Data Analysis and Solutions for TCS Challenges: A Report

This report analyzes challenges faced by Tata Consultancy Services (TCS), a leading IT services company, and provides data-driven solutions. The report addresses key issues such as low accuracy and false positives, proposing the use of machine learning techniques like confusion matrices to improve classification accuracy. It also tackles deficient consulting operations by suggesting powerful problem-solving techniques and focusing on key drivers. The report highlights the importance of effective communication during recessions and discusses the challenges and solutions related to artificial intelligence (AI) integration. Furthermore, it explores how data analysis and machine learning can be applied to overcome market competition, reduce churn rates, and improve service quality. The solutions are supported by relevant references to academic research and industry insights.

Running head: SOLVING CHALLENGES IN TCS USING DATA ANALYSIS
1
Name
Course
Institution
Date

SOLVING CHALLENGES IN TCS USING DATA ANALYSIS
2
Introduction
Different Companies face different problems from managerial, accounting, decision making, etc.
Business data analysis can be used as a solution to succumb to this problem. Data analysis has been
adopted by many businesses to help their business grow and also to survive in the competitive market.
In this paper, we are going to draw some solutions from the problems faced by the business.
Data collection
The data that is used for the analysis is obtained from the business’ database. Data on the current sales,
profits, number of employees, customer feedback, churn rate, etc. are very crucial during the data
analysis.
Solutions
i) Low accuracy and False positives
Low accuracy and False positive are solved using machine learning. A technique known as a confusion
matrix (used with binary data, i.e., positive and negative) is deployed to solve this issue. Various
measures such as precision, sensitivity, error rate, accuracy, etc. are derived from it. The next step
undertaken after constructing the confusion matrix is to determine the accuracy of the classification.
Accuracy is obtained by calculating the number of all accurate prediction divided by the total number of
the data set (Saito and Rehmsmeier, 2015). This will method will prevent low accuracy and false
positives.
ii) Deficient consulting operations
Here, powerful problem-solving techniques are used. During the analysis, we will focus on the time,
energy on the key drivers and the big wins. One of the solution to be used is considering cutting the cost
of two or three key drivers of the Company and look at their impact on the company. One can also use
the 80-20 rule which is also known as the secret of achieving more with less. The distribution of this
technique as the first solution. This is how it works: 80 % of the profit are obtained from 20 % of the
clients, and 80 % of the Company’s cost comes from 20 % of its operation (Dunford, Su & Tamang,
2014). Another way is to collect, and analyzing the hard data from the Company.
iii) Communication during Recession
During an economic downturn in business, it's very important to communicate with the staffs. Recession
reduces the performances of the employees. To avoid this, the boss needs to create a right channel of
delivering messages it the staffs. These channels include newsletters, desktop alerts, screensavers and
desktop tickers (Moreno et al. 2010).
iv) Artificial Intelligence
Even though many businesses embrace technology, it has somehow created problems in business. AI
requires more compute power since it carries out a lot of calculations and does a lot of processing. To

SOLVING CHALLENGES IN TCS USING DATA ANALYSIS
3
solve this, the new generation of computers need to be installed. Also, people power has been a
problem, and therefore, the companies that are embracing technology especially AI should train more
people to gain the effectiveness of AI.
v) Powerful Competition
You need to apply data analysis to beat the market competition. Using machine learning techniques
such as decision tree, a company can reduce the churn rate and improve on its services. A company can
also frequently evaluate its performance to keep its sales on track (Brown, Chui & Manyika, 2011).

SOLVING CHALLENGES IN TCS USING DATA ANALYSIS
4
References
Brown, B., Chui, M., & Manyika, J. (2011). Are you ready for the era of ‘big data’. McKinsey Quarterly,
4(1), 24-35. Available from:
http://www.euincoop.eu/documents/euclid_ws_bang/Are_you_ready_for_the_Era_of_Big_Dat
a%20.pdf
Dunford, R., Su, Q., & Tamang, E. (2014). The pareto principle. The Plymouth Student Scientist, 7(1), 140-
148. Available from:
https://www.researchgate.net/publication/304356999_The_Pareto_principle_and_a_hazard_m
odel_as_tools_for_appropriate_scheduled_maintenance_in_a_manufacturing_firm
Moreno, Á., Verhoeven, P., Tench, R., & Zerfass, A. (2010). European Communication Monitor 2009. An
institutionalized view of how public relations and communication management professionals
face the economic and media crises in Europe. Public Relations Review, 36(2), 97-104. Available
from: https://linkinghub.elsevier.com/retrieve/pii/S0363811110000238
Saito, T., & Rehmsmeier, M. (2015). The precision-recall plot is more informative than the ROC plot
when evaluating binary classifiers on imbalanced datasets. PloS one, 10(3), e0118432. Available
from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0118432