Top 14 business intelligence and data articles, 23 - 30 March

With the pace of modern business changing faster than ever we want to take a look at which business intelligence technologies people are turning to to help them adapt and stay ahead. That's why we're launching the Pulse of BI 2015 survey. And we want you to take part.

Organizations that were born digital are built around their IT platform, and all their business processes are IT-driven and data-powered. Every action, every decision, is based on the processing of data sets about users and customers, about usage patterns, external conditions, etc. But not every organization was born digital.

The battle for attention is a big part of the success of any dashboard. As Mico Yuk says, "the only metric that matters in BI is user adoption" - if your dashboard isn't used, it doesn't matter how good it looks or how efficiently is conveys information.

The last blog post contained first part of the analysis. This article contains the second part which introduces analysis for dimension D2 (Management) of the research goals Q2/Q3.Research questions Q2 and Q3 (Part 2)D2 (Management) was the dimension that together with D4 (People) contains most of the risks identified by the interviewees.

The telecoms industry has been a major investor in big data tech, but it needs to look beyond purely the technology Everyone is talking about big data. It spans all industries and exists in vast quantities. But whether it is the evolution of Management Information, Business Intelligence, or even a revolution, the real benefit of big data remains to be seen.

Paul Gittins Is IT risking irrelevancy? We have heard this all before; Cloud will make IT redundant; outsourcing and the rise of the virtual IT department, etc. Are we seeing something different with the rise of big data?

According to an article from Readwrite, the amount of money going to big data projects is steadily increasing despite widespread failure to achieve many results. For big data-related projects in global organizations, a total of $31 billion was spent in 2013 and that amount is expected to top $114 billion by 2018.

Big Data has arrived at a critical juncture. Organizations that jumped at its potential to revolutionize business are now increasingly disappointed by Big Data projects' implementation challenges. Many business leaders are also surprised by how their organizational frameworks have been ill-equipped to handle the insights generated by even the most successful Big Data and business intelligence (BI) initiatives.

In modern society, data pours into organisations from a wide variety of sources and in such volumes, at such speed and with such complexity that it is impossible for traditional database tools to make sense of or manage. The term given to this is "Big Data", which can be neatly characterised by three Vs: increasing variety, velocity and volume.

After four months of rejection by employers, a man named Kim did one simple thing that changed his luck. He added "Mr." to the front of his name on his resume. And then he got a job. Discrimination and bias are still alive and well in the U...

With the price of crude plumbing lows not seen since 2009, Royal Dutch Shell, Whiting Petroleum Corp and many others are turning to rocket fuel, Big Data, lasers, spectrometers and other new or revamped technologies to do more for less.

Engineers and construction companies will be able to tell what's under the ground before they start digging after a data-analysis company was given access to troves of information. The aggregated data could save companies millions of pounds by avoiding ancient burial grounds and other archaeologically important sites.

Print this page Email article Computer processing power doubles roughly every 18 months. This is the widely known Moore's Law, which has proven to be reasonably accurate for over 35 years. The effect has been a staggering decrease in the cost of processing data.

So many new applications and platforms offer sophisticated ways to analyze and manipulate Big Data, but they lack capabilities to adequately standardize, enrich and match complex data sets. As a result, organizations realize that they first have to ensure the reliability and quality of data lakes and enterprise data hubs before their contents can be utilized by any downstream applications.