- According to recent research, just 7.3% of organisations say the state of their data and analytics is excellent, and only 22% of companies are currently seeing a significant return from data science expenditure.
- Poor returns on data & analytics investment are often the result of applying 20th-century thinking to 21st-century challenges and opportunities.
- Modern data science and analytics require secure, efficient processes to turn raw data from multiple sources and in numerous formats into useful inputs to a data product.
- Developing, orchestrating and iterating modern data pipelines is an extremely complex process requiring multiple technologies and skills.
- Other domains have to successfully overcome the challenge of delivering high-quality products at speed in complex environments. DataOps applies proven agile principles, lean thinking and DevOps practices to the development of data products.
- A DataOps approach aligns data producers, analytical data consumers, processes and technology with the rest of the organisation and its goals
Chief Data Officer
11:20AM - Day 1