Lets understand Data Observability and Why it is Important.

What is Data Observability?

Data observability is an organization’s ability to fully quantify the health of the data, proactively detect issues and quickly apply a mitigation plan before issues become a factor in lost revenue, ROI or branding.

Data is the lifeline of the modern enterprises. Organizations invest significant money and time in developing data analytics platform and decision support systems in order to make timely business decisions.

 

Why Data Observability is Important?

In today’s data-driven world, data observability has emerged as a critical strategy for ensuring data reliability. Availability of data and its quality have a direct impact on a company’s decision-making approach and the outcome of its operations. Quality data has the potential to change everything, from customer experience and bottom-line profitability to even employee behavioral workflows.

The practice of measuring and monitoring data systems to ensure their dependability, integrity, and accuracy is known as data observability. By applying best practices of data observability, organizations could deploy remedial measures for mitigating the issues and deploy best practices to avoid making the same mistake again, allowing them to make more forceful decisions, enhance processes, improve their marketing campaigns, and optimize products and services

Data Observability Frameworks

Health Of Data Operation

Monitoring and Measuring health of data operation can be crucial for maintaining reliable and high-quality data systems. Collecting meta data, run time, frequency, performance of data pipelines can assist organizations in identifying:

· Bottlenecks

· Inefficiencies

· And areas for improvement in data pipelines

· Abnormality in executions

As a result, data processing can be optimized, operational efficiency can be increased, and data warehouse management becomes more agile.

Together, data observability and data operations help organizations meet regulatory requirements by ensuring data:

· Accuracy

· Consistency

· Traceability.

Data Flow Monitoring

The Data Flow monitoring framework helps to deliver metrics critical to the individual objects in the data flow and provides insights on below two key areas.

· Data completeness — Did we get complete data for each object?

· Data timeliness — Did the data arrive at right time?

Freshness — This metrics tells us how old data is ad when was the data last updated. Without knowledge of when the data was last refreshed, business stake holders would have no idea on how accurate their analysis are. Older the data powered the outcome.

Volume

Monitoring data volume flowing through a pipe line at every hops of the data provides confidence on the completeness of data. If the volume differs abnormally across the different hops of a data flow, then that could very well indicate a n issue in pipeline and alert the right members before business users use the data for any key decisions.

Schema Drift

In a controlled well managed environment, before deploying any schema changes, pipe lines are tested and confirmed to make sure that there is no unforeseen issues arise in pipeline execution. However, often things are not ideal and due to one or other reason schema changes without proper approval and impact analysis Data observability tool should alert of any unauthorized schema changes and proactive identifies the impact to the pipeline.

Data Profiling

Once we find Pipelines are executed successfully, data flow met all criteria on volume and timeliness, data profiling at column and row level provides the insights on data quality rules for key columns. These insights help us understand the data quality at most granular basis and provides detailed insights in a very actionable way.

Data Observability at column and row level helps us understand
· Actual column value range vs expected column value range
· Master data enumeration checks i.e. whether data coming in column meet master data validation criteria
· Invalid characters — Are there invalid characters in data
· Invalid email, phone number or zip codes
· Uniqueness criteria — Does data meet Uniqueness criterias defined in business rules
· User defined custom rules
· Length of data fields
· Null or Blank checks

Organizations need to create a clear metrics to assess and monitor data quality metrics. These data health metrics should be in line with business goals and data needs. By automating compliance checks and validation processes, the data observability platform can lower the risk of noncompliance penalties.

Data Reconciliation

As data gets processed every day through various pipelines, data can break for hundreds of reasons. With a limited team size and multiple competing priorities, data engineers are often not able to reconcile all data(or any data) every day. As a result, many times business users find out about the data issues before the data engineering team knows about them. But at that point, it is too late for them to build the trust back.

Data Observability platform should provide ability to reconcile data with source data or historical trend to confirm data cell level accuracy and confirm if data is accurate and data is consistent.

Reconcile data between source and target

Solution needs to connect to diverse data sources and automatically reconciles data between source and target. The solution leverages its own AI engine to determine the reconciliation issues and alerts appropriate stakeholders through multiple channels which include email, texts, and Slack channels.

Data reconciliation within the analytics platform

Sometimes connecting to source systems is not possible due to several reasons such as source systems are owned by different groups and they don’t allow or source systems are too rigid for any external connection. In that scenario, solution needs to reconcile incoming new data with historical trends to determine data anomalies and reconciliation issues.

Implementing a data observability framework

Let’s bring this full circle: data observability is a collection of activities and technologies that help you understand the health and the state of data within your system. Data observability is a byproduct of the DataOps movement, and it has been the missing piece for making agile, iterative improvements to your data products possible.

We’ve learned that data observability isn’t a silver bullet, and neither is DataOps. Technology alone will not solve your problems. You can have the best monitoring dashboards that report on all of your metadata, equipped with the most powerful automation and algorithms. Still, without organizational adoption, it’s only good for the pipelines you own. Vice-versa, everyone can be bought into DataOps as a practice, but if you don’t have the technology to support it, it’s just nice-to-have documented philosophy that doesn’t impact output.

Key Components of a Data observability solution

Pic : DAMA Data Quality Dashboard from 4DAlert

While organizations can monitor data in multiple ways, but in order to have meaningful insights on health of data, the solution need to include these key functionalities:

· Ability to add rule based on rule catalog — Solution must have predefined rule catalog that could be customized for varieties of use case.

· Anomaly Detection — Solution should be able to detect the anomalies but a number that too high or low for the context or unexpected master data or blank field that should never be blank

· Real time Alerting — As anomalies are detected, solution should be able to alert in real time so that an corrective action could be applied.

· Trend Analysis — Solution should leverage AI/ML capabilities to compare new data with historical trend and detect issues and create alerts for the anomalies

· Issue Tracking — Once issues are identified, solution should allow us to log the issue and track the issue and take it through resolution life cycle.

· Dashboard and Metrics — Solution should be able to deliver a set of dashboards that provides clear and actionable tasks for any issues identified.

· Central repository — For most organizations, observability is siloed. Teams collect metadata on the pipelines they own. Different teams are collecting metadata that may not connect to critical downstream or upstream events. More importantly, that metadata isn’t visualized or reported on a dashboard that can be viewed across teams.

Pic: DAMA Data Quality Dashboard from 4DAlert

There may be standardized logging policies for one team, but not for another, and there’s no way for other teams to easily access them. Some teams may run algorithms on datasets to ensure they are meeting business rules. But the team that builds the pipelines doesn’t have a way to monitor how the data is transforming within that pipeline and whether it will be delivered in a form the consumers expect. The list can go on and on.

Without the ability to standardize and centralize these activities, teams can’t have the level of awareness they need to proactively iterate their data platform. A downstream data team can’t trace the source of their issues upstream, and an upstream data team can’t improve their processes without visibility into downstream dependencies.

What Does the Future of Data Observability Look Like?

As data volumes continue to grow and organizations dependency on data grows, data observability will become even more essential for organizations of every size. More and more businesses are realizing the the benefits how data-driven decision-making helps business stakeholders, but they won’t be able to use that data effectively unless data confirms to data quality standard. Increasingly, organizations will see that manually reconciling, monitoring and managing data across multiple data sources manually requires large resource and time and therefore not feasible.

Data observability functions and tools such as 4DAlert will take over as the predominant method to automate monitoring pipe lines, reconcile huge volumes of data, reduce siloed data monitoring, and improve collaboration across the organization.

Observability tools will continue to improve by supporting more data sources, automating more capabilities like governance and data standardization, and delivering rapid insights in real-time. These elements will help organizations support growth and leverage revenue-generating opportunities with fewer manual processes.

Transform Your Organization’s Monitoring Capabilities with 4DAlert’s AI/ML enabled Data Observability solution

Organizations handle integrates multiple systems, pulls data from varieties of sources and loads a huge volume of valuable data every day. But without the right tools, managing, monitoring and finding data quality issues manually can take huge amount time and resources. Growing data volumes make it more important than ever for companies to find a solution that streamlines and automates end-to-end data management for analytics, compliance, and monitoring needs.

4DAlert’s data house platform provides Data Observability, Data Governance, Data Catalog, Data Modelling and CI/CD modules that support you manage your data platform. Specifically, Data Observability module within the data house enables you monitor pipelines built across various integration points and alerts you when there is abnormality in pipeline execution. Solution helps you automate your data reconciliation needs, detect data quality issues and delivers a set of predefined dashboards that helps manage your data platforms.