2014 was a big year for business intelligence.
With the New Year now underway, we took a look back at some of the most compelling and widely shared business intelligence stories of 2014.
Data science is quickly becoming one of the most attractive new job fields. Cicero discusses why the career is growing at this moment in time, and how it is revolutionizing the Business Intelligence world. He names several key trends that have contributed to this trend: the rise of accessibility to data, its increasing importance in both product creation and in framing the privacy debate, and the need for a complex role that combines skills of management and empathy with mathematical and engineering expertise. The story clearly articulates the roots of an important change in the business world as a result of BI’s development.
Noted BI expert Cindi Howson discusses the top predicted trends for 2014, focusing on data discovery, cloud computing, and mobile BI. She examines how 2014 will be much like 2013, and her predictions turned out to be largely accurate. Visual data discovery continued to grow, and cloud and mobile BI became essential tools for professionals wanting 24/7 access to insights. Her analysis of simplicity in BI will likely hold as a prediction for 2015 as well.
BI is getting huge. Just how huge? Forbes cites a cross-section of sources that give statistical proof of the impact of BI, analytics, and big data on the market. Among the key market estimates are cloud BI growing to $2.94 billion by 2018, and the overall big data-related hardware industry growing to $50.1 billion by the end of 2015. Research also found that spending for this technology will continually to grow across industries. And companies that use big data are five times more likely to make faster decisions. Put briefly: the industry, and its benefits, are only getting bigger.
What’s next in the big data world? It’s well established that the market is going to grow, but what exactly is that going to look like? Where will the innovation be? Gil Press analyzes six areas that could see significant development in the coming year. His predictions include growth in security apps, Internet of Things analytics, the genesis of buying and selling of data, self-service tools as an answer to the skills shortage, and image, video, and audio analytics. Finally, he predicts a need for communications experts to tell data “stories” in clear language.
IBM has partnered with Twitter on a project to turn a massive amount of tweets into business intelligence. The two corporate giants are working together to release a portfolio of data analysis tools that will help companies across industries capitalize on both a key data source and a key data mining platform. The effort is also aimed at making IBM a “cloud-centric” business that will thrive in a world soon to be dominated by the cloud.
Data driven healthcare is revolutionizing the medical industry. Caregivers now have access to information that they never did before, Patient information like heart rate and even sleep and exercise patterns can be monitored by personal devices. Data is also operating on a much larger scale, being used to predict and quarantine public health concerns like flu outbreaks and other pandemics. There are issues, of course, with there being too much data, and technology that’s not complex enough to capitalize on its full potential. That’s why the article suggests new innovations in medical data analysis are on the way in BI in 2015.
Salesforce’s new analytics product, Wave, was released in the fall of 2014. Miller argues that Salesforce was somewhat late to the game, compared to its competition, in developing an analytics tool. But that doesn’t take away from its innovation as a visualization tool that can convey business analytics both on small mobile screens and large PC screens. The story covers the importance of Salesforce’s move into the analytics market and the factors that could limit its ultimate success.
Data is not business intelligence, anymore than raw flour is bread. It must pass through several stages on its journey from statistic to insight. Marr’s take on the process is invaluable for those looking for a clear and simplified explanation of how BI works. He describes each layer in detail: the data sources layer, data storage layer, data analysis layer, and finally, the data output, or presentation, layer. Essentially, the layers are gathering, filing, examining, presenting.
The impact of business intelligence products in the world of finance, marketing, and IT has been well-established. But tools like predictive analytics are making a huge splash in the public sector, as well. This Guardian article looks at how predictive tools have been applied in places as diverse as emergency rooms and tax bureaus across Australia.
BI is complex, and users new to the technology are bound to make mistakes. Jennifer Lonoff Schiff lays out nine of the most common errors made and explains how to avoid them. The article is a survey and analysis of the responses of several BI experts and business leaders.