Rooftop's Predictive Analytics Model
To develop predictive data intelligence solutions with valid results, a methodical and effective approach is required. For this purpose, we have developed Rooftop's Predictive Analytics Model. With this method, we ensure an optimal process from defining the assignment to delivering the results.
With a well-defined method for developing predictive data intelligence solutions, we obtain a structured and transparent workflow. This is required when many highly specialized competencies are involved in the work. It ensures quality in the results as well as the results being efficiently achieved.
The model is used for smaller assignments, pilots and project implementations. The working approach in the data projects is agile. Therefore, our development method is in line with agile implementation methods such as SAFe. Equally important, our method secures that the predictions of the data intelligence solution is highly scalable.
Rooftop's Predictive Analytics Model Guarantees You
- Most recent and proven research applied when developing empirical model and algorithm.
- Confidence measures on all outcomes; hence we specify by the percentage for which the algorithm gives the correct result based on the data applied.
- Built-in quality assurance, and a solid foundation for project gate checks because we continuously verify if the produced results are suitable for the intended purpose.
- Structure and firmness in the project work, which gives transparency in the project's deliveries. In this way, the client obtains continuous insight in the results and the progress of the project.
Rooftop's Predictive Analytics Model
The predictive data intelligence solutions, which deliver the best results, are achieved by leveraging a structured and transparent workflow while developing the solution. Below are the essential points of our model.
- Become clear on the business decisions the predictions are to support in releasing your 'business pain' or your customer's business benefits.
- Review state-of-the-art research and consider theoretical aspects relevant for developing the model and building the algorithms.
- Involve the company's domain experts of the topic and the business data scientists.
- Prepare a business data science proof of concept covering what is expected to be performed to succeed in the assignment.
- Collect available and relevant data based on our theoretical consideration. This often include several different data sources
- Structure data – making them readable for analytics.
- Clean out weaknesses in the collected data (missing periods, outliers).
Develop model and algorithm
- Explore the data to find relationships between variables which constitute the starting point of building the model.
- Train the model on limited data.
- Test the model's performance on test data.
- Iteratively adjust variable selection and determine the final parameters in the model.
- Present the empirical model, potentially as software code to be integrated in the client's existing solution.
- Produce a datafile to be integrated in the client's existing application.
- Deliver a user interactive visualization of data results.
- Make data-driven decisions based on the developed analysis, which is to be manually performed by the employee.
- Automate the execution of the identified data-driven decisions.
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Close Collaboration is a Must
A well-defined Data Science model, such as Rooftop's Predictive Analytics Model, is necessary but not sufficient. The involvement of highly specialized data competences, domain experts, and business management is required. Through interdisciplinary cooperation, synergies evolve and the best results are achieved.
We work closely with management when analyzing business opportunities anchored in data. We uncover alternative paths that the project can potentially move towards. This is relevant for data projects since we don't know in advance whether the available data sufficiently supports the business idea. However, it might support a related – and still relevant business case.
We work closely with the client's domain experts. The synergy between the domain expert's knowledge and a data scientist’s skills creates the foundation for the solution. Often, several domain experts participate, and preferably the change agent of the project participates as well.
We work closely with the change agent as the solution in a data project isn't only an algorithm and IT. There is always a need for preparing the business and transform daily operation to incorporate the new solution. Not having focus on this, will prevent business readiness and delay the business benefits from day one.
We work closely with the IT organization to establish interim data exchange for the pilot, participate in the technical implementation, and provide support and documentation for hand-over of the technical solution.
We happily collaborate with the client's other suppliers, who are potentially part of implementing the data project.
Rooftop Analytics offers services relevant – and necessary to be able to execute ambitious data intelligence projects.
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