Evidence from the Organisation Flashcards
(18 cards)
Why is it important?
- Identify organsiational problems or challnges
- Determine organisational consequences of a problem
- identify potential causes of a problem
- Find plausible alternative solutions
- Monitor the effectiveness of management decisions or solutions
- Contextualises problems
Finance and Accounting
Statements of cash flow- money received or given out.
Income statements- profit or loss and income
Statements of a firm’s financial position- list of an organisation’s assets and liabilities
Human resources
People.
Who they are, their characteristics, relationship to the organisation.
Job satisfaction, years of experience etc.
Sales and Marketing
Competitors, customer relationships, brand awareness.
Risk
Calculations, what has gone wrong.
Big Data
Particular kind of organisational data.
Volume- size. Huge.
Variety- comes from a variety of sources.
Velocity- speed at which data are generated
Where to find?
Externally and internally.
Techniques and tools for acquiring
Business Intelligence Systems- support decision-making process by organising, processing and analysing the data and turning them into useful information.
Structured Query Language- most info systems store data in an underlying database. It is possible to query this database, extracting data from the database in a readable format. Requires support of IT department.
Statistical Software
Review of documents and reports.
Surveys.
KPI
Key Performance Indicators- when we tie a metric to a target, goal or norm critical to organisation’s success. They are very important, as they are contextualised. Used to see how performance has changed over time and if so in what direction and at what rate.
Things that we need to be aware of when acquiring organisational data
Data protection- laws and regulations aimed at keeping employees’ sensitive information safe and the privacy of customers secure. Data Protection Act (1998)- UK.
Costs and benefits.
Accessibility.
Politics- internal politics. lack of executive sponsorship- main reason why data analytic projects often fail.
Appraising Organisational Data
Similar to scientific literature in that both must be based on principles of scientific evidence. Ensures trustworthiness.
A Logic Model
A short narrative explaining why or when a problem occurs and how this leads to a particular outcome. Conceptualises problems and processes. Helps to tie assumptions about problems to real tangible relationships.
Guides data collection. Links strategy to measurement- aligns goals with metrics. Supports evaluation and learning. Improves transparency and accountability.
Irrelevant Data
Due to technological advancements, organisational data creates multiple metrics which give a false impression of understanding. Organisations are often tempted to create metrics from easily available data rather than make the effort to gather more relevant data. Do not collect organisational data just because it is easy to do so.
Inaccurate Data
Organisational data is prone to bias. Looks more objective than it is.
Aggregation
Organisational data may be captured and combined from multiple sources. Avoid combining them on the assumption that they are comparable (like comparing apples.
Missing contextual information
Context is so important, as otherwise data can be empty and meaningless.
Small Number Problem
Stems from the probability theory known as the Law of Large Numbers. The larger the sample size the more accurate its predictions. Sample value deviates on average from the pop. value.
Occurs;
- When organisations compare units unequal in size.
- When organisations collect data from a sample rather than from the whole organisation
- When organisations have access only to a small sample of the total market population
How to overcome:
- Sample from whole population
- Aggregate internal data to achieve larger sample sizes
- Pool data from several firms to develop a larger data set
- Clearly state sample size and report confidence intervals
Misleading Graphs
- Omitting the baseline i.e. y axis not starting at 0
- Numbers do not add up in pie charts
- Cumulative versus interval data
