Big Data Analytics What it is and why it matters
Big Data Analytics: What it is and why it matters
Big data analytics examines large amounts of data to discover hidden patterns, possible correlations and other insights. With todays modern world it is very easy to get access to it and analyze it almost inmediately.
History and evolution of Big Data Analysis.
The concept of big data has been around for years; Organizations now understand that if they collect data that streams into their business, they can apply analytics and get value from it. Around 1950 business used to do it to but the difference with the Big data that we know today is efficiency and speed, this gives the organization a competitive edge they didn't had before.
Why is Big data analyics important?
It helps organizations to take advantage of their data and look for new opportunities. That leads into smarter business moves, decisions, efficient operations, higher profits and happier customers. The most valuable data is display in these sets:
Cost reduction: Big data technologies brings cost advantages when storing large amounts of data
Faster, better decision making: Business analyse the data storaged and make decisions based on what they have learned.
New products and services: With the storaged data and the analysis that took us to better decisions and for instance better business, we can create new products that meet our customers needs previously registered in the data collected.
Big data analytics in todays world
Who is using it?
Life science: Clinical research is a slow and expensive process with trials failing for many different reasons. Advanced analytics unlocks the potential of improve speed and efficiency by delivering more intelligent and automated solutions.
Banking: Financial institutions collect and access analytical insight from large volumes of unstructured data in order to make financial decisions. Big data allows them to access the information they need whenever they need it by eliminating overlapping redundant tools and systems
Manufacturing: Manufactures are used to struggle with problems on a daily basis like motion basis, complex supply chain, etc. Big data allows competitive organizations to discover new cost savings opportunities and revenue opportunities.
Health care: A variaty of information from patient records, health plans, insurance information, and many others. Big data analyze these information structured and unstructured in the quickest way so health care providers can give accurate and lifesaving diagnoses or tratment options.
Government: One of the biggest struggles among government agencies is giving the very best while sticking into the budget. The law enforcement agencies tend to combat the crime with relatively scarce sources. Big data helps them to streamline operations while giving the agency a wider view of criminal activity.
Retail: Customer service has evolved in the last few years because shoppers expect the seller to comprehend them and have what they need whenever they need it. Big data help retailers to meet those demands. They collect an enourmous quantity of data from customer loyalty programs, buying habits and other sources, retailers not only have an indepth understanding of their customers but also to predict trends, recommend new products and boost profitability.
How it works and Key technologies
Machine learning: It trains a machine how to learn, it makes it possible to quickly and automatically produce models that can analyze bigger and more complex data and deliver faster and more accurate results.
Data management: Data has to be really high quality and well-governed before it can be reliable analyzed. With data coming in and out of the organizations there is need to be a process that give us quality standard data. Once the process is done the organization should asign one data manager that keeps all the organization on the same page.
Data mining: Helps you to analyze large amounts of data in order to find patterns and this information is used to do further analysis and answer coplex business questions. With this help you skip all the repetitive data emphasizing in whats relevant and then use that information to predict outcomes and accelerate the informative decision making.
Predictive analytics: Uses data, statistical algorithms and machine learning techniques to identify how possible are some future options to happen based on the historical data and this makes the organization feels like they are taking the best decision.
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