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. These are vital to extracting potentially essential insights from data. As a field, Data Science covers a wide range of academic topics and specialties. Its main aim is to interpret raw data to achieve business-relevant insights. Data Scientists apply extensive Computer Science and data visualization. With these, professionals can navigate enormous amounts of raw data. Yet, a Data Scientist also uses Artificial Intelligence, especially Machine Learning and Deep Learning! In most scenarios, experts develop different models using several methods and algorithms. Ultimately, the goal is to pinpoint the most 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. Let's see some examples!
1. Data Science in Entertainment
Platforms like Spotify and Netflix have boosted entertainment thanks to Data Science tools. Data impacts everything, including the music you like and the shows and movies you watch. Most platforms analyze databases to recognize users' 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. Also, with users' data, these platforms identify trends and genres users may like.
2. 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.
3. Data Science in Online Shopping
Have you ever wondered why sites like Amazon give you related-products recommendations? eCommerce websites consider users' recent searches or liked products. That's why they put their most suitable products on the first row according to users' needs.
Data Science plays a vital part in any eCommerce platform's back end. Their most notorious connection is enabling system notification whenever users explore a site. These systems accumulate constant databases with users' information. With these inputs, they understand and learn about shopping habits and preferences. Hence, users receive personalized suggestions. This strategy enhances sales while providing complete User Experiences (UXs).
What is Quality Assurance?
Quality Assurance's processes 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, it reduces rework in significant quantities. Hence, time and costs go down as well. The goal of QA goes beyond catching errors early on. Ensuring clean, safe, and bug-free code is only one task. Also, it handles ensuring compliance with Quality Standards throughout the whole process.
QA is a continuous cycle of different steps. Often, its process includes planning, designing, testing, and analyzing. Further, this position takes action when the outcome deviates from the quality criteria. It helps identify potential trials before affecting users or causing costly delays. In turn, this strategy guarantees a positive and bug-free end-user experience. Ensuring good condition and on-schedule product releases can also save time and money. Thus, the role of QA is essential for every development process.
Examples of Quality Assurance
When does a QA put their hands on code? What are some examples of it? Let's unveil them!
1. Quality Assurance for Human Error
Human Error is a behavior that QA ensures to avoid. This term refers to frequent mistakes that can often occur. Yet, these are easy to recognize, diagnose, and take immediate preventive actions. These include entering letters into a database where there can only be numbers.
2. Quality Assurance for System Errors
Quality Assurance systems often increase both company's legitimacy and consumer trust. 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.
3. 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. Besides, 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 that comes to value for decision-making in development teams. DS' insights are vital to understanding 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, users won't have pleasant experiences. Moreover, it can mean the loss of potential clients, customers, or users.
The same logic goes the other way around. Quality Assurance ensures that development projects meet quality standards. Nonetheless, the outcome may not meet the required needs without proper data collection. A venture may have a top-notch QA outcome. But, if data is separate from the process, user workflows may get lost in the way. Hence, 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!
The Future of Data Science and Quality Assurance
QA identifies improvement-needing areas in development processes. Also, it handles the software's reliability. DS analyzes datasets to draw meaningful insights. Further, it develops algorithms to understand better how users interact with products. Data Science and Quality Assurance are both vital elements of successful software development. With their complementary skill sets, they ensure well-designed and user-friendly software. By leveraging both disciplines' strengths, businesses can create more reliable technology. These outcomes can both provide valuable user insights and function as intended.
Conclusion
Both fields' professionals have different roles, but one shared goal. That is, of course, delivering high-quality development outcomes on time and within budget. Hence, joint work can be vital in securing successful development outcomes. Moreover, it can create more streamlined processes and higher-quality results. Take advantage of their overlapping skill sets! With the right combination, your business will likely see significant growth.
