Clients are looking at testers to grow the business. Before you tell yourself “what nonsense”, let us prove the point.

Testing profession is in the middle of a profound disruption that is already well underway. Testers themselves are aware of it. A recent survey from London-based recruitment firm Harvey Nash indicates that 67% expect to be automated out of a job in the next 10 years.

Fundamentals like regression testing are going to be automated out, and manual testers are going to need to learn model-based automation skills with a lot more analytics. We all know that AI and Machine Learning could potentially do better job in testing a piece of code than what a QA person is capable of doing.

So it happened like this. Jobs were initially created for the people, by the people, and done by people. Then we designed tools to help them do the job better. . And now the insatiable human desire to build something better has led to the development of algorithms which is taking over the tools and is making them redundant. Manual testing jobs are gradually getting wiped off. They are replaced by algorithms that are faster and more efficient.

The testing professionals are definitely at an inflexion point. They have 2 evident choices.

1. Completely shift focus to Data Science and become a Data Scientist. In this case, they may have to step out of their comfort zone and start over in a new space.

2. Use their years of QA experience, add Data Science skills and domain expertise to build a unique proposition for the market.

Our recommendation would be to go with #2. Primarily for two reasons –

a. Years of experience in QA should not be thrown away. It would add more value to augment the QA/Testing skills with another skill such as Data science to create a niche for oneself.

b. We are in the data and algorithm economy. So irrespective of what profession we are in, everybody has to have skills in this space to be able to survive. There’s more value in adding data science to current set of skill competencies as opposed to throwing away the QA experience altogether.

5 Reasons why a software tester cannot ignore the importance of Data Science

1. Data is everywhere

Data Science is an interdisciplinary field and has pervaded all aspects of life. Data is messy. No matter what your role is, it is very difficult to get data in the best suited format for analysis. Data Science is all about making assumptions on what the data might look like. Testing is required every step of the way and testers on the team can play their role in testing those assumptions. Let’s take a scenario where a Data Scientist writes a piece of code to derive a model, and then more data comes in. A tester is required to check if the code will work on the new data set.

2. There is a skill overlap

A software tester need to think critically and should always doubt the system under test. They need to uncover hidden issues. A Data Science professional is expected to do the same. The Data Science programming languages Python & R are extensively used by the testers for writing tests & debugging codes.