What is business observability?
From a financial perspective, technology is a black hole. Many Nonprofit organizations struggle with quantifying the business impact of technology. For example, only 37% of the 2022 Observability Forecast survey respondents said their telemetry data includes business context to quantify the business impact of events and incidents.
This is often due to a lack of visibility and data silos between technology and business teams. Connecting the dots back to the business after the fact can be difficult and reactive, resulting in obsolete, non-actionable insights. It shouldn’t be an afterthought.
Business observability is the art of capturing, measuring, and managing the live interaction between technology and capital (telemetry data and business analytics).
It can help eliminate data silos by actively aligning monetary costs and revenue impacts to the performance of systems, applications, and processes. In other words, it correlates performance with key business results in real time.
When you implement business observability practices, you get visibility into how your applications and infrastructure impact your business so you can make data-driven decisions and achieve significant financial gains.
Monitoring vs observability vs business observability
But first, let’s look at an example of the difference between monitoring, observability, and business observability.
Business observability in action
Now let’s look at some common use case examples by industry.
Step 1: Establish business objectives.
The first step in the business observability process is to define clear business objectives and assess risks. What do you want to improve? Establishing specific, measurable business objectives and risks can help ensure that your business observability process focuses on aspects directly related to achieving those objectives and mitigating potential risks.
Examples of business objectives include:
- Revenue goal: Increase subscription revenue by 15% in Q2.
- Risk: Decrease security breach incidents by 50%.
- Customer impact: Improve customer satisfaction (CSAT) score by 10 points.
- User engagement: Increase monthly active users (MAUs) by 20%.
- Operational efficiency: Decrease order fulfillment time by 30%.
Step 2: Capture data from all sources.
Next, you’ll need to assess how much of your tech stack you’ve instrumented and are currently monitoring.
Measuring the percentage of telemetry data captured from all sources across your entire business is known as telemetry data coverage, which is a key business observability metric. It helps you gauge how much of your systems, applications, and processes you are monitoring and the amount of data available for analysis.
What is telemetry data?
Telemetry data is external outputs of applications and infrastructure that include metrics, events, logs, and traces (MELT), which are the essential data types of observability. Instrumenting everything and using MELT to form a fundamental working knowledge of connections — the relationships and dependencies within your system and business as well as its detailed performance and health — are prerequisites to practicing observability.
Telemetry data coverage 🟰 the number of data sources you’re currently monitoring ➗ your total number of data sources
The higher your telemetry data coverage, the more complete the picture of your organization’s performance and the more accurate and valuable the insights from the data. However, most organizations generally report achieving only 25–35% coverage, which is low. For example, only 27% of the 2022 Observability Forecast survey respondents said they capture their telemetry across the full tech stack. Aiming for 70% or more can give you a more complete picture.
- Low coverage: 35%
- Medium coverage: 35–70%
- High coverage: >70%
You can use telemetry data coverage to:
- Pinpoint gaps or areas that require additional data.
- Benchmark performance over time.
- Identify trends and patterns in the data.
Your organization’s telemetry data can come from two main sources:
- Controlled environments encompass your organization’s tech stack, including owned services, software, and infrastructure. They usually constitute only one-third of the business landscape and are easier to monitor and analyze due to direct data access and customizable monitoring and analytics tools.
- Uncontrolled environments include external systems, tools, and services not directly controlled by your organization. They constitute approximately two-thirds of the business landscape and can be challenging to monitor and analyze due to limited direct access, often requiring APIs or alternative methods for data collection and analysis.
Most observability solutions concentrate on controlled environments, neglecting uncontrolled data sources, which can lead to an incomplete understanding of your business landscape.
As organizations increasingly rely on third-party technology tools and services, the amount of data produced in uncontrolled environments has surpassed that produced in controlled environments. This can make it harder to monitor and analyze all the data necessary for a comprehensive understanding of your business performance.
Low telemetry data coverage in an uncontrolled environment can lead to black boxes and blind spots, which are essentially anything not covered by MELT.
- Black boxes refer to critical systems or services that are vital to your business but lack visibility. These can be difficult to monitor and analyze if your organization can’t access the data or configure monitoring and analytics tools to meet your specific needs. Examples of black boxes include proprietary devices, antiquated systems, and offline machines.
- Blind spots refer to systems and services that offer analytics and telemetry but require additional effort to access, capture, view, and store the information they provide. These blind spots can include cloud services, IoT devices, social media platforms, and other external sources that your organization doesn’t control directly.
Identifying and minimizing black boxes and blind spots can increase your telemetry data coverage significantly, which can lead to reducing loss of money, time, and customers.
Impact of high and low telemetry data coverage
Low telemetry data coverage can lead to greater business loss due to a lack of visibility into critical systems and services. This can result in missed opportunities, inefficient processes, and low customer satisfaction. Additionally, low telemetry data coverage can make it difficult to identify and troubleshoot issues, which can lead to increased downtime and costs.
Types of business loss:
- Revenue
- Security (theft and crime)
- Credibility (customer confidence)
To protect your business, you should increase your telemetry data coverage. By increasing visibility and access to data from all sources, you can gain a more complete understanding of your business performance, identify areas for improvement, and make more informed decisions.
Step 3: Quantify the financial impact of business metrics.
Once you’ve increased telemetry data coverage, you’re ready to turn data into dollars by measuring the real-time financial impact of performance issues or outages. This is called metric monetization.
Metric monetization refers to the process of quantifying the financial impact of specific metrics on a business. These metrics could include:
- Order cost: Cost of placing orders via apps and kiosks.
- Outage cost: Financial loss during system downtime.
- Cloud-to-cash ratio: Efficiency of cloud services spending in generating revenue.
- Revenue replication: Identification and replication of successful revenue-generating strategies across products, segments, or regions.
- Service-level loss: Financial impact of failing to meet SLAs or KPIs.
Understanding the financial impact of different metrics can help you prioritize your efforts to improve the performance of critical systems and services. Additionally, this knowledge can help you make data-driven decisions about investments in new technology and infrastructure.
In short, metric monetization is a crucial part of business observability. It enables you to understand the financial impact of specific issues and make informed decisions about how to resolve them. This leads to a more streamlined, quantified, and profitable business.
Step 4: View live metrics.
Once you’ve achieved metric monetization, you need advanced visualization to capture real-time activity, track progress toward business objectives, and identify emerging trends or issues. A telemetry data platform is where you can bring together and effectively grow the seeds of business observability.
It’s essential to have a telemetry data platform that provides both telemetry data collection and programmability. An off-the-shelf platform with canned dashboards won’t suffice.
Why is programmability important?
Programmability enables you to program or customize your telemetry data instead of just measuring and monitoring it, as well as deploy custom business performance tools and applications that take full advantage of the unified intelligence harnessed in the platform.
Your telemetry data platform should be a space where you can eliminate black boxes and blind spots as quickly as they appear and where navigating between controlled and uncontrolled environments feels exactly the same. The kind of magic that can finally bridge technology with business and achieve metric monetization.
You should also be able to use advanced dashboards, querying, and AIOps capabilities to view your metrics in real time. Predictive modeling, correlation analysis, and anomaly detection can help you capture, process, and analyze multi-dimensional data, plus reveal the relationships between system performance, business goals, and risks.
Step 5: Make data-driven business decisions.
Next, you’ll be ready to prioritize and address issues or improvements immediately based on their business impact. Below are some examples of the types of informed decisions enabled by business observability.
- Enhanced business performance: Pinpoint bottlenecks and inefficiencies that may be impacting revenue generation, customer satisfaction, or operational costs, prioritize them, and then take corrective action, which can significantly enhance your overall performance.
- Proactive, real-time risk assessment and management: Detect anomalies and potential issues in real time and proactively mitigate risks before they escalate, which can help prevent costly outages, service disruptions, and customer dissatisfaction, as well as safeguard your organization’s reputation and financial health.
- Optimized customer experience: Monitor the entire customer journey to gain deep insights into customer behavior and preferences and use them to inform strategies that enhance the customer experience, which can lead to increased customer loyalty.
- Accelerated innovation: Identify opportunities for innovation and swiftly act on them, which can lead to developing new products, services, or business models that drive growth and differentiation in the market.
- Business model validation and strategic planning: Use predictive business modeling to analyze data and predict future trends and outcomes, which can help you anticipate market shifts and customer behavior changes and then adapt accordingly.
Step 6: Achieve positive business outcomes.
Finally, you’ll be able to quantify the results of attaining key business objectives. Some common outcomes of business observability include:
- Reduced costs
- Increased revenue
- New revenue streams
- Quick return on investment (ROI)
- Increased profitability
- Decreased total cost of ownership (TCO)
- Regulatory compliance
You’ll likely wonder how you ever did without it.
If you are a nonprofit organization, looking to level up your Observability tools, sign up for a free account here:
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New Relic offers free tools and full platform features along with 1TB of free monthly data injest and 5 user accounts to all qualifying global nonprofit customers! Just email us at hgruber(at)newrelic(dot)com to find out more!
