Artificial intelligence (AI) is advancing at a breakneck pace, and it is swiftly emerging as a potential disruptor and vital enabler for almost every business in every sector. At this point, the technology itself is not a barrier to the mainstream use of AI; rather, it a collection of challenges that, unfortunately, are far more human, such as ethics, governance, and human values, are.
The inventive and ever-evolving nature of technology creates as many opportunities as obstacles. The fact that software permeates every part of life demonstrates that corporate leaders should closely monitor the level of quality engineering to avoid a halt in ongoing improvement. The software industry and the digital quality assurance are confronted by an unprecedentedly fast-evolving technological macrocosm.
Data-driven decision-making is fundamental for any business that wants to thrive in today’s cut-throat environment. In fact, there is enough evidence today that proves that data-driven decision-making powered by artificial intelligence (AI) platforms can help businesses expedite their operations, thus saving valuable time and money. Such decisions involve leveraging past information to predict the challenges and opportunities that await an enterprise in the future.
Today, every business gathers a sizable quantity of data. From app usage statistics to repeat purchases stats, from time spent on the app to the location of buyers, almost every data point is collected and stored in databases. We all know the importance of data in the growth of a business. From stocking inventory in anticipation of demand to focusing on more profitable customers and areas, and optimizing employee productivity, data gives us accuracy.
We’ve all heard about the significant WhatsApp breakdowns that have happened in the recent past, during which the app was unavailable for the public for an hour. However, from a technical standpoint, WhatsApp returned in less than an hour. What would have enabled the engineers at WhatsApp to quickly restore the services? Technically speaking, the team experienced an extremely stressful production failure because of this.
Today organizations are looking for better solutions to guarantee that their data and information are kept safe and structured. Using a data lake contributes to the creation of a centralized infrastructure for location management and enables any firm to manage, store, analyze, and efficiently categorize its data. Organizations find it extremely difficult to deal with data because the information is kept in silos and in multiple formats.
Codeless automation testing is performing automation tests without having to write any code. It can be instrumental in executing continuous testing as most automation scripts fail due to the deficiency of proper coding standards. It will also enable us to concentrate more on test creation and analysis instead of fearing getting the code to work, possessing it, and scaling it when required. So, if we are relatively codeless in automation testing, we will find this blog helpful.
Artificial Intelligence (AI) is quite powerful and is constantly evolving and currently knows no bounds. It is focused on outperforming its limits using the power of Machine Learning (ML). AI is empowering computers to do things that human beings are unable to do efficiently and effectively and machine learning is aiding the computers to do so by breaking the rules of traditional programming.
One of the biggest preventable losses that hurts insurers worldwide is fraudulent insurance claims. The P&C segment accounts for the most fraudulent insurance claims, with auto insurance and workers’ compensation making up the biggest percentage of fraudulent claims that have an annual impact on the insurance business.
