Systems | Development | Analytics | API | Testing

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.

The five things you need to know about data observability in a data pipeline are: Becoming a data-driven organization is a vital goal for businesses of all sizes and industries—but this is easier said than done. Too many companies fail to attain the fundamental principle of data observability: knowing the existence and status of all the enterprise data at their fingertips.

Nowadays most people in organizations understand how visibility into data adds overall value and there is a general dedication to be and remain data driven and increase overall data literacy. At the same time, sometimes there are limitations to how much organizations want to invest or augment their investment in data projects. It's important to make sure that companies have support across departments to budget appropriately for their data needs.

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 five reasons why data integration is a critical element for data analytics are: According to research by IDC and Tableau, 83 percent of CEOs say that they want their company to be “more data-driven.” The study finds that data-driven organizations have observed many positive impacts, from faster time to market to more new customers. Of course, becoming a truly data-driven company is easier said than done—and data analytics is the way to do it.