OCR LEVEL 3 CAMBRIDGE TECHNICAL

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1 Cambridge TECHNICALS OCR LEVEL 3 CAMBRIDGE TECHNICAL CERTIFICATE/DIPLOMA IN IT UNDERSTANDING THE BUSINESS ANALYTICS PROCESS FOR BIG DATA J/505/5326 LEVEL 3 UNIT 39 GUIDED LEARNING HOURS: 60 UNIT CREDIT VALUE: 10

2 UNDERSTANDING THE BUSINESS ANALYTICS PROCESS FOR BIG DATA Reference code J/505/5326 LEVEL 3 AIM AND PURPOSE OF THE UNIT With advances in technology, users are expecting more personal interactions with the products and services they consume. Business analytics is the way in which modern businesses use the data they gather about consumers to improve their marketing, delivery of services and customer interaction. The development of these business analytics processes has been driven by the emergence of larger, less structured sets of data which traditional analysis tools have struggled to cope with. These data sets are commonly referred to as Big Data. The aim of the unit is to provide the learner with an understanding of business analytics, how it is rapidly developing and how businesses are using it to expand and increase income/market share. They will consider the approaches, risks and benefits, and be able to look at the wider usage of Big Data as it relates to them and their preferred career pathway. 2

3 Understanding the Business Analytics Process for Big Data Level 3 Unit 39 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 the concept of Big Data P1 explain terminology relating to Big Data M1 describe technological challenges of Big Data 2 Understand how Big Data is analysed by the business sector P2 identify how business sectors can benefit from Big Data analytics M2 explain how different types of analytics are applied across different business sectors D1 justify the use of data analytics using data sets containing sensitive data 3 Understand the impact of business analytics P3 identify benefits of business analytics within the business environment D2 propose business opportunities through the analysis of sourced Big Data P4 explain risks within business analytics M3 create outline plans to help manage risk within business analytics 3

4 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 the concept of Big Data Terminology data sets capture e.g. historical, live, analysed, purchased storage curing validation verification analysis Technological challenges size of data sets speed of collection processing requirements output types capture types i.e. video, audio, textual Uses of Big Data eg Marketing Sales Business Intelligence Product development LO2 Understand how Big Data is analysed by the business sector Business types private public charities Business sectors e.g. IT Healthcare Engineering/manufacturing Accounting Education Environmental Sources of Big Data banking i.e. personal details government records i.e. tax, pensions and benefits environmental reports i.e. weather patterns, wind speeds, pollution recorded live data i.e. manufacturing, sports testing results i.e. education research i.e. health screening Types of analytics descriptive predictive prescriptive LO3 Understand the impact of business analytics Uses of business analytics Business Intelligence Forecasting Linear, integer, constraint programming Problem solving Identification of patterns Sector usage e.g. IBM Smarter Commerce IBM Smarter Care Siting of wind farms Cancer research Business impacts e.g. production planning supply chain optimisation distribution network design portfolio optimisation resource allocation scheduling, sequencing timetabling, routing, and dispatching Business benefits e.g. Merging internal and external data Scope of content Competitive Advantage Operational Efficiencies Customers Improved confidence personalised service retention satisfaction managing expectations 4

5 Understanding the Business Analytics Process for Big Data Level 3 Unit 39 Financial ROI Performance management Forecasting Analysis Competitors Purchasing power Sales Management Fraud identification recognition reduction Compliance regulatory, privacy Risks e.g. organisational Market risk Credit risk Liquidity risk Collateral management Actuarial modelling Operational risk Governance, legislation and compliance Policy and compliance management 5

6 DELIVERY GUIDANCE Big Data is an area that learners need to fully appreciate to enable them to identify how it can be used to benefit sectors and individuals. The best start to understanding is research and discussion on what Big Data as a concept is. To begin, the tutor could discuss familiar sources of Big Data that the learners will understand in context or relating to a sector or hobby that they have. Some examples could be the creation of social media profiles, the purchasing of goods online, the completion of a tax return. They should then be able to identify other Big Data collection sources across other sectors. They should then be encouraged to think what can be done with this data and the processes that the sourced information may be subjected to. This will identify to them the phrases and terminology in Big Data collection and analysis and they should be encouraged to research this further perhaps as smaller groups to find and understand the additional terminology and detail relating to Big Data. As an understanding of the processes, terminology and activities are developed, tutors should encourage learners to consider the amount of data that could be captured/ sourced by a business. They should identify the source of data for a business, and discuss the required frequency of collection and analysis for each. An appreciation of the scale of information across a range of sources and the processing of this information should be used as a basis to discuss with the learners the technological requirements for organisations and the technological challenges that will be experienced. By contextualising the sources and data types, the scope and therefore challenges are more visible to a learner. The research and discussions should then start to focus more on the use of Big Data by the business sector and tutors should provide examples of usage in the media that have served to benefit specific business types. Learners should be taught the range of benefits that could be achieved to different business divisions such as sales, finance, and marketing. They should be taught the different types of analytics that may be used and by contextualising these; they will be able to appreciate how data that is sourced could be used for other purposes either within a business sector or cross sector. They should be encouraged by the tutor to look at the actual content of the data being sourced by businesses and specific examples such as personal finances or medical records should be considered in detail for learners to appreciate the sensitivity of some of the collected data. This will enable the tutor to then open up discussions as to whether sensitive data collected by business is appropriate for use for a different purpose and the moral, legal and ethical implications of any sourcing and analysis. The tutor should then encourage the learners to consider the more positive outcomes of using sensitive data for different purposes, such as medical research or identity protection, or financial approval. Research in to the advantages and disadvantages should be carried out by small groups of learners with collaboration and consolidation with the wider group to appreciate the different perspectives each may have. They should also review the developments that have already been established and try to identify where the concept has been applied to other sectors for a similar or completely different purpose and they should identify opportunities where data analytics could be used for existing data sets. The exploration of the sectors and application, and the nature and content of the data should then be clear in the learners mind. They could create mind maps or flow diagrams as individuals to show the scope of the data for a sector or data set and expand this as more of their own ideas emerge. The last topic that needs to be delivered based on their understanding is risk. They should understand the risks to business from their collection, storage and processing but may should also be encouraged to discuss risks to individuals and the legislation that is in place to support them. They should be taught about the legislation that exists, what the regulations are, the implications for employees and businesses if they are breached. This should be discussed with the tutor in addition to any research as the learners need to fully understand and appreciation the concept and implications of the risk. By identifying all the areas not simply legislation, they should be able to develop checklists for organisations based on different types of data set. 6

7 Understanding the Business Analytics Process for Big Data Level 3 Unit 39 SUGGESTED ASSESSMENT SCENARIOS AND TASK PLUS GUIDANCE ON ASSESSING THE SUGGESTED TASKS Assessment Criteria P1, M1 For P1 learners must explain with examples the appropriate terminology relating to Big Data. The learner should ensure that examples of the application within business are also included in their explanation and that the terminology is clearly explained and defined. Evidence could be in the form of a report or leaflet. For merit assessment criterion M1, learners must describe the range of technological challenges of Big Data. This may be in the form of a report or leaflet and give examples of the challenges, the implications for the Data sets and capture and the technology that is required to support different data sets. Assessment Criteria P2, M2, D1 For P2 learners must document real businesses and the benefits that they and the wider world have experienced through the use of Big Data and business analytics. They should identify the benefits of business analytics across a number of different business sectors. A report or promotional leaflet would ensure the depth of detail and content. opportunities through the analysis of sourced Big Data and would be expected to explore information that may be gathered for a particular business or sector. They should propose opportunities for analysis and how they could be used to enhance a business. The evidence should clearly identify the learner s own ideas about how the data could be used and should include examples relevant to business. This may involve the application of business analytics across unrelated sectors or the development of simple existing ideas into larger and wider opportunities for a business. This could be in the form of a business proposal or a research document. For P4 learners must explain risks within business analytics with examples. Evidence could be in the form of a presentation or leaflet and should consider the full range of risks identified in the teaching content. For merit criterion M3 learners must create an outline plan to assist a business in managing risks when working with Big Data. This should be in the form of a report and should cover the criteria identified in the teaching content as to how risks can be managed or reduced. For merit assessment criterion M2 the evidence could be an extension of P2 where the learner identified the use of business analytics across the sectors. They must explain how businesses use certain types of analytics for different purposes based on their sector or business objectives and what the benefits of these different approaches may be in helping a business develop. For distinction criterion D1 learners must justify the use of data analytics using data sets containing sensitive data. They should show clear understanding of the different styles and sources of sensitive data and why they should be considered sensitive. They should then justify why the data sets should be captured and used based on these considerations. Assessment Criteria P3, D2, P4, M3 For P3 learners must identify benefits of business analytics within the business environment. The benefits could be within one organisation across identified divisions with differing benefits or across a range of business types and sectors. This could be in the form of a report which could be enhanced with mind maps to show cross sector or division benefits. For distinction criterion D2 learners must propose business 7

8 RESOURCES Business Intelligence: Cognos Business Intelligence, Cognos Express, Cognos Insight Various learning resources for BI (demos, tech talks as well as papers) and software download links: ibm.com/ibm/university/academic/pub/page/ban_business_intelligence Linear, integer, constraint programming: ILOG Optimization Studio Predictive and advanced analytics: SPSS Modeler Course material: SPSS predictive analytics Redbook: Risk management: CityOne game: Centres may wish to sign up free of charge to the academic initiative for the full range of resources and videos: 8

9 Understanding the Business Analytics Process for Big Data Level 3 Unit 39 MAPPING WITHIN THE QUALIFICATION TO THE OTHER UNITS Unit 6 ecommerce Unit 25 Data analysis and design Unit 23 Database design Unit 33 System design Unit 34 System analysis LINKS TO NOS 4.2 Data Analysis 4.4 Systems Analysis 4.5 Data Design 4.7 Systems Design 6.1 Information Management 6.2 IT Secutiry Management 9

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