Business Intelligence in the Fashion Industry

This report discusses the role of business intelligence in the fashion industry and its impact on decision making. It explores the concept of data quality management and its processes.

Business Intelligence in the
Fashion Industry

Table of Contents
INTRODUCTION...........................................................................................................................3
MAIN BODY...................................................................................................................................3
Data quality management.......................................................................................................3
Process of data quality management......................................................................................4
CONCLUSION................................................................................................................................5
REFERENCES................................................................................................................................6

INTRODUCTION
Business intelligence is sum of business processes, architecture and technologies which
are used to convert raw data into significant information which lead the business to earn high
profits within their business operations. Business intelligence is having significant impact on
strategic and operational decision making. Business intelligence uses past data instead of
assumptions and forecasting. In current businesses business intelligence is the emerging trend as
it used so as to prepare and summarise large reports within fraction of seconds. This report
includes emerging trends of business intelligence and their uses and disadvantages over fashion
industry so as to enhance industry capture (Choi and Shen, 2016).
MAIN BODY
Since the year of 1990 business intelligence has emerged within technical and IT centric
industry. In recent times this concept is used by all the industries whether it is retail or food
processing industry, Pharmaceutical or fashion industry. The business intelligence is used for the
purpose to demonstrate analytical findings within reports, graphs and chart so as to give better
understanding of business state. This term is used within businesses so as to get quick and easy
foresight of business’s current position by analysing past data and records. If discussion is about
latest trends in business intelligence then there are many trends such as data quality management,
data discovery, AI machine learning, predictive analytics reporting augmented analytics, data
automation and many more. These practices are used by businesses so as to customise their data
solution as per their need and to get cutting edge technology which is made for their end
customers. In the fashion industry many inventions are introduced through which businesses are
creating values over the time. For example Louis Vuitton’s is known for their innovation and
creativity whereas H&M is known for their retail innovation and Levi’s is known for their
product customisation.
In current scenario fashion designer are innovators, entrepreneur as they are able to
manage so many aspects at once. They recreate things in such a manner that it enhances their
operational and personal competence within marketplace. In today’s fast changing world fashion
industry is a market where new technologies are accepted vastly so as to provide benefits to tehir

customers. In current technological changes fashion industry has emerged with prominent
changes such as modification in fibre, textile and fashion industry which lead them to bring
revolution within market place and get a distinct market edge. The major criteria for success of
innovation within fashion industry are to make their customer aware about these emerging trends
so that they initiate their fashion leadership in order to influence their future buyer. In future
trends of garment and fashion industry may include bio- monitoring clothing, biomedical
garments for ill children, integrated electronic textile etc. In order to manage this Data quality
management is having their prominent role so as to examine market need and enhancements.
Data quality management
Data quality management is the sum of right business processes, right people and right
technologies within businesses so as to achieve common business goals of enhancing data
quality which is an important aspect within business organisation. For the businesses of fashion
industry it is required that collection of data is from authentic sources so as to get effective and
clear results in which need of their potential customers can be easily identified so as to satisfy
them with any new business idea or modification in existing business idea. Data quality
management involves all the processes from collection of data to implementation of emerged
data procedures. It can be said that quality of data is very critical as this helps the company to
determine appropriate insights and data. In this digital age data quality management is having so
many benefits such as this helps the business in taking more effective decisions so as to target
their audience in a better way (Daraio and et. al., 2016). Furthermore businesses do have high
profitability by using DQM as their tool of data collection as they are able to implement data in
order to get competitive edge within marketplace. Quality of data can be measured with the help
of following parameters:
 Accuracy: Accuracy means credibility of the data and its sources. It is to be checked
properly that collected data has been collected from authenticated sources or not. Data
accuracy is helpful in fashion industry so as to get appropriate information for future use
and development.
 Completeness: It should be ensured that collected data is complete so that to avoid any
gap in between. At the time of collecting data this should be kept in mind that all the
aspects are taken which is material for business.

 Relevancy: Further collected data must have relevancy to the company and its business.
As in context of fashion industry relevancy refers to need and liking of customer about
fashion. So it should be included while collecting data from past records (Gao, Xie and
Tao, 2016).
 Timeliness: This means occurrence of that particular changes that means the gathered
data has been updated with prominent changes and records so as to maintain its validity.
As in context of fashion industry this industry is having most frequent changes due to
changes in market demand and customer preferences. So within this context data should
be latest so as to determine appropriate reports and results in an effective way.
 Validity: This means the collected data is matching with prescribed rules so as to avoid
any overlapping of data. In fashion industry it is possible that data may have various
forms but at the time of recording they should be at ground level so as to match
prescribed rules.
Process of data quality management
In order to manage data and maintain its quality so as to determine effective and profit
generating results, several steps are required to be followed which are explained as under:
1) Profile the data: Under this step action plan is made so as to decide that what type of
data is required and for what purpose it is required. For instance in fashion industry if a
company wants to make clothes which are made of a fabric that is rare in market and
super expensive. So here data will be collected about buying habits of customer so as to
take out appropriate results.
2) Establish metrics and define targets: Metrics are used so as to compare and assess
performance with other company of same business. So in fashion industry there are many
competitors of a company as this industry is wide. So as to implement DQM one is
required to compare their performance with rivals so as to take out effective data for
processing.
3) Design and implement data quality rules: Under these step rules of quality within data
is decided. As context in fashion industry for instance a company is willing to introduce
their new range of clothes which is specifically for winters then rule for data collection
can be the area where these clothes are used (Silvola and et. al., 2016).

4) Integrate DQ rules in DI processes: This step is all about entering those rules in system
so as to initiate the process of data management for making further report and result.
Under this step rules of data quality will be set within data integration system so that to
start process of data management.
5) Review exceptions: Under this step exception in data collection is identifies so as to
refine the report. As in context of fashion industry if a company is planning to make
denim jackets then their sale will be high at those places where winters are a season but
exception is some people likes to wear jacket even in moderate season (Stecyk, 2018).
6) Monitor data quality versus target: In the last step monitoring of data is done so as to
take out those results for which this whole process is taking place. Suppose a company
within fashion industry is making jewellery and now they are planning to diversify their
business to shoes manufacturer. In this they have expectation that they will not be
succeeding in this target market as they have so many huge competitors. But at the same
time when data is collected by following all the steps they found something else.
So in this way the whole process of data quality management takes place which is having
prominent role within businesses and their success.
CONCLUSION
From the above discussion it can be concluded that business intelligence is having
prominent role within era of current businesses. Business intelligence is beneficial to be adopted
so as to avoid gaps in poor quality data and adopt those regulations which are adopted across the
worldwide. Further data quality management is adopted so as to get prominent quality data for
further processing (Todeschini and et. al., 2017) . This helps the business to remain up to date
with latest trends of business intelligence so as to get market edge.

REFERENCES
Books and journals
Choi, T.M. and Shen, B., 2016, July. A system of systems framework for sustainable fashion
supply chain management in the big data era. In 2016 IEEE 14th International
Conference on Industrial Informatics (INDIN) (pp. 902-908). IEEE.
Daraio and et. al.,2016. The advantages of an Ontology-Based Data Management approach:
openness, interoperability and data quality. Scientometrics. 108(1). pp.441-455.
Gao, J., Xie, C. and Tao, C., 2016, March. Big Data Validation and Quality Assurance--Issuses,
Challenges, and Needs. In 2016 IEEE symposium on service-oriented system
engineering (SOSE) (pp. 433-441). IEEE.
Silvola and et.al., 2016. Data quality assessment and improvement. International Journal of
Business Information Systems. 22(1). pp.62-81.
Stecyk, A., 2018. A Development of Business Intelligence Systems. European Journal of
Service Management. 28(1, 4/2018). pp.305-311.
Todeschini and et. al., 2017. Innovative and sustainable business models in the fashion industry:
Entrepreneurial drivers, opportunities, and challenges. Business Horizons. 60(6).
pp.759-770.