At first glance, Data Science and Quality Assurance are not related.
Yet, nothing could be farther from the truth in the IT and Software Development field!
From gathering data to ensuring quality standards, these fields can be the key to success.
But how? Let's see how these two so-called separated fields can be the blend your business didn't know it needed!
What is Data Science?
Data Science is a discipline that combines programming, maths and statistics elements to extract essential insights from data.
As a field, DS covers a wide range of academic and business edges. The core goal is to translate raw data into relevant insights.
Data Scientists apply extensive Computer Science and data visualization to navigate enormous amounts of raw data.
Yet, Data Scientists also use Artificial Intelligence, especially Machine Learning and Deep Learning.
Ultimately, the focus is to pinpoint critical info, extract it and make it part of business strategies.
Examples of Data Science
Data Science goes beyond business strategies! It's also all around us and we can see its effects in our daily life.
Data Science in Entertainment
Platforms like Spotify and Netflix have boosted entertainment thanks to Data Science.
Data impacts everything, including the music you like and the shows and movies you watch.
Currently, most platforms analyze databases to recognize user preferences and behavioral patterns.
As a result, for instance, Netflix creates and curates high-targeted suggestions.
Spotify also relies on its users' data to tailor its well-known weekly playlists.
With user data, these platforms identify trends and genres users may like.
Data Science in Search Engines
We're accustomed to using phrases like "Google it!" if someone needs answers on a specific topic.
Although the expression's wide usage, we often skip how Data Science helps to make it possible.
In fractions of a second, search engines return relevant results for searched questions.
This ability includes big-name companies like Google, Bing, Yahoo, AOL and Ask.
None of these search engines would be what they are today if Data Science hadn't been present.
Data Science in Online Shopping
Have you ever wondered why sites like Amazon give you related-products recommendations?
eCommerce websites consider user recent searches or liked products to put their most suitable products on the first row.
Data Science plays a vital part in any eCommerce platform's back end.
The most notorious connection is by enabling system notification whenever users explore a site.
These systems accumulate constant databases with user information to learn about shopping habits and preferences.
As a result, users receive personalized suggestions that enhance sales and provides better User Experiences.
What is Quality Assurance?
Quality Assurance's operations ensure the delivery of high-quality software products.
Also known as QA, it involves automation tools, development techniques, and manual testing.
The aim is to detect flaws in the product before its release into production.
This IT role plays a huge role in every Software and Product Development!
Not only does it identify issues in both the planning and developing stages.
Further, QA reduces rework in significant quantities to save time, energy and costs.
Ensuring clean, safe, and bug-free code is only one task— QA also ensures compliance with Quality Standards.
This continuous cycle includes planning, designing and testing to take action when the outcome deviates from the quality criteria.
This strategy guarantees a positive and bug-free end-user experience and on-schedule product releases.
Examples of Quality Assurance
When does a QA put their hands on code? What are some examples of it? Let's unveil them!
Quality Assurance for Human Error
The term Human Error refers to manual mistakes that humans often can make.
Think of, for instance, entering letters into a database where there can only be numbers.
These are easy to recognize, diagnose and take immediate preventive actions.
Quality Assurance for System Errors
QA teams ensure the systems and/or goods adhere to the manufacturer's specifications.
Here, QA teams subject a product to various events to spot potential flaws.
Also known as stress testing, it often occurs under heavy loads or usage situations.
A perfect example can be if potential clients can't access a company's site.
Quality Assurance in Manufacturing
In manufacturing, Quality Assurance focuses on preventing product mistakes and flaws.
All these processes need to occur before providing customers with goods or services.
Usually, a well-run factory that adheres to proper protocol produces high-quality goods.
Examples of it include quality inspections of assessments, facilities and specialized software.
Data Science and Quality Assurance
As we saw, Data Science collects, analyzes, and converts raw data into helpful insights.
Meanwhile, Quality Assurance guarantees products' accuracy, completeness and integrity.
At first glance, these two fields may not seem related.
Yet, Data Science and Quality Assurance are two hand-in-hand disciplines.
While DS interprets inputs, QA ensures its accuracy.
Data Science analyzes data to understand customer behavior, trends, and patterns.
Yet, none of this data can be helpful in successful outcomes without QA.
If a business has data but doesn't catch errors on time, it can mean the loss of potential users!
The same logic goes the other way around.
Quality Assurance ensures that projects meet quality standards.
Nonetheless, outcomes may not meet the required needs without proper data collection!
A venture may have a top-notch QA outcome. Yet, if data is separated from the process, user workflows may get lost in the way.
If this happens, users will have a more challenging time recognizing what to do and how to do it.
And, of course, this setback also translates into poor UX and potential user loss.
One could say that Data Science comes "before" Quality Assurance in the dev life cycle.
Yet, if you put these fields together from scratch, you can create efficient projects!
Conclusion
Both QA and DS professionals have different roles, but one shared goal: delivering high-quality products.
Joint work can be vital in securing successful workflows and outcomes.
Take advantage of their overlapping skill sets! With the right combination, your business will likely see significant growth.
