Why Data Loses Its Meaning and How to Keep It Intact

Data, Information, and the Logic in Between

Every organization has data. But data on its own—a collection of numbers and text in a database—is of limited use. To become valuable, it must be transformed into information. That transformation happens through business logic.

Think of business logic as the set of rules, calculations, and procedures embedded in your core applications. It’s the code in your SAP system that defines how "Net Revenue" is calculated from gross sales, returns, and discounts. It's the process that determines a customer's credit status based on their payment history. This logic is the business context that gives raw data its meaning. Without it, you’re just looking at numbers.

The Problem: The Great Context Divide

The central challenge of modern data and application engineering is that we have become experts at separating data from the logic that defines it. When we extract data from a source system like SAP and load it into a cloud data platform like AWS for analytics, we almost always leave the business context behind.

The raw data tables for sales orders and customer invoices make the journey, but the complex SAP application logic that understands how those tables relate to each other does not. This creates a dangerous "context chasm." The data is now in the hands of analysts and data scientists who are forced to guess, assume, or attempt to reverse-engineer the original business rules. This inevitably leads to:

• Flawed Insights: Analytics are built on incorrect assumptions about what the data represents.

• Wasted Resources: Teams spend countless hours trying to rebuild logic that already exists and works perfectly in the source application.

• Eroding Trust: When a dashboard built in AWS shows a different "Net Revenue" than the official report from SAP, stakeholders lose faith in the entire data platform.

Bridging the Chasm with Active Synchronization

To solve this, you need more than a simple data pipeline; you need a context pipeline. The solution is to maintain an active, unbreakable link between the data and the logic that gives it meaning.

We recognize that systems like SAP are not just databases; they are sophisticated applications that house decades of invaluable business logic. Our approach is not to move and replicate that logic, but to create a persistent bridge to it.

1. Treating Logic and Data as a Single Unit: Understand that the value of your SAP data is intrinsically tied to the application context it comes from. We don't see them as separate entities to be handled by different tools.

2. Synchronizing Semantic and Logical Metadata: When Ailien connects your SAP and AWS environments, it does more than just map schemas. It captures the rich business context—the definitions, the relationships, the calculation logic—from the SAP source. This context is then actively synchronized and linked to the corresponding data assets in your AWS Glue Data Catalog.

3. Making Context Accessible to Everyone: Through Ailien, an analyst querying a table in AWS can instantly see the authoritative business logic and definitions from SAP. There’s no more guesswork. They can understand how a specific metric was calculated at its source, ensuring their own analysis is consistent and accurate. The logic is treated like metadata, traveling with the data it describes.

By refusing to separate data from its meaning, Ailien ensures that the business context defined in your core applications is never lost in translation. We bridge the chasm, creating a single, unified data landscape where insights are built on a foundation of verifiable truth.