Data Launcher.

An agile and scalable data pipeline centralises data from all sources into a data warehouse, transforms raw data into business concepts defined in your data dictionary with ELT processes, surfaces easily-digestible business insights in automated dashboards, facilitates deep-dive analytics and encompasses advanced data science algorithms.

For more details on the components of a modern analytics data stack, please see our insights article here.

We evaluate tools against the following factors: agility and scalability, cost-effectiveness, ease of maintenance and collaboration by the team. We also take into consideration your current analytics needs, longer-term data vision, existing tech ecosystem and any particular preferences by the team. We remain tool agnostic but have a preference towards cloud and open source technologies.

We take care of all the heavy lifting from data strategy to implementation. To get us up to speed quickly on the first week, we typically need a one-hour strategic session to go over your business goals and challenges and a one-hour technical session to understand your existing data stack and best practices. On an ongoing basis, we have a 45-minute weekly sync to provide you with project updates and a shared Slack channel for ad-hoc questions.

We are experienced at joining forces with your team, understanding your business, goals, existing data processes and challenges, and helping you return value from data quickly. Having a project manager (e.g. head of growth or lead data analyst) and a technical point of contact would facilitate an efficient on-boarding process and on-going collaboration.

Yes. We work with companies of all sizes and data maturity hence are used to being onboarded quickly within your existing data stack. We collaborate closely with your team to ensure that we follow existing development guidelines and are not affecting your current work stream. We can also help optimise your data stack at your request.