Today, data science is one of the major QA services into which a software testing company would invest their time, collective effort and R&D. Data science is all about mining large volumes of raw information in creative ways to produce value for businesses.
A QA company carrying out data centric testing today relies on data science to solve complex analytical issues with the help of algorithm development, data inference and other emerging technologies.
Putting data science to work How does your Gmail filter out annoying and meaningless spam keeping your inbox manageable? How does your favourite shopping site recommend products that interest you? How does a music streaming service such as gaana.com show up music channels and artistes that you love? This is data science at work! It examines your behaviors and preferences and bases actions and results on this. Vast amounts of raw data is typically streaming into enterprise data warehouses and stored there in order to enable immediate or future mining of what is essentially a trove of potentially valuable, problem solving information. ETL testing (Extract Transform Load) facilitates the transfer of data from various sources to a central data warehouse while using strict transfer protocols to help verify, validate and quantify the data while making sure that there is neither data loss nor any kind of duplication.
Mining this data using quantitative data analytics testing will offer insight into existing issues and help guide strategic, problem solving decision making. This data mining essentially reveals trends and helps understand complex behaviors, which in turn helps analyst draw accurate inferences. For instance an entertainment streaming service or an online fashion store will examine vast amounts of data to reveal what viewers are interested to base future business decisions on. Data warehouse testing will also result in the development of data products and algorithm solutions operating at scale. It helps businesses plan production volumes, based on information gleaned about future demand patterns.
How a QA company helps strengthen data science As software environments and business practices have changed and evolved; providers of QA services have had to keep pace with this evolution to remain current and relevant. A software testing company will review available data to create predictive models and mathematical optimisations. Business leaders find the practical solutions they are looking for, teams can set up SOPs and streamlining of existing processes. This helps minimise waste and removes redundancy. Private and government entities find that mining data for creative solutions helps manage work forces and resolve performance issues. This methodology also helps minimise the risk of performance glitches and failures upon system deployment.
Applying data science to quality assurance processes reviews existing processes, data sources and business models helps to identify and understand clarify business problems in the appropriate context. The QA services provider would then use multiple technologies such as solvers, languages and analytics engines to compare findings and to recommend alterations or improvements. All the vast quantities of data are put to good use to create model implementation, suggest improvements and possible further analyses. A combination of open source or commercial software, advanced analytic algorithms, numerical computing and other tools may be deployed for the best outcomes and to create the best and most holistic solutions.
There are several reason why using an independent QA company is recommended. Your business has access to trained and experienced professionals who keep their teams updated with the latest technology trends. They have a bird’s eye view of the market, which brings valuable insight about the competition and other stakeholders. Their experience with other businesses helps to create a bank of valuable information and the creation of best practices that bring real advantages to businesses looking to grow, expand and increase profitability.
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