Quality Control and Quality Assurance are important parts of any manufacturing or service industry, aimed at making sure that products or services meet previously set standards and customer expectations.
Quality Control (QC) refers to the systematic process of monitoring, inspecting, and testing products or services during or after production to identify and rectify defects or shifts from established quality criteria. Quality Assurance (QA) takes into account a broader perspective, emphasizing proactive measures and systematic steps to prevent defects.
In this detailed guide, we will discuss what quality control and quality assurance are, their important features, and their key differences.
What is Quality Control?
Quality control or QC is the act of monitoring the results of product development to make sure they meet predetermined quality standards. It works reactively- i.e., it spots and fixes mistakes after making them, ensuring that customers receive high-quality items.
Organizations frequently use a systematic quality control procedure to maintain output quality. In data management and software engineering, quality control makes sure that project-specific methods and procedures are followed and that the finished result satisfies the required standards.
Quality control and quality assurance are different as the former places more emphasis on investigating the final products than assessing the methods used in development.
What are the Main Features of Quality Control?
QC’s primary goal is to validate data. While ensuring accuracy and completeness, it checks whether consistency was maintained while making quality products. It identifies missing values, data outliers, and data entry errors whenever the output quality doesn’t match the set standard. It generates reports pinpointing data quality issues, which data analysts or engineers can later address.
- QC often employs sampling to assess a subset of data for quality rather than scrutinizing the entire dataset.
- In many cases, QC relies on manual inspections and human intervention to correct data errors.
For Data Management Solutions and Quality Expertise, Contact Inferenz today!
What is Quality Assurance?
Quality assurance, or QA, is the proactive strategy to protect data quality throughout its lifecycle. It focuses on creating guidelines and meeting them, alongside best practices, to stop problems with data quality.
Quality assurance comprises a series of tasks and protocols carried out during and after project management or product development. Besides addressing post-market customer concerns, its goal is to guarantee the production of high-quality products and successful project outcomes.
Quality control and quality assurance are different as the former is reactive, and the latter is a proactive, preventive process that aims to reduce errors in the final product by focusing on process improvements and ensuring project-related processes and techniques are implemented correctly.
What are the Main Features of Quality Assurance?
QA is about designing data collection, integration, and transformation processes with built-in quality checks and controls. Inferenz is a strict believer and enforcer of these steps. As a top data analytics company and solutions expert, we believe that by maintaining quality control and quality assurance during these steps, we can deliver top-notch quality projects!
- QA includes training professionals in data management to teach them the importance of data quality and its role in its maintenance.
- It involves ongoing monitoring of data processes and systems to identify and address issues as they arise.
Both quality control and quality assurance prevent data quality issues, reducing the need for multiple corrections.
What are the differences Between Quality Control and Quality Assurance?
The reactive and proactive characteristics of quality control and quality assurance are how they fundamentally differ from one another. While QC responds to problems found, QA adopts a proactive strategy to try and avoid mistakes.
While QA is concerned mainly with the development processes, QC is primarily concerned with the product and testing. At last, QA is an internal organization-wide practice, while smaller teams usually complete QC duties.
- The most glaring contrast between quality control and quality assurance is the timing of their activities. QC operates reactively, occurring post-data collection or processing, while QA is proactive and aims to prevent data quality issues upfront.
- QC often involves manual, labor-intensive processes that require human intervention to identify and rectify errors. QA is quite the opposite and is process-driven, implementing automated quality checks.
The following table helps identify the key differences between quality control and quality assurance:
|Aspect||Quality Control (QC)||Quality Assurance (QA)|
|Timing||Reactive, post-development||Proactive, pre-and post-development|
|Focus||Detecting and correcting errors||Establishing processes and standards|
|Scope||Examines the final product||Examines the entire process|
|Main goal||Ensuring product meets quality standards||Ensuring adherence to processes and standards|
|Involvement||Typically performed by data analysts or data engineers||Involves all personnel handling data|
|Approach||Manual inspections and error correction||Process-driven, automation of quality checks|
|Relationship to each other||Often follows after QA||Precedes QC and lays the foundation|
Importance of Finding a ‘Quality’ Data Management Company
Inferenz is regarded as the top data analytics company in the UAE. Our experience across retail, e-commerce, healthcare, and many other industries has taught us the importance of quality control and quality assurance. We regard quality as our top parameter of service. It is also why our clients prefer our services over other data management companies in Dubai, UAE, and other GCC nations.
We have a considerable client base in the US as well. The first project that comes to mind while talking about quality control and quality assurance is from there. It was our data warehouse and analytics project for a leading premium healthcare service provider in the US.
In this project, we strictly reinforced quality measures mainly because they directly affected a large number of consumers connected to healthcare in the USA. For this project, we fetched and separated data across different source systems. Even if the process was time-consuming, there was absolutely zero margin for error since any compromise in patient details was non-negotiable.
After we successfully delivered our project with all quality control and quality assurance checks completed, our project helped our client increase their data analytics power and usage with the help of AI.
If your company is looking for similar services and dedication, contact Inferenz today!
Let Inferenz Guide You on Your Data Management Journey
To guarantee product quality, quality control, and quality assurance are important. While QA is more concerned with proactive error prevention through defined processes, QC focuses on real-time monitoring and correction to satisfy standards.
As a leading data analytics company in the UAE, Inferenz upholds these values. We guarantee that our data management projects, from collection and engineering to analysis, are error-free.
We have a strong clientele in the US and GCC countries thanks to our dedication to quality. For similar commitment and delivery, get in touch with us today.
Q1. Which is First? QA or QC?
In data management and other services, QA comes first as it helps set up continuous quality-checking processes. QA prevents errors as it measures quality throughout the development process. QC, on the other hand, is conducted at the end of the production phase. It helps identify and correct data that may have slipped QA.
Q2. Why is QC a Part of QA?
QA is a series of error checks that are run throughout the production cycle, and QC is a part of it. QC is generally considered the last step of QA.
Q3. What are the 4 Types of Quality Assurance?
The four categories of quality assurance are Pre-production inspection (PPI), During-production inspection (DPI), Pre-shipment inspection (PSI), and Container loading/loading supervision (LS).