Data Integration

Data observability: The new frontier of data health

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Don Pinto

4 min read

Every business relies on data, and organizations are collecting more of it now than ever. Whether capturing new leads or making pricing decisions, companies increasingly turn to data for improving their decision making and optimizing their day-to-day operations. Nevertheless, collecting data alone isn't sufficient to turn a data-rich organization into a data-driven one.  

Since data can be spread across and moved to different locations within an organization, most organizations can’t see the state of their data until something goes wrong. As a result, unhealthy data can hinder critical decision making, and the “garbage in, garbage out” dynamic that typically builds up as data flows through the system can increase data handling costs.  

Data health describes how well an organization's data supports its business objectives. Data is healthy if it is easily discoverable, understandable, and of value to the people that need to use it — and these characteristics are sustained throughout its lifecycle. Therefore, starting with a healthy dataset and continuously monitoring it is key to saving time and resources for the business. To effectively accomplish this, data observability is critical.  

What Is data observability? 

While the term data observability has been popping up in many different data contexts, it might still be unfamiliar to many. Data observability is the practice of being able to continuously evaluate your data and provide insights on how it is evolving. This means more than just data dashboards — it is the what, where, why, and how behind your data systems. It requires a holistic view of many different aspects, such as quality, lineage, monitoring, and notifications. This gives you a 360-degree view of your entire data lifecycle, from data ingestion and transformation to data access and disposal. An effective data observability solution enables consumers to trust the data they receive and to use it confidently, while data producers can identify bad data and remediate it quickly. 

Why a good data observability solution is needed 

Here are a few reasons why businesses require a good data observability solution: 

  1. Uncover data blind spots: As the data stack in an organization evolves to meet modern business needs, it can become challenging to understand how and where data is generated, transformed, moved, and stored. Unfortunately, this leads to data blind spots that might be overlooked, causing incorrect results or even data system failures if not corrected.

  2. Controlling data drift: In the past, data infrastructure was designed for handling small amounts of data (usually operational data from a few internal sources), and data was not expected to change much. However, as the data variety, volumes, and velocity increase, new formats, schemas, and transformations will likely emerge, contributing to data drift that could lead to the wrong business decisions.

  3. Meeting regulatory compliance and audits: With the growing need to meet regulatory compliance requirements, businesses are routinely required to identify and handle sensitive data. For example, it might be necessary to anonymize PII data, Being unable to do this on a large scale means high stakes and increased risks for the organization.

Typically, if organizations want to understand data quality and improve their data, they need to manually run data quality reports on datasets and be reactive in their troubleshooting efforts. With Talend Data Console, that becomes a proactive effort.

Exploring data observability with Talend Data Console 

Does your organization need data observability? If you’re already a Talend customer, the good news is that we have something exciting for you!  

Through the Talend Data Console Early Adopter program, we are inviting existing customers to discover and test selected new features ahead of general availability.  

The Talend Data Console makes it easy to meet your data observability objectives and to measure the effectiveness of data programs and quality interventions by : 

  1. Obtaining a complete 360-degree observability view of your data assets, including health and monitoring. This provides data professionals with a layer of data observability to gain a better understanding of the data landscape and areas for improvements. 

  2. Detecting data health issues across inventoried datasets and making suggestions of datasets to improve according to custom thresholds on quality metrics and social activities.

  3. Leveraging Talend Trust Score™ evolution capabilities to see how the quantified trust aspects of your data have changed over time and what exact data interventions have impacted the score. This allows you to identify data drifts quickly. 

To summarize, the new Talend Data Console provides a holistic view of the organization’s data. This exciting advance will allow data professionals to automatically and proactively monitor the health of their data over time and provide the data trust needed for self-service data access. 

Talend customers: sign up today for the Talend Data Console Early Adopter program! And if you're not using Talend yet, contact our sales team to learn more.

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