As shown by the recent surveys, cloud analytics have become an important focus of IT professionals. Managers in most companies and organizations have long realized the significance of data being accurate and truthful in order for a business to function well. The abundance of information is available online and sometimes it gets difficult to choose the freshest and the most important data to deal with. This is why managers always seek to find the best ways to analyze data thoroughly.
Analytics refer to examining the basic patterns of data. To discover the meaningful connections between those patterns, analytics rely on statistics, computer programing and operations research. Analytics have wide variety of applications, though the most important one is business data analytics.
Data analytics represent the whole science of examining data in order to make them suitable for a particular use in business. Contemporary data analytics involve dynamic recordings of the data. The term “analytics” has somewhat different meaning than the term “data analysis”. The latter is a bit broader, covering different steps of analysis while “analytics” refers to methodology as a whole and is carried through different algorithms and software.
Cloud Analytics in Business Intelligence
Data analytics is hype in Business Intelligence (BI) and incorporates different data functions and processes. A modern organization deals with huge amounts of unstructured data and it has to use extensive computation. One way of dealing with extensive amounts of unstructured data is to use cloud analytics.
Cloud analytics involve different tools and applications stored on remote servers. They are offered on a pay-per-use model which makes them affordable and scalable. Gartner defines cloud analytics as “any analytics effort in which one or more of these elements is implemented in the cloud, be it public or privately owned.” They also state that the six key elements in cloud analytics are: data sources, data models, processing applications, computing power, analytic models, and sharing or storing of results.
Data analytics have long been dealing with large amounts of unstructured information known as big data. Big data used to be related to science only but now even the small businesses may generate large amounts of data every day. Storing and managing these data is often related to cloud solutions. Cloud computing systems significantly progressed and became one of the most practical ways to manage big data. First there were MPP relational databases that largely improved processing of big data. Then there came Hadoop and NoSQL which further facilitated big data management.
One example of how to deal with a huge amount of unstructured data is technology used by SecureAlert for tracking ex-convicts. Namely, they’ve been monitoring 15 000 ex-convicts through the information from their ankle cuffs. As the convicts were moving around in the town, all these locations were displayed like dots on the screens in SecurityAlert rooms. This means that they had to operate with huge amounts of GPS signals.
The predictions are that the amounts of data will grow at a very high rate. Every day regular people generate a lot of new pieces of information and even an average business may deal with massive consumer databases. This trend of generating huge amounts of information is expected to grow, which means that the solutions for managing big data need to progress as well.