From IT Signals to Business Impact: The Role of Domain-Centric Adaptors

Introduction

In observability, tracking technical metrics—like request rates, latencies and error rates, are crucial. However, these metrics on their own often fail to capture the full picture from a business perspective. A backend system might show high latency, but without understanding its impact on business-critical transactions or customer experience, the metric remains just a technical anomaly. Teams may spend valuable time addressing issues that don’t directly affect business outcomes while overlooking problems that do.

Unlike technical monitoring, Business Observability (linking business impact to technical performance) bridges this gap and unifies all stakeholders to the common priorities. Metrics such as transaction success rates, cart abandonment rates, or micro-transaction latencies provide meaningful insights into customer behavior and business performance. For instance, a payment system might log a 0.5% failure rate in transactions. However, when these failures occur during peak traffic periods or involve a top-performing partner, their impact becomes far more significant. Hence, understanding the linkage between business performance (number of customers affected or the potential revenue loss) becomes vital as enterprises transform into digital-first enterprises.

In observability, the data collected about system performance through metrics, logs, and traces can become truly actionable with business context. It leads to clarity on how technical issues impact business priorities, helping teams focus on what matters most. 

This is where integrating business context into observability practices becomes essential, bridging the gap between raw technical data and actionable business insights.

Building Business Context

Deriving business context begins with deep domain knowledge. Observability teams (SREs, ITOps etc) must understand the specific details of the domain they’re monitoring—whether it’s fintech, healthcare, or another industry. In fintech, for example, logs, transaction data, and transaction payloads have specific formats and protocol alignment that need decoding. 

Effectively extracting intelligence from these data sets to obtain real-time meaningful data and metrics requires the right tools— this is where domain-centric adapters within VuNet’s Business Observability platform, vuSmartMaps™ bring a differentiated value. They are one of the key components of ContextStreams,  a comprehensive data processing pipeline that receives, processes, enriches, correlates, and transforms the data.  The ContextStreams pipeline with the domain-centric adaptors acts as the “business context engine” of our Observability platform. 

Let’s discuss these adapters in more detail to understand how they make complex data meaningful and actionable

ContextStreams enables converting raw data into meaningful insights. The domain-centric adaptors add business relevance to these data making the insights business-centric, they turn complex data into clear, actionable insights, helping organizations to monitor and respond effectively.

The domain-centric adapters are designed with a deep understanding of specific domains. Within vuSmartMaps™, these adaptors are in-built to support a variety of banking systems such as Payment switch, Lending Management, Card Management, Core Banking, etc., and support a variety of payment protocols such as ISO8583, ISO20022, 3D Secure, and more. Over the years, several such adaptors have been added to the platform through a thorough understanding of financial transactions, allowing us to make complex banking and financial transaction workflows easy to monitor, understand, and derive data-driven decisions.

For example, consider an ISO8583 adapter designed to handle the complexities of card transactions. ISO8583 is a standard used in the financial industry for encoding transaction data, but in its raw format, the data can be cryptic and difficult to interpret.

Just look at the raw data on the left-hand side of the below image. Does it not look like a child had scribbled digits on a page?  Without decoding, it’s just a jumble of digits.  With the magic of the domain-centric adaptor, this chaotic string of numbers is transformed into a structured, readable format that reveals meaningful transaction details as shown in the right hand side of the below image.

Here is another example of the adaptor at work helping to identify key trends from raw log data and providing real-time insights on merchant performance. 

How Adapters Derive These Insights

To generate such insights, the ContexStreams pipeline with the in-built adapters performs several key functions through a structured data processing pipeline. Here’s a simplified overview of the process with an example log:

1. Data Collection: Agents start by gathering raw data from various sources. In this case, logs of individual transactions from a variety of merchant types, transaction statuses, and amounts are collected

2. Data filtering/masking: The raw data often contains redundant, irrelevant, or noisy information. During this stage, adapters filter out any unnecessary data and standardize fields, such as transaction types and merchant names, to ensure consistency. This is essential for accurate aggregation and analysis. Masking of data is also done as applicable for Personally Identifiable Information (PII).

Here, unnecessary segments (like extra identifiers or device IDs) are removed, simplifying the data for processing in the next steps.

3. Data Enrichment and Transformation: The data is enriched by adding contextual details, such as geographical information, timestamps, and merchant categories, to each transaction. This enrichment provides deeper insights, enabling teams to analyze trends across different locations, transaction times, and merchant types.

Simultaneously, the adapters transform raw data fields into readable formats by converting encoded values (e.g., transaction types and failure codes) into human-readable terms like “Purchase Transaction” or “Insufficient Funds.” Together, these processes make the data more meaningful and actionable for business stakeholders.

4. Data/Transaction Correlation: Adapters can correlate data from multiple transaction steps (transaction legs) to provide additional information These micro-transactions are async in nature and distributed, allowing for complex business logic and multi API calls.

For example, if Person A transfers money to Person B, the process involves multiple steps, such as verifying the account details, initiating the transfer request, processing the payment through the bank’s network etc. Each of these steps generates its own transaction log or trace.

Understanding the need for the observability of such domain-centric transactions, the platform comes pre-built with transaction session plugins that go deeper in calculating the turnaround times of async transactions at scale, which maps directly to user experience issues, creating visibility on the hotspots and for further optimizations.

Benefits of Domain-Centric Adapters 

Let’s explore these benefits in more detail

Links Business and IT metrics:

Domain-centric adapters bridge the gap between raw data and meaningful insights by segmenting information into transaction types, merchant categories, or regions, making it easier for IT teams to link technical performance with business impact. For example, the adapter might transform raw logs into a structured format showing that latency issues are affecting transactions for a particular merchant in a specific region, enabling teams to focus on resolving high-impact problems.

Accelerates Incident Management

Domain-centric adapters transform raw data by decoding complex data points, correlating related events, and segmenting information across various dimensions. This process provides enriched, actionable insights that make incident management faster and more precise. For example, an adapter processes transaction logs and segments them by merchant category and region, providing data such as transaction success rates for grocery stores in various regions. Real-time alerts generated from this data ensure teams can prioritize and address the issue effectively

Enhances Customer Experiences Through Business Insights

Improves customer satisfaction by helping get business-relevant insights from the raw log data. For example, segmenting transaction delays by payment method can reveal that a specific card network is causing slowdowns during peak hours. These insights enable organizations to collaborate with the card provider to resolve the issue, ensuring seamless transactions for customers and improving their overall experience.

Empowers Decisions with Unified Business and IT Context

The decision-making is streamlined because domain-centric adapters help align IT performance with business results. For instance, an adapter can transform transaction data to show system response times alongside transaction statuses. This allows downstream systems to analyze how delays in system response (an IT metric) during peak periods correlate with an increase in abandoned transactions (a business metric).

Future of Observability and Domain-Centric Adapters

As observability continues to evolve, domain-centric adapters are set to play an even more critical role. With new sources of data constantly emerging the scope of observability is expanding beyond traditional systems. These new sources introduce diverse data formats and unique standards, which means that domain-centric adapters must also evolve to handle increasingly complex data landscapes.

Looking to the future, artificial intelligence (AI) holds great potential in the development of these adapters. By automating parts of the transformation process, AI can analyze raw data patterns and learn to interpret new data structures more rapidly than manual methods allow. This can streamline the creation of adapters, enabling them to keep up with new observability sources in real-time. Additionally, AI could enhance domain-centric adapters by continuously refining the data transformation rules based on system behaviors and anomalies, ensuring that insights remain accurate and contextually relevant.

Conclusion

Domain-centric adapters are essential in transforming complex, industry-specific data into actionable insights. By bridging the gap between raw data and meaningful information, they allow observability teams to understand and act on what truly matters. As observability evolves, so too will these adapters, with advancements like AI offering new ways to enhance and automate their capabilities. In a data-driven world, domain-centric adapters stand as powerful tools, enabling businesses to make sense of complexity with clarity and precision.