Systems | Development | Analytics | API | Testing

Picture this: You are part of a BI team at a global garment manufacturer with dozens of factories, warehouses, and stores worldwide. Your team is tasked with extracting insights from company data. You begin the ETL (Extract, Transform, Load) process but find yourself struggling with the manual effort of understanding table structures and revisiting and modifying pipelines due to ongoing changes in data sources or business requirements.

The year is 1424. You’re shipping goods across the world, and the ship in question gives you a bill of lading. It’s a piece of paper containing details about what your goods are, where you’re shipping them from, and where they’re headed. Fast forward to 2024. You’re shipping your goods across the world, and the shipping company gives you a bill of lading. It’s still (most likely) a piece of paper.

Enterprises have complex IT and technology needs. To reach business goals, organizations must often stitch together a patchwork of software and IT systems. So, it’s understandable that the thought of adding yet another technology—low-code—might cause trepidation. But low-code excels at integrating seamlessly with any technology. Low-code tools speed up development work and enable rapid application development.

Data pipeline automation plays a central role in integrating and delivering data across systems. The architecture is excellent at handling repetitive, structured tasks, such as extracting, transforming, and loading data in a steady, predictable environment, because the pipelines are built around fixed rules and predefined processes. So, they will continue to work if you maintain the status quo, i.e., as long as your data follows a consistent structure.

For insurance companies, balancing customer expectations with the rigorous requirements necessary to mitigate risks poses a challenge. Especially when you’re using manual underwriting processes. By turning to artificial intelligence (AI) in insurance underwriting, you avoid costly delays, streamline your employees’ processes, improve accuracy, and create an optimal customer experience. In this blog post, you’ll learn how AI facilitates greater efficiency in underwriting.

Remember the early 2000s, when data was difficult to make sense of? It was like an exclusive domain of a few selected IT experts. Because back then, accessing and understanding data was a monumental task, loaded with delays and complexities. Today, data is a strategic asset for technical experts and business users. This shift is driven by the fact that we need answers NOW.

How are AI governance and data governance related? Better still, what’s more important for an organization to focus on, AI-powered data governance or AI data governance? These are important questions, but before we answer these, let’s understand how AI and data governance are related to each other.

ERP providers constantly seek ways to enhance their offerings, streamline operations, and provide superior value to their clients in the ever-evolving landscape of enterprise software. One of the most transformative strategies in this domain is the integration of iPaaS (Integration Platform as a Service). This blog post explores how partnering with an iPaaS can significantly boost an ERP provider’s capabilities, offering strategic advantages and substantial ROI.

Financial services organisations are constantly pressured to streamline operations while maintaining stringent security and compliance standards. As back-office processes become more complex, a flexible, scalable, and automated integration platform becomes essential. Linx addresses these challenges by providing an advanced platform that delivers highly customisable solutions tailored to each organisation’s needs.

It’s a new world—again. Data today isn’t what it was five or ten years ago, because data volume is doubling every two years. So, how could ETL still be the same? In the early ‘90s, we started storing data in warehouses, and ETL was born out of a need to extract data from these warehouses, transform it as needed, and load it to the destination. This worked well enough for a time, and traditional ETL was able to cater to enterprise data needs efficiently.