Most people like their privacy and, as a society, we usually value the right to privacy. Therefore, observing data can be tricky business. Even if you have permission to observe data, it is still necessary to understand the validity and quality of observed data.
To explore the topic of data observability, this article covers the following topics:
What is Data Observability?
Data observability is part of DataOps that involves understanding an organization’s overall data health. It takes into consideration the five essential pillars of data health.
Five Essential Pillars of Data Health
- Freshness: Refers to the age of the data and how up-to-date it is. Freshness is important because outdated or stale data can lead to incorrect analysis and decision-making. To ensure freshness, data teams need to regularly monitor and refresh their data sources.
- Dissemination: This relates to how data is shared and distributed across an organization. It’s important to ensure that the right people have access to the right data at the right time to make informed decisions. Dissemination also involves ensuring that data is properly secured and protected.
- Quantity: The volume of data that an organization has and how it’s stored. It’s important to have enough data to make informed decisions, but having too much data can lead to confusion and overwhelm. Data teams need to ensure that data is properly organized and stored so that it can be easily accessed and analyzed.
- Schema: The structure of the data and how it’s organized. Having a well-defined schema is important because it allows data teams to easily analyze and interpret data. It also helps ensure consistency across different data sources.
- Lineage: The history of the data and how it has been transformed and processed over time. Understanding data lineage is important because it allows data teams to trace the origins of a piece of data and ensure its accuracy. It also helps ensure compliance with regulations and standards.
These pillars give organizations a consistent means of assessing data health and quality, improving the quality of data and decreasing its unavailability. Data teams can ensure high quality data by employing data observability tools and platforms.
Understanding Data Observability
Data observability is a catchall phrase for understanding the quality and status of the data stored in a system. In its most basic form, data observability is an umbrella term that encompasses a variety of operations and methods that, when combined, make it possible for users to detect, debug, and fix data source problems in close to real time.
Data observability focuses on ensuring that an organization’s data is complete, accurate and available. It involves monitoring, measuring and analyzing data in real time to identify any issues or anomalies. The goal is to improve the quality and reliability of an organization’s data, as well as increase the efficiency and effectiveness of data-related processes. It is achieved through the use of specialized tools and platforms that allow data teams to monitor data pipelines and systems, detect errors and anomalies and quickly resolve issues. Ensuring that data is observable, data teams can gain a better understanding of their data and use it to make informed decisions.
Data Observability Best Practices
We outline some best practices associated with data observability.
Automated Monitoring in Real Time
Automated monitoring in real time is necessary to ensure that any issues or anomalies are detected as soon as possible. The faster that data teams are altered to possible issues the faster they can respond and prevent them from escalating. Automating the monitoring process allows for continuous monitoring, which is essential for ensuring data observability.
Involves setting up automated notifications to notify data teams of issues or anomalies as soon as they occur. Proactive alerting allows data teams to quickly respond to issues before they become critical, reducing downtime and improving data quality.
Proactive alerting works by setting up specific triggers or thresholds that, when reached, will automatically generate an alert. For example, a data team might set up an alert for a sudden increase in errors in a data pipeline, or a drop in data freshness below a certain threshold.
Proactive alerting can be customized to fit the specific needs of an organization and can be set up to notify the appropriate teams or individuals based on the severity or type of issue. By setting up proactive alerts, data teams can identify and address issues quickly, reducing the risk of data downtime and ensuring that data is always available and accurate.
Collaboration Across Teams
Collaboration helps to ensure that everyone in data-related processes are working towards the same goal of improving data quality and reliability. It involves working closely with different times, such as data teams, development teams, and business stakeholders, to identify and resolve issues related to data observability.
By collaborating across teams, data observability can become a shared responsibility that involves everyone in the organization. This can help ensure that data is of high quality, reliable, and available when needed, ultimately leading to better decision-making and business outcomes.
Utilize Data Lineage
Data lineage is an important tool that allows data teams to understand the origin and transformation of data as it moves through different systems and processes. By tracing the lineage of data, data teams can identify the source of issues or anomalies and quickly resolve them. These could include identifying the source of data issues, ensuring compliance with regulations and standards, tracking data quality, improving data governance and advance planning for data lineage.
Using data lineage data teams can gain a better understanding of their data and use it to make informed decisions that drive business success.
Continuous improvement involves a cycle of monitoring, identifying areas for improvement, implementing changes, and then monitoring again to ensure that improvements have been made. This cycle is repeated continuously, with the goal of continually improving data quality, reliability, and availability.
Continuous improvement is important in data observability because it ensures that data-related processes are constantly being optimized for better performance. By continually monitoring and improving data processes, data teams can ensure that data is of high quality, reliable, and available when needed, ultimately leading to better decision-making and business outcomes.
Data observability has a critical practice for organizations that want to make data-driven decisions with confidence. Satori can help an organization’s overall data health by taking into consideration the five essential pillars of data health and employing data observability tools and platforms, data teams can ensure high quality data, decrease unavailability, and improve data quality, reliability, and availability.
Data observability is an ongoing process, using Satori’s data security platform you can ensure continuous monitoring, identifying areas for improvement, implementing changes, and monitoring again. Using Satori to implement data observability practices, organizations can make data-driven decisions that drive business success.