Understanding Big Data Analytics - Report Example

The requirements of capturing, storing, questioning/analyzing and retrieving data have been increased, especially for the unstructured data such as video. The increasing data requirements impact traditional or relational databases and relegate them to the venture crumb mound as an up-and-coming variety of vendors’ crumble the traditional software and database dominance. In order to deal with huge and unstructured data, a number of companies have materialized the influencing product developed with specialized hardware, proprietary and open source technologies to capture and analyze the huge data sets known as Big Data. The Big Data is one of the hot topics of the year 2012 that is a compilation of multiple large and complex data sets so that useful information may be extracted for decision making of the organizations (Dignan, 2011). The organizations use the databases to decide the way forward of the organization. The decision making in the organizations should be derived from the analysis of trends in the database – the Big Data. The databases are reliable if they have been designed properly and the captured and stored information contained in them is accurate without redundancy and anomalies. The databases facilitate the analyst to manage the data in whatever the way the analyst wants but keeping in view the constraints of the design of the database. The document presents the details of the above mentioned my viewpoints on why and how much the decision making of the organizations should be dependent on the results obtained from the databases – the Big Data. Every organization deals with information regarding products, people including employees, customers, prospective benefactor(s), who (might) protract organization’s functions and services. Moreover, each and every decision from solving a particular problem for deciding the future of an organization is based on availability, accuracy and quality of information. “Information is an organizational asset, and, according to its value and scope, must be organized, inventoried, secured, and made readily available in a usable format for daily operations and analysis by individuals, groups, and processes, both today and in the future” (Neilson, 2007). In computing, the organizational information is neither just bits, bytes saved in a server nor limited to client data, the hardware and the software that store it. A data or information to which an (large) organization deals is too huge to control it manually and a process of gathering, normalizing and sharing that information to all its stakeholders. It might be difficult to manage this imperative huge information manually; moreover, the manual maintenance of information might not be reliable and accurate. Therefore, I believe that the organizations should use databases for decision making be driven by ‘evidence’ derived from analysis of trends in the huge database. This is the reason that databases are formulated and high in demand. A database facilitates to store, handle and utilize implausible diverse organization’s information easily. A database can be defined as “collection of information that is organized so that it can easily be accessed, managed, and updated” (Rouse, 2006). Keeping in view the above facts, it can be stated that the crucial information needs to be accurate and stored correctly in reliable storages for its enduring usage. The database is one of the best storage mechanisms that are reliable as compared to the manual management of data. But, the quality and accuracy of data are too critical and fundamental for a database developed/maintained by any organization; either the database is developed for achieving a small goal with limited scope or it is a multi-billion dollar information system. It can be said that the value of data is directly proportional to the quality of data. It is one of many reasons that an inadequately designed database may present incorrect information that may be complicated to utilize, or may even stop working accurately. Therefore, the reliability of the database is directly proportional to the accuracy of the information it contains and the quality of the design of the database. Once the database has been developed with proper design that contains correct information without data redundancy and anomalies, moreover, the data collection methods have to be standardized to input the correct data. Then it can be stated that the database is one of the reliable resources on which the organizations can depend. Moreover, it would be ideal that the organizations initially run the Information System parallel to their existing/previous system(s) to validate the outputs/results of the new system. And if the organization is satisfied with the new system so it can be stated that the new system is dependable. It is significant that the Information System should be evaluated time to time to get effective results, as the information is increasing and updating with the passage of time. There are certain features and capabilities of the database that makes it dependable for the decision making in the organizations which have been identified and categorized by a number of authors in their papers. The organizations have always a stance that they are getting productive returns on their investments, particularly the telecommunication organizations which deal with enormous data. The database is the only solutions that help the organizations to deal with massive data and facilitates to develop and re-define the organizational strategies and future road map in favor of the organizations. According to the Gary Spakes, there are four areas in which the big data can benefit the organizations by detecting, preventing and rectifying financial frauds, it facilitates the banking sector or financial service providers by calculating risk on a large portfolio of loans, it helps the organizations to execute high-value marketing campaign and in the telecom sector the big data assists the management to improve the delinquent collections (Spakes, n.d). Many organizations have successfully implemented the technology of Big Data and gained a strong return over investment by redefining and updating their business strategies to attract new loyal customers. One of the best examples is the Wal-Mart – one of the initial pioneers utilizing the benefits of Big Data by means of predictive analytics in order to recognize customers’ inclination on a basis of area and provincial so that particular items can be stocked in the branches accordingly. The implementation of Big Data turned out to be an effective and productive tactic that capitulate heavy return over investment, moreover, they redefined their strategy and detached themselves from retail services. The Wal-Mart became well-known in terms of the technology undertaken by them, the data processing and analytical turn out to be a solution and opportunity to enhance the business, therefore, the other organizations also adopted the same technology (Smith, 2011). Moreover, the telecommunication organizations use data warehouses for storage of information by utilizing the different techniques (OLTP – online transaction processing systems), the data cleansing methods are used to eliminate anomalies and the ETL (Extract, Transform and Load) process is used to obtain the results/reports. On the basis of these reports the top management decides the road map of the organization and develops strategies and policies in terms of telecommunication packages and quality services offering to the public. These steps can only be facilitated by the data warehouse databases applying data mining techniques. The policies and strategies developed with the help of reports generated by the databases facilitate to get customer satisfaction, hence increase business income and productivity. The analytics of database facilitate not only to know the past behaviour of the customer but also develop a model to envision the behaviour of the customers’ consulate in the future (Lamont, 2000). However, according to the Barry Devlin in 2011, the data warehouses are being in use for the last twenty five (25) years, it is an accepted architecture that facilitates the decision makers. He provided the analysis on “how big data and traditional data warehousing can coexist”. As per his analysis the big data would utilize the data warehouse – the successful architecture and re-utilize by giving re-birth to the data warehouse. He concluded that the reality is different from the death of data warehouse and relational databases, moreover: “Big Data is actually a superset of the information and processes that have characterized data warehousing since its inception, with big data focusing on large-scale and often short-term analysis. With the advent of big data, data warehousing itself can return to its roots — the creation of consistency and trust in enterprise information.” (Devlin, 2011) From the analysis it can be derived that the big data would not demolish the existing technologies, rather it would add something to the existing technologies so that the enhanced advantages may be taken from the existing technologies. Therefore, the vendors of the databases and data warehouses would not be frightened from the big data; rather they would smooth the progress of big data companies. The study also reveals that the organizations having data warehouse can be benefited from the big data technology as they don’t need to abolish the existing setup rather they need to update. Moreover, the big data is based on the successful existing technology but with huge scope therefore it can be trusted in terms of decision making for the organizations. There are several demerits of the Big Data which the organizations should consider before adopting the Big Data for decision making. As the big data is using more commonly the new technologies which are relatively less mature, therefore these technologies carry more risk that might directly impact the organization decision through the big data. The majority of the executives of the organizations believe that the decisions in the organization should be data-driven but doubts persist because the technology of Big Data is expensive as compared to the data warehouse and relational database, therefore, the organizations avoid taking risks. Moreover, there exist a certain group of organizations that believe their organizations are not data oriented as they do not have enough of a big data culture; therefore, the big data is not strategically fit for their organizations. On the other hand, there are certain challenges as well that need to be considered while implementing the big data. One of the most significant factors is to identify the questions the organizations want to know from the Big Data and every organization have certain questions for improving their business as well as deals with data. Moreover, I believe that the bigger the risk the bigger advantages you get. Therefore, keeping in view the above facts and my initial arguments I believe that the organizations should use the evidence and data driven approaches such as the big data for making decisions of the organizations. References Neilson, P. (2007). Data architecture. Retrieved from http://sqlblog.com/blogs/paul_nielsen/archive/2007/11/25/data-architecture.aspx Lamont, J. (2000). Data warehousing in the telecommunications industry. Retrieved from http://www.kmworld.com/Articles/Editorial/Feature/Data-warehousing-in-the-telecommunications-industry-9153.aspx Spakes, G. (n.d). Four ways big data can benefit your business. Retrieved from http://www.sas.com/news/feature/big-data-benefits.html Dignan, L. (2011). Big data vs. traditional databases: Can you reproduce YouTube on Oracles Exadata? Retrieved from http://www.zdnet.com/blog/btl/big-data-vs-traditional-databases-can-you-reproduce-youtube-on-oracles-exadata/52053 Smith, D. (2011). 5 Real world uses of big data. Retrieved from: http://gigaom.com/cloud/5-real-world-uses-of-big-data/ Devlin, B. (2011). Will data warehousing survive the advent of big data? Retrieved from: http://strata.oreilly.com/2011/01/data-warehouse-big-data.html Nobel, C. (2010). How IT shapes top-down and bottom-up decision making. Working Knowledge: Harvard Business School. Retrieved from http://hbswk.hbs.edu/item/6504.html?wknews=110110 Webster, J. (2011). Understanding big data analytics. Retrieved from http://searchstorage.techtarget.com/feature/Understanding-Big-Data-analytics Read More