According to the Ponemon Institutes, "Cost of a Data Breach" report, a data breach's total global cost averaged $3.86 million in 2020. However, a data breach's implications go far beyond financial losses; it can severely hinder an organization's operational capacity and compliance structures.
According to The Economist, “the world’s most valuable resource is no longer oil, but data.” Despite the value of enterprise data, much has been written about the so-called “data science shortage”: the supposed lack of professionals with knowledge of how to use and manipulate big data. A 2018 study by LinkedIn estimated that there were more than 151,000 unfilled jobs in the U.S. requiring data science skills.
The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain futured. However, the sudden surge in demand comes with its own set of challenges.
The key differences between Hadoop vs. SQL: Organizations rely on big data to power their business, but many teams struggle with the complexities of data management. Thankfully, Hadoop and SQL handle large data sets more efficiently. These tools manage data in unique ways, which makes it difficult for us to compare them on a like-for-like basis. However, organizations looking to streamline their tech stacks might have reason to choose one over the other. In this article, we compared Hadoop vs.
Large and small businesses face daily reminders that their data’s security is under attack by digital pirates, hackers, and cybercriminals. These threats require an immediate response, whether it's an unexpected hacking threat or a newly discovered database vulnerability. In this guide, we’ll help you understand these threats and how to prevent them in 2021.
We're living in a data-driven age. In every sector, we've seen new companies emerge, executing lightning-fast strategies based on sophisticated analytics. These data mavericks have disrupted and sometimes even devoured their more traditional rivals. To stay afloat, you need a state-of-the-art data infrastructure. That means having the right platforms, the right data pipelines, and the right analytics engines. But when you have all that data, what do you actually do with it?
There are a few standard structured data formats and discussions galore on which of them is more advantageous. Within Xplenty, users are able to process JSON and XML data formats with ease, and this article shares an example showing the functions that facilitate processing XML on Xplenty.
Every business wants to stay agile. They invest in analytics to learn about their customers and their internal state, and they use these insights to make bold and innovative decisions. But then they run into a common problem: how to put those decisions into action. This is where a solutions analyst comes in. These multi-talented creative thinkers will look at the current state of play and identify the smartest path forward.
Your data can quickly get out of control when you’re working with multiple cloud storage services and applications throughout your organization. Complex cloud ecosystems can make it difficult to know what data you have, how it’s being managed, whether it’s safe, and how to use it effectively. Cloud data management platforms can stop this frustrating scenario in its tracks.
The key differences between Fivetran, MuleSoft, and Xplenty: Hiring a data scientist or engineer can cost up to $140,000 per year —something many businesses can't afford. Still, organizations need to pull data from different locations into a data lake or warehouse for business insights. An Extract, Transform, and Load (ETL) platform makes this process easier, but few organizations have the technical or coding know-how to make it happen.
