Data Observability for Data Quality.edited
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Data Observability for Data Quality: Reliable Data
for All Your AWS Data Platforms
Data is the epicentre of business success. It allows organizations to establish baselines, benchmarks and
goals to keep moving forward. But it’s not enough to have data; you need to develop and access high-
quality data to make better decisions across the business. While there is no shortage of ideas and tools
on how your business can have the correct data at the right time, there still needs to be a system in place
that ensures the data you have is of the quality that you need it to be.
In the current fast-paced digital landscape, organizations depend on multiple data sources that require
the development and maintenance of several data pipelines, storage solutions, databases, etc. Managing
all these different structures is complex and ultimately impacts the integrity and value derived from data.
To unleash the true potential of your data, data observability plays a crucial role.
What Is Data Observability?
Data observability is the extent of visibility you have on the health and performance of your data at any
point in time. It enables organizations to understand, monitor and manage their data across multiple IT
tools throughout the data lifecycle. Data observability is a blanket term for all the broad categories of
activities and technologies that allow you to proactively detect, troubleshoot and resolve data problems
in near real-time before downstream data users are impacted.
Data observability plays a critical role in ensuring the trustworthiness and accuracy of data. It is more
than data monitoring and emphasizes real-time insights, actionable metrics and collaborative efforts.
Importance of Data Observability for AWS Data Platforms
Amazon Web Services (AWS) is a comprehensive cloud computing solution widely used by organizations
across the globe. By moving to AWS, organizations enjoy lower operation costs and stronger operation
resilience. Data observability on AWS will ensure data quality and reliability for organizations using the
cloud solution. This will help the organizations make data-driven decisions, detect issues early and
optimize data workflows for increased efficiency.
Data observability within the AWS ecosystem has several benefits that include:
Enhancing trust in data enables organizations to confidently make decisions based on data
insights and unlock the full potential of their data to drive successful outcomes.
Data observability is a proactive and holistic approach that enables businesses to mitigate risks
associated with data issues before they can affect business.
Data observability helps maintain the quality, consistency and reliability of data available in the
pipeline such that data is available on-demand and in the correct format.
Promotes efficient operations by minimizing disruptions, errors and downtime in the cloud.
Supporting effective analytics and insights.
Data observability motivates organizations to achieve data quality, which ensures adherence to
regulations through effective data governance.
The Benefits of Using Data Observability to Maximize Data Quality
Incomplete or inaccurate data creates loopholes in analytics that lower trust in data and lead to poor
decisions. You can, however, improve data quality through data observability by continuously monitoring
and measuring data quality, leading to better-quality data. Data observability provides enterprises with a
360-degree view of their data ecosystem, allowing them to identify and resolve issues that could cause
pipeline breakdown quickly. This ensures consistency, reliability and quality of data in pipelines.
Data quality measures the health of data sets and determines whether it is good enough for the
intended use in operational and analytical applications. That determination is made by examining data
based on various quality dimensions that may include accuracy, completeness, validity, reliability,
timeliness and consistency.
Best Practices for Implementing Data Observability On AWS
To achieve data observability in AWS, you must monitor tools and dashboards to aggregate and visualize
log data to recognize anomalies easily. By following AWS data observability best practices, you can
achieve quality and reliable data.
Monitor Your Data Pipelines
Get a dashboard that provides an operational view of your data pipeline or system. You will need first to
set up the monitoring tools and dashboards. AWS has a range of monitoring services, which include
Amazon CloudWatch, AWS X-Ray, AWS CloudTrail, etc. You must select the appropriate tool by evaluating
its features, integration capabilities and ease of use. You will then define your monitoring objectives,
configure the tool and set up custom metrics to monitor specific aspects of your data pipeline.
Alerts And Notifications
You must set up alerts to notify you of a data anomaly or error. You must define critical data metrics,
define the threshold value for each metric to trigger alerts and configure alerts to be sent to relevant
stakeholders upon detection.
Define And Validate Data Quality Rules
To ensure the accuracy and completeness of data in AWS, you must validate it against predefined values
and criteria. Perform quality checks, verify formats, detect errors in incoming data and cleanse the data
to eliminate inaccuracies or duplicates. You may also need to profile, audit, track and monitor the data.
Governing And Protecting Data
As part of data observability in AWS, you will also need to establish practices and policies for data
governance. This will include roles and responsibilities for enforcing the governance framework.
Using Data Observability to Identify and Resolve Data Quality
Issues
Data quality issues
could arise due to several factors and cannot be fixed with technology alone. You
need people and processes, too. Through data observability, organizations can address each dimension
of data quality based on the five pillars it stands for.
Freshness: This explains the need to have the latest available data as soon as possible without
any gaps. This ensures the completeness and relevance of data.
Distribution: This helps improve the accuracy and validity of data by relating to the data's
attribute health. The goal is to determine if the metrics derived from the data's attributes are
within the acceptable range of values.
Volume: This pillar examines the amount of data in the source vs the target to ensure
completeness and uniqueness of data.
Schema: Changes in data fields can affect the downstream processes. By verifying the structure
of the data, you uphold the consistency, accuracy and validity of the data.
Lineage: This is a holistic pillar that touches on every dimension of data quality. Lineage provides
traceability of data to its origin by following the path the data takes, allowing data teams to trace
any errors within the data ecosystem easily.
The Agilisium Advantage
AWS data observability is crucial for staying ahead of performance or availability issues that could
weaken the value of your AWS environment. While the system has observability solutions that don’t
require installing anything in the AWS cloud, you must activate and access them through the AWS
console. However, there is a drawback to the AWS observability solutions. They can only provide
essential monitoring and observability functions and only work with AWS.
Data observability is the backbone of an agile data team. To gain more from data observability, consider
using third-party solutions like
Agilisium
, which provides extensive
data observability
and management
features across multiple cloud platforms.
To learn more about how our platform can help your organization achieve complete visibility into every
aspect of your system,
contact us today
.
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