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Rooftop's Predictive Analytics Model

To execute data projects 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 Analytics solutions, we obtain a structured and transparent workflow. This is required when many highly specialized competences are involved in the work. It ensures quality in the results as well as the results being efficiently achieved.

The model is used for both pilot projects 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 Predictive Analytics 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 Analytics solutions, which deliver the best results, i.e. predictions, are achieved by leveraging a structured and transparent workflow while developing the solution. Below are the essential points of our model.

1.

Understand
and evaluate
the assignment

  • Be clear on the 'business pain' which the project is supposed to resolve.
  • Review state-of-the-art research and consider theoretical aspects relevant for developing the model and building the algorithms.
  • Involve the company's professionals and knowledge of the topic.

2.

Collect
and prepare
data

  • Collect available and relevant data based on our theoretical consideration.
  • Establish how to access data.
  • Structure data – making them readable for analytics.
  • Clean out weaknesses in the collected data (missing periods, outliers).

3.

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 performance of the model on test data.
  • Iteratively adjust variable selection and determine the final parameters in the model.

4.

Present
the
results

  • 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 graphic presentation of data results.

5.

Start
making
decisions

  • 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, Subject Matter 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 Subject Matter Experts. The synergy between the Subject Matter Expert's knowledge and a data scientist’s skills creates the foundation for the solution. Often, several Subject Matter 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 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.

THE STRENGTH LIES IN THE PERSONAL MEETING

If you are interested in understanding how we work and ensure top quality in our deliveries, contact Simon Friis Wittrock.

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Rooftop Analytics offers services relevant – and necessary to be able to execute ambitious data projects.

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