- Data culture is decision culture.
- The fundamental objective is collecting, analysing and developing data to make better decision.
- Identify business problem. What is and how you can solve it. Solving business problems must be a part of your data strategy.
- Data culture need to be c-suite imperatives and the boards.
- The democratization of data
- C-suite should consider a hybrid organizational model in which agile teams combine talented professionals from both business side and the analytics side.
- Top down approach doesn’t work.
- Data, applied to business problems, creates innovation. With that you get excitement.
- Data culture and risk
- The effective data culture put risk at their core.
- Handling data usage in ethnical way, following regulatory request is a must.
- Algorithm bias must be observed and supervised.
- Culture catalysts
- Company requires people who can bridge both worlds – data science and on-the-ground operations.
- Someone’s got to lead a charge.
- Sharing data beyond company wall? Not so fast
- Marrying talent and culture
- Injecting new people and transforming existing ones.
- Three areas of skills:
- Business
- Business leader – lead analytics transformation across organization
- Delivery manager – deliver data and analytics-driven insight and interface with end user
- Technology
- Data engineers – collect, structure and analyse data
- Business
- Analytics
- Workflow integrators -build interactive decision-support tools and implement solutions
- Visualization analysts – visualize data and build reports and dashboards
- Business and technical
- Data architects – ensure quality and consistency of present and future data flows
- Business and analytics
- Analytics translators – ensure analytics solve critical business problems
- Technology and analytics
- Data scientists – develop statistical models and algorithms
