What Can You Expect?
Roadmaps begin with the current situation and navigate the technical solutions that bring about the desired change. Our projects range from modest prototypes to deploying and managing full multidisciplinary teams that implement the concept into a convenient autonomous product.
We help you bridge the divide between your current data systems and the maximum utility and efficiency your information can provide. Many organizations have plenty of data, qualitative or quantitative, but lack expertise in optimizing it. We gather experts from every data science field into a multidisciplinary team to investigate and collaborate. We determine the quality of the data, articulate the goals and then define cases in which we achieve those goals. Each case culminates into a proposal with either a fixed fee or hourly invoicing.
We create a roadmap for data-driven work through interacting with your organization. Information is gathered through a combination of workshops, training sessions, interviews and use case discovery. Aurai’s multidisciplinary team guides and advises you through this process. This collaborative investigation leads to a roadmap, which combines IT and business operations so that data-driven work becomes effortless.
We use the Team Data Science Process model to shape our data science assignments. The model is iterative and flexible, which fits well with the exploratory nature of data science assignments. Also, the model covers all major aspects of an assignment in four phases: Business Understanding, Data Acquisition & Understanding, Modeling and Deployment. We define the key deliverables our clients receive at each phase to make clear what we are producing and when it will be ready. By incorporating TDSP in our services, we ensure that we deliver a working artificial intelligence solution that also integrates well into the client’s current infrastructure and architecture.
Data-Driven Scrum (DDS) helps us keep a good overview of the tasks and planning. DDS is based on Scrum and uses sprints, refinements, a backlog and a Kanban board. We use two modifications to DDS to enhance the data science projects. For example, some tasks in the TDSP model involve more work than others. Not every sprint needs to be the same length; the length or duration should be based on the task’s workload. Plus, certain meetings (such as refinement) are not linked to the sprint itself, but arise when the team needs them. This allows for quicker adjustments to match reality, a necessity for any data science project.