The Five Use Cases in Data Observability: Mastering Data Production

ย The Five Use Cases in Data Observability: Mastering Data Production (#3)

Introduction

Managing the production phase of data analytics is a daunting challenge. Overseeing multi-tool, multi-dataset, and multi-hop data processes ensures high-quality outputs. This blog explores the third of five critical use cases for Data Observability and Quality Validationโ€”data Productionโ€”highlighting how DataKitchen’s Open-Source Data Observability solutions empower organizations to manage this critical stage effectively.

The Five Use Cases in Data Observability

 

Data Evaluation: This involves evaluating and cleansing new datasets before being added to production. This process is critical as it ensures data quality from the onset.

Data Ingestion: Continuous monitoring of data ingestion ensures that updates to existing data sources are consistent and accurate. Examples include regular loading of CRM data and anomaly detection.

Production: During the production cycle, oversee multi-tool and multi-data set processes, such as dashboard production and warehouse building, ensuring that all components function correctly and the correct data is delivered to your customers.

Development: Observability in development includes conducting regression tests and impact assessments when new code, tools, or configurations are introduced, helping maintain system integrity as new code of data sets are introduced into production.

Data Migration: This use case focuses on verifying data accuracy during migration projects, such as cloud transitions, to ensure that migrated data matches the legacy data regarding output and functionality.

 

The Challenge of Data Production

Data production encompasses processing and refining raw data into valuable insights, including dashboard production, warehouse constructions, and model refreshes. During these processes, monitoring and validating data at each step of the production process is vital to detect any discrepancies, errors, or inefficiencies that might compromise the final products.ย  The challenges in data production are multi-faceted:

  • Ensure the accuracy and consistency of data across different tools and stages.
  • Meet service level agreements (SLAs) and maintain robust toolchain performance.
  • Quickly locate and address data or process errors before they affect downstream results.

 

Critical Questions in Data Production

Effective data observability in production requires answers to several critical questions to ensure data integrity and operational efficiency:

  • Are key performance metrics within expected ranges?
  • Is the business logic producing correct outcomes?
  • Are all required data records and values present and accurate?
  • Does the data maintain integrity without conflicting with other datasets?
  • Are production models accurate, and do dashboards display correct data?
  • Have I Checked The Raw Data And The Integrated Data?
  • Did I compare key metrics with real-world information that is known to be correct?
  • Did I Verify That All Required Records And Values Are Available?
  • Did I Ensure That Data Does Not Conflict With Itself?
  • Is My Model Still Accurate?
  • Is My Dashboard Displaying The Correct Data?
  • Is The Delay Because The Job Started Late Or It Took Too Long To Process?
  • Looking Across My Entire Organization, How Many Pipelines Are In Production?
  • Is There A Troublesome Pipeline (Lots Of Errors, Intermittent Errors)?
  • Did Every Job That Was Supposed To Run?
  • How Many Tests Do I Have In Production?
  • What Are My Pass/Fail Metrics Over Time In Production?
  • Is the data in the report that I am looking at “Fresh”?
  • Do I Have A Troublesome Data Supplier?
  • Did I Verify That There Is No Missing Information And That The Data Is 100% Complete?
  • Does The Data Conformity To Predefined Values?
  • Where Did The Problem Happen?
  • Is My Output Data The Right Quality?
  • Did Jobx Run After All The Jobs In Groupy Completed?
  • How Many Jobs Ran Yesterday, How Long Did They Take?
  • How Many Jobs Will Run Today, How Long Will They Take, And When Are They Running?
  • How Many Tests Do I Have In Production?
  • What Are My Pass/Fail Metrics Over Time In Production?
  • Is There A Troublesome Pipeline (Lots Of Errors, Intermittent Errors)?
  • Can I Check The Final Key Metrics Against A Previous Period For Variance?

How DataKitchen Solves Data Production Challenges

DataKitchenโ€™s DataOps Observability tackles these challenges head-on with its innovative DataOps TestGen software, which automates the generation of 32 distinct data quality validation tests. These tests are designed based on thorough data profiling and can be executed directly within the database environmentโ€”ensuring no costly data movement and swift detection of issues.ย  These include:

  • Automated Data Quality Tests: DataOps TestGen automatically creates comprehensive tests for data freshness, schema accuracy, volume, and more, significantly speeding up the validation process.
  • End-to-End Data Journeys: The platform models your entire data analytics process as digital twins, which act as live schematics for monitoring and troubleshooting. This feature allows for rapid identification and rectification of errors across the data pipeline.
  • In-Database Execution: By executing tests within the database, DataKitchen ensures minimal disruption and maximum efficiency, avoiding the performance overhead of data duplication.

 

Benefits of Effective Data Observability in Production

Implementing DataKitchen’s observability solutions during the data production phase offers numerous benefits:

  • Prevention of Customer-Facing Errors: Quick identification and correction of data issues prevent errors from reaching end-users, enhancing customer trust and satisfaction.
  • Enhanced Team Productivity: Thanks to efficient error detection and automated testing, teams spend less time troubleshooting and more time on strategic activities.
  • End-to-end view: This perspective reduces the ‘morning dread’ of discovering overnight errors, ensuring a smoother operational flow and peace of mind for data teams.

 

Conclusion

Effective โ€˜across and downโ€™ observation of the data production process is pivotal for any data-driven organization. DataKitchen’s Open Source Data Observability software provides a robust framework for monitoring, testing, and refining data workflows. By leveraging intelligent, automated tools, businesses can ensure their data processes are error-free, leading to reliable insights and informed decision-making. For organizations looking to improve their data quality and operational efficiency, embracing DataKitchenโ€™s observability solutions is a strategic step toward achieving excellence in DataOps.

 

Next Steps:ย  Download Open Source Data Observability, and Then Take A Free Data Observability and Data Quality Validation Certification Course

Sign-Up for our Newsletter

Get the latest straight into your inbox

Open Source Data Observability Software

DataOps Observability: Monitor every Data Journey in an enterprise, from source to customer value, and find errors fast! [Open Source, Enterprise]

DataOps Data Quality TestGen: Simple, Fast Data Quality Test Generation and Execution. Trust, but verify your data! [Open Source, Enterprise]

DataOps Software

DataOps Automation: Orchestrate and automate your data toolchain to deliver insight with few errors and a high rate of change. [Enterprise]

recipes for dataops success

DataKitchen Consulting Services


Assessments

Identify obstacles to remove and opportunities to grow

DataOps Consulting, Coaching, and Transformation

Deliver faster and eliminate errors

DataOps Training

Educate, align, and mobilize

Commercial Pharma Agile Data Warehouse

Get trusted data and fast changes from your warehouse

 

dataops-cookbook-download

DataOps Learning and Background Resources


DataOps Journey FAQ
DataOps Observability basics
Data Journey Manifesto
Why it matters!
DataOps FAQ
All the basics of DataOps
DataOps 101 Training
Get certified in DataOps
Maturity Model Assessment
Assess your DataOps Readiness
DataOps Manifesto
Thirty thousand signatures can't be wrong!

 

DataKitchen Basics


About DataKitchen

All the basics on DataKitchen

DataKitchen Team

Who we are; Why we are the DataOps experts

Careers

Come join us!

Contact

How to connect with DataKitchen

 

DataKitchen News


Newsroom

Hear the latest from DataKitchen

Events

See DataKitchen live!

Partners

See how partners are using our Products

 

Monitor every Data Journey in an enterprise, from source to customer value, in development and production.

Simple, Fast Data Quality Test Generation and Execution. Your Data Journey starts with verifying that you can trust your data.

Orchestrate and automate your data toolchain to deliver insight with few errors and a high rate of change.