The Importance of Business and IT Alignment to Build Successful Data Pipelines

Okay, I’ll admit, I am pretty biased when it comes to how people within organizations work together to ensure successful data projects. I have been involved in too many projects that failed to take into account the importance of collaboration across departments and functions. They were stuck on data and only the data.

Leverage or liability - transforming data chaos into data excellence with Waterstone Mortgage

Data is a double-edged sword. It has tremendous potential, but if mismanaged or misused, can wreak havoc on the operations, costs, revenue, and reputation of an organization. In highly regulated industries, data can be an even bigger liability. Learn how Julia Fryk, Data Architect and Engineer at Waterstone Mortgage, championed superior data management, taking the company from data chaos to data excellence. The transformation from data that could not be trusted to high-quality assets that data consumers have confidence in has made a remarkable difference and extracted more value from data.

Snowflake's Retail Data Cloud

The retail and consumer packaged goods (CPG) industry is experiencing a global shift in how consumers, retailers, and brands interact. Long-term trends such as digitization and e-commerce, higher customer expectations, and supply chain transformation are accelerating. Businesses with a strong data foundation are rapidly adapting, and launching new digital capabilities and services.

The Power of Unlocking and Unifying Data

Every day, humans produce 2.5 quintillion bytes of data. Just to put that number into perspective, there are 18 zeros in a quintillion. Unifying data can allow you to take advantage of it and benefit your business. The vast majority of organizations have collected nearly endless amounts of data. Yet, these same organizations are starving for information that can be used to make more informed decisions. Information may be stored in databases that don’t talk to each other.

Welcome to the decade of data

To quote Hemingway: change happens gradually, then suddenly. We see this in the world around us. Think back to 2019. There’s no denying how much the pandemic reshaped our professional and personal lives, with technology driving this change at massive scale. Yet these changes, despite their ubiquity, are really the culmination of trends like cloud and automation that were well underway.

Transformational triumph: eBay's data fabric modernization

In today’s economy, every business is eager to accelerate beyond and above the competition. Critical to this effort is the data your business runs successfully on, its backbone, and a good team behind the magic. Join eBay, the world's leading online marketplace, to hear how they accelerated their operational data with impressive results. Not only did the company experience no downtime, but they reduced data volumes by a whopping 50% — resulting in less friction, smoother operations, and a team built to enable success and scale.

Protect Your Assets and Your Reputation in the Cloud

A recent headline in Wired magazine read “Uber Hack’s Devastation Is Just Starting to Reveal Itself.” There is no corporation that wants that headline and the reputational damage and financial loss it may cause. In the case of Uber it was a relatively simple attack using an approach called Multi Factor Authentication (MFA) fatigue. This is when an attacker takes advantage of authentication systems that require account owners to approve a log in.

6 Best Data Extraction Tools for 2022 (Pros, Cons, Best for)

A data extraction tool can help you speed up one of the most error-prone engineering processes: collecting raw data from different sources. In this article, we are going to analyze the following 6 market leaders in data extraction: Before we dive in, let’s look at all the problems you can avoid by implementing a data extraction tool.

Data Science Maturity and Understanding Data Architecture/Warehousing

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 magazine column about data warehousing, held the first conference about this topic, and was the first person to teach data warehousing classes. Data science is immature. This statement is not pejorative; it is simply a statement of historical fact. As such, it is not arguable.