Data is emerging as the world’s newest resource for competitive advantage among nations, organizations and business. It is estimated that every day we create 2.5 quintillion bytes of data from a variety of sources. These are from the computer notes to posts on social media sites and from purchase transaction records to pictures.
These collection of data sets which are so large and complex and are difficult to process using the on hand database management tools are known as Big data. The challenges in Big data includes capture, curation, storage, search, sharing, transfer, analysis and visualization of the data.
Big data has few key characteristics such as volume, sources, velocity, variety and veracity. The first among these is volume. Experts predict that by 2020, the volume of data in the world will grow to 40 Zettabytes. This affects every business, governments and individual. Based on a recent study,2.8 Zettabytes of data were created in 2012 and only .5% of that data were used for analysis. Unstructured data, such as texts, notes, logs makes up a large chunk of this data volume and these requires text mining to analyze the data.
The business data is also growing at these same exponential rate too.Along with the volume, the number of sources, from where the data is extracted are also growing. Data is increasingly accelerating the velocity at which it is created, as the process are moved from batch to a real time business. The demands of the business from these data also has increased, from an answer next week to an answer in a minute.
Business intelligence (BI) provides OLAP-based, standard business reports, ad hoc reports on past data. This ad hoc analysis looks at the static past of data. This has its purpose and business uses but doesn’t meet the needs of a forward-looking business.
Forward looking big data analytics requires statistical analysis, statistical forecasting, causal analysis, optimization, predictive modeling, and text mining on the large chunk of data available. There are performance issues, when these high volume past data are used in the relational data model, for a forward-looking big data analytics, for future in the current system landscape in many organizations.
Big Data Analytics will help organizations in providing an overview of the drivers of their business by introducing big data technology into the organization. This is the application of advanced analytic techniques to a very large data sets. These can not be achieved by standard data warehousing applications. These technologies are hadoop, mapreduce, massively parallel processing databases, in memory database, search based applications, data-mining grids, distributed file systems, distributed databases, cloud etc.
The Technology drivers for Big data Analytics
With the Big data analytics the relevant information from data warehouse in terabytes, petabytes and exabytes can be extracted and analyzed to transform the business decisions for the future.
The reason that big data is currently a hot topic is partly due to the fact that the technology -MapReduce, Hadoop, In memory database, Massively parallel processing database, database grids, search based functionality etc are now available to process these large data sets which are mostly a combination of structured and unstructured data. With these technologies, it is now possible to bring insights from these data in to the day to day decision making process.
Predictive Analytics identifies meaningful patters of Big data to predict future events and assess the attractiveness of various options. Predictive analytics can be applied to any type of unknown data, whether it be in the past, present or future.Predictive Analytics provides the Business Intelligence about the future using the insights of Big data.
Big Data Analytics will help organizations in providing an overview of the drivers of their business by introducing big data technology into the organization. This is the application of advanced analytic techniques to a very large data sets. These can not be achieved by standard data warehousing applications. These technologies are hadoop, mapreduce, massively parallel processing databases, in memory database, search based applications, data-mining grids, distributed file systems, distributed databases, cloud etc.
With the Big data analytics the relevant information from data warehouse in terabytes, petabytes and exabytes can be extracted and analyzed to transform the business decisions for the future. The reason that big data is currently a hot topic is partly due to the fact that the technology -MapReduce, Hadoop, In memory database, Massively parallel processing database, database grids, search based functionality etc are now available to process these large data sets which are mostly a combination of structured and unstructured data.
The business benefits of Big Data Analytics include turn dormant data into new opportunities making use of big data analytics, intuitively design very complex predictive models using casual factors, Big Data integration capabilities with traditional databases and other systems, Hadoop Distributed File System , wide range of Big data applications and analytics to analyse more history data and many more.