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The Report Is Based On The Critical Examination Of The Big Data

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Question:

Discuss About the Report Is Based On the Critical Examination of the Big Data?

 

Answer:

Introduction

The report is based on the critical examination of the Big Data (BD) implications to the business organization. It will explicate the relationship between BD and Business Analytics (BA) and discuss the benefits and challenges of the BD to the business organizations from the accounting standpoint. It follows a review of the literature and analysis of the academic journal articles drawn from library databases and industry articles on the Big Data topic.

The report takes the structure as follows. In the first section, a clear definition of the BD is presented based on BD explanations incorporating the linkage between BD and BA. Examples of additional commercial software available in the market to deal with BD is also given. A case example of an organization that uses the BD is then presented with particular focus to the case background, the challenges and benefits to the organization due to Bid Data Management (BDM)  

 

Big Data

Big Data (BD) is a phrase that presents a description to an enormous volume of data. Such data is both unstructured and structured. This data inundates the business on daily basis. However, it is never the amount of data which is significant. The most significant thing is what the organization use the data for (Macedonia, Johnson and Rajapakse 2017). The BD can be examined for the insights which culminate to better decision alongside strategic business moves. The BD can as well be described on the basis of data management challenges. These challenges are as a result of rising volume, velocity as well as variety of data that cannot be solved with the traditional databases.

Whereas there is abundant definitions for the BD, a great proportion entails the concept usually tagged as the “three Vs” of BD. The Volume: varies from terabytes to petabytes of the data. Variety: entails data from a vast range of sources as well as formats like social media, web logs, online, transactions, ecommerce and financial transactions (Members 2017). Velocity: increasingly, organizations have strict needs from the time data is generated, to the point actionable insights are delivered to users. Hence, data requires to be gathered, stored, processed as well as analyzed within comparatively short windows-varying from daily to real time (Xie, Draizen and Bourne 2017).

There is a close relationship between BD and BA. The Business Analytics uses the Big Data as the inputs to produce effective outputs that inform the strategies and implementation in the company. Amazon Web Services (AWS) is one the central commercial software that helps the organization deal with BD. The AWS offers a vast and completely integrated portfolio of the cloud computing services. These services are helpful in building, securing as well as deploying the big data applications.

AWS saves the organizations the need to procure the hardware or infrastructure maintenance and scaling. This allows the organization to focus on the resources to uncover novel insights. AWS adds new features and capabilities constantly and, hence, the organization is usually capable of leveraging the latest technologies without making lasting investment commitments. The BD analytics companies like Cloudera and Hortonworks avail the commercial software which help business store, process as well as analyze data via the Hadoop. This is an open source software system which is capable of sorting as well as handling enormous amounts of information.  

Ups’ Company Case

Background

The organization chosen for the case is the United Parcel Service Logistic Company (UPS). The United Parcel Service, Inc. is the global largest package deliver firm and a provider of the supply chain management solutions. UPS has many pieces alongside parts that are constantly in motion. It stores an enormous amount of data. UPS derives its data from sensors in its vehicles. The data stored by UPS not only monitors the day-to-day performance, but further triggered a principal redesign of the company drivers’ route structures. The initiative used by UPS was tagged ORION (On-Road Integration Optimization and Navigation).

The initiative was arguably the global biggest operation research project. The initiative depended heavily on the online map data for the reconfigurations of the pickups as well as drop-offs of the drivers in real time. The organization used expansive fleet telematics alongside advanced algorithms to collect and compute innumerable amounts of data to provide the organization drivers with the optimized routes.

The project helped UPS drivers to utilize the most optimized delivery routes with respect to the fuel, distance and time by helping UPS to develop the ORION project using BD. It helped the UPS drivers to determine the optimal manner to deliver as well as pick-up packages within the set of stops defined by the commence time, commit time, pick-ups windows as well as special clientele needs. The ORION system depended on the online map data that was UPS-customized to compute miles as well as travel time for planning most-cost effective routes.  The project had challenges and benefits relating to the Big Data Management.

 

Challenges And Benefits To Bdm

Challenges of Big Data

The ORION project embraced by UPS required extensive hardware alongside architectural provisions. UPS faced specific change-management challenges arising from the Big Data for the ORION project. The UPS had to guarantee uptake at many diverse project phases to achieve the maximum cost as well as emission reductions. This presented a challenge as each phase had its own distinct change-management challenges. In the development phases, the research and development leads had to design a technology solution which functioned better than the prevailing practice and prove to the UPS’ business leaders that it had potential (Wamba et al.  2017).

At the demonstration stage, the porotype testing alongside business-case validation had to be performed in lab and subsequently tested in the field, initially for smaller and later, for large cohorts of the UPS drivers. At the adoption phase, the challenge was operationalization as well as rolling out. The project had to convince thousands of the UPS staff to embrace the ORION integration into their daily work.

The project required a heavy investment as it required the Big Data. UPS invested heavily in ORION meant to shorten the routes of drivers thereby saving millions of fuel. UPS did not, however, disclosed the much the ORION project cost specifically but was just described as a “good-sized project” for the UPS’s $1 billion yearly technological expenditure. The Big Data required in the ORION project required over 500 staff that had to work for its deployment.

UPS also faced a challenge of having the system use the real-time data which had to wait until later versions were brought. The ORION could not anticipate bad weather, traffic as well as additional variables which could mean the diversity in slowing down the route of the driver. The presence of Big Data made it impossible to initially integrate the dynamic data in ORION (Sookhak et al. 2017).

 

Benefits of Big Data

From the ORION project, the UPS greatly saved cost of over 8.4 million gallons of the fuel. This was achieved as the UPS managed to cut 85 million miles off the day-to-day routes. The UPS estimated that saving solely a single daily mile a driver saved the organization $30 million. This implied a substantial overall dollar savings (Parikka et al. 2017). 

The ORION has helped the UPS to solve individual route in seconds. It is constantly operational in the background hence evaluating the routes prior to drivers exiting the facility. The ORION project benefits the UPS by constantly evaluating the best manner for the route to operate on the basis of the real-time information. Whereas most of American are sleeping, the ORION project is solving tens of thousands route optimization a minute.     

The ORION has saved and continue to save UPS around 100 million miles per annum. This is a decrease of ten million gallons of the consumed fuel. It further decreases the carbon dioxide emissions by around 100, 000 metric tons. The initials results have shown that miles have decreased with individual route utilizing ORION and a decrease of merely one mile a driver a day over a year saves the organization up to fifty million dollars. ORION further benefits the customers by enabling more personalized services including on peak business days (Ivanov, Tsipoulanidis and Schönberger 2017).

The UPS My Choice® service have allowed the customers to enjoy online and mobile access thereby seeing their incoming UPS  home deliveries. This enables the customers to actively choose the preferred deliveries, reroute shipments as well as delivery location and dates adjustment as needed. Presently, millions of customers have taken the advantage of the UPS My Choice® service (Holland et al. 2017). ORION technologies will endure to make feasible even increasingly personalized services, with international on the future roadmap.

 

Conclusion

Via the Big Data and Business Analytics,  the company gathers data from various sources and analyzing it thereby finding answers that enable: (i) reduce cost (ii) reduce/save time (iii) new product development and offering optimization (iv) smart decisions making. As reflected above, UPS Company has combined the BD with high-powered analytics thereby accomplishing business-associated tasks (Gwilt, Prendiville and Mitchell 2017).

The company has been able to determine the roots causes of the failures, defects and issues in the near-real time. The company has also used the BD and BA to generate coupons at the sale point on the basis of the buying habits of the customers. The company has also been able recalculate the entire risk portfolios in seconds (Asadi-Someh et al. 2017). The company has benefited from the combined BD and BA to detect fraudulent behavior prior to it effecting the organization. 

 

References

Asadi Someh, I., Wixom, B., Davern, M. and Shanks, G., 2017, January. Enablers and Mechanisms: Practices for Achieving Synergy with Business Analytics. In Proceedings of the 50th Hawaii International Conference on System Sciences.

Gwilt, I., Prendiville, A. and Mitchell, V., 2017. Making sense of Data through Service Design: opportunities and reflections.

Holland, C., Levis, J., Nuggehalli, R., Santilli, B. and Winters, J., 2017. UPS Optimizes Delivery Routes. Interfaces, 47(1), pp.8-23.

Ivanov, D., Tsipoulanidis, A. and Schönberger, J., 2017. Routing and Scheduling. In Global Supply Chain and Operations Management (pp. 389-434). Springer International Publishing.

Macedonia, C.R., Johnson, C.T. and Rajapakse, I., 2017. Advanced Research and Data Methods in Women's Health: Big Data Analytics, Adaptive Studies, and the Road Ahead. Obstetrics & Gynecology.

Members, B.D.C., 2017. The BIG Data Center: from deposition to integration to translation. Nucleic Acids Research, 45(Database issue), p.D18.

Parikka, A., Habart, E., Bernard-Salas, J., Goicoechea, J.R., Abergel, A., Pilleri, P., Dartois, E., Joblin, C., Gerin, M. and Godard, B., 2017. Spatial distribution of far-infrared rotationally excited CH+ and OH emission lines in the Orion Bar photodissociation region. Astronomy & Astrophysics, 599, p.A20.

Sookhak, M., Gani, A., Khan, M.K. and Buyya, R., 2017. Dynamic remote data auditing for securing big data storage in cloud computing. Information Sciences, 380, pp.101-116.

Wamba, S.F., Ngai, E.W., Riggins, F. and Akter, S., 2017. Transforming operations and production management using big data and business analytics: future research directions.

Xie, L., Draizen, E.J. and Bourne, P.E., 2017. Harnessing big data for systems pharmacology. Annual Review of Pharmacology and Toxicology, 57, pp.245-262.

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