Data Analysis for Big-Data Management and McKinsey Case Study

Running head: DATA ANALYSIS
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Data analysis
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DATA ANALYSIS
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Data analysis
Big-data management revolution case study
The data-driven companies entirely rely on the big data in their decision making as they
can predict, measure, and hence get more knowledge about their business performance, which
aids them to translate the business to a better performance. On the other hand, data analytics
relies on the experience or knowledge of the highly skilled managers or executives in driving
decision making processes whereby these managers revert to the big data in cases where their
knowledge and experience does not agree to the evidence provided by the big data. According to
(Sheriff, 2018, pp.79-91) the big data will intelligently improve the business performance, for
instance, in an online retail bookshop, the big data will provide information about the books sold
most, the least sales, the most viewed books, and even provide the basis of creating an algorithm
that can track the customer’s choices and preferences. Such information could help the
executives to stock the most sold items and even can predict the next purchases of the customers,
which can greatly improve the business performance.
However, in order to create a new culture of decision-making, some managerial
challenges should be overcome (Janssen et al., 2017, pp.338-345). Leadership in an organization
is the key index, as leaders need to set clear goals, provide suitable human insights to the
performance and drive their employees and stakeholders towards achieving the set goals and
objects. Moreover, good leaders will identify opportunities; understand the market dynamics, and
articulate visions through handling of their customers, employees, and the shareholders. Another
challenge is the talent management. With the existence of big data, more efficiency can be
achieved if the personnel available is highly skilled in the utilization of these large pieces of

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information available, which would lead to better decision-making processes. In addition, the
technology available in storing this big data will greatly promote decision-making process as the
retrieval of only relevant and useful information is fast compared to traditional means of big data
storage.
McKinsey case study
According to McKinsey case study, the issues to be investigated in assessing the
condition of the current system in Loravia School are entirely centered on the quality and
quantity of the education available and how it affects the performance of the education system in
the country in relation to other well-performing education systems in well-established economies
in Eastern Europe. However, from the given chart, it is observed that the number of students
variation reflect the geographical distribution of the populations of the given countries. The data
from the chart indicates that the ratio of teachers/students per student funding are vital in
improving the quality of education thus the teacher quality and curriculum should be investigated
(O'Cadiz, 2018).
The reduction in the number of schools would imply that nearly 38% of schools be closed
which would be probably an unrealistic move as it would lead to the rise of other problems such
as poor attendance and shortage of staff since the students and the teachers would have to cover
long distances in order to get to school and also cause higher financial budgets. Based on the
case study, the assessment should also be extended to post-secondary level to assess the
transformation of the students and their performance even on their competence in the
employment fields and market.