Systems | Development | Analytics | API | Testing

The rise of game engines has sparked new innovations across industries. Amazon Lumberyard — the Amazon game engine — has recently transitioned to open source. Open 3D Engine (O3DE) may be new on the scene. But as companies continue to move to the cloud, many are looking at this new Amazon game engine to transform their pipeline.

Through FMEAs, product development teams are able to determine potential failures within a project and improve them to mitigate risk. The most efficient way to conduct a failure mode and effects analysis is through an automated tool. This blog will provide an overview of FMEAs and highlight the many benefits they can have on your product.

The development of a digital product has been redefined to involve only 4 phases, as TCGen and Product Plan propose: However, having an easier-to-follow process is not the only improvement that you can implement: cost and time efficiency can be taken a huge step further when you incorporate analytics insights. So, with this infographic, we propose some tools that can help you analyze data sets to enrich the phases of each development process.

“As a gamer, I wish for it. But as a developer, I wouldn’t want to be working on backward compatibility. It’s soul-crushing maintenance work, man!” – A developer on our team who shall remain unnamed! Let’s call her Dev-I for now. Last week, I was talking to internal Appian developers on backward compatibility (BC) when one of them shared this quote.

For modern businesses faced with increasing volumes and complexity of data, it’s no longer efficient or feasible to rely on analyzing data in BI dashboards. Traditional dashboards are great at providing business leaders with insights into what’s happened in the past, but what if they need actionable information in real time? What if they want to use their data to estimate what may happen in the future? Companies are taking notice.

If you have any experience with comparing open source data visualisation tools then it is very likely you will have encountered both Kibana and Grafana during your research and discovery phase. As two of the most popular solutions for logs and metrics analysis, it can be difficult to distinguish between the two and make the choice to use either Grafana or Kibana depending on the analysis task at hand.

Does your team know how to use data analytics to their advantage? For the vast majority of companies, the answer may be “no.” According to Accenture, only 21% of people are confident in their data literacy skills, and just 32% of companies have realized tangible, measurable value from their data. While the definition of data literacy varies depending on who you ask, at its core, the term means equipping anyone in your organization to know how to use data in a business context.