Data observability has been a hot topic for the past few years in the data space. Several recent blogs have explained why this capability is critical to the success of modern data and analytics solutions, which increasingly comprise a complicated collection of disparate tools in a hybrid multi-cloud environment. With the profusion of data sources, various modes of data transformations, and a diverse set of consumers, it\u2019s a miracle that tasks still get accomplished. Until they don\u2019t. Thus, the need for full-stack observability.<\/p>\n
Despite a wealth of available information, many organizations are still unclear about appropriate use cases to introduce data observability into their technology stack. That is the goal of this document \u2014 to examine key use cases of data observability. This document is not a primer on the topic. If you are keen to learn more, please refer to\u00a0What is Data Observability?<\/a>,\u00a0Data Observability Accelerates Modern Data Stack Adoption<\/a>, and\u00a0What Have I Observed about Data Observability?<\/a><\/p>\n While the modern data stack abounds with a plethora of specialized technologies, businesses don\u2019t care about the technology soup. They want to derive maximum value from their data assets. For which they expect data to be trustworthy, accessible, and explainable. Hence, one of the most effective ways to convey the importance of data observability is through an examination of\u00a0business-focused<\/em>\u00a0use cases. As an example, a technical use case of data observability may be to enable data governance, but the business user is interested in decreased latency between the creation of data and its consumption.<\/p>\n Data observability extracts key metrics from complex pipelines that help improve business operations as follows:<\/p>\n All the benefits mentioned above are intertwined. For example, automation can help deliver data faster and with higher quality leading to lower costs. In order to achieve these goals, the data observability implementation should reinforce organizations\u2019 overall DataOps and data governance initiatives.<\/p>\n The primary goal of the sales teams in any organization is to generate revenue. To do so, they rely on the marketing team to analyze vast amounts of incoming signals and qualify them as leads and prospects. Faster analysis of this data leads to quicker decision-making, which translates into revenue. To achieve this use case, the IT team needs to deploy an efficient and reliable architecture that can process fast-changing data in the shortest time possible. Amazon CTO Werner Vogels\u2019 statement \u201ceverything fails, all the time\u201d is especially true when organizations are running numerous pipelines on high-velocity data on ever-increasing volumes of data.<\/p>\n So, how does data observability play a part?<\/p>\n There is a high opportunity cost in delaying decisions. This may be because of the unavailability of data or the systems that process data. Data observability can deliver reliability in the build and runtime stages of the data lifecycle as follows:<\/p>\n Errors can emanate because of multiple reasons \u2014 poor data quality, resource failures, or human errors. The data observability tool\u2019s single-pane-of-glass view provides visibility into quality, performance, and resource utilization aspects of data flows across pipelines so that appropriate actions can be taken in case of an anomaly. In absence of this visibility, errors may go undetected, leading to incorrect analysis. Or when errors take place, they may require time-consuming manual triage and remediation steps.<\/p>\n Your data observability tool should improve the speed of data delivery to consumers. This is especially true when additional data sources are added to the mix. How quickly an organization can provision the newly available data determines its competitive advantage.<\/p>\n Businesses are moving faster than ever in history. Oftentimes, they are moving faster than their IT teams. Unfortunately, this sometimes causes the CFO to be the first one to detect a broken dashboard. In such a situation, remediating the fix becomes a reactive fire drill that has a high opportunity cost and reduces the team\u2019s productivity. Broken pipelines compromise trust in the abilities of the IT teams.<\/p>\n Stop using your business users as your QA team. Data observability provides a unique opportunity for business and IT teams to align early in the lifecycle and remediate issues before promoting pipeline into production. Data observability can detect or even predict errors faster than traditional approaches, and business SMEs can provide the needed business domain knowledge to fix them. The goal is to help deliver reliable architecture, where data is always available and trustworthy. Accessible and quality data works on multiple dimensions:<\/p>\n Focus on data reliability improves trust and fosters a strong data culture. As your data observability matures, it leads to a more sophisticated design of a fault-tolerant data and analytics environment.<\/p>\n IT costs have increased precipitously as complexity has increased. They have also increased as more users take advantage of the ease of cloud computing, only to see consumption-based operational expenses rise unpredictably. As a result, many organizations\u2019 focus has shifted from performance optimization to cost optimization.<\/p>\n Cloud computing has taken the effort away from building the data analytics infrastructure. This is good news because this effort adds little strategic value to the bottom line. Cloud service providers (CSPs) provide excellent building blocks but lack adequate guardrails to protect against cost overruns. This has led to the rise of FinOps, the financial management discipline around getting maximum business value from modern data stacks.<\/p>\n Data observability products play a key role as they are the map of how resources are being consumed and the cost they incur in the process. A well-charted data and analytics journey saves build costs in the short term and runtime costs in the long term. Data observability helps derive cost savings by exposing necessary metrics to help across:<\/p>\n\n
Faster time to analytics<\/h1>\n
\n
Always-available quality data<\/h1>\n
\n
Cost optimization<\/h1>\n