Big Data Analysis
As one of the most “hyped” terms in the market today, there is no consensus as to how to define big data. The term is often used synonymously with related concept such as Business Intelligence ( BI) and data mining. It is true that all three terms is about analyzing data and in many cases advanced analytics . But big data concept is different from the two others when data volumes, number of transactions and the number of data sources are so big and complex that they require special methods and technologies in order to draw insight out of data (for instance, traditional data warehouse solutions may fall short when dealing with big data). This leads us to the most widely used definition in the industry. Gartner (2012) defines Big Data in the following: Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation. Organizations have a long tradition of capturing transactional data. Apart from that, organizations nowadays are capturing additional data from its operational environment at an increasingly fast speed. Some example are listed here: Web data, Text data, Time and location data, Smart grid and sensor data, Social network data. The answer lies in how the data is used. The processes, tools, goals, and strategies that are deployed when working with big data are what set big data apart from traditional data. Specifically, big data is defined by the following features: Highly scalable analytics processes, Flexibility, Real-time results, Machine learning applications, Scale-out storage systems, Data quality. 1. Cost Reduction: Big data helps in providing business intelligence that can reduce costs and improve the efficiency of operations. Big data analytics can provide insights on the impact of different variables in the production process thus helping industries take better decisions. 2. Improved Decision Making: Big data analytics can analyze past data to make predictions about the future. Thus businesses can not only make better present decisions but also prepare for the future. 3. New Products and Services: Businesses can analyze past data about product launches and customer feedbacks to launch better products in future. Along with this, the real time market analysis allows business to understand shifts in demand and supplies of products and changes in consumers’ behavior which helps in customer oriented marketing. Despite the hype, big data does offer tangible business benefit to organizations. It enables enhanced insight, decision making, and process automation. The characteristics of big data is the three V: Volume, Velocity and Variety. The “big” in big data is not just about volume. While big data certainly involves having a lot of data, big data does not refer to data volume alone. What it means is that you are not only getting a lot of data. It is also coming at you fast, it is coming at you in complex format, and it is coming at you from a variety of sources. Data comes from variety of sources, and can be used in various industry applications. Often it is the combination of data sources that counts. Along with big data, there is also a so-called paradigm shift in terms of analytic focus. That is a shift from descriptive analytics to predictive and prescriptive analytics. Big data necessitates a new type of data management solution because of its high-volume, high-velocity and/or high-variety nature. This new type of data management solution bears the trademark of highly scalable, massively parallel, and cost-effective. New technologies, such as Hadoop, are not replacing other technologies, such as relational database, but rather are being added alongside them. • Book: Introduction to Big Data Xiaomeng Su, Institutt for informatikk og e-læring ved NTNU • Website (blog): http://blog.syncsort.com/2018/03/big-data/big-data- vs-traditional-data/ • Website: https://www.newgenapps.com/blog/what-is-big-data- analytics-benefits-challenges.