The Data Vault: top 8 data articles, 22 - 29 June

Despite growing investment in BI, adoption rates have been stuck stubbornly at about 20% for several years, but regardless of what's to blame the message is clear: BI adoption rates need to increase. One way to increase adoption is to get information into the hands of people who haven't been serviced by BI in the past - in a way that works for them.

Paul Barsch As humans, we have no problems staying busy. In fact, most of us are wired to "do something first" and ask questions later. In other words, our busyness often comes at the expense of a well thought out plan to tackle projects from the mundane to complex.

Over the past few weeks we've published a series of articles on this blog, aiming to broaden the definition of self-service business intelligence (BI). It's a topic that's on everyone's lips nowadays but somewhere in the hype cycle it has become almost synonymous with Data Discovery, and the notion that regular business users should be given access to raw, unstructured data to gather their own business insight.

The big data analytics space is evolving at a tremendous speed, so is the terminology used in the space. This terminology is sometimes really hard to understand. Here is a list of some of the big data terminologies simplified: 1. Anonymization: Comes from the word anonymous.

Picking up from where we left off in Big Data Glossary - Part 1, here is a list of some more of the big data terminologies simplified: 16. Legacy System - In the context of computing, this refers to outdated computer systems, programming languages or application software that are used instead of available upgraded versions.

Big data is everywhere and used in nearly every type of business. While many people might not be aware of its prevalence, it's clear from the way companies are engaging in big data analytics that it is playing a big role in achieving new levels of success.

Apple's old slogan was "Think Different" - and while it is now retired, and the ethos may not be as apparent in the company's products as it once was, it is true for their approach to Big Data.In some ways, despite being the most profitable tech company in the world, Apple found itself having to play catch-up with Big Data.While Apple traditionally employed teams of highly paid experts in aesthetics and design to produce systems that they thought people would want to use, competitors like Google examined user data to see how people actually were using them.This g

Big Data is not just a greater volume of data that is being created, but also a rapidly-expanding variety of data sources.