Sample/practice exam 2017, questions and answers PDF

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Q 12. 'Producers' are one type of BI user. Outline 3 other types of BI users and how they would make use of BI The following is just an example of which content should be included. You need to elaborate more on it. • Consumers: little desire to create, task oriented – need to make decisions quickly (eg: customers, public, suppliers, employees) • Builders: central or business IT Collaborators: add to and improve the data from BI, add their knowledge, add comments and links, rate the data (eg: social media technologies)

Q 13. What are the benefits and risks for organisations associated with Self-Service BI (eg: Microsoft PowerBI and Tableau). Benefits • Quicker delivery of BI for end user • Can make use of user's existing knowledge of software rather than learn anew one (eg: Microsoft Excel) • Gives more ‘power’ to the business user to set priorities • Allows end user to iterate through their requirements themselves rather than use BI/IT resources to do this – this also helps once they need the BI team to build something more permanent • Provides a means for users with very sensitive data to keep control of it rather than require it to be stored in central BI systems • Using a common self-service tool allows users to collaborate • In short term can be much smaller cost to set up and maintain • Builds a positive experience for users towards BI/ Users become more invested in possibilities of BI • Expectation from end-users that central BI systems should become more responsive (also a risk) Risks Users creating multiple sets of data that is not necessarily managed so have duplicate copies or slightly changed copies of data throughout organisation

Data unlikely to be conformed to standards or have metadata created and stored Requires a greater level of skill, especially for data acquisition and preparation Creates an end-user demand to increasingly access central BI data by passing the managed BI front end Issues of security, performance and licensing not necessarily managed Data management skills In longer term complexity increases cost Vendor market for self-serve tools still maturing Expectation from end-users that central BI systems should become more responsive (also a benefit)

B1. Explain in 4 – 5 sentences what Data Governance is and why it is important in the context of Business Intelligence.(2 marks)

Data governance is a mechanism to manage data within an organisation. It deals with many different aspects on data and sees data as an asset which has a value to the organisation. The aspects include handling incoming data in various forms, dealing with security, infrastructure to hold and manage data, exchanging information within and outside an organisation and data quality. It is important for BI because it can lead to an infrastructure that is more flexible, reliable and scalable.

Data governance 数据管治（What is it? What are the benefits? - It is a control that ensures that the data entry 数据输入 by an operations teammember or by an automated process meets precise 精确的；明确的 standards, suchas business rule, a data definition and data integrity constraints in the data model. - Successful data governance strategy - Prioritise, cost and manage - Data as an asset - Apply for organisation of different sizes

3．BI Lifecycle – most relevant parts  Planning and requirements definition Typical questions in this phase - Are we ready to start the project? - What are the business priorities and expected benefits from BI? - Who are the groups/users needing BI? - How much will BI cost compared to savings and other benefits? - Which BI will be delivered to meet these needs? - Who will deliver the BI? - What approach is needed to plan and manage (and maintain) BI?  End users of BI Typical questions: - What do they want to do? - Which decision do they need to make? - How often will they ‘touch’ BI? - What are their technical and data interpretation skills? End user groups: - Builders: central or business IT (unfortunately often seen as bottleneck) - Collaborators 合作者: improve the info, add context and their knowledge, add comments and links, rate the data (eg: social media technologies) - Producers: power users, create reports, analytics - Consumers: little desire to create, task oriented – need to make decisions quickly (eg: customers, public, suppliers, employees)  Data selection Typical questions: - Which data is the best to use? (independently if available or not) - Where to get the data from? Does somebody provide the data? Does the organisation collect the data already?If no, what do we need to do in order to collect it?  Self-Service BI (vs. Traditional DW Lecture 2, point 11)- Self-Service BI Tools: Cognos Insight, MS PowerBI, QlikView, Tableau, etc.

week9 Business priorities 优先顺序 and expected benefits? (Further benefit examples)  Increased profits due to better decision support - Use of information that’s more reliable

- Creation of central customer, product, service data - Faster, more accurate reporting  Protection from competitors - Information/analysis available to partners and suppliers to improve supply chain - Faster response to changing market conditions  New product/service development - Data-driven decision making to avoid poorly planned initiatives - Performance measurement system that supports organisational alignment – so all organisation is aware and aligned with key information - Better targeting of potential customers

week10 Data warehousing & BI Mgmt, Data Architecture Management, Database Operations Management - Flexibility, scalability －can grow and change easily - - Reliability, performance data is available when needed - - Data available as it changes and at point in time, in a business friendly view: Real-time: what’s happening now, what’s the latest info Snapshot: what was happening/what happened in the past

Section C Five questions on Business Analytics (10 marks)(Only one question is provided here as an example) C1. What is Decision Tree Modelling and why is it useful?

Decision Tree modelling is a predictive analytics technique used in data mining. It can classify datasets into categories based on existing data. It is useful to predict into which category someone or something falls into based on historic decisions. For example, loan applications or tax return applications are typical scenarios of a decision tree algorithms where the algorithm can predict if a loan application is successful or not, or into which tax payment group an application falls into....