Systems | Development | Analytics | API | Testing

Trans World Airlines (TWA). Pan American Airlines. Sports Authority, Staples, Woolworth's, and Banana Republic. Aside from some minor similarities, these companies are all very diverse.

Every year analysts, vendors, thought leaders, and everyone in between like to surmise the upcoming trends for the year. I am going to do something a little different this year. I am discussing some trends, just like everyone else, but basing them on what we are seeing with customers and how they are succeeding with the Integrate.io platform. Not just succeeding, but levering complex and diverse data sets to enable better business decisions and support growth.

Five things to know about this topic: Just about every process used within a business generates some form of data. While some may see this information as useless, data analysis tools can turn it into a resource that helps your brand make better decisions in every aspect of its operations. Not all analytical tools are equal. However, the ones on this list can help you generate incredible insights that result in better decision-making.

A recent study on call center statistics found that 91% of consumers reported poor customer service in 2021. Providing high-quality service is essential, especially today, to retain customers and drive more business. Quality service is only one important metric in running a profitable call center. No matter your goal, the first step is understanding what's going on in your call center.

Do you know how much data your company stores? Do you know the types of data being utilized for any given purpose? Can you picture how data flows from one system to another? The goal of data modeling is to help you understand aspects like these. By giving you a visual representation of data within your systems, data modeling tools help you better store, manage, and utilize your data by optimizing the underlying architecture.

In general, the concepts of data literacy and creating successful data pipelines seem totally disconnected. Data literacy involves insuring that data consumers have the knowledge and capabilities to understand and interact with data in a way that will provide them with the answers and value they need to do their jobs and benefit their organizations. While data pipelines require technical expertise to move, connect, and store data across the company's data ecosystem.

This is a guest post for Integrate.io written by Bill Inmon, an American computer scientist recognized as the "father of the data warehouse." Inmon wrote the first book and first magazine column about data warehousing, held the first conference about this topic, and was the first person to teach data warehousing classes.

The data-driven culture cultivated in modern-day organizations is focused on deriving the best possible business insights from their data. With data scattered across the globe, these organizations' most significant challenge is to break the silos of their decentralized data and gather new data for analysis in real-time. To address the data silo problem, data engineering brought forward solutions like ETL, ELT, and data integration tools.

Let's simplify ETL to identify seven criteria that shouldn't be overlooked. Although these requirements seem simple, my purpose is to highlight that ETL requirements will differ if your company is looking at operational data pipelines vs. analytical use cases.

Architecture is an ever-living, ever-evolving entity. Architecture is constantly changing. As time passes and technology advances, it is inevitable that the underlying architecture of technology also evolves and mutates. Learn more about ever-evolving architecture below.