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.

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.

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.

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.

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.

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.

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.

We Provide consulting, implementation, and management services on DevOps, DevSecOps, Cloud, Automated Ops, Microservices, Infrastructure, and Security

Services offered by us: https://www.zippyops.com/services

Our Products: https://www.zippyops.com/products

Our Solutions: https://www.zippyops.com/solutions

For Demo, videos check out YouTube Playlist: https://www.youtube.com/watch?v=4FYvPooN_Tg&list=PLCJ3JpanNyCfXlHahZhYgJH9-rV6ouPro

If this seems interesting, please email us at [email protected] for a call.

Relevant Blogs:

Why Training Labs are mandatory for good training 

Solving the Kubernetes Security Puzzle 

19 Most Common OpenSSL Commands for 2023 

Public Key and Private Key Pairs: Know the Technical Difference