When it comes to truly understanding your organization's health and performance, data observability is needed, especially with the rise in AI going into 2024.
In this article, we'll unpack the dynamics of data observability and its components, understand its benefits in your business, and explore real ways to use observable data. Read on to find out more about the potential of data observability.
Data observability is the term for your ability to fully understand, monitor, and manage the quality, reliability, and performance of data across various data pipelines. Observability provides a transparent view of data flows to ensure their accuracy and validity.
Comprehensively speaking, data observability is a proactive approach to data management that allows businesses to gain insights and recognize issues in their data ecosystem. This can be done in real-time, contributing to enhanced decision-making.
Data observability enables the ability to inspect, diagnose, and rectify data inconsistencies within an organization's information system. Data observability also acts as a cornerstone for boosting business intelligence — here's what data observability can unlock for your business:
In short, the better question might be, “What can’t I do with observable data?!”
(Related reading: What’s Observability?)
Data observability can be broken down into 5 main pillars:
Freshness refers to the timeliness of data, i.e. how up-to-date and recent given data is at any point in time. This pillar enables businesses to monitor the latency of their data flow, ensuring that the information being used for decision-making is as current as possible.
Distribution focuses on understanding how data is being transmitted and moved across various data sources. It involves:
Volume refers to the size of data being processed and stored by an organization. This pillar enables you to monitor your storage capacity and cost — and identify any issues with data scaling.
Schema refers to the structure and organization of data, including its format, type, and relationships. This pillar helps you ensure data consistency across different systems and identify any discrepancies or errors.
Lineage refers to the history and origin of data, tracking where it came from and how it has been transformed throughout its journey. Data lineage allows you to best understand the reliability and accuracy of your data, as well as identify any potential data quality issues.
Now, let's have a look at the key components that make up data observability:
Accelerating the advancement of your business through data observability starts with a fundamental element: monitoring data quality. With continuous monitoring, your systems can discern inconsistencies and faults, thus elevating the veracity and reliability of observable data.
Tracing data lineage is essential: it allows tracking of a data item's lifecycle, from its source to its transformation, until it reaches a visualization state. Understanding how data moves, and changes in your organization can have a significant impact on your business strategy. With this feature, you’ll have:
In the case of an unexpected result, it’s much easier for engineers to efficiently examine the data’s path to identify the problem in the pipeline if there's good data observability.
To simplify this process, use observability tooling (like those from Splunk) to establish data lineage. These tools enable teams to understand better how the resulting output was constructed — allowing engineers to dive into the operations at each step in the workflow.
(Read about distributed tracing, the technology that enables data tracing.)
Real-time visibility is crucial to data observability. This visibility enables tracking of data in real-time as it moves along the data pipeline, providing transparency into any issues that may arise.
Here are some benefits of real-time visibility:
(Check out real-time visibility & monitoring with Google Cloud & Splunk.)
Achieving consistency in your business’ information is paramount in the age of digital transformation. Data consistency underpins trust in business process outcomes, decision-making, and overall data reliability.
Of course, the most affective approach for assuring consistent data is to have data observability. With that, you can:
Detecting anomalies and outliers is a crucial facet of data observability. Leveraging the right tools can highlight unexpected data variations, smooth out data streams, and reduce the impact of outliers on your overall analytics. With this feature, you can:
Let's now explore some of the benefits of having data observability in your organization:
With data observability, businesses can base all decisions on accurate and reliable data. Enhanced data observability instills trust in data across the organization.
As a result, some added reliability and accuracy benefits are:
With data observability, businesses unlock the power to make informed and proactive decisions. By continuously tracking data flows, you can:
In support of decision-making, data observability enables real-time insights into business operations, predictive analytics for strategic planning, and the ability to detect and address risks before they impact business.
Data observability has a significant impact on operational efficiency, as it helps organizations eliminate redundant processes, streamline workflows, and accelerate data-driven decision-making. Some ways that data observability can improve operational efficiency include:
Through consistent monitoring and tracking, data observability enhances the security of organizational data. It also enables businesses to adhere to data governance regulations, protecting sensitive information and maintaining customer trust.
Some ways that data observability contributes to data security and governance include:
(Know the differences: data governance vs. data management.)
The ultimate goal of data observability is to provide transparency into the entire lifecycle of data. If you do data observability the right way, you’ll get a total view of your data’s movements, transformations, and usage—that is, actual transparency in your data. This transparency enables you to:
To successfully implement observable data, you’ll need three things: a keen understanding of your data infrastructure, the appropriate tools and instrumentation, and the right monitoring systems that flag data inconsistencies.
The first step is to understand your data infrastructure, including where data is sourced from, how it moves along the pipeline, and where it resides.
Adopting data observability facilitates a 360-degree view of your data infrastructure. It allows you to align the disparate elements of your data ecosystem, ensuring efficient data flow and usage.
Next, select tools that are best suited for your organization's needs. Consider tools that can easily integrate with your existing data infrastructure and provide comprehensive monitoring capabilities. Some factors to consider when choosing observability tools include:
Splunk Application Performance Monitoring is a perfect example of this technology.
Lastly, use effective monitoring systems to pivot your business towards data-driven decision-making.
They help track data changes, performance metrics, and data pipeline developments impacting your business. Here are some actions to take:
Monitoring systems enable businesses to quickly identify and address potential issues, ensuring the reliability and accuracy of data.
Say goodbye to blind spots, guesswork, and swivel-chair monitoring. With Splunk Observability Cloud and AI Assistant, correlate all your metrics, logs, and traces automatically and in one place.
Finally, here are some best practices to ensure successful data observability implementation in your organization.
Defining key data quality metrics allows organizations to measure and track the quality of their data. Some questions to answer include:
Establishing these metrics ensures that businesses can identify and address potential issues, reducing the risk of incorrect or incomplete data.
Data observability requires the involvement and cooperation of all teams within an organization. By promoting a culture of data democratization, businesses ensure that everyone has access to data insights and can contribute to improving the quality and accuracy of data. This includes:
(Related reading: organizational change management.)
Establishing an effective data governance policy can embed transparency into your organization's core. A comprehensive framework that outlines data management standards, mechanisms for reporting issues, and auditing procedures is important.
Additionally, it is crucial to involve all stakeholders in the creation of data governance policies to ensure buy-in and alignment toward a common goal.
A regular audit of data pipelines helps with their upkeep. It allows for early detection of bottlenecks, leading to quicker resolution and streamlined operations.
It guarantees seamless data pipeline processes and facilitates unhindered access to essential data points.
With a focus on maintaining pipeline health, audits help recognize deviations in data quality. This paves the way for accurate business forecasting and data-driven decision-making.
Data observability is a critical aspect of modern data management and governance. It provides complete transparency into the lifecycle of data, helping businesses ensure regulatory compliance, identify areas for improvement, and make more informed decisions.
With proper understanding, tool selection, monitoring systems setup, and best practices adoption, organizations can reap the full benefits of data observability in their operations.
See an error or have a suggestion? Please let us know by emailing splunkblogs@cisco.com.
This posting does not necessarily represent Splunk's position, strategies or opinion.
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