Leveraging analytical expertise

Data Fluency

Konstantinos Kattidis

Data Analytics Lead

Beyond the skill of exploring and describing data

• Going beyond basic exploratory analysis or methods to describe data
• Complex analytics methods include:
  • Analysis of large datasets
  • Building KPI dashboards
  • Developing predictive models

Being aware of the techniques

• Data-fluent individuals do not need to master the intricacies of advanced analytical methods
• But they should know the existence and understand the potential of such methods
• And collaborate with data experts
• This helps to apply more effective analytical solutions

Identifying use cases for advanced analytics

• Data-fluent individuals can identify business use cases for advanced analytics
• For example:
  • A marketing manager recognizes the potential of predicting customer churn to target retention strategies

Collaborating with data experts

• Collaboration with data experts is the key to success
• Business individuals provide domain expertise
• Data experts bring technical knowledge
• This collaborative environment nurtures innovation, as business insights and technical expertise converge to solve complex challenges

Understanding the limitations

• The effectiveness of advanced analytical methods hinges on the quality of input data
  • Addressing missing values, and handling outliers are essential
• It requires:
  • Specialized skills
  • Computational resources
  • Time

Example: balancing accuracy and time constraints

• An operations manager at a retail store requests the development of a demand forecast to help make informed restocking decisions
• A complex model can potentially yield higher accuracy but may demand extensive time and resources
• As simpler approach is used to balance accuracy and time constraints

Let's practice!

Data Fluency