The questions you want to answer determine which type of analytics to use
A widely acknowledged, purpose-based classification divides analytics it into four different types: descriptive, diagnostic, predictive or prescriptive analytics.
A widespread view is that structured and semi-structured data constitute only about 10% of the data aggregated by companies. The remaining 90% of all data available is unstructured. Structured data breaks down well into fields and can be stored for example in a data warehouse.
Big data analytics has achieved wide acceptance as a means to generate insights into what clients want and to derive good, evidence-based business decisions from it. However, while confidence in analytic decision-making is growing, there is also evidence to suggest that investments into big data analytics are not yet paying off.
An increased interest in big data and advanced analytics was evident in 2014, forcing companies of all sizes to react if they want to remain competitive. More and more businesses have been seized by the phenomenon, also reflected by hiring trends: According to a HR data analytics provider, the demand for IT project managers with big data expertise has increased by over 120% in the past 12 months.
Not only businesses are embracing analytics to gain competitive edge.
Now that the World Cup in Brazil has come to an end, both sports and technology bloggers alike seem to have one big question: Did Big Data give the German national team the competitive edge to win the World Cup? Media reports confirm the winning team’s use of the customized analytics software Match Insights, engineered by business software giant SAP.
Will analytics revolutionize the ailing record industry?
Our habits of buying and listening to music have changed drastically over the last few years. It is no longer common to go out and buy a physical copy of your favorite artist’s new CD. Thanks to streaming sites such as Spotify, anyone can listen to it for free. More and more people are “torrenting” music files, i.e. sharing them with others through the internet.
An century-old method of visualization is getting new attention.
It is a common misconception that business intelligence technologies can be used to find a diamond in a coal mine. Business analytics is more for confirmation than for random exploration. In other words, the experienced analyst first formulates a hypothesis that two or more things are related or that some underlying behavior is driving a pattern and then collects the data.
Asked what inspired him to build the Google self-driving car, Sebastian Thrun mentions being at the DARPA Grand Challenge in 2003, a long distance competition for driverless cars, funded by the United States Department of Defense. Witnessing how these cars went up in flames after only a mile, he was convinced he could do better.
More and more industries are discovering the predictive power of large-scale data analysis.
In the era of Big Data, uncovering new and interesting correlations with huge data sets offers unprecedented opportunities. More and more businesses are jumping on the bandwagon and have begun to analyze and visualize data in ways that matter to them, for example to structure decisions, improve pricing or avoid waste.
One of the main topics dominating the world of business and IT today is big data. This is not surprising, given the increasing amount of digital business and communication processes causing a genuine data deluge.
Customer experience is driving growth
A handful of technologies we are increasingly relying on are reshaping IT: smartphones, tablets, mobile networks, social networking and big data analytics. Early adopters of these technologies have excelled in business. They have responded to a customer base that is reinventing how they choose to learn about new products, remain up-to-date on existing ones, and stay loyal to brands they value.
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