Requirements for Data Analysis Flashcards
Name the four types of stakeholder within the stakeholder matrix
Technical people
Direct management
General/casual users
Influential Stakeholders
Describe ‘technical people’ (stakeholder matrix)
Relevant requirements i.e colleagues on the same level
Lower Influence
Keep satisfied (regular meetings)
Describe ‘general/casual users’ (stakeholder matrix)
Monitor with occasional updates
i.e general public
Occasional engagement
Describe ‘Direct Management’ (stakeholder matrix)
Direct influence on the project
Requires regular updates and quick adoption of requirements i.e senior managers
Describe ‘Influential stakeholders’ (stakeholder matrix)
Can directly affect the future of the project i.e MDs, CEOs, shareholders
Requirements not directly relevant
Describe ‘Importance’ in relation to the stakeholder matrix
Relates to the importance that the details of the project must conform to their request
Someone else’s interest in the details
Describe ‘Influence’ in relation to the stakeholder matrix
The sway that the person has over the overall running and general outcome of the project.
Describe ‘business problems’
Business problems are current or long term challenges and issues faced by a business. This may prevent a business from executing strategy and achieving goals.
Name two negatives of using data alone to solve business problems
1) business problems are often complex, with numerous possible solutions
2) Data is too ‘low-level’ to provide direct answers.
Name for the four stages in analysing a business problem
1) Generate a hypothesis based on the overall problem
2) Confirm whether the hypothesis is what they want reviewing
3) Gather data
4) Complete the analysis, then refer back to the business problem to evaluate whether you’ve found a solution
Define ‘causal analysis’
Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect (are there any underlying factors)
State the two different types of requirements for data analysis and explain them
1) General - A set of policies and standards that the analyst and the output must adhere to
2) Technical - The processes and operations you must follow during the project - relating to what specific IT systems are used, for example.
Define ‘policy’ in the context of data analysis requirements
A set of rules enforced by management that guide the overall direction of the business
Define ‘standard’ in the context of data analysis requirements
Mandatory actions or rules that give formal policies support and directions. Need to be quantifiable. i.e 99.5% of complaints must be dealt within 24hr
Define ‘procedures’ in the context of data analysis requirements
Detailed step by step instructions to achieve a given goal or mandate. Standardised recipes
Define ‘guidelines’ in the context of data analysis requirements
Helpful advice to achieve something. Are negotiable. Negotiable is good as it can result in better methods/ways being developed/discovered.
Define ‘requirements elicitation’
The practice of collecting the requirements of a system from users, customers and other stakeholders.
Define ‘explicit’ knowledge
Codified Knowledge found in documents, databases etc. IT is essential for transfer and storage
Define ‘tacit’ knowledge
Experienced based knowledge. Knowledge for day to day learning. Derived from OTJ training.
Describe the difference between an ‘As-is’ model and a ‘To-Be’ model
An ‘As-is’ model captures an accurate visual picture of an organisation’s process, whereas a ‘To-Be’ model models the impact of any future process changes before you make them.
Describe a ‘Use-case’ diagram
A model showing the interaction a customer has with a system
Describe a ‘Context’ diagram
A model showing the system we’re building in the context of everything around it. How does it interact with everything else?
Describe a ‘Functional Decomposition’
A model of the system you’ve designed, broken down into different bits of code/programming that make up the model.
Describe a ‘Sequence Diagram (Swim flow diagram)’
Shows how information is being passed from one area to the next in terms of time.
Describe the difference between data validation and data verification
Data validation is the process of checking that data makes sense when it goes into a database. I.e, an email address has to contain an ‘@’.
Data verification is checking that the provided data is actually correct. I.e, if you set up a new online account, you may get an email to confirm your email address.
