Data Culture

Tableau Drive Methodology

Breaking away from the traditional waterfall development model, we shift towards a more agile analytical approach. This addresses issues such as the "separation of requirements and implementation, significant requirement loss during development, and long cycles," and creates a data-driven culture that is "user-centered, with clear responsibilities between business and IT, and a short cycle with quick results."

Data Foundation

Uncovering the self-driving power of data, and building self-service capabilities in four stages.

Discovery: An evaluation to assess whether the company is ready to promote an analytics culture and establish measures to help the company prepare adequately.

Prototyping & Quick Wins: A phase where super users gain the support and training needed to become confident analytics advocates. This stage focuses on developing "quick win" initiatives that demonstrate the business value of analytics, which can then be replicated and expanded.

Foundation Building: Preparing processes, organizational structures, and technological infrastructure to support broad deployment. At this stage, mature security, data governance, and other policies will be developed, alongside preparations for extensive training and enablement.

Scaling: Developing a unique, quantifiable analytics capability that is owned and driven by the business.