Phase 1. Data Discovery - Before we start the project, it is very important to understand the multiple specifications, needs, priorities and required budget of your business. You as a customer must own the ability to ask the right questions. Our team will also assess whether you have the required resources available in terms of people, technology, time and data to support the project. In this phase as a data scientist, our main goal would be to boost the sales of your business. Some of the factors affecting your business sales could be:
- Store location
- Working hours
- Product placement
- Product pricing
- Competitors’ location and promotions and many more!
Phase 2. Data preparation - In this phase, we need analytical sandbox in which we can perform analytics for the complete duration of your project. Here our team explore, preprocess and condition data before beginning to modeling. Further, we also perform ETLT (extract, transform, load and transform) to get data into the sandbox. Among the tools we use R for data cleaning, transformation and visualization. This tool helps us to spot the outliers and establish a relationship between the multiple variables. Once we have successfully cleaned and prepared the data, its right time to do exploratory analytics on it.
Phase 3. Model Planning - In this phase, we determine the methods and techniques to draw the relationships between different variables. These relationships will then set the base for the algorithms which our team will apply in the upcoming phase. Here we also apply Exploratory Data Analytics (EDA) using multiple statistical formulas and visualization tools.
Phase 4. Model Building - In this phase, our team develops datasets mainly for training and testing purposes. Here we consider whether our existing tools will be sufficient for running the models or will we require a more robust environment (such as quick and parallel processing). We also analyze multiple learning techniques like classification, association and clustering to build the final model.
Phase 5. Operationalize - In this phase we provide you with the final reports, briefings, code and technical documents. Additionally, from time to time a pilot project is also implemented in a real-time production environment. This real time project will deliver a comprehensible picture of the performance and other related constraints on a small scale prior full deployment.
Phase 6. Communicate Results - At this phase it is crucial for us to evaluate whether we have been able to attain our goal that we had planned in the data discovery phase. Therefore, in the last phase we identify all the vital findings, communicate to the stakeholders and determine if the outcome of the project has become a success or a failure. Here, the data scientist is required to be a connection between multiple teams and should be able to smoothly communicate his complete findings to key stakeholders and decision makers in the organization so that the desired actions can be taken on the basis of the recommendations given by the data scientists.