Tanmoy answered on Jun 12 2021
MBA633 - Real World Business Analytics and Management Assessment
In the real world business analytics and management process we will consider how to implement the big data analytics tool. We will also discuss the challenges faced during implementation of the big data analytics tool in University of Putra, Malaysia and the metrics used to overcome those issues.
Big data analytics is a process which provides an organization with readymade data which will be used for analyzing and reporting purpose. It helps to reduce the cost and simplify the data by converting the raw data into actionable information. It actually allows analyzing voluminous data to useable resources which may be related to market trends, co
elations, customer taste and preferences and useful information.
Big data involves various tools and techniques which are used for processing and analysing and several processes for handling and management of the system. The various phases of big data analytics tool are data acquisition, data analysis, visualization and interpretation of the data during data management process. Therefore, it helps to obtain not only quality data but also to analyze the performance of the process. But, there are several problems encountered as the big data analytics tool performs. One of them is lack of data provenance which means loss of historical records. Hence, missing information, e
or during processing and inconsistencies can restrict the speed of storage, capturing of data and receiving useful information. Thus, we will discuss about these problems and how to mitigate those to derive e
or free data. We will discuss on an analysis conducted on the performance of big data analytics tool by big data analytics, subject matter experts in the University of Putra, Malaysia. (Hidden Brains Blog; Big Data Analytics: Challenges and Implementation; 22nd Jul 2017)
The various big data analytics implementation challenges faced while implementation of this software in their system are as follow:
1. Operational issues: Data sets are becoming more diverse and enormous. Hence it is getting difficult for the organizations to incorporate the large quantity of data in the analytical process. These results in gaps and provides the organization with inco
ect information on been ignored.
2. Shortage of professional to handle big data analytics: There is huge demand for big data analytics professionals for the post of big data scientists and analysts in the IT industry. But, big data analytics requires multi disciplinary skills and the industrial experiences. It becomes extremely difficult to find candidates to fulfil the position with such rare skills.
3. Data security and privacy concerns: Big data contains huge chunks of data. One of the biggest challenges is securing the sensitive information from hackers. Hence, the organizations are implementing various security measures such as encryption, identity and gaining control and segmentation of the data.
4. Data quality and storage management: Since the companies are increasing, there is growth and exchange of data taking place in volume. Now, these data are stored through warehouses or data-lakes. The ware houses or data-lake gathers huge chunks of information from structured as well as unstructured sources and tries to integrate both. This result in e
or, inconsistency in data, data duplication, missing records and logical inconsistency. This is known as lack of data provenance. (E Learning Industry; 7 Top Big Data Analytics challenges faced by business enterprises; By Aasish Kumar; Jun 8th 2018)
Key metric for defining the problem
)The key metrics to verify the big data analytics process are as below:
The above metric of big data analytics can be explained as follows:
A. Acquisition: The data acquisition process begins with identification of the sources of data and collecting them from data warehouses, through online actions like tweets, customer reviews, web crawlers, sensors and clicks streams, log files etc. it is captured in a timely manner and is delivered in the next phase for more processing.
B. Preparation: This process involves cleansing of data by integration of data, pre-processing, sorting out the e
ors, verification of missing data and outliers. Then after reducing the features and combining the extracted data it is transfe
ed to the next phase.
C. Analysis and modelling: In this phase the extracted data is analyzed through various data models and statistical tools. The various analytical data...