Data analytics

                              

                                        

Data is in vast amount and the world is producing a terabyte of data every data but it should be processed so it could be of good use. This is where data analytics comes into play.

Data analytics is the field of computer where we compare, search, sort different types of data and then categorize the data into a different list where it can be easily be accessed by people all around the world.

Together with social, mobile and cloud, analytics and associated data technologies have emerged as core business disruptors in the digital age. As companies began the shift from being data-generating to data-powered organizations in 2017, data and analytics became the center of gravity for many enterprises. In 2018, these technologies need to start delivering value. Here are the approaches, roles, and concerns that will drive data analytics strategies in the year ahead.

 Data analytics over the years

The amount of data generated today from all industry domains, also known as big data is huge, encompassing data gathering, data analysis, and data implementation process. Over the years, big data analytics trends are changing, from a departmental approach to business-driven data approach, embracing agile technologies and an increased focus on advanced analytics. Business enterprises need to implement the right data-driven big data analytics trends to stay ahead in the competition.

Previously, big data was primarily deployed by big businesses, who could afford the technology and channels used to collect and analyze the information. Today the scope of big data is changed leading to business enterprises large and small rely on big data for intelligent business insights. This has led to big data evolving at an unbelievably fast pace. The best example of the growth is big data in the cloud which has led to even small businesses taking advantage of the latest technology trends.

Big data analytics is becoming a focal point of importance for companies and people alike.

                               

How it works and key technologies

There’s no single technology that encompasses big data analytics. Of course, there’s advanced analytics that can be applied to big data, but in reality, several types of technology work together to help you get the most value from your information. Here are the biggest players:

                               

Data management. Data needs to be high quality and well-governed before it can be reliably analyzed. With data constantly flowing in and out of an organization, it's important to establish repeatable processes to build and maintain standards for data quality. Once data is reliable, organizations should establish a master data management program that gets the entire enterprise on the same page.  

Data mining. Data mining technology helps you examine large amounts of data to discover patterns in the data – and this information can be used for further analysis to help answer complex business questions. With data mining software, you can sift through all the chaotic and repetitive noise in data, pinpoint what's relevant, use that information to assess likely outcomes, and then accelerate the pace of making informed decisions.

Hadoop. This open source software framework can store large amounts of data and run applications on clusters of commodity hardware. It has become a key technology to doing business due to the constant increase of data volumes and varieties, and its distributed computing model processes big data fast. An additional benefit is that Hadoop's open source framework is free and uses commodity hardware to store large quantities of data.

In-memory analytics. By analyzing data from system memory (instead of from your hard disk drive), you can derive immediate insights from your data and act on them quickly. This technology is able to remove data prep and analytical processing latencies to test new scenarios and create models; it's not only an easy way for organizations to stay agile and make better business decisions, but it also enables them to run iterative and interactive analytics scenarios. 

Predictive analytics. Predictive analytics technology uses data, statistical algorithms and machine-learning techniques to identify the likelihood of future outcomes based on historical data. It's all about providing the best assessment on what will happen in the future, so organizations can feel more confident that they're making the best possible business decision. Some of the most common applications of predictive analytics include fraud detection, risk, operations, and marketing.

Text mining. With text mining technology, you can analyze text data from the web, comment fields, books and other text-based sources to uncover insights you hadn't noticed before. Text mining uses machine learning or natural language processing technology to comb through documents – emails, blogs, Twitter feeds, surveys, competitive intelligence and more – to help you analyze large amounts of information and discover new topics and term relationships.