DZone
Thanks for visiting DZone today,
Edit Profile
  • Manage Email Subscriptions
  • How to Post to DZone
  • Article Submission Guidelines
Sign Out View Profile
  • Post an Article
  • Manage My Drafts
Over 2 million developers have joined DZone.
Log In / Join
Please enter at least three characters to search
Refcards Trend Reports
Events Video Library
Refcards
Trend Reports

Events

View Events Video Library

Zones

Culture and Methodologies Agile Career Development Methodologies Team Management
Data Engineering AI/ML Big Data Data Databases IoT
Software Design and Architecture Cloud Architecture Containers Integration Microservices Performance Security
Coding Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
Culture and Methodologies
Agile Career Development Methodologies Team Management
Data Engineering
AI/ML Big Data Data Databases IoT
Software Design and Architecture
Cloud Architecture Containers Integration Microservices Performance Security
Coding
Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance
Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks

Last call! Secure your stack and shape the future! Help dev teams across the globe navigate their software supply chain security challenges.

Modernize your data layer. Learn how to design cloud-native database architectures to meet the evolving demands of AI and GenAI workloads.

Releasing software shouldn't be stressful or risky. Learn how to leverage progressive delivery techniques to ensure safer deployments.

Avoid machine learning mistakes and boost model performance! Discover key ML patterns, anti-patterns, data strategies, and more.

Related

  • The Hidden Cost of Dirty Data in AI Development
  • Data Governance Essentials: Policies and Procedures (Part 6)
  • Optimizing Data Management for AI Success: Industry Insights and Best Practices
  • Maximizing Enterprise Data: Unleashing the Productive Power of AI With the Right Approach

Trending

  • How the Go Runtime Preempts Goroutines for Efficient Concurrency
  • Blue Skies Ahead: An AI Case Study on LLM Use for a Graph Theory Related Application
  • How to Practice TDD With Kotlin
  • Immutable Secrets Management: A Zero-Trust Approach to Sensitive Data in Containers
  1. DZone
  2. Data Engineering
  3. Big Data
  4. How Data Scientists Can Follow Quality Assurance Best Practices

How Data Scientists Can Follow Quality Assurance Best Practices

Data scientists must follow quality assurance best practices in order to determine accurate findings and influence informed decisions.

By 
Devin Partida user avatar
Devin Partida
·
Mar. 19, 23 · Analysis
Likes (1)
Comment
Save
Tweet
Share
4.0K Views

Join the DZone community and get the full member experience.

Join For Free

The world runs on data. Data scientists organize and make sense of a barrage of information, synthesizing and translating it so people can understand it. They drive the innovation and decision-making process for many organizations. But the quality of the data they use can greatly influence the accuracy of their findings, which directly impacts business outcomes and operations. That’s why data scientists must follow strong quality assurance practices.

What Is Quality Assurance?

In data science, quality assurance ensures a product or service meets the required standards. It refers to verifying data is accurate, complete, and consistent. The data must be free of inconsistencies, errors, and duplicates, and the scientists must properly organize and document it well.

A 2019 survey found around 23% of an organization’s IT budget was dedicated to quality assurance and testing. Although the number has decreased from 35% since 2015, quality assurance remains one of the most critical aspects of data science. Clear data governance and documentation increase the efficiency of data analysis, helping to improve the quality of the investigation and the insights it generates.

Quality Assurance Practices for Data Scientists to Follow

Data scientists must follow a few important steps to ensure the quality of the data they’re using.

1. Define Clear Objectives

Before beginning a data analysis project, scientists must define clear objectives for what they want to achieve. This process helps determine the necessary data type, sources to use, and methods to employ. A clear understanding of the goal also helps ensure the data is relevant and valuable.

To get started, creating a map of all data assets and pipelines, a data lineage analysis and quality scores is helpful. It identifies the data source and how it might change along the analytics pipeline. Modern data catalogs can automate and streamline the process.

2. Verify Data Sources

Where did the data come from? Data analytics pipelines are complicated and there may be up to three types of data in a system. One of the most vital steps in quality assurance is verifying the data sources — they must be reliable, accurate and appropriate.

Data lineage solutions help identify quality issues at any point in the analytics pipeline, preventing negative downstream impacts. That’s why many organizations are adopting this technology.

3. Perform Data Cleaning

The process of identifying and correcting inconsistencies, errors, and inaccuracies in data is known as data cleaning. It involves removing duplicates, structural errors, unwanted observations, and outliers. Data cleaning also entails filling in incomplete data, fixing spelling mistakes, and formatting data consistently. Data scientists must carry out this step before conducting an analysis to ensure the data is accurate.

4. Solidify Data Governance Practices

Managing data availability, usability, integrity, and security is known as data governance. Establishing good data governance processes helps ensure data scientists use accurate and consistent information.

To create these practices, data scientists can establish policies for data access, storage, and sharing. For example, having a metadata storage strategy lets people quickly locate their datasets. They can also create procedures for data auditing and quality control.

It’s important to automate much of this process because relying too heavily on manually taking inventory and remediating data can lead to failure. Automating data governance helps data scientists work at an appropriate speed and scale with more data than ever before.

5. Establish Service Level Agreements 

Setting up service level agreements (SLAs) with data providers can be useful. An SLA should define data sources, formats and quality, and subject matter experts should evaluate before applying transformations and putting the data into their systems.

6. Validate Analysis Results

Algorithms have their place, but they aren’t foolproof. Data scientists must validate the results of every complete analysis to ensure accuracy. They may need to test the findings with different test methods or parameters, compare the results to other data sources, or check their results for errors.

This job isn’t just for the IT department. All levels of a business should have access to data, thereby eliminating siloes and letting everyone participate in the analysis. It’s important to establish a data-driven culture that values discussion, observation, and refinement throughout the entire organization.

7. Seek Additional Feedback

Outside observers can catch errors and offer suggestions for improvement. Third-party feedback helps ensure the data analysis is practical, relevant, and accurate. Data scientists can ask stakeholders and subject matter experts for feedback when an analysis is complete.

Crunching the Numbers

Because data scientists perform such a critical role in so many industries, there is a lot at stake if they generate inaccurate data. The outcomes of their analyses impact decisions in health care, computer science, government, and so much more. Quality assurance practices help data scientists ensure the data they present is accurate and relevant. That’s more important than ever in a world overrun with information.

Data analysis Data governance Data science Data quality

Opinions expressed by DZone contributors are their own.

Related

  • The Hidden Cost of Dirty Data in AI Development
  • Data Governance Essentials: Policies and Procedures (Part 6)
  • Optimizing Data Management for AI Success: Industry Insights and Best Practices
  • Maximizing Enterprise Data: Unleashing the Productive Power of AI With the Right Approach

Partner Resources

×

Comments
Oops! Something Went Wrong

The likes didn't load as expected. Please refresh the page and try again.

ABOUT US

  • About DZone
  • Support and feedback
  • Community research
  • Sitemap

ADVERTISE

  • Advertise with DZone

CONTRIBUTE ON DZONE

  • Article Submission Guidelines
  • Become a Contributor
  • Core Program
  • Visit the Writers' Zone

LEGAL

  • Terms of Service
  • Privacy Policy

CONTACT US

  • 3343 Perimeter Hill Drive
  • Suite 100
  • Nashville, TN 37211
  • support@dzone.com

Let's be friends:

Likes
There are no likes...yet! 👀
Be the first to like this post!
It looks like you're not logged in.
Sign in to see who liked this post!