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.

APIs are powerful for ETL (extract, transform, load) and data integration workflows. API integrations make it possible for the seamless exchange of information between websites, databases, and applications. The Xplenty API allows you and your enterprise to monitor Xplenty clusters and jobs. Through the Xplenty data processing package and Xplenty web application, you can call the Xplenty API to.

A slow car has never won a Formula One race. The Olympics doesn’t reward slow times in swimming, track or any other clock-timed sport. Likewise, slow data speeds don’t win over customers or colleagues in the real-time business world. Microsoft’s own research once reported that a person visiting a website on a connected device is likely to wait no more than 10 seconds to see it before moving to a competitor’s site.

Does it feel like your business is paying too much for cloud services? You are not alone. Cloud costs are expected to increase at a compound annual growth rate of 10.5% to 13.1% through 2025, according to the International Data Corporation (IDC). While getting a handle on those cloud costs may be tricky, you don’t have to worry — we’ve got you covered.

With COVID-19’s ever-changing conditions – growing infection rates, shifting and new vaccine mandates, variant outbreaks and office closures and re-openings – HR has stepped up and taken on a significant role in helping organizations navigate every employee’s personal and work life needs. COVID-19 accelerated the evolution already underway in HR, with HR growing beyond being a policy and procedure hub into a strategic business partner.

Ever since E. F. Codd introduced the first relational model for storing data at IBM in 1970, the industry has picked up the database technology and used it for its competitive advantage. The relational database management system - or RDBMS - was the default technology for storing and accessing data for a long time. It supported transactional data storage, the building of data products, and was the go-to model for data that was used in data-driven decisioning.