Data Insights for Business Decisions: Data Analysis Presentation

This presentation delves into the critical role of data insights in business decision-making, focusing on the type of data collected and used by organizations, distinguishing between big data and traditional market research. It examines how information is captured and stored, highlighting the use of va...Read More

DATA INSIGHTS FOR
BUSINESS DECISIONS

Introduction
 Data insight can be defined as a kind of deep understanding of an organizations or individual’s
gain by analysing data or information on a particular issue.
 It further help an organization to make effective and better decisions that can further help them to
enhance their insight, lead actions that can increase efficiency of business that can further
enhance efficiency, revenue and profit of an organization.
 This presentation will focus upon type of data collected and used by organization, orthodox
market research and ways in which it differ from big data, ways in which information is captured
and stored with key references to types of databases and management information systems,
impact of technological development on the role and use of data in the business decision making
process, ways in which information and data is used within the day to operations of
organisations, ways in which data capture can be captured and used within organization.

Type of data collected and used by
organization if it is big data and if it is first,
second- or third- party data
Type of data used in the industry Is it Big data Is it 1st, 2nd, or 3rd party data
Details of customers is one of the most
important type of data which is being
used in this industry
Yes, data or information stored about
customers is a big data and it is used to
identify type of customers, their preference
and ways in which they use organization’s
products and services.
It is a 1st party data in which data about
customers is collected and managed by
themselves. This data is collected in the form
of customer feedback, organization’s
application and website.
Non-traditional type of personal data of
customers such as photos, videos etc.
Yes, it is a kind of big data and an important
type of data which is collected by Facebook
because it directly help them in
understanding type of data which is being
preferred by their customers, posted and
shared by them.
It is third party data which is being collected
by organization for understanding type of
data that customers prefer posting and
sharing. This data is collected by Facebooks
application or website.

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Data collected by other organizations
with whom they have collaborated with
or who uses Facebook as a platform for
sponsorship
Yes it is a big data which is being used by
organization for expansion of their
business and customer segment as well.
it is a second party data which is used by
them for attracting a greater number of
customers and expand business by
collaborating with other organizations.

Description of orthodox market research and
ways in which it is different from Big Data
Orthodox market research can be defined as a kind of process which is used for evaluating feasibility of
new products or services using orthodox devices for market status, forecasting, product offering etc.
 Orthodox market research uses traditional methods or devices for understanding human behaviour a factor that
influence market trend of behaviour of customers. Whereas, Big data uses different kinds of analytical models
or software that helps in finding new information from variety of sources. It can effectively help in analysing
emerging trend by analysing current data collected by the organization.
 Some of the most common methods used for orthodox market research are interviews, focus groups,
questionnaire, survey. Other than this, today internet is also used as source of conducting orthodox market
research. Whereas big data uses, clustering, forecasting, analytics, regression analysis, hypothesis testing and
many other methods are used for big data.

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 Orthodox market research is not completely dependent upon technology or advance
modern system or software. But in order to collect data or market research, this
orthodox market research definitely use modern tools or technology (Bloom and et. al.,
2018). Whereas, big data is majorly dependent upon model and advance technology for
gathering, analysing and storing data or information. Most commonly used advance and
modern technology used by big data are cloud computing and business intelligence
tools or software’s.
 Organization’s using orthodox market research methods or tools majorly require people
with skills such as questionnaire design, time management, quantitative and
quantitative analysis, psychological understanding etc. Whereas, organizations that are
using big data require skills such as data analysis, software engineering, machine
learning, data visualization, cloud computing etc.

Explanation of ways in which information is
captured and stored with key references to types of
databases and management information systems
 Transaction processing system is one of the most commonly used systems that are
being used by organizations for capturing data or information related to transactions. It
majorly helps in capturing truncation activities during business operations of
organizations.
 This system directly captures, modify data and store it within real time organizational
systems. Mostly it stores data within cloud- based servers or databases.
 There are various kinds of information systems that stores, modify and uses this data or
information are: management information system, transaction processing and
management systems.

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 Cameras are another type of systems that helps in capturing data or information of
people in terms of pictures or videos.
 This type of data is completely stored within cloud- based servers or cloud- based
databases because this data is extremely large in size and continuously growing in
nature. Some of the organizations that make CCTV cameras have developed their own
cloud- based databases that helps them to store their client’s or customers pictures or
videos so that they can access it whenever they want it to be used or viewed.
 It is important to store this data or information in cloud- based servers because this type
of data is normally in in GB’s, or TB’s.

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 Loyalty cards it another way of capturing customers data in terms of their shopping,
preferences and buying behaviour. Loyalty cards help organization retaining their customers
and enhancing their loyalty towards organizations.
 All the data gathered by organization with the help of loyalty cards is sorted within their own
internal databases that are specially designed to stored customer information in an
appropriate and sorted manner.
 Mostly of the organizations like Facebook uses customer information system for storing,
capturing and analysing customers data or information sorted within database in an
appropriate manner.

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 Biometric or facial data is another type of data which is collected by Facebook. This
data is captured by Facebook as a login feature of its application and website.
 This data is stored in biometric and facial recognition database systems in order to be
sorted and used in an appropriate manner.
 This data is mostly used by facial recognition information system so that they can
easily recognize their customers and provide them with an advanced version or feature
of their application.

impact of technology development on the role and use of
data in business decision making process of technology
sector
 Technology development means any kind of discovery, invention, composition,
technology, advancement, enhancement of process. It include any kind of development
protectable by copyright, patent and production under specific regulation. This may
have significant impact on the labour market. Where share of different information in
regards of business growth and development.
 By using big data, the primary of companies to offer a better improvement in customer
service. This can help to increase the overall profitability, enhance customer experience
in context of primary goals.

Strategic importance to the Facebook within
technology sector
 Tracking or monitoring cookies- Generally, Facebook can easily track its user’s data
across web with the help of tracking cookies. If users can be logged into Facebook,
where it can easily accessible the tracking activities of client.
 Facial recognition- one of the latest investment of Facebook in strategic plan in term of
facial recognition. In context of technology sector, Facial recognition is better approach
in order to find client profile with image. Sometimes, it is also sharing from one person
to another.
 Analysing the user’s behaviour- it has been predicted the data accurately on the range
of personal attributes that are highly sensitive (Rejikumar, Aswathy Asokan and
Sreedharan, 2020). It just analysing user’s Facebook and conduct a research on focus
on the different design patterns. This can very accurately predict satisfaction of
individual people, emotional stability, gender and age.

improving data capture and use in technology
sector
 By using graph API- this is the best way to improve data capture process in which
represent the idea of social graph. In order to get information, but it is important to
interact with graph by using HTTP-based API. It is one of the data visualization
approach in order to represent the capturing data in the form of hierarchical elements.
Initially, the first node is that when represent the individual object with unique ID.
Second edge can be established a connection between one node to another. By using
graph API, it will be able get valuable information or data captured from Facebook.
 Getting the data of latest posts IDs- this is all about the way in which get data from
different post. It suppose as digital markets, it is able to check about trending in order
to increase high click rates (Merendino and et.al., 2018). Therefore, Facebook can
easily track data about latest posts. This will help to improve the speed, performance of
data capturing.

References
 Alharthi, A., Krotov, V. and Bowman, M., 2017. Addressing barriers to big data. Business
Horizons. 60(3). pp.285-292.
 Venkatesh, K., and et. al., 2019. Challenges and Research Disputes and Tools in Big Data
Analytics. International Journal of Engineering and Advanced Technology. 6. pp.1949-1952.
 Ionescu, L., 2019. Big data, blockchain, and artificial intelligence in cloud-based accounting
information systems. Analysis and Metaphysics. (18). pp.44-49.
 Paas, L., 2019. Marketing research education in the Big Data era. International Journal of Market
Research. 61(3). pp.233-235.
 Bloom, G., and et. al., 2018, June. Design patterns for the industrial Internet of Things. In 2018 14th
IEEE International Workshop on Factory Communication Systems (WFCS) (pp. 1-10). IEEE.
 Groomer, S.M. and Murthy, U.S., 2018. Continuous Auditing of Database Applications: An
Embedded Audit Module Approach1. In Continuous auditing. Emerald Publishing Limited.