Business Projects Involving Big Data - Assignment Example

Business Projects Involving IS/IT work Business Projects Involving IS/IT work Big Data is critical to business success and business survival Introduction New management skills are critical in turning large volumes of businesses data into a competitive gold. Big Data is one tool that business organizations need to possess for success and survival. Big Data provides managers with an opportunity to measure and understand their businesses. The business managers translate that knowledge and use it to improve performance and decision-making (Cohen 2010, p. 58). Big Data addresses speed, mobility, security, and scalability. It also enhances business stability and flexibility. Big Data integrates elements of both structured and unstructured data. Actualization time to information is vital to extracting value from different data sources (Kuketz 2012, p. 1). Such data sources include identification radio frequency (RFID), mobile devices, expanding list of automated sensor technologies, and the web. All business organizations should take Big Data and its ability to create value earnestly in case they want effective competition. However, businesses that collect more data than they can effectively manage need to focus on training and hiring skilled data scientists. Importance of Big Data to business success and survival Big Data Enhances Business Success and Survival Big Data is vital if a manager wants to ensure that their business success and survival becomes a reality. Business executives can use big data to quantify and therefore achieve more precise management than ever before. Use of Big Data can enable business executives arrive at smarter decisions and aim at effective interventions in domains that intuition and gut dominated before (McAfee & Brynjolfsson 2012, p.58). Big Data from displays more advantages than what scientists analyzed initially. Big Data reveals advantageous differences over scientist analysts in terms of velocity, variety, and volume. Better Competitive Edge against Rivals Current research shows that more data now flood the internet more frequently than the last 20 years. The information frequency on the internet provides an optimum condition for companies with Big Data to display a competitive edge over its rivals (Aluya, 2014 p. 72). Such companies execute high-value marketing drives. The companies control sophisticated sales operation thus running millions target campaigns. However, processing data becomes difficult as data volume grows, and the campaign target 10-15 million receivers. Institutions operating on big data utilize high-performance analytics hence achieving tremendous gains in the database marketing. Consequently, it enables teams to validate extra variables for massive reliability in their models. In addition, high performing analytics eliminates the restrictions on variables and observations that the company processes. Enhancement of Transparency Big Data enhances radical transparency especially where there is a wide availability of data. As time passes, information becomes easily accessible across sectors thus acting as a threat to companies that rely on proprietary data as an asset for competition (Plunkett, 2014 p. 266). The real estate sector, for instance, merchandise on information asymmetries such as an exclusive right to access transaction data and ask buyers behaviors. Big Data enable analysts to bypass agents hence engaging sellers and buyers to exchange views on property values. As a result, the process creates parallel real estate sources to the proprietors. Cost and valuation of data are becoming easily accessible across a range of industries. Detection, Remediation and Prevention of Financial Fraud Big Data helps in detecting, remediating, and preventing financial fraud. In many areas, criminals try activities involving company defraud through constant evolution of its portfolio of strategies and scheme. With the increase in the volume of sophisticated schemes, many companies manage huge data volumes and as well as uncovering hidden patterns by turning powerful analytics. The increase in data volumes provides room for a fraudulent transactions scrutiny. Consequently, it enhances chances of detecting, preventing, and remediating financial fraud by using SAS Grid Computing. Business fraud and IT teams are also able to collaborate efficiently on their endeavor to upgrade their fraud detection. The resulting solution is that the application of SAS Grid computing enables the fraud and IT teams to follow fraudsters and their developing schemes. The team further conducts transactions such as scanning to prevent more acts of fraud before they financially affect negatively on the company. Improved Processing and Storage Technologies Businesses that have integrated big data enjoy data archiving, compliance, continuity in planning and cloud initiatives. Such initiatives provide an optimum ground for big data project courtesy of storage and processing that are the primary uses of Big Data (Ohlhorst, 2013 p.23). The enhanced processing and storage are the current focus of the nature distribution and, therefore, the application distribution over multiple systems and locations. Major vendors associated with big data such as IBM and HP further provide a foundation for big analytics. Availability of Vast Information Big Data and analytics open doors to all manners of new information about business most interesting issues. Big Data enables customers to define a better profile and timing for making sale of goods and services. Health maintenance and treatment enterprises utilize big data to define customized methods of treatment. Moreover, big data assist professionals to identify better ways to design and do products delivery. It is, therefore, imperative to integrate big data into operating processes of business organizations and companies. Timely Perceptiveness from Data Variety Timely perceptiveness from the variety amounts of data is another remarkable advantage that comes with Big Data. Such variety of data includes those in the company database, third party sources, and those from external sources besides remote sensors, the internet, and social media. In addition, Big Data enable business enterprise managers to forecast on events that impact on either operation of the business or performance. Besides, a business that has integrated Big Data enjoys speedy convergence of BDI results with the aid of SAP and HANA for velocity. In addition, businesses with big data benefit from Hadoop for volume capability to handle large volumes of data with automatic verification and validation. Improved Decision-Making Big Data businesses can control risks through optimization of challenging decisions of unintentional events in a more rapid manner. Organization becomes effective if it consolidates information and decisions that are relevant in the same position. The Big Data business corporations often create and transfer information and expertise where it should be (McAfee, 2012. p.66). A good leader of an organization should have flexible data patterns not too soft to commit mistakes and correlate causation and misleading data. Big Data management can help in achieving this organization. It is evident that obtaining the best decision-making results from data. Increase in Technological Advancement Upgrading to Big Data in most companies is boosting volume, variety, and velocity. Hadoop is capable of combining commodity hardware with less expensive open source software to boost the volume, variety, and velocity. However, managers must be ready to identify highly skilled IT specialists to ensure that use of Hadoop is a success to their organizations. Power of Big Data Does Not Erase Need for Human Insight Despite the use of Big Data, leaders remain capable of spotting great opportunities and understanding the new market developments. Furthermore, leadership teams still set clear goals as well as envisioning success. Leaders nurture their creative skills and truly suggest articulate and demanding vision. Challenges of Big Data in Business Success and Survival However, as much as businesses that have integrated Big Data enjoy a wide variety of benefits, the approach as well comes with numerous challenges. Some of those challenges have far-reaching impacts such that failing to solve them amicably may end the business in technical mayhem. Big Data Is Disruptive When Introduced Into a Business Issues of scale, data center design, and storage are among the disruptive issues that come with the introduction of Big Data (Ohlhorst, 2013 p. 73). Disruptions involve costs associated with hardware and software. Some of the disruptions have human related problems particularly with the staff and other support services. Consequently, business organization may suffer losses touching on Return in Investment (RIO) and Total Cost of Ownership (TCO). Company bosses, therefore, should accelerate ROI while decreasing TCO to have a balance between integration of big data and business success. Scarcity of Data Scientists The second outstanding challenge is the issue of identifying hiring scarce data scientists. Data scientists who can manage big data within the organization are scarce in number, and that is a source of challenge to the managers (Chen, 2014 p. 8). Managers must learn how to identify data scientists besides attracting them to take up responsibilities of managing the company’s big data. Many businesses fail to understand skills these experts should have to perform the management task. Consequently, business organizations meet huge expenses in identifying and keeping such scarce human experts. Big Data Easily Get Disorganized and Overwhelms the Management Team In cases where there is no taxonomy and context, there is never data documentation due to some uncollected specific interests (Jay, 2013 p. 48). Before thorough analysis, Big Data becomes unreliable and tedious. Collection of data is usually an engineering function that requires highly skilled personnel to conduct. Big Data and Hadoop Organizations often store raw data without updating them. The data, therefore, lack assemblage for the aim of saving and storage. Lack of aggregation results comes because of raw data that may be extremely too large to handle. Consequently, a single server will fail to process the data within the desirable amount of time. For instance, processing one terabyte might take a whole day for the server to process. As a result, inefficiency due to time consumption will be the outcome. Conclusion Despite the challenges that come because of integration of Big Data, time to utilize big data approach has come to make storage and saving of information effective and efficient. The competing edge over business rivals improved decision-making, efficient storage and accessing stored information makes it one suitable application that will help in raising standards in the business environment. Managers of business enterprises, therefore, must upgrade as institutions train more data scientists. Bibliography Aluya, Joseph. (2014). The Influences of Big Data Analytics Is Big Data a Disruptive Technology? Authorhouse. Chen, M. (2014). Big data: related technologies, challenges and future prospects. http://dx.doi.org/10.1007/978-3-319-06245-7. Davenport, T, & Patil, D 2012, Data Scientist: The Sexiest Job Of the 21st Century, Harvard Business Review, 90, 10, pp. 70-76. Dumbill, E., Croll, A., Steele, J., Loukides, M. K., & Slocum, M. (2012). Planning for big data. http://www.contentreserve.com/TitleInfo.asp?ID={2C1909F7-7288-40FA-BAAE-D8B373944B48}&Format=50. Hurwitz, J., Nugent, A., Halper, F., & Kaufman, M. (2013). Big Data for Dummies. Hoboken, N.J., Wiley. Jay, Liebowitz. (2013). Big Data and Business Analytics. CRC Press Karimi, Hassan (2014). Big Data: Techniques and Technologies in Geoinformatics. CRC Press. Mayer-SchöNberger, V., & Cukier, K. (2013). Big data a revolution that will transform how we live, work, and think. Boston, Houghton Mifflin Harcourt. McAfee, A, & Brynjolfsson, E 2012, Big Data: The Management Revolution. (cover story), Harvard Business Review, 90, 10, pp. 60-68. Mohanty, S. 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