How to implement business observability

Business observability is the continuous monitoring and analysis of business processes, data flows, and system performance to gain insights, optimize operations, and understand business implications. Unlike traditional monitoring, observability offers a holistic view of an organization’s ecosystem, ensuring that decision-makers have real-time data and actionable insights beyond operational impact.

In a business context, this means optimizing specific business-related data (which is often treated and stored differently than regular operational data) to boost the bottom line.

As organizations become increasingly data-driven, effective data management isn’t an afterthought for businesses. Companies must understand how their systems interact to detect anomalies before they affect customers. That means proactive monitoring is necessary to uncover opportunities for improvement and protect revenue.

Business observability addresses these challenges by analyzing operational data from applications, servers, databases, and microservices alongside connected customer and business data. Telemetry data provides a comprehensive view of the environment, helping teams identify root causes, dependencies, and performance bottlenecks along with business impact. With AI-powered analytics, organizations can transform unknown unknowns into opportunities.

Business observability also lets organizations include data from outside their usual operational scope. For security purposes, customer data and financial data is often stored separately from operational data. Business observability layers in your business data and contextualizes it within your organization. You can see everything your business is doing and drive decisions accordingly.

Across industries, business observability has a wide range of use cases to help you make data-driven decisions with confidence.

By detecting customer friction points, business observability gives teams the visibility they need to improve customer experiences, further personalize them, and ultimately reduce churn.

For example, Wells Fargo adopted a business observability solution to identify issues within its distributed microservices environment, fast. It needed end-to-end visibility into all financial transactions and the ability to report on risks in near real time. By enabling real-time transaction tracing, Wells Fargo enhanced its ability to detect and resolve issues faster, ultimately accelerating digital solution delivery for both customers and employees.

From evaluating the impact of marketing efforts on conversion rates, to spotting irregularities in billing and pricing, to monitoring POS systems, getting real-time revenue data is a key benefit of a business observability solution. 

Audience data is one type of business data that can be used to optimize revenue. DISH Media, for example, implemented business observability to consolidate audience information and insights to attract and retain advertisers. It supported and increased advertising revenues with targeted ads while improving developer productivity by eliminating manual analysis.

Modern-day businesses are dependent on seamless performance. But digital sprawl, telemetry data noise, and complex architectures can be a significant challenge to quick resolution. With business observability practices in place, businesses can detect and resolve system slowdowns before they impact users, track interactions across distributed architectures to prevent downtime, and bolster security and compliance monitoring. 

Achmea, for example, uses Elastic Observability to proactively fix system performance issues. Business observability analyzes its technical infrastructure and proactively resolves issues for 12 million customers and 16,000 employees. By keeping its multiple innovative (and expensive) tools running smoothly, business observability maintains optimal performance to maximize its return on digital investments.

The more complex the supply chain, the more challenging it is to ensure operational efficiency. Business observability offers businesses end-to-end visibility and the ability to identify inefficiencies and predict inventory needs.

For example, Albert Heijn Technology (AH Tech) implemented business observability to monitor its entire application landscape and resolve issues proactively for more than 2,000 stores and thousands of points of sale. To seamlessly integrate data from its supply chain distribution centers, stores, and operations centers, AH Tech needed a scalable solution with complete visibility. Sharing data transparently enabled different areas of the organization to collaborate on a large scale.

Security and compliance can be particularly challenging in complex digital architectures. Organizations must continually monitor their systems, transactions, and permissions to detect anomalies and potential fraud or breaches. By identifying vulnerabilities in business processes, business observability helps teams get ahead of risks and ensure regulatory compliance. This is especially important with business observability, as it handles sensitive customer data and personally identifiable information (PII).

Advanced analytics of operations can help organizations identify inefficiencies in their processes, as well as trends in employee engagement and satisfaction. Business observability can give teams the insights they need to improve productivity and work conditions, ultimately driving innovation. 

Overall, business observability enables companies to shift from a reactive to a proactive approach. This enhances operational efficiency, optimizes system performance, and improves customer experiences across industries.

Business observability is built on four key processes: data collection, monitoring, analysis, and visualization. Together, they provide meaningful insights.

Data is at the heart of any business problem and its solution. It’s also the lifeblood of any observability practice. Data is gathered from various sources, including application logs, customer transactions, website interactions, and machine sensors.

Telemetry data is at the core of data collection for business observability. That’s why telemetry signals — metrics, logs, and traces — are referred to as the pillars of observability: 

• Metrics: Metrics are quantifiable measurements that help you track performance and understand what is happening in your systems. Metrics are collected from the host, application, network, servers, containers, and external dependencies and include CPU, disk, and memory usage; response times; error rates; throughput; and more. 

• Logs: Logs are detailed records of events, transactions, and errors that provide context for troubleshooting and root cause analysis. Logs help you understand the why behind what’s happening in your systems.

• Traces: Distributed tracing follows user requests across multiple services, helping businesses identify bottlenecks. They help you understand where issues are happening. 

• Business data: This includes data from data warehouses, databases, CRM systems, ERP platforms, marketing automation tools, financial systems, point-of-sale terminals, customer support tickets, and product analytics. It provides context on business operations, customer interactions, and other domain-specific information to help you understand the business impact of technical issues.

Once data has been collected, business observability relies on real-time monitoring tools to track business processes, IT systems, and performance metrics to identify issues before they escalate. Automated monitoring and carefully designed alerts are key to reducing the swivel-chair phenomenon many IT analysts face.

Machine learning and AI bolster the advanced analytics that process collected data to extract patterns and identify trends and anomalies. These AI-driven tools help businesses predict and mitigate potential risks and uncover actionable insights from their data.

Business observability gives teams a single pane of glass view into their systems and operations. Dashboards and reports present insights in an understandable format, enabling teams to make data-driven decisions quickly.

Collecting vast amounts of data can make it difficult to extract meaningful insights. That’s why prioritizing critical business metrics and using AI-powered analytics to filter unnecessary data is key. Identifying which business metrics are critical boils down to the specific use case you’re tackling with business observability.

The solution: Automate data processing. Manually processing the data systems generate is hugely inefficient, if not impossible. AI and machine learning can help lighten the load and streamline data processing.

Some companies are still in the process of their digital transformation. Others are contending with hybrid cloud systems. Data signals are varied, in different formats, and very high in volume, making integration a significant challenge. 

The solution: Use cloud-based, open standard observability tools. While turnkey solutions may offer a quick start, they limit flexibility. Open standard observability solutions ensure that you own your data and are tailored to your specific needs. 

Implementing observability requires skilled professionals, infrastructure, and budget. Short-term costs can be difficult to justify to decision-makers. Starting small, focusing on high-impact areas, and gradually scaling up paves the way for a successful implementation — and long-term savings. 

The solution: Prioritize critical business processes. Start small. By monitoring key business functions before expanding coverage, you can iterate on and learn from your observability practices, setting yourself and your teams up for success when you scale.

Business observability is key to optimizing processes and productivity and reducing costs. So how do you implement it?

Identify specific business goals and outline clear objectives based on these goals. You’re integrating your business data into your observability solution for a reason. How will you define success?

Define key performance indicators (KPIs) such as customer retention rates, response times, or revenue per user. These goals should be tangible but also reflect a more proactive approach to business. For example, you should aim to prevent shortages or outages rather than just knowing what to do if they occur.

The answers you get will only be as good as the data you collect, and the data you collect must be relevant to your outlined objectives. Determine what kind of business data you are interested in capturing. For example, location data might be more relevant to your business than social media data if you’re a brick-and-mortar business interested in increasing foot traffic.

By establishing efficient data pipelines to collect, process, and analyze data from the right sources, you’ll know what kind of data is coming from where. Business data is often treated and contextualized differently from operational data. Therefore, this step might also include data unification to standardize data operations.

Adopt monitoring platforms that provide comprehensive insights across business processes, IT systems, and customer interactions. Integrate monitoring tools that handle your operational and business data to accomplish business observability.

Observability is an ongoing process. By regularly refining your processes and strategies based on the insights and feedback gathered from your data, you can scale up and down as needed.

Ultimately, a successful business observability plan relies on the alignment of IT, operations, and business teams to ensure a unified approach. Understanding dependencies in the data and implementing changes based on those insights require collaboration across your organization’s verticals. Business observability doesn’t stop at the tech — it begins as a company mindset.

Business observability is a complex undertaking. Businesses can use insights from observability to drive organizational decision-making and optimize performance. 

For example, BITMARCK implemented business observability to deliver a superior customer experience, streamline compliance with data privacy regulation, and supercharge productivity while opening the door for new innovation implementations such as AI. This supported its ambition to build software that delivers a better customer experience and rises to the challenge of the modern digital environment.

Like BITMARCK, businesses hoping to scale might implement business observability to streamline processes and free up resources to innovate, which can have major positive impacts on your operations and, ultimately, your revenue.

These best practices can help you get there:

• Define clear goals. As with any technological transformation, clearly defined objectives aligned with business outcomes ensure that your observability efforts drive value.

• Optimize processes with automation. Data volume and velocity can be addressed with automation. Consider AI-driven monitoring tools to automate data processing, anomaly detection, root cause analysis, and more.

• Prioritize security and privacy. Security should be at the heart of your business observability implementation plan. Baking it in from the start helps safeguard sensitive information, limit vulnerabilities, and ensure compliance with data governance policies.

Elastic Observability is an open and extensible, full-stack solution designed to provide real-time visibility into your entire digital ecosystem.

With Elastic, break down data silos, centralize logging, and use AI-driven analytics to detect anomalies before they impact operations. Elastic’s flexibility allows businesses to scale at their own pace and tailor their solutions to their goals.

Unify your data and speed up innovation with one end-to-end solution.