What is Big Data Analytics?
Big Data has the potential to change how an organization perceives and use data. Though Big Data is generated from multiple sources, the three main varieties to which they boil down to is –
Transaction data – data generated from financial order (invoice or payment), activity records and logistics (deliveries, storage record or travel record, financial data from share/stock market).
Machine data – data generated from industrial equipment, real-time data from sensors (sensors embedded in roads to monitor traffic) and web logs. Social Data – data generated from social media sites like Facebook, Twitter, Linkedin, online blogs etc. Big Data Analytics is the process of collecting and analyzing high volume of data i.e. big data, to unlock the hidden patterns, unknown connections, market trends and other useful business information which traditional systems can't do. It helps organizations understand the dynamics of the business, view day-to-day operations from different perspectives, automate decision-making and reduce IT costs.
Impact on current data extraction techniques
Big Data analytics is the application of advanced analytic techniques such as predictive analytics, data mining, and statistical analysis. Conventional BI software also play a role in the data analysis process, providing information based on operational data in the form of reports and consoles but it limits the quantity and varieties of analysis that can be performed on structured data. Big Data Analytics usually involves the combination of more advanced data processing and machine learning algorithms, distributed over a cluster of computing nodes enabling an organization to continuously drive innovation and make the best possible decision. The findings can lead to more business opportunities, effective marketing, improving customer service and better operational efficiency.
Several organizations are now adopting an enhanced technology that includes Hadoop and related tools such as YARN, MapReduce, Spark and Pigas as well as NoSQL databases.
Big Data Analytics: Case studies
Few industry sectors where big data analytics is being used to great effect are –
Fashion retailer Burberry Group Plc is using data analytics software to boosts its sales. By installing modern technology and equipment's it enhances its stores displays and make its marketing more personalized. The data analytics software builds a database of customers past purchases, internet searches and interest, allowing Burberry to market its product on personal basis.
Germany won the FIFA world cup in 2014 as a result of teamwork, talent - and big data. It utilizes the service designed by SAP, Match Insights that runs on Intel processors. Tiny sensors developed by Intel can be embedded in player boots, racquets and even in the ball to gather real-time data. The huge amount of data created by players helps coaches and training staff to gain insights on key moments in games. This data will deliver insights on performance, capabilities and other areas that need addressing.
In healthcare data transparency is utmost important to ensure proper patient care is being delivered by healthcare service. The NHS has re-launched the "Care.data" initiative to improve the delivery of public sector healthcare. Big data analytical tools help extracting the key information such as how many patients a doctor has seen, It links GP data with hospital data, and build new ways for analyzing the pathway of care that patients travel on.