Pearson BTEC
Level 3 Extended
Diploma in Information
Technology
BIG DATA ANALYTICS –
UNIT 10 ASSIGNMENT
A
Learning Aim A: Explore the use of
cloud technologies and tools in
organisations
ASSESSOR:
ISSUE DATE: 27TH FEBRUARY 2024
HAND IN DEADLINE: 18TH MARCH 2024
,NAME: LAYODE
STUDENT NO:
Table of Contents
Table of Contents.............................................................................................2
Introduction.....................................................................................................3
P1.....................................................................................................................3
Purpose of big data and business analytics..................................................4
Five v’s of big data........................................................................................5
Reasons why organisations analyse data........................................................6
Strategy planning..........................................................................................6
Enhance decision making.............................................................................8
Improving productivity..................................................................................9
Product and/or service benchmarking..........................................................9
M1..................................................................................................................12
Barriers of using big data and data analytics..............................................12
Data privacy and security concerns............................................................12
Budgeting.................................................................................................13
Quality of data.........................................................................................13
Staff knowledge and skills........................................................................13
Legislative, Ethical and security issues.......................................................14
Consumer privacy....................................................................................14
Data security............................................................................................14
Data encryption.......................................................................................15
Data loss..................................................................................................16
Types of data..............................................................................................17
Types of storage data.................................................................................20
, Safe procedures for data storage.............................................................22
Adequate upkeep of its systems.................................................................22
Data warehouse, Data Marts and Data Lakes.............................................25
Advantages of a data Warehouse.........................................................26
What is not a data warehouse...............................................................26
Data Mart.................................................................................................27
Data Lakes...............................................................................................28
D1..................................................................................................................30
Challenges of big data................................................................................30
Analyzing Big Data......................................................................................32
Conclusion.....................................................................................................34
Introduction
I have been asked by the director of an educational charity that I am
currently doing my internship to investigate how big data and data analytics
might be used to improve the way the business targets their effort so they
can be effective. In this report I will be exploring how data analytics can
enhance our targeting efforts and overall impact. This report will provide a
brief evaluation of why organizations use data analytics, the challenges they
face, and the considerations involved and draw upon insights from other
similar organizations, as well as supply a comparative analysis.
P1
Big data analytics is a complex method that helps businesses make wise
business decisions by using data to reveal hidden patterns, correlations,
market trends, and customer preferences. What-if analysis, statistical
algorithms, predictive models, and sophisticated analytics approaches are all
included. Millions of patient records, medical claims, clinical outcomes, and
, care management information, for example, are gathered, combined,
processed, and analysed in the healthcare sector. Predictive analytics,
accounting, and other processes all make use of this data. Even with the
difficulties related to data accessibility, quality, and kind, big data analytics
is still a useful tool for companies because of its many advantages.
Purpose of big data and business analytics
In today's world, big data and business analytics are essential resources for
organisations. By examining huge quantities of data from many sources,
they facilitate well-informed decision-making and offer insightful information
for marketing campaigns, product development, and strategic choices. By
finding errors in workflows and processes, cutting expenses, and raising
productivity, they also increase productivity and efficiency. Businesses may
better understand their consumers' needs, tastes, and behaviours by using
big data and analytics, which also improves the customer experience.
Organisations may offer targeted marketing efforts, personalise products and
services, and create outstanding customer experiences by utilising this
knowledge.
Risk management and fraud detection are also essential. Businesses may
protect their assets and reputation by spotting any fraud and security
breaches early on by analysing trends and abnormalities in data. Analysing
consumer behaviour, rival performance, and market trends yields
competitive advantage and significant insights for strategic planning and
strategy adaptation. Big data analytics powers innovation and product
development by revealing new trends, customer preferences, and unmet
needs. This helps companies create new goods and services that satisfy
customer demand and outperform rivals. Big data analytics can also be used
to improve the supply chain by giving real-time visibility into logistics,
demand forecasting, supplier performance, and inventory levels. Key
indicators are monitored, and relevant data is analysed to guarantee
compliance with industry regulations.
Level 3 Extended
Diploma in Information
Technology
BIG DATA ANALYTICS –
UNIT 10 ASSIGNMENT
A
Learning Aim A: Explore the use of
cloud technologies and tools in
organisations
ASSESSOR:
ISSUE DATE: 27TH FEBRUARY 2024
HAND IN DEADLINE: 18TH MARCH 2024
,NAME: LAYODE
STUDENT NO:
Table of Contents
Table of Contents.............................................................................................2
Introduction.....................................................................................................3
P1.....................................................................................................................3
Purpose of big data and business analytics..................................................4
Five v’s of big data........................................................................................5
Reasons why organisations analyse data........................................................6
Strategy planning..........................................................................................6
Enhance decision making.............................................................................8
Improving productivity..................................................................................9
Product and/or service benchmarking..........................................................9
M1..................................................................................................................12
Barriers of using big data and data analytics..............................................12
Data privacy and security concerns............................................................12
Budgeting.................................................................................................13
Quality of data.........................................................................................13
Staff knowledge and skills........................................................................13
Legislative, Ethical and security issues.......................................................14
Consumer privacy....................................................................................14
Data security............................................................................................14
Data encryption.......................................................................................15
Data loss..................................................................................................16
Types of data..............................................................................................17
Types of storage data.................................................................................20
, Safe procedures for data storage.............................................................22
Adequate upkeep of its systems.................................................................22
Data warehouse, Data Marts and Data Lakes.............................................25
Advantages of a data Warehouse.........................................................26
What is not a data warehouse...............................................................26
Data Mart.................................................................................................27
Data Lakes...............................................................................................28
D1..................................................................................................................30
Challenges of big data................................................................................30
Analyzing Big Data......................................................................................32
Conclusion.....................................................................................................34
Introduction
I have been asked by the director of an educational charity that I am
currently doing my internship to investigate how big data and data analytics
might be used to improve the way the business targets their effort so they
can be effective. In this report I will be exploring how data analytics can
enhance our targeting efforts and overall impact. This report will provide a
brief evaluation of why organizations use data analytics, the challenges they
face, and the considerations involved and draw upon insights from other
similar organizations, as well as supply a comparative analysis.
P1
Big data analytics is a complex method that helps businesses make wise
business decisions by using data to reveal hidden patterns, correlations,
market trends, and customer preferences. What-if analysis, statistical
algorithms, predictive models, and sophisticated analytics approaches are all
included. Millions of patient records, medical claims, clinical outcomes, and
, care management information, for example, are gathered, combined,
processed, and analysed in the healthcare sector. Predictive analytics,
accounting, and other processes all make use of this data. Even with the
difficulties related to data accessibility, quality, and kind, big data analytics
is still a useful tool for companies because of its many advantages.
Purpose of big data and business analytics
In today's world, big data and business analytics are essential resources for
organisations. By examining huge quantities of data from many sources,
they facilitate well-informed decision-making and offer insightful information
for marketing campaigns, product development, and strategic choices. By
finding errors in workflows and processes, cutting expenses, and raising
productivity, they also increase productivity and efficiency. Businesses may
better understand their consumers' needs, tastes, and behaviours by using
big data and analytics, which also improves the customer experience.
Organisations may offer targeted marketing efforts, personalise products and
services, and create outstanding customer experiences by utilising this
knowledge.
Risk management and fraud detection are also essential. Businesses may
protect their assets and reputation by spotting any fraud and security
breaches early on by analysing trends and abnormalities in data. Analysing
consumer behaviour, rival performance, and market trends yields
competitive advantage and significant insights for strategic planning and
strategy adaptation. Big data analytics powers innovation and product
development by revealing new trends, customer preferences, and unmet
needs. This helps companies create new goods and services that satisfy
customer demand and outperform rivals. Big data analytics can also be used
to improve the supply chain by giving real-time visibility into logistics,
demand forecasting, supplier performance, and inventory levels. Key
indicators are monitored, and relevant data is analysed to guarantee
compliance with industry regulations.
