About the tool
The Data Science Process supports establishment of data-oriented management systems. Using digital technologies to transition to a Circular Economy requires a clear data- and business analytics strategy, the right people to effect a data-driven cultural change, and willingness in the organisation to appropriately structure itself to align the analytics capability with the overall business strategy. To achieve such a strategy, it is necessary to establish data-oriented management systems, which can be supported by the Data Science Process tool.
How to apply the tool?
The steps of the process can be performed iteratively as indicated by the arrows on the Data Science Process template. The steps should not necessarily be performed in a given time period but can be a continuous process.
Step 1: Business Understanding
The first and most important step within the data science process is to understand the business and Circular Economy objectives. The current situation must be assessed (together with domain- and business experts), to decide how to analyse it and how to measure future success.
Step 2: Data Understanding
Collect initial data, facts, and figures are to create a deeper understanding of the problem. Examine the properties, amount and quality of the data, to verify that the target goal and analysis is achievable.
Step 3: Data Preparation
Explore the data, to observe and uncover any patterns in the data, in light of the business understanding.
Step 4: Data validation
Validate the data, by re-involving the business- and domain experts. The aim here is to validate that a proper understanding has been reached, of the data and problem in hand, together with proper Circular Economy objective alignment.
Step 5: Modelling
Move over to the actual algorithm and artificial intelligence development. Often, multiple models and solutions are tested and evaluated for quality, precision, and best fit, with respect to the initial business goal.
Step 6: Evaluation
Evaluate, the results of the modelling phase, according to the business initiative and Circular Economy objectives. This entails the re-involvement of the business- and domain experts. Often, new business and Circular Economy objectives may occur, as a result of the new patterns and insights discovered.
Step 7: Deployment
Gather final information from the evaluation phase and deploy artificial intelligence to production, before offering the solution to customers and stakeholders.
Step 8: Analytic Profiles
Describe the analytic profiles. These are structures that are meant to consolidate the learnings, methods and techniques achieved, allowing you to more easily reuse experiences to catalyse the development of future artificial intelligence solutions.
When to apply the tool?
The Data Science Process should be used in the process of deploying the data to develop the data-oriented management systems and to evaluate its performance.
The Data Science Process is one of the tools presented in the CIRCit project to support Smart Circular Economy (WB4).
Read more about the results of this focus area, explore other tools which can help in implementing Smart Circular Economy and download the inspiring workbook here.