Data Driven Decisions in Business: Analysis of Bangles Company Report

This report examines the application of data-driven decision-making within the context of Bangles, a UK-based jewelry company. It begins by outlining the increasing importance of data analysis, highlighting trends such as the exponential growth of big data, high expectations for analytical insights, the availability of analytical tools, and the critical need for data security. The report then details the analytical approach employed, specifically the Problem-Solved Framework, which involves business understanding, data understanding, and data preparation. Furthermore, the report explores the process of data analysis, including data cleaning techniques such as removing duplicates, fixing structural errors, and filtering unwanted outliers. The methodology and findings presented aim to provide a practical guide for leveraging data to enhance business strategies and decision-making processes within the jewelry industry.

DATA DRIVEN DECISIONS
FOR BUSINES

TABLE OF CONTENTS
 INTRODUCTION
 TRENDS SHAPING THE INCREASING IMPORTANCE OF DATA
ANALYSIS
 ANALYTICAL APPROACH
 ANALYSIS
 REFERENCES

INTRODUCTION
 Data driven decisions are the decisions which is made by a business with
the help of data analysis in the business.
 In this project the company which is selected is Bangles which is a
jewelry company based in UK.

Trends shaping the increasing importance
of Data analysis
 Some trends which were successful in shaping the data analysis increase
in companies like Bangles are,.
 Big data is everywhere :
 In the recent times the generation of data has increased a lot each day.
Currently the generated data in a day is estimated at 2.5 Exabyte's.
 High Expectations :
 But the flow of data in the organization ideal leaders have understood that
analytics tools result of the data explosion has been that many businesses
and companies have shown great urgency and excitement for it.

CONTINUE..
 Analytical tools :
 For the flow of data I delete this has understood that harnessing data
for the organization is effective by deploying multiple analytics tool.
These tools are there scattered across different parts of business.
 Security :
 Most of the companies have very high security for the data, however
data breaches it is very common in headlines. This trend increases the
data analysis of the company because with the help of these data an
organization can increase security with the help of support data driven
communication and collaboration with traditional firewall.

Analytical approach
 The approach which can be use for the data analysis is the Problem-
Solved Framework. This process is that process in which problem if not
solved it is considered as failure. This approach consists of the following
steps,
 Business Understanding :
 The first step in this process is to understand the business of the company
which is using this approach for data analytics. Bangles is a company that
deals with all different kinds of jewelry both in online and offline market.

CONTINUE..
 Data understanding :
 In this approach data is said as the raw material which is
used for building solutions. The focus of data
understanding is the estimation of cost and benefit each of
the data source.
 Data preparation :
 It is the process of analyzing, evaluating, rectifying,
altering and determining the prime data to be used for
business purposes. It helps in ensuring accuracy in the
datasets which helps in making precise decisions.

Analysis
 Data is known as the facts and information which is collected
together with the help of statistics. Data cleaning is the process
which is used for fixing incorrect and corrupted form of data. It
is found incorrect due to incorrect format, duplication, or
incomplete data. The steps which are taken by this company for
cleaning incorrect data are,
 Remove duplicate and irrelevant observation:
 For the process of cleaning data, the most important and easy
approach of cleaning is to remove the unwanted information
from the data, including the duplicate observations and the
irrelevant observations.

CONTINUE..
 Fixing structural errors:
 Structural errors are the repeated data errors which can be noticed while
transferring file. Duplicate errors, typos can be reduced with the help of data
cleaning tools. These tools analyze the records and cleans the database
which makes the datasets more accurate and easy to use.
 Filter unwanted outliers:
 Outliers can be very informative and removing them inappropriately can be
a difficult and wrong decision. Outliers are unusual values that can cause
problems and misrepresent a dataset, that’s why it is important to understand
why they occurs.

REFERENCES
 Hogg, D.W. and Foreman-Mackey, D., 2018. Data analysis recipes: Using
markov chain monte carlo. The Astrophysical Journal Supplement Series.
236(1). p.11.
 Dorie, V., and et.al.,2019. Automated versus do-it-yourself methods for
causal inference: Lessons learned from a data analysis
competition. Statistical Science. 34(1). pp.43-68.