A Level Data Practices Analysis PDF

CONTEMPORARY DATA PRACTICES ANALYSING DATA  LEARNING OBJECTIVES 1. Understand how binary data is stored. 2. To be able to calculate storage requirements. 3. To grasp the importance of monitoring data usage and size. 4. To explore various storage methods and their practical applica...

CONTEMPORARY DATA PRACTICES ANALYSING DATA  LEARNING OBJECTIVES 1. Understand how binary data is stored. 2. To be able to calculate storage requirements. 3. To grasp the importance of monitoring data usage and size. 4. To explore various storage methods and their practical applications.  STARTER Why do we Analyse Data? What is its importance?  DATA ANALYTICS Data Analytics is the process of examining data sets in order to find trends and draw conclusions. Within companies worldwide, this is utilised to make smarter decisions and allows for patterns within data to be better understood.  DATA ANALYTICS Within the Business sector, data analytics are used to help companies to make better decisions, such as identifying top-selling products. Within the Research sector, it is used to assist in the validation of theories through the analysis of large data sets.  DESCRIPTIVE ANALYTICS Descriptive Analytics summarise and organise data to provide valuable insights. It’s main purpose is to describe, explain and present key characteristics of a dataset, providing an understanding of the key themes.  DESCRIPTIVE ANALYTICS The benefits of using descriptive analysis are: Understanding Trends This allows for the identification of product sale trends during specific seasons.  DESCRIPTIVE ANALYTICS Summarising Data This allows for the condensing of large datasets into manageable summaries. Identifying Anomalies Detects unusual patters within specific sectors.  DATA VISUALISATION Data Visualisation is the visual representation of data through charts, graphs and histograms. This method is used to plot common themes from descriptive analysis.  TASKS A supermarket sells a range of food and non-food products, including fresh and frozen food, drinks, bakery, home and entertainment products, and electrical goods. PART A Consider how descriptive data analytics can provide useful information for the supermarket’s management team. PART B Explain how the supermarket’s management team can use data visualisation.  MANAGEMENT INFORMATION SYSTEMS A Management Information System (MIS) is an organised collection of people, procedures and resources designed to support the decisions of managers. These allow for basic data to be structured into reports that give guidance for decisions.  MANAGEMENT INFORMATION SYSTEMS Management information systems contain information from Company Operations. This includes sales figures, expenses, investments and workforce data. If you need to know how much profit your company has made each year for the past five years.  MANAGEMENT INFORMATION SYSTEMS What-if scenarios show how different variables change when a decision is made. You can enter reduced staff levels or increased promotion budgets and see what happens to revenue, expenses and profit for different levels of cuts or increases. Some management information systems have this feature built- in, while others provide the information required for running scenarios on other applications such as spreadsheets.  MANAGEMENT INFORMATION SYSTEMS Any decisions made in a business will affect the projected company results and may require modifications to business strategy. Management information systems either have trend analysis built-in or can provide information that enables such an analysis. Typical business strategies include projections for all fundamental operating results.  MANAGEMENT INFORMATION SYSTEMS Management information systems give you the data you need to determine whether your decisions have had the desired effect. If specific results are not on track, you can use management information systems to evaluate the situation and decide to take additional measures.  PROJECT MANAGEMENT SOFTWARE Project Management Software is used to assist in the planning, executing, monitoring and controlling of projects  PROJECT MANAGEMENT SOFTWARE The Key Features of a Project Management Software are: Setting the Critical Path of the Project This allows for the definition of the sequence of essential tasks. Delays within these tasks, often delay the whole project.  PROJECT MANAGEMENT SOFTWARE Visualising Interdependent Tasks This shows which tasks rely on each other, thus ensuring that they are completed in the correct order of Outline the Project Schedule The project schedule is the plan or the roadmap of the project.  PROJECT MANAGEMENT SOFTWARE Set Milestone Deadlines Milestones are mini-deadline or checkpoints throughout a project. These are used to make sure that you are completing essential tasks. Task Breakdown This allows for the breakdown of Milestones into smaller, manageable tasks.  PROJECT MANAGEMENT SOFTWARE Set Responsibilities This allows for you to set the responsibility / accountability for the completion of a task to specific team members. Allocation of Staff This allows for allocation of team members to specific tasks based upon their skills.  PROJECT MANAGEMENT SOFTWARE Allocation of Resources This allows for allocation of the necessary resources in order to complete a task. This can include books, technology, money and even time.  TASKS A local drinks company has developed a new product. Their marketing team has been tasked with executing a nationwide advertising campaign. PART A Discuss the primary role of a Project Management System for the Marketing Team. PART B How can a PMS be used to aid in the organisation and tracking of project tasks and resources?  DATA WAREHOUSE A data warehouse is a centralised repository where large volumes of structured data from various sources are stored, organised, and optimised for efficient retrieval and analysis. The term data warehouse is used to refer to the technologies used to hold massive amounts of data.  DATA WAREHOUSE An extract, transform, load (ETL)-based data warehouse uses three layers to house its key functions. The staging layer or staging database stores raw data extracted from each of the disparate source data systems.  DATA WAREHOUSE The integration layer integrates the data sets by transforming the data from the staging layer. The access layer helps users retrieve data.  DATA WAREHOUSE An extract, load, transform (ELT)-based data warehouse doesn’t use a separate ETL tool for data transformation. Instead, there is a staging area inside the data warehouse itself. In this approach, data gets extracted, and then loaded into the same data warehouse.  DATA WAREHOUSE They are designed for reporting and data analysis, making it easier for businesses to make informed decisions.  DATA WAREHOUSE - EXAMPLE A retail company collects sales data from multiple stores, online transactions, and customer reviews. They can use a data warehouse to consolidate all this data into one place. This allows them to analyse trends, track inventory, and optimise pricing strategies across the entire business.  DATA MINING Data mining is the process of discovering patterns, trends, and valuable insights from large datasets. It involves using various techniques, including statistical analysis, machine learning, and pattern recognition, to extract meaningful knowledge from data. The term data mining refers to techniques used to gain information from the large mass of data.  DATA MINING - EXAMPLE A social media platform like Facebook uses data mining to analyse user behaviour. They can identify patterns in user interactions, such as what content gets the most likes. Information is used to personalise users' news feeds, target advertisements, and improve user engagement.  BIG DATA Large data sets, aka "big data," are collections of data that are so massive and complex that traditional data processing tools and methods are insufficient to manage and analyse them effectively.  BIG DATA The term big data refers to the many modern sources of data. This includes mobile phones and the internet. The data collected from these sources can come structure, semi-structured and unstructured.  BIG DATA The volume of big data, refers to the quantity of data that is generated from a variety of sources. With the large amount of data collected for usage, we need to look at its validity - this means is it correct and accurate for its intended usage.  BIG DATA There are multiple sources of information utilised in large data sets, this mixture of data to be processed can be referred to as its variety. With such large amount of data being analysed, the data can be variable, meaning it can be constantly changing.  BIG DATA Big data can be complex, difficult to process and difficult to understand. This is referred to as its complexity. The term big data can often be referred to as a continuous stream of data. The speed of the generation of data within big data, is measured as its velocity.  BIG DATA - EXAMPLE Meteorologists gather data from satellites, weather stations, and other sources to make predictions. Analysing this massive amount of data helps in forecasting weather patterns, predicting storms, and issuing warnings to protect communities. EXAM QUESTION Large data sets can be analysed in different ways. Describe how large data sets can be analysed and used. [ 4 Marks ]  WITHOUT A CDN When a user visits the website, their request is directed to a single server. The user may also be far away from the server Slower Loading due to the increased distance the data has to travel. Performance issues or downtime due to increased load on the server  WITH A CDN Website’s content is replicated and stored on multiple servers distributed across the regions worldwide. User are directed to the geographically closest PoP. Improved perforamnce as the time it takes to send / retrieve data is reduced. Being able to distribute load / traffic across many servers. Being able to easily scale it up (Add more servers)