Unit 10 - Big Data and Business Analytics
Investigation of Big Data and Business Analytics for Organisations
Introduction
I started an internship at an educational charity. The director of the charity is interested on how
big data and analytics might be used by the organisation to improve the way they target their
efforts so that they have can be more efficient when they work.
I will be authoring an evaluative report for the director evaluating how the use of business
analytics will benefit the organisation by drawing, bringing, and comparing from other
organisations.
There will be a total of nine points that I will be talking about. These points are:
The reasons why organisations analyse data, including strategic planning, improving
productivity and product and/or service benchmarking
General challenges of analysing data including costs, skills required and security issues.
The ethical and legislate factors that govern the gathering, access, and storage of data
Types of data and sources available including qualitative, quantitative, internal, and
external data
Storing data including structured and unstructured data, system-based factors, and
security.
Data warehouses and Data marts
Analysing big data and the challenges involved including the skills and technology
needed.
The several types of business analytics (descriptive, diagnostic, predictive, prescriptive)
The benefits and challenges to organisations that may arise from the collection, storage,
and analysis of organisational data, with reference to big data
,Reasons why organisations analyse data, including strategic planning,
improving productivity and product and/or service benchmarking
There are many reasons that organisations analyse data as it helps organisations and businesses
to optimize their business performance and increase the value of their business.
Strategic planning
Strategic planning is how an organisation set goals after analysing all the data that they have
acquired and plan to achieve them.
Analysing an organisations or businesses data can help to identify Certain things such as trends
and patterns which then the business and organisation can use this data to make more strategic
decisions which could then increase the businesses revenue. Analysing this data can also help
companies to be more strategic when selling goods or services that they provide.
Analysing data is also important because companies and organisations also collect data on their
customers. This is important because analysing customer data helps to figure out what their
customers likes and what they purchase the most from the company. This then helps
organisations to figure out what their consumer market is like and can use that information to
target their customers by advertising the products that consumers by most. They can advertise
the products that they have found from the data or make the products cheaper when signing
up to a campaign or loyalty card. Also, by analysing data about trends companies can also use
this to their advantage by providing seasonal items so that more customers can be intrigued by
the produce. So, by companies and organisations analysing data they can then increase their
revenue and gain more customers so that is one reason analysing data is important for
businesses to satisfy existing customers and then potentially gain new customers along the
way.
Businesses also need to analyse data is that most of the data that they collect becomes
redundant meaning that most the data is useless for the business. This is another reason
businesses analyse data so that they can see what data useful and what data is useless. This is
important because by identifying which data is useful and which data is useless businesses and
organisations can then use that data to cut costs, enhance their business performance and
identify potential risks to the company. Analysing data will also help to manage the data more
efficiently as there will be massive amounts of data so analysing it will help to manage and
categorize it making it easier to see what data important and what data is not important as said
before.
Improving departmental productivity
Improving departmental productivity is to improve the efficiency and speed of accessing and
analysing the data in certain departments within an organisation.
, Analysing the data can improve departmental productivity within an organisation or business.
Improving departmental productivity means to improve finance department, the marketing and
sales department, information and technology and the operational and supply chain
management.
Finance department
Analysing data can improve the financial department of a business or organisation. One way
analysing data can help with a company's finances is analysing customers data in real-time to
improve their experience when they purchase a good or service from the organisation or
business and then try to build and maintain loyalty from the customers by increasing their
experience of the organisation. Also analysing large amount of customer data will help to create
new personalized products and services to then target specific individual customers which is
also a strategy as said in the strategic planning section, but this also helps a business's finances
as it will then drive in sales which then lead to increase in revenue and profit for the business.
Also, by analysing data from the financial department the organisation will be able to then be
able to improve its risk assessment after collecting and analysing the data which can help
change the organisations environment and make it better for employees. Also, by analysing
finance departments data it can help to understand customers deeper and will help the
organisations heads of departments to make informed decisions which will then promote
better business outcomes. This can then help to decide and predict the amount of cashflow that
the business will gain increasing the value of the organisation by analysing the previous cash
flows and revenue which helps predict future cash flow.
Marketing and sales department
Another department that is improved after analysing data is the marketing and sales
department. This is because by analysing the data received from the marketing and sales
department it can help when deciding on decisions such as who to market to, where to market
to and what platform to. Data from marketing and sales department also show if that the
quality of marketing is good or bad which is shown if the business gains more customers or not
so by analysing the data from the marketing and sales department we can tell if the marketing
is effective or not.
Information and technology
Information and technology are an important department as it is used to be efficient when
collecting and analysing data as technology helps to sort out data. Information and technology
is key because for example if the data is collected on a paper based system it can then
important data can be lost and it will be a very bad thing if that happens because if the data is
lost then organisations and companies won't be able to analyse it properly and will then lost
out on huge amounts of money as the data that could have been analysed and then used to
Investigation of Big Data and Business Analytics for Organisations
Introduction
I started an internship at an educational charity. The director of the charity is interested on how
big data and analytics might be used by the organisation to improve the way they target their
efforts so that they have can be more efficient when they work.
I will be authoring an evaluative report for the director evaluating how the use of business
analytics will benefit the organisation by drawing, bringing, and comparing from other
organisations.
There will be a total of nine points that I will be talking about. These points are:
The reasons why organisations analyse data, including strategic planning, improving
productivity and product and/or service benchmarking
General challenges of analysing data including costs, skills required and security issues.
The ethical and legislate factors that govern the gathering, access, and storage of data
Types of data and sources available including qualitative, quantitative, internal, and
external data
Storing data including structured and unstructured data, system-based factors, and
security.
Data warehouses and Data marts
Analysing big data and the challenges involved including the skills and technology
needed.
The several types of business analytics (descriptive, diagnostic, predictive, prescriptive)
The benefits and challenges to organisations that may arise from the collection, storage,
and analysis of organisational data, with reference to big data
,Reasons why organisations analyse data, including strategic planning,
improving productivity and product and/or service benchmarking
There are many reasons that organisations analyse data as it helps organisations and businesses
to optimize their business performance and increase the value of their business.
Strategic planning
Strategic planning is how an organisation set goals after analysing all the data that they have
acquired and plan to achieve them.
Analysing an organisations or businesses data can help to identify Certain things such as trends
and patterns which then the business and organisation can use this data to make more strategic
decisions which could then increase the businesses revenue. Analysing this data can also help
companies to be more strategic when selling goods or services that they provide.
Analysing data is also important because companies and organisations also collect data on their
customers. This is important because analysing customer data helps to figure out what their
customers likes and what they purchase the most from the company. This then helps
organisations to figure out what their consumer market is like and can use that information to
target their customers by advertising the products that consumers by most. They can advertise
the products that they have found from the data or make the products cheaper when signing
up to a campaign or loyalty card. Also, by analysing data about trends companies can also use
this to their advantage by providing seasonal items so that more customers can be intrigued by
the produce. So, by companies and organisations analysing data they can then increase their
revenue and gain more customers so that is one reason analysing data is important for
businesses to satisfy existing customers and then potentially gain new customers along the
way.
Businesses also need to analyse data is that most of the data that they collect becomes
redundant meaning that most the data is useless for the business. This is another reason
businesses analyse data so that they can see what data useful and what data is useless. This is
important because by identifying which data is useful and which data is useless businesses and
organisations can then use that data to cut costs, enhance their business performance and
identify potential risks to the company. Analysing data will also help to manage the data more
efficiently as there will be massive amounts of data so analysing it will help to manage and
categorize it making it easier to see what data important and what data is not important as said
before.
Improving departmental productivity
Improving departmental productivity is to improve the efficiency and speed of accessing and
analysing the data in certain departments within an organisation.
, Analysing the data can improve departmental productivity within an organisation or business.
Improving departmental productivity means to improve finance department, the marketing and
sales department, information and technology and the operational and supply chain
management.
Finance department
Analysing data can improve the financial department of a business or organisation. One way
analysing data can help with a company's finances is analysing customers data in real-time to
improve their experience when they purchase a good or service from the organisation or
business and then try to build and maintain loyalty from the customers by increasing their
experience of the organisation. Also analysing large amount of customer data will help to create
new personalized products and services to then target specific individual customers which is
also a strategy as said in the strategic planning section, but this also helps a business's finances
as it will then drive in sales which then lead to increase in revenue and profit for the business.
Also, by analysing data from the financial department the organisation will be able to then be
able to improve its risk assessment after collecting and analysing the data which can help
change the organisations environment and make it better for employees. Also, by analysing
finance departments data it can help to understand customers deeper and will help the
organisations heads of departments to make informed decisions which will then promote
better business outcomes. This can then help to decide and predict the amount of cashflow that
the business will gain increasing the value of the organisation by analysing the previous cash
flows and revenue which helps predict future cash flow.
Marketing and sales department
Another department that is improved after analysing data is the marketing and sales
department. This is because by analysing the data received from the marketing and sales
department it can help when deciding on decisions such as who to market to, where to market
to and what platform to. Data from marketing and sales department also show if that the
quality of marketing is good or bad which is shown if the business gains more customers or not
so by analysing the data from the marketing and sales department we can tell if the marketing
is effective or not.
Information and technology
Information and technology are an important department as it is used to be efficient when
collecting and analysing data as technology helps to sort out data. Information and technology
is key because for example if the data is collected on a paper based system it can then
important data can be lost and it will be a very bad thing if that happens because if the data is
lost then organisations and companies won't be able to analyse it properly and will then lost
out on huge amounts of money as the data that could have been analysed and then used to
