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How observability analytics helps teams uncover answers

Discover the importance of observability analytics, which combines traditional analytics data to deliver actionable insights.

When it comes to critical applications and environments, organizations can’t afford to leave any stone unturned, as IT unknowns can have significant consequences. Any gaps, slowdowns, or potential issues can have a significant effect on an organization’s success — or lack thereof.

This is where observability analytics can help. Here’s a look at how it works, what it does, and its role in ensuring reliable operations.

What is observability analytics?

Observability analytics enables users to gain new insights into traditional telemetry data such as logs, metrics, and traces by allowing users to dynamically query any data captured and to deliver actionable insights. By connecting the dots between multiple points of observation, teams can take action to identify any potential issues or insights and understand it all.

With an all-source data approach, organizations can move beyond everyday IT fire drills to examine key performance indicators (KPIs) and service-level agreements (SLAs) to ensure they’re being met. They can identify and analyze trends to determine what short- and long-term futures may look like. And they can create relevant queries based on available data to answer questions and make business decisions.

Breaking down the benefits of observability analytics

Observability analytics offers organizations several benefits, including the following:

Uncovering unknown unknowns

Unknown unknowns are answers without questions. While measuring app response time under different circumstances provides a latency value, for example, it doesn’t tell you why the app is slow, fast, or somewhere in between. These unknowns are often tied to the root cause of IT issues.

Observability analytics can help teams solve for unknown unknowns. By analyzing the big picture of IT operations — including logs, metrics, traces, security reports, and usage data — and combining the output with advanced AI tool sets, organizations can conduct exploratory analytics that go beyond the surface to discover the why behind the what.

Breaking down data silos

Data silos remain a challenge for organizations. According to recent survey data, 79% of knowledge workers said teams in their organization are siloed, and 68% said these silos negatively affect their work. Part of the problem is time wasted searching for data, with workers reporting an average of 11.6 hours lost per week.

If teams can’t find the data they need when they need it, productivity declines. Collaboration is also challenging if teams need to work in tandem but have access to different data sets and applications.

Observability analytics can help identify and break down silos, providing common ground for inter-team efforts.

Democratizing data consumption

Democratizing data consumption means making data available and accessible. While many employees are familiar with IT processes, few are trained data practitioners. Observability platforms make it possible to capture and contextualize data, creating a shared foundation for staff.

Weighing the challenges of observability analytics

Implementing observability analytics comes with potential challenges. Common concerns include managing tool sprawl, creating context, and validating output.

Managing tool sprawl

More observability tools means more data — and more complexity. Consider that the average multicloud environment includes at least 12 different applications and services. To effectively monitor and manage these services, organizations often rely on multiple monitoring tools, each with its own feature set and focus. In some cases, these features overlap. In others, there may be gaps in observability that organizations can’t see.

Part of implementing effective observability, therefore, is minimizing the number of tools required to deliver actionable insight.

Creating context

Data without context is just noise. Best-case scenario: This noise distracts from but doesn’t derail effective analytics, costing companies time. Worst-case scenario: It becomes part of the decision-making process, potentially costing time and money.

Put simply, context is king. If solutions can’t provide context for collected data, they can do more harm than good.

Validating output

Analytical outputs aren’t guaranteed. Consider the recent rise of natural-language input AI tools. While these solutions make it easy for users to ask questions and get answers, these answers are only as accurate as the data available to the AI model. If the data is incomplete or inaccurate, so is the output. As a result, regular validation is critical to ensure accurate results.

Three components of successful observability analytics

Effective observation doesn’t happen automatically. Three components are critical to set the stage for successful observability analytics.

1. Automation

Based on the sheer volume and variety of data available to observability tools, IT automation is critical to ensure efficient operations. While human oversight is required to ensure outputs meet expectations, relying on manual processes to collect and correlate data is no longer feasible. In practice, teams need a combination of infrastructure and operations, digital processes, and automation tools to ensure information is effectively handled at every step of the observability process.

2. Streamlined data collection

Organizations also need tools that enable streamlined data collection. In practice, this means finding and implementing solutions capable of collecting data from multiple sources — such as cloud environments, co-located data centers, and on-site servers — and extracting key insights from this data, regardless of format.

This means observability solutions must be equally proficient at extracting structured usage from cloud services as they are at capturing data from on-premises mainframes.

3. Data lakehouse

Data lakes are a cost-efficient way to store information, while data warehouses provide contextual, high-speed querying capabilities. To make the most of observability analytics, organizations need both.

A data lakehouse such as Dynatrace Grail can combine the best features of lakes and warehouses, allowing them to effectively handle analytical and machine learning workloads.

Common use cases to consider

While observability analysis has no set boundary and can be used in any business application, several use cases are commonplace.

Exploratory analysis. Exploratory analytics enables teams to explore IT environments with observability solutions to discover unknown dependencies, interactions, or outcomes that help identify root causes.

Metrics-based performance thresholds. By observing current processes and outcomes, organizations can map these results onto service-level objectives, SLAs, service-level indicators, and KPIs to establish baselines and outliers. This allows for the creation of performance-based thresholds tied to concrete, observable metrics.

Consider the Dynatrace Carbon Impact app, which provides an overview of your current carbon footprint compared to previous time frames and recommendations to reduce your overall impact. By leveraging observability analytics, organizations can establish a baseline carbon footprint level along with emissions targets and upper limits that trigger a notification event when reached.

Predictive analysis. Complete visibility of IT infrastructure sets the stage for predictive analytics that can help inform key business objectives. For example, observability analytics can provide data on current infrastructure usage and any bottlenecks or performance problems. Equipped with this data, teams are better prepared to anticipate application, bandwidth, and security needs over time, streamlining the infrastructure scaling process.

Taking observability analytics to the next level

With Dynatrace, observability comes standard with built-in solutions. Here are the main functions teams can use to discover new insights:

Data forecasting. With Dynatrace Grail and Davis AI, organizations can predict capacity demands and make proactive changes. Using the Dynatrace Query Language (DQL), they can analyze all data stored within the Dynatrace Grail data lakehouse for any given time series. Davis AI then analyzes the time series and automatically chooses the best prediction model.

Predictive analytics. Davis AI can also help predict the impact of multiple workflows on business operations. This assists with long-term capacity planning and helps IT leaders make short-term decisions that boost operational efficiency.

Exploratory analytics. Using Dynatrace Notebooks, teams can carry out exploratory analytics for observability, security, and business data analysis. With Notebooks, users can collaborate using code, text, and rich media to build and share insights.

With observability analytics powered by Dynatrace, teams are better equipped to discover unknown unknowns, understand their impact, and take action that improves business operations. For more information on observability analytics from Dynatrace, check out our website.