COIT20253 - Business Intelligence using Big Data
Added on 2020-03-04
13 Pages3866 Words75 Views
Data Science and Big DataDatabases
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Big Data for Sentiment AnalysisExecutive summaryBig data is defined to be a large volume of data that are complicated and cannot be analyzed or efficiently handled and processed using traditional tools. There are differenttypes of big data out which there is structured, semi-structured and unstructured data. Depending on each type of data collected, they will be handled using different types of data bases. For instance, structured data are handled using relational databases while unstructured data are handled using non-relational databases. Big data are collected using google, social media e.t.c. and then directed to the databases for storage. Data from outside sources are first extracted and changed by ELT before they are loaded intothe databases. For proper management of unstructured data that are collected from various sources including social media are stored in No-SQL databases. Such databases are always focused to carry out massive scaling, simplify application development and data model flexibility. Consumer centric design is the process of setting goods and services towards the needs, wants and demands on the side of the consumers both as a result of designing and improving the quality of product, service and even the contents in order to fulfil customers’ product experience. Recommendationsystem is important for online business and for the continuity of business in case of power outage is by using power backup.IntroductionBig data is the enormous volume of data that are complicated and cannot be analyzed or efficiently handled and processed by traditional tools. Depending on how data are
collected and the tools that were used to collect data, they keep on streaming in and piling up from time to time for storage. Since reaching the customers in their various places physically is difficult, companies and business organizations use social media and other platforms to collect data in order to meet the demands of their customers. Due to this therefore, big data sentiment analysis prove to play important role in organizing and exhaustively deriving the meaning of the collected data for decision making in business (Cambria et al, 2013). Unlike small and medium data, big data covera wider and detailed range of customer requirements that can be important in solving most of the problems facing the customers outside there about the products produced by a particular business. Interest of adopting the use of big data by various business organizations have grown in the past few years since its emergence (Chen et al, 2012). As a result of this, many technologies such as the Hadoop, Real-Time solutions, Cloud Solutions and many more have come to light and are gaining popularity with the continued rise of big data adoption by business organizations (Kunzea, 2015). Change in data collection tool has also resulted to change in the type of big data collected. Toolslike social media on the internet collect large volume of unstructured data that are then organized by No-SQL databases for storage (Wu et al, 2014). Woolworths is one of the companies in Australia that put big data into practice where they spend heavily on it to enjoy its fruits. The supermarket is currently facing a slow market growth as a result of competition in the market. Venturing into big data was now aimed at capturing all the information from customers through the social media that could help them handle the problems that are currently there or might be detected by the data in the future.
Discussion of topicsData collection systemWith reliance on Quantium as one of the Australia’s oldest and biggest data company, Woolworths assigned them to collect big data and help in carrying out big data analytics. The Quantium Company collects data from the field through the internet portals such as google and social media about Woolworths’ products from their customers. Data that are mostly collected through such data collection tools are unstructured where the users of the social media just come up with any issue concerning the business that was not structured or even taken care of in the earlier preparation of the data (Tufekci, 2014). The said portals where the customers drop theircomments about the products that are sold by the business (Woolworths) will be streamed to the business’ database for storage and can be retrieved at any time in future for analysis and interpretation. Once the data have been collected, the specialistshandling big data will therefore continue further to make use of the data through carrying out big data and sentiment analysis.Storage systemWhere and how big data are stored stands to be a big question to all those who wish to adopt its use. Retrieval of structured data through traditional methods include rational databases, data warehouses etc. collected data are taken from operational data stores where they are uploaded using Extract Transform Load (ELT) which are responsible for
extraction of data from outside sources and change the data to fit the needs in operationthereafter loading data into the databases (Katal et al, 2013). Before the data is made available for data mining, the data is cleaned, transformed and made ready for analytical functions (Sagiroglu, 2013). The traditional Enterprise Data Warehouse (EDW) do not allow the incorporation of new sources of data until when the data is cleansed and integrated. Irrespective of quality of data, all data sources are attracted bybig data environments since they are made magnetic. Storage of big data should be in aflexible way in such a way that data can rapidly be adapted and easily produced by the analysts (Herodotou et al, 2011). Due to this, agile database is needed where its physical and logical contents can be easily adapted with fast data evolution. The study and analysis of enormous dataset by the analysts require the incorporation and use of complicated statistical methods to excavate data up and down. Deep big data repositoryalso need to be done in order to have sophisticated algorithm runtime engine served. For proper management of unstructured data that are collected from various sources including social media are stored in No-SQL databases (Moniruzzaman and Hossain, 2013). Such databases are always focused to carry out massive scaling, simplify application development and data model flexibility. Data management and storage are separated in No-SQL, this is contrary to relational databases that manage structured data. Tasks of managing data that are to be written in specific database languages are written in the application layer since such databases focus much on high performance scalable data storage (Moniruzzaman and Hossain, 2013). Elimination of disk input/output enables real time response from the database since data in server memory is managed by in-memory databases. Silicon based main memory are used for storage
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