1. Business Analytics
1.1. Discriptive Analysis - historical insight
1.2. Predictive Analysis
1.3. Prescriptive Analysis - outcome recommendations from the two above
2. KPI's
2.1. Ideally between 15-20
2.2. Types
2.2.1. Quantitative
2.2.2. Qualitative
2.2.3. Leading (can predict the outcome)
2.2.4. Lagging (present success/failure after)
3. Establishing Performance Measurement
3.1. Key issues
3.1.1. What is important to the organisation
3.1.2. Measure causes, not effects
3.1.3. Adopt tools & techniques which will enable the above to be determined & measured
3.1.4. Ensure coherence of measurement throughout organisation
3.2. Establish VISION, then STRATEGIC goal, then FUNCTIONAL goal
4. Goal Setting
4.1. Traditional
4.1.1. Can be self limiting
4.1.2. Generally absolute figures
4.2. Stretch objectives
4.2.1. Generally relative figures
4.2.2. Must be done correctly or risks demotivating staff
5. PRP
5.1. Doesn't work on individual level, can lead to drop in performance
5.2. Group works better, encourages individuals/departments to work together towards the goals of the company
6. Data
6.1. Always consider how it will push the business forward - does it provide useful intelligence
6.2. Raw data can be used to provide insight into
6.2.1. What is happening
6.2.2. WHY it is happening
6.2.3. Provide predictive insights through analysis
6.3. Must be careful with the data
6.3.1. Veracity is important
6.3.2. Shouldn't be allowed to make the decisions, but should help inform those who ultimatly make the decision
6.4. Not everything which is captured is important
6.5. Too much data can be as bad as not enough - must be clear as to what is being measured
7. Issues with Measurement
7.1. Is it subjectively or objectively important?
7.2. Could the measures have unintended, adverse consequences
7.2.1. Inherent conflict within measures?
7.2.2. Organisational barriers separating measures
7.2.3. Lack of understanding
7.2.4. Particular measures have been given undue weighting