Running Head: DATA ANALYTICS LIFECYCLE 1
BIG DATA ANALYTICS 2
BIG DATA ANALYTICS
Surya Teja Putta
University of the Cumberlands
Big Data and Analytics
This study reviews the field of big data analytics which is a multidisciplinary field which applies scientific techniques, processes, algorithm, and system to get knowledge and trends from a given set of data. It as well involves applying statistics, data analysis, machine learning, and other related techniques so as to understand a given set of data. The study concentrates on framing a business analytics problem which is one of the crucial parts of the data analytics lifecycle. On the issue of framing the problems, the study elaborates on the significance of correctly framing the analytic problems since it is a very crucial part in data science. Additionally, the study gives a case study on the issue of framing questions at ABC incorporation where the organization is planning on shutting down the fiber internet supply to willful defaulters.
Big Data Analytics
Big data analytics is a multidisciplinary field which applies sine6tific techniques, processes, algorithm, and system to get knowledge and trends from a given set of data. It involves applying statistics, data analysis, machine learning, and other related techniques so as to understand a given set of data. Framing a business analytics problem is one of the crucial parts of the data analytics lifecycle.
A lot of study has been carried out in the field of big data analytics. In 2001 Laney classified data using the 3Vs model, which composed of volume, velocity and variety. He also outlined the steps of data analytics including the framing of analytics problems. Sagroglu and Sinanc (2013) also did a wide research on big data research and the framing of the analytics problems where they concentrated on the security issues. Additionally, Rani (2016) also insisted on the need of proper framing of business analytic problem in data science. However, though several studies have been carried out before, there still need to carry more research on the business analytics problems as a part of big data analytic technique.
For the success of any project in an organization framing the problem is very crucial. This process involves starting the analytics problem to be solved. It involves noting down the problem statement and sharing it with the stakeholders (LaValle, 2011). In this case, different stakeholders understand the problem differently and give different views and solutions. Due to this, it is important to state the analytic problem as well as why and to whom it is important. Basically, the team needs to precisely elaborate on the situation and its main challenges (EMC Education services, 2015).
When stating the problem, it is essential to identify the main objective of the problem, and also point what is supposed to be done to attain the needs. The success and objectives of the project should be prioritized. One has to assess what the team is trying to achieve with the given project and the expectations of the concerned group. This should happen before the problem is documented and shared with the group members. The problem should be adjusted so as to fit in the sponsor's objectives. Additionally, a good framed analytics problem is that it establishes failure criteria. This is crucial since many people understating a project tend to think only on the success criteria and what the condition will look like when the associates are successful. In this case, they assume that everything will run as planned which is not possible since it is not possible to plan for everything that will happen during the project (EMC Education services, 2015). Additionally, framing a business analytics problem is important since it sets criteria for both success and failure, this assists the participants to shy away from unproductive efforts and remain aligned with the project sponsors.
Poorly framed business analytics problems may bring a lot of complications in an organization (LaValle, 2011). My organization of choice is the ABC Corporation which deals with telecommunication services. An example of a poorly framed problem in this organization is the case where the ABC incorporation is planning on shutting down the fiber internet supply to wilful defaulters. I.e. the clients who have the means to pay but they have failed to pay the bill for some time. This implies that a good portion of the clients should not be considered for this shutdown operation since they may be under the threshold income limit which qualifies them for delay or deferred payments.
Apparently, ABC incorporation wants to keep the cost of this operation. Shutting this connection may incur several costs such as; operational cost, lost revenue, seasonality factor cost of operations and possible litigation cost. Thus, in such case, the organization have to formulate an optimization problem subject to the constraints of the organization. The main question is whether the shutoff should be carried out every month, given the available constraints. 0ne consideration would be that a portion of the capacity is used up by the travel, thus the shutoff may be scheduled in a way that boosts the number of them that can be performed. Additionally, not every shutoff is equal; they should refrain from some of them. Since if the fiber is left connected, the consumers will be likely to pay the bill eventually. The organization will have a hard time identifying those which will be disconnected and those which will not be disconnected and deciding on among those to be disconnected which should be prioritized. In such a case the organization will have to be very keen when framing the problem since if poorly framed, the organization will incur a lot of loses.
In conclusion, big data analytics is very crucial part in the field of information technology. It helps the organization to gain insights on the available data and assist in making organizational strategic decisions. Framing business analytics problems is one of the important elements of big data analytics which every organization should embrace and take it seriously since it shapes the organization towards its goals thus enabling the organization to attain its objectives.
EMC Education Services. (2015). Data Analytics and Lifecycle, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing, and Presenting Data (pp. 26-60). Canada: Wiley
LaValle S, Lesser E, Shockley R, Hopkins MS, Kruschwitz N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Manag Rev. 52, 20-32.
Laney, D. (2001). 3-D data management: Controlling data volume, velocity and variety. META Group Research Note, 6(70).
Rani, B. R. (2016, March 9–11). Big Data and Academic Libraries. In International conference on Big Data and knowledge discovery. Indian Statistical Institute.
Sagiroglu, S., & Sinanc, D. (2013). Big Data: A review. In IEEE international conference on CTS.