Data observability is a collection of technologies and activities that allows data science teams to prevent problems from becoming severe business issues.

1. With numerous data products relying on hundreds and thousands of external
and internal data sources, modern organizations now have a more significant
number of data use cases. To meet their growing data needs, they have
adopted advanced technologies and big data infrastructures.
The increasing complexity of the data stack, the sheer volume, variety, speed,
and quantity of data generated and collected, opens the door to more complex
issues like schema changes, random drifts or poor data quality, downtimes,
duplicate data, and other complex issues. The complexity of data
management is also exacerbated by the many data storage options, data
pipelines and an array of enterprise applications.
Data engineers and business executives responsible for maintaining and
building data infrastructures and systems are often overwhelmed. They do
their best to keep data systems functional and operational as much as
possible. There are no perfect systems, and data volumes can be
unpredictable. No matter how much money data teams have invested in the
cloud, how sophisticated an analytics dashboard is or how well-designed it is,
everything fails--if unreliable data is ingested, transformed, and pushed
downstream.
Modern data pipelines are interconnected and not intuitive. Because of this,
data from both internal and external sources can become inconsistent,
inaccurate, missing, or change suddenly, which could eventually impact the
correctness and accuracy of dependent data assets. Data and analytics teams
must be able to dig deep to find the root cause of any data issues and then
resolve them.
It isn't easy to achieve this without a comprehensive and complete view of the
entire data stack and its lifecycle. Data observability is valuable for data teams
and organizations to ensure data quality and a reliable data flow throughout
their day-to-day business operations.
Data observability is essential, organizations and teams should pay attention
to it in order to achieve their data-driven visions.
What is Data Observability?
While observability is most commonly used in engineering and software
systems, it is also essential in the data niche. Software engineers can monitor
the health and performance of their applications using tools like DataDog,
AppDynamics and NewRelic -- data teams must also do the same.

2. Data observability is the ability of an organization to keep a constant pulse on
their data systems through tracking, monitoring and troubleshooting issues to
reduce downtime, improve data quality, and eventually prevent issues from
happening.
It is also a collection of technologies and activities that allow data and
analytics teams to track data-related failures and walk upstream to determine
what is wrong at each level (quality, infrastructure, and computation). This
helps data teams to measure the operative and effective use of data and
understand what’s happening across every stage of the enterprise data life-
cycle.
Similar to the three pillars of observability, data observability has 5 pillars.
Each pillar answers a series of questions that allow data teams to get a
holistic view of data health and pipelines when they are combined and
continuously monitored. Let’s have a look at these questions:
 Freshness: Was all data received and is it current? What
upstream data was omitted/included? When was the last time
data was extracted/generated? Was the data received on time?
 Volume: Has all the data been received? Are all the data tables
complete?
 Distribution: To whom was the data sent? How useful and
complete is the data? Is the data reliable? What was the process
of transforming the data? Are the data values within an
acceptable range of value?
 Lineage: Who are the downstream ingesters of a data asset?
Who generates the data? Who will use the data to make business
decisions? What are the stages at which downstream ingesters
will use the data?
 Schema: Does the data format conform to the schema? What has
changed in the data schema? Who made the changes?
What Is the Importance of Data Observability?
Data observability goes beyond monitoring and alerting. It allows
organizations to understand their data systems fully and allows them to fix or
even prevent data problems in increasingly complex data situations.

3. 1) Data observability increases trust in data so that businesses can
make data-driven business decisions confidently.
While data insights and machine-learning algorithms can be invaluable,
inaccurate or mismanaged data can have devastating consequences.
Public Health England (PHE), which tracks daily Covid-19 infection rates,
found an error in their data collection. This error caused 15,841 cases
between September 25 and October 2 to be overlooked. According to the
PHE, the Excel spreadsheet used to collect data exceeded its data limit. The
result was that the daily number of new cases was much higher than initially
reported. Tens of thousands of people who had tested positive for Covid-19
did not receive contact from the government's "test & trace" program. Data
observability allows organizations to track and monitor situations efficiently
and quickly. This allows them to make more informed decisions.
2) Data observability allows for the timely delivery of high-quality data to
support business workloads.
Every organization must ensure that data is easily accessible and in the
correct format. Almost every department in an organization relies on high-
quality data for business operations. Data scientists, data engineers, and data
analysts depend on the data to provide insights and analytics. A lack of quality
data can lead to costly business process breakdowns.
For example, your company has an ecommerce site with multiple data
sources (stock quantities, sales transactions, user analytics), which
consolidate into a data warehouse. To generate annual reports, the sales
department requires sales transaction data, the marketing department relies
on user analytics data to run effective marketing campaigns and data
scientists rely on data to build and deploy machine learning models that will
help them recommend products. It could cause harm to the various aspects of
the business if one of the data sources is incorrect or out of sync.
Data observability is a way to ensure the quality, reliability, and consistency of
data within the data pipeline. It gives organizations a 360-degree overview of
their data ecosystem. This allows them to drill down and fix any issues that
could disrupt their data pipeline.
3) Data observability allows you to identify and fix data issues before
they affect your business.

4. Pure monitoring systems have a significant flaw that they can only detect
unusual conditions or situations you know about or anticipate. But what about
those cases that you can't see coming?
A mistake caused by Amsterdam's City Council in 2014 led to the loss of
EUR188 million. Inadvertently, the error occurred because the software used
by the council to distribute housing benefits to low-income families was
programmed in cents rather than euros. Families received significantly more
than they anticipated due to the software error. People who were expected to
receive EUR155 received EUR15,500. Even more alarming is that
administrators were not notified of this error by the software.
Data observability can detect situations you don't know about or wouldn't
consider looking for. It can also prevent problems from becoming severe
business issues. Data observability allows you to track the relationships
between specific issues and provides context and pertinent information for
root cause analysis.
Top Data Observability Platforms for Monitoring Data
Quality at Scale
We understand how difficult it can be to find the right observability tool for your
company. Here is a list of the top platforms for data observability in 2022.
1) Monte Carlo
Monte Carlo's observability service offers a complete solution to prevent a
damaged data pipeline. This tool is an excellent choice for data engineers as it
allows them to check dependability and avoid expensive data downtime.
Monte Carlo has unique features, including data catalogs, alerts, and out-of-
the-box observability on multiple criteria.
2) Databand
Databand's goal is to make data engineering more efficient in a complex
infrastructure. Databand's AI-powered platform provides data engineers with
tools to optimize their operations and get a single view of all their data flows.
Its goal is to identify the core elements of data pipelines and where they have
failed before insufficient data can get through. The contemporary data stack
also includes cloud-native technologies like Apache Airflow or Snowflake.
3) Honeycomb
Honeycomb provides developers with the visibility needed to identify and fix
problems in distributed systems. The firm claims that Honeycomb helps

5. developers understand and fix complex interactions in dispersed services. Its
full-stack cloud observability technology provides logs, traces, events and
automated instrumented codes using Honeycomb beelines as its agent.
Honeycomb supports OpenTelemetry for the generation of instrumentation
information.
4) Acceldata
Acceldata is a data observability platform that provides data monitoring, data
dependability, and data observability solutions. These tools were created to
assist data engineers in gaining cross-sectional and extensive views of
complex data pipelines. Acceldata's products combine signals from many
layers and workloads into one pane of glass, allowing multiple teams to
collaborate on data problems.
Acceldata Pulse also provides performance monitoring and observability,
which helps to ensure data reliability at scale. This tool is designed for the
financial and payment industries.
5) Datafold
Datafold is a data observability tool that helps data teams assess data quality
and implement anomaly detection and profiling. Datafold's capabilities allow
teams to perform data quality assurance using data profiling. Users can also
compare tables within a database or multiple databases and generate smart
warnings with just one click. Data teams can also track ETL code changes
during data transfers and connect them to their CI/CD to quickly examine the
code.
6) SigNoz
SigNoz, an open-source full-stack APM/observability system that tracks
metrics and traces, is available as an open-source project. Open-source
means that users can host the program on their infrastructure without sharing
their data with third parties. Full-stack technologies include telemetry, backend
storage, and a visualization layer that allows consumption and actions. SigNoz
uses OpenTelemetry(a vendor-agnostic instrumentation library) to create
telemetry data.
7) DataDog
DataDog's observability software includes infrastructure, log management,
and application performance monitoring. DataDog gives you a complete view
of distributed applications by tracing requests from end-to-end distributed

6. systems. It also displays latency percentiles and open-source instrument
libraries. This is the "necessary monitoring and security platform for cloud
applications," according to its creators.
8) Dynatrace
Dynatrace is a SaaS application for enterprises that targets large companies
and addresses many monitoring needs. Their AI engine, Davis, can automate
root cause investigation and anomaly detection. The company's technology
may also be a unique solution to infrastructure monitoring, application
security, and cloud automation.
9) Grafana Laboratories
Grafana's open-source analytics and interactive visualization web layers are
well-known for accommodating multiple storage backends for time-series
data. Grafana supports connections to Graphite, ElasticSearch, InfluxDB and
Prometheus. It also supports traces from Jaeger, X-Ray, Tempo, and Zipkin. It
also offers plugins, dashboards, alarms, and other user-level access for
governance. Grafana Cloud offers solutions like Grafana Cloud Logs, Grafana
Cloud Traces and Grafana Cloud Metrics.
10) Soda
Soda's AI-powered platform for data observability is an environment that
allows data owners, engineers, and data analysts to work together to solve
problems. Soda.ai describes the technology as "a platform that enables teams
to define what good data looks like and handle errors quickly before they have
a downstream impact." This tool allow users to examine their data and create
rules to validate it quickly.
Implementation of a Data Observability Framework
Data observability is an "outcome" of the DataOps movement. Even though
you can have the most advanced automation and algorithms to monitor your
metadata, it will only benefit with organizational adoption. However, anyone
can adopt DataOps as an organization, but it will be a well-documented
philosophy that doesn't impact output without the technology to support it.
So, how do you implement a data observability framework that improves your
data quality at all levels? What metrics should be tracked at each stage of the
data observability framework?
These are the key ingredients for a highly-functional data observability
framework:

7. i) DataOps Culture
ii) Standardized Data platform
iii) Unified Data Observability Platform
Before you can even consider producing high-value data products, you must
have widespread adoption of the DataOps Culture. This requires everyone to
be involved, especially leadership. They will be the ones who create the
systems and processes that support development, maintenance, feedback,
and other activities. A bottom-up movement is powerful, but you still need
budget approvals to make the necessary technological changes to support
DataOps.
Leadership can help the organization move towards a standardized data
platform if everyone buys into the idea. What does this mean? To ensure that
all teams have end-to-end accountability and ownership, infrastructure must
be in place to allow them to communicate openly and speak the same
language. Standard libraries are needed for API and data management (i.e.,
querying the data warehouse, reading/writing to the data lake, pulling
information from APIs, etc.) A standardized library is also required to ensure
data quality along with source code tracking, data versioning, and CI/CD
processes. With all this in place, your infrastructure is ready for success.
You now need an open, unified platform for monitoring your system's health
that allows your entire organization to access it. The observability platform will
act as a central metadata repository. It would include all of the features
mentioned earlier (like monitoring and alerting, tracking, comparison and
analysis), so data teams could view how other platform sections affect them.
To effectively monitor the functioning of the Data Observability Framework,
you should monitor the following metrics:
1) Operational Health:
 Execution Metadata
 Pipeline State
 Delays
2) Dataset Monitoring:
 Availability
 Freshness

8.  Volume
 Schema Change
3) Column-level Profiling:
 Summary statistics
 Anomaly detection
4) Row-level Validation:
 Business rule enforcement
 Stop "bad data"
To ensure operational health, it's best to collect execution metadata. This
metadata includes information about pipeline states, length, delays, retries,
and the times between runs. You should monitor the completeness and
availability of your data along with the volume and changes to the schema.
You should collect summary statistics for columns and use anomaly detection
to alert you of any changes. The column trends would include the Mean, Max,
and Min. Row-level validation would require you to ensure that previous
checks were valid and adhered to your business rules. This is very contextual,
so you will need to exercise your discretion.
Conclusion
Data observability is essential for any data team to be agile and iterate quickly
on their products. Without data observability it's difficult for teams to rely on
their infrastructure or tools because errors can't be tracked quickly. This
results in less flexibility in developing new features or improvements for
customers. You're effectively wasting money if you are not investing in this
critical piece of the DataOps framework in 2022.