tailored solution for your organization.
