Our way of working
RevoData is here to empower your data and AI journey, from establishing your data strategy and building your data platform to training your team.
One team
RevoData and your team are one team. We implement the most fitting Data & AI solution, with the aim of training your team and enabling you to independently continue development. We always remain available in the background, but you can always operate fully independently.
Idea
Philosophizing together about what the future might look like.
- Establishing hypotheses and what you expect to find in the data.
- Does this data already exist or does it need to be created?
- Defining actions to be taken based on the insights gained.
- Establishing value; also called the business case.
In Production
The underlying infrastructure and operational controls do not need to be built and maintained - we do this for you:
- Less operational overhead
- Better scalability
- Cost Savings
- Greater flexibility
- Better security
1. Design
Preparation of a design and detailed plan for implementation of a data initiative.
- Identifying data sources
- Defining the data structure
- Determine how the data will be transformed and stored
- Work out how data is used within your company
2. Build
Build the data project components planned in the design phase:
- Use reusable components from RevoData
- Develop Data Pipelines and AI models for your application
- Measuring & improving data quality
3. Test
Ensuring that the data platform operates correctly and meets the requirements established in the design phase.
- Checking that data is correctly transformed and loaded
- Testing the applications and tools that have been developed
- Identify and resolve any issues before the project is rolled out
4. Deploy
Ensuring that the data platform is ready to use and accessible to your people.
- Putting the data platform into production and making it available to your employees
- Providing user documentation
- Training your people
- Guiding the change to a data-driven organization
5. Review
Assess whether the data platform meets its stated objectives
- Collecting feedback from users to identify any problems or areas for improvement
- Make any necessary adjustments to the data platform
- Establish follow-up steps to maximize value
