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This is a guest post with exclusive content by Bill Inmon. Bill Inmon “is an American computer scientist, recognized by many as the father of the data warehouse. Inmon wrote the first book, first magazine column, held the first conference, and was the first to offer classes in data warehousing.” -Wikipedia. Our key points: One of the characteristics of most computing and analytical environments is that the environment consists of only one type of data.

While advancing technology is beginning to bring us electric vehicles, most vehicles on the road still operate on fossil fuels. This includes the commercial vehicles used in major fleet operations. Traditionally, gathering metrics regarding a driver's driving style, vehicle speed, cruise control usage, diagnostics, drive cycle, a vehicle's engine, and other factors has been quite difficult.

This is a guest post with exclusive content by Bill Inmon. Bill Inmon “is an American computer scientist, recognized by many as the father of the data warehouse. Inmon wrote the first book, first magazine column, held the first conference, and was the first to offer classes in data warehousing.” — Wikipedia. Our critical points: Data warehouses are the whack-a-mole of technology.

Customer service has always been the underdog that significantly affects a business’s performance but is always overlooked due to a lack of time and resources. While businesses focused on generating more revenue, they missed an important contributor to that revenue- a positive customer experience. Using the customer’s perspective to stay aligned with the evolving customer needs is essential for your business to foster a customer-centric culture.

Ecommerce companies like yours collect and store data for analytical activities such as business intelligence (BI). However, many organizations don't know how to harness the power of data or improve the efficiency of analytics. Data observability solves the problems of the modern data infrastructure by helping you understand the current state or health of data in your Ecommerce enterprise. You can detect and diagnose issues that might occur during data integration and improve data quality for analysis.

For many years now, vendors and consultants have avoided the practice of data integration. Integrating data is complex. Systems are often undocumented, which makes searching them difficult. Assumptions must be made in order to understand business decisions from long ago. Data integration requires the proverbial four-letter word no one wants to hear: work. But anything worth doing should involve a bit of sweat and tears, right?

Ecommerce businesses have been dominating the online community for quite some time now. Over the years, we have seen many enterprises transform to operate online, and customers rely more on online marketplaces due to accessibility and comfort. Perhaps the most important outcome of this digital transformation was the concept of Software-as-a-service (SAAS). SAAS has allowed organizations to sell digital solutions and create a client base.

Here are five things to know about data observability: Data observability solves many of the issues of modern data infrastructure. Still, few Ecommerce organizations understand this process or how to improve it. Here's what you need to know: Data observability, in a data science context, helps you understand the current state of all the data in your Ecommerce enterprise. It monitors and manages any problems that might occur during the data integration process.

Guest post by Bill Inmon “Bill Inmon is an American computer scientist, recognized by many as the father of the data warehouse. Inmon wrote the first book, held the first conference, wrote the first column in a magazine, and was the first to offer classes in data warehousing.” Source: Wikipedia. An article headline — “BIG DATA/AI BLAME FAILURES ON BAD DATA” — caught my attention.

The healthcare industry is beginning to digitally transform with its adoption of continuously advancing technologies. Healthcare organizations are moving toward a more connected and collaborative healthcare ecosystem for improving the way they provide care. Any data-driven organization knows the importance of high-quality data pipelines in data science. The large amounts of heterogeneous healthcare data, call for healthcare organizations to adopt data-driven approaches to automate medical workflows.