Data, data, and more data: In today’s landscape, data is driving critical decisions and is considered the lifeblood of an organization. With an increasingly digital economy, more and more enterprises are making key financial and customer decisions based on data input, making the accuracy and timeliness of data more vital than ever.
However, when data issues arise, they cause confusion, downtime, and extra work. Late or missing data often necessitates the intervention of an organization’s engineering or development teams — time that is costly and disruptive to their regular workload. Meanwhile, downstream teams that rely on this data may be brought to a screeching halt, leading to lost productivity and time taken away from advancing innovation.
That’s precisely where data observability comes into play. Think of this trending, new initiative as the premium insurance needed to protect an enterprise’s data health, quality, and integrity. An effective data observability solution provides a higher level of data monitoring sophistication, giving data teams and engineers a new approach to gauging the operational health of a given data source or pipeline, independent of built-in alerts and monitoring.
Keep reading for a deep dive into data observability.
The Emergence of Data Observability
Data observability solutions are primarily driven by the increase in data pipeline complexity and scope. Over time, data pipelines have evolved. Today, many enterprises collect hundreds to thousands of streams of data with multiple transformation, aggregation, and processing stages and — a greater interdependence between each data set. At the same time, the growing reliance on data as a driver of critical business decisions has led to the rapid expansion of data teams.
With robust data observability processes in place, enterprises set themselves up for success by enabling a quick response to unforeseen data issues. In fact, according to Gartner, by 2024, enterprises will increase their adoption rate of observability tools by 30%.
It’s easy to see why this solution is increasingly popular: it enables data teams to gain additional tools, technologies, expertise, and processes that are integrated with existing systems. Data observability acts as an automated defense system, allowing users to identify, troubleshoot, and resolve data errors even quicker than before. The result? Data downtime is minimized or prevented, and the integrity of the data is preserved. It’s a proactive solution minimizing the number of resources spent on intervening to correct an issue.
How Does Data Observability Work?
With such increases in pipeline complexity, issues arise more often than one might think. Consider the multitude of internal and external data sources your organization collects. With this daily deluge of data sources, all it takes is a seemingly minor change made downstream, making the output inaccurate. Now, the data is compromised, and critical decisions become jeopardized based on faulty data.
Enter data observability, where systems are tuned to quickly alert your data teams to data anomalies, false positives, and pipeline problems. An end-to-end data observability solution enables users to see their data’s journey in detail, with several factors (e.g., lineage, schema, queries, etc.) available within a single view. This provides insights that aid in identifying ways to remedy the cause of data issues.
Going Deeper Than Data Orchestration and Monitoring Tools
Data orchestration and monitoring tools simply aren’t enough. Data orchestration tools generally don’t go deeper than the process level to gauge the health of inbound data necessary to prevent data downtime. Monitoring tools don’t go deep enough either. Many organizations already deploy monitoring tools that use analytics to identify problems within software environments. The downside is that monitoring tools often don’t dive into the process to prescribe the health of the inbound data necessary to prevent downtime.
Put simply, monitoring is designed to find problems, while observability tools not only identify problems but provide context — illuminating the root cause — so that issues can be quickly resolved.
The Five Pillars of Data Observability
Underlying the workings of data observability are five vital tenets which are responsible for measuring and alerts about your data’s health and reliability. Without further ado, here’s an introduction to the five pillars of data observability.
- Recency: Recency (also known as “freshness”) measures the timeliness of your data. Is it up-to-date? This is a key consideration, given that old data can lead to inaccurate decisions. Recency also looks for gaps in data that can signal problems.
- Distribution: Distribution measures can identify anomalies that may indicate an unexpected change in your data source upstream or incomplete data.
- Volume: Volume measures, like distribution measures, ensure the amount of data you’re receiving is in line with historical expectations.
- Schema: Schemas define how your data is structured and organized among tables, columns, and views. Changes in the source data’s structure are often the cause of downtime.
- Lineage: Lineage gets at the “where” of data problems. By identifying which areas of data collection are impacted and where those changes were made, lineage is key to pinpointing and resolving data issues.
Combined, the five tenets of data observability provide the framework needed to build an effective data observability solution that can pinpoint and resolve issues before they cause distress and downtime throughout your organization.
Data Observability: An Automated Defense System
Enterprises can protect their most valuable asset by integrating a holistic data observability solution into complex data systems and pipelines. That’s where Maven Wave’s data team comes in. Through our data practices, Maven Wave can help your teams integrate a data observability solution to safeguard your data’s health, quality, and integrity, allowing you to harness it to drive informed business decisions and stay competitive. In today’s data environment, there’s nothing more critical.
To learn more about data observability and see it in action via a recent enterprise POC (Proof of Concept), download Maven Wave’s ebook here.
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