Cambridge TECHNICALS OCR LEVEL 2 CAMBRIDGE TECHNICAL CERTIFICATE/DIPLOMA IN IT UNDERSTANDING BIG DATA K/505/5383 LEVEL 2 UNIT 29 GUIDED LEARNING HOURS: 60 UNIT CREDIT VALUE: 10
Understanding Big Data K/505/5383 LEVEL 2 Aim and purpose of the unit As technology advances, an increasing amount of information is captured and stored about individuals relating to personal and business life. Big data is this large pot of information that is collected and this unit allows the learner to understand the dimensions of Big Data, understand where and how it is currently used, the benefits to organisations and to explore potential usage for different purposes. www.ocr.org.uk 2
Understanding Big Data Level 2 Unit 29 ASSESSMENT AND GRADING CRITERIA Learning Outcome (LO) Pass Merit Distinction The learner will: The assessment criteria are the pass requirements for this unit. The learner can: To achieve a merit the evidence must show that, in addition to the pass criteria, the learner is able to: To achieve a distinction the evidence must show that, in addition to the pass and merit criteria, the learner is able to: 1 Understand what is meant by Big Data 2 Understand how Big Data is used 3 Understand how Big Data is processed P1 explain the term Big Data P2 identify sources of Big Data P3 explain how Big Data has been used to benefit society P4 compare and contrast languages used to query Big Data P5 describe predictive analytics M1 explain the techniques used for Big Data analysis M2 describe benefits to business of Big Data M3 explain how the review of queries could broaden the use of Big Data M4 explain why predictive analytics is a growing industry D1 describe the technological challenges to organisations from capturing Big Data D2 describe risks to users of using Big Data and those whose data is stored 3
Teaching content The unit content describes what has to be taught to ensure that learners are able to access the highest grade. Anything which follows an i.e. details what must be taught as part of that area of content. Anything which follows an e.g. is illustrative, it should be noted that where e.g. is used, learners must know and be able to apply relevant examples to their work though these do not need to be the same ones specified in the unit content. LO1 Understand what is meant by Big Data What does the term mean Four dimensions: Volume the amount an organisation gathers/stores Variety the types of data for analysis Velocity the speed at which data is captured Veracity the reliability of the data sourced and analysed Different types of data e.g. text, machine generated, audio, video, twitter, internet, sensory Techniques and stages of analysis e.g. Checking Cleaning Sorting Modelling Mining Characteristics Analytics Technological challenges e.g. memory storage space physical location scope of data LO2 Understand how Big Data is used Sources e.g. social media, loyalty cards, online commerce, questionnaires, government records and subscriptions Examples of application in society: Vestas pinpoint optimal location for wind turbines Ford electric cars Cancer research Healthcare data baby Risks Risks e.g. -- organisational -- Market risk -- Credit risk -- Liquidity risk -- Collateral management Actuarial modelling Operational risk Governance, legislation and compliance Policy and compliance management LO3 Understand how Big Data is processed Software e.g. Hadoop, MapReduce, NoSQL, JaQL, Hive, Pig, BigInsights, Streams Categories of data e.g. Retail habits i.e. preferred shops, spend, shopping patterns Medical criteria i.e. blood group, conditions Personal details i.e. date of birth, height, weight Financial information i.e. salary, credit rating, debt, mortgage, fraud Environmental i.e. temperatures, rainfall, sunlight hours, wind speeds, tides www.ocr.org.uk 4
Understanding Big Data Level 2 Unit 29 Predictive analytics techniques Definition Statistics Modelling Data Mining Predictive analytics usage e.g. Science Healthcare Finance Sales and Marketing 5
Delivery guidance Understand what is meant by Big Data Learners should be encouraged to give their initial interpretations of Big Data as a group sharing ideas and concepts before working as teams to research the topic and processes further. They should fully appreciate all aspects of Big Data, the dimensions, types of data and the business it is fast becoming. They should research and share the range of techniques used to source, sift and analyse the data, identifying considerations within the process to ensure that the information sourced is maintained in its original form to ensure nothing is lost and why this is important. They should then identify how data is checked for validity, and the analytic processes that are used for this. Another consideration that should be widely discussed is the purpose of modelling and the usage of these models in addition to the example techniques and stages identified in the teaching content. This list is not exhaustive and as the learners contextualise the types of data to those they can relate to they may identify additional stages and options. The contextualisation of the information sourced, the analysis purpose and the required outcomes will make it easier for learners to appreciate the breadth and depth of Big Data. Sharing their ideas with others in a wider group should also help them appreciate the scope of Big Data and support later ideas on adaptation. The very name Big Data implies the scope of the subject and learners should identify how the technology which generates and sources the data could also be one of the biggest challenges. They should consider how much information is sourced by different parties on a daily/hourly basis and how they would deal with this information and technological problems that will be encountered by all organisations. Understand how Big Data is used With a good appreciation of the sector and the data, learners should be encouraged to initially identify the commercial sources of Big Data which will then encourage them to appreciate the sources of Big Data that they had not identified such as social media, online gaming and how these apparently social interactions are as important to larger businesses for Big Data as those initially identified. This is a good time for the learners to appreciate the safety and security aspects of the data they personally provide to faceless databases and organisations. There is a potential here for a lot of negativity but is important for them to appreciate the scope of the topic. With the wider picture, learners should then look at how Big Data has been used to benefit individuals and society; this can be through a number of case studies widely available in the media and publicised by the IBM Smarter Planet initiative. They can then identify how in a similar way businesses can also benefit. This may tie in to research on Social Media and Business and this sector should be a consideration in their research into the use of Big Data by business. Every aspect of personal and business life contains an element of risk. The degree of risk and the implications must be a consideration in any decision making process. One of these business considerations is the legal implications of sourcing, saving and analysing data and the validity to the business. An individual needs to consider the risk of providing personal data in a range of situations and consideration should be given to its wider use. Through group research and discussion learners should consider the legal implications and other risks they identify through this group work. Understand how Big Data is processed With a comprehensive picture of Big Data, the sector and the industry learners should then consider the detail regarding how to process the data, the technologies which are widely used to do this. They should compare the functionality and flexibility of these for a range of analytical purposes. They should look at case studies of where languages are used, what information they process and how the core information could be re-queried for additional purposes extending the value of the information and the benefits to an organisation. Finally they should further research analytics with a focus on predictive analytics which they may already have identified in earlier investigations. They should understand the purpose of this specific type of analytics, how it has evolved and why it is important to business. They should also identify reasons why the sector is growing and should be an important consideration for certain business types. Discussion and identification of businesses and purposes will also widen their understanding of the sector. www.ocr.org.uk 6
Understanding Big Data Level 2 Unit 29 Suggested assessment scenarios and task plus guidance on assessing the suggested tasks Assessment criteria P1, M1, D1 For P1 learners must explain the term Big Data and include definitions and interpretation of these definitions by the learner. This could be in the form of a presentation or leaflet. For merit assessment criterion M1 learners must explain the techniques used for Big Data analysis. This could be a visual annotated representation as a flow chart describing the processes within the techniques. For distinction criterion D1 learners must describe the technological challenges to organisations from capturing Big Data. They should consider a range of criteria not restricted to those limited in the teaching content. Assessment criteria P2 For P2 learners must identify the range of sources currently used by business to gather information for use and further analysis. Learners will be expected to identify a wide range of at least five sources and include some detail as to the type of information gathered. This could be in the form of a presentation or leaflet For merit assessment criterion M3 learners must explain how the review of queries could broaden the use of Big Data. This may be evidenced as a presentation where the learner explains existing usage of Big Data and suggest additional usage for it. This may be enhanced by the learner identifying the types of data stored to support their explanation of its broader use. For P5 learners must describe predictive analytics. They should include the purpose and usage for predictive analytics and the evidence may be in the form of a report or presentation. For merit assessment criterion M4 learners must explain why predictive analytics is a growing industry. This could be an extension of P5 and learners should clearly demonstrate where identified industries and organisations use predictive analytics and why it is becoming more widely use. Assessment criteria P3, M2, D2 For P3 learners must explain the ways in which Big Data is used across a range of sectors to benefit society as a whole. This should include examples of current usage. This could be in the form of a presentation. For merit assessment criterion M2 learners must describe benefits to business of Big Data identifying how specific businesses have benefited commercially. This may be evidenced as an extension of P3. For distinction criterion D2 learners must describe possible risks of using Big Data, the risks identified by the learner may focus on business or social risks but should describe the risk and why it is considered a risk. Although the evidence could be presented as an extension to P3 and M1 it ideally sits as a document or a section in its own right. Assessment criteria P4, M3, P5, M4 For P4 learners must compare and contrast languages used to query Big Data. They should identify a range of languages used giving examples of the usage. This could be evidenced in the form of a leaflet or a short report. 7
RESOURCES List of white papers, reports, case studies, podcasts etc: www-01.ibm.com/software/data/bigdata/library.html Vestas case study: www.ibmbigdatahub.com/video/ibm-helps-vestas-turnclimate-big-data-capital Seattle Children s Hospital use case for Big Data: www.ibmbigdatahub.com/video/seattle-childrens-hospitalturns-big-data-better-care Getting Big Value from Big Data: http://ibm.co/wrkgrb Google usage and analytics - www.google.com/analytics www.ocr.org.uk 8
Understanding Big Data Level 2 Unit 29 mapping within the qualification to the other units Unit 20: Database systems Unit 21: Doing business online Unit 25: Systems software and hardware for development Unit 27: Developing programming solutions links to nos 4.2 Data Analysis 4.5 Data Design 5.1 Systems Development 5.2 Software Development 6.1 Information Management 7.6 Availability Management 7.7 IT/ Technology capacity Management 9
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