We are at the final and critical step in the analysis of models and data in a data science project. A model’s ability to generalize lies in its predictive power. Whether we describe a model depends on how it will generalize unknown data in the future.

Data is considered by a non-technical layman when presenting the data. We offer the results to answer our business questions at the beginning of the project and to provide us with the practical insights we find in the data science process.

A workable understanding is a key result that we demonstrate how data science can deliver predictive and later prescriptive analytics. We learn how to replicate a positive outcome or how to stop a negative outcome.

In addition, you will interpret your results differently and keep them guided by your business questions. It is critical that the results are delivered in a manner that would be beneficial to the company, otherwise else the stakeholders do not benefit.

Technical knowledge alone is not enough in this phase. One of the key skills is to tell a straightforward and realistic tale. In the absence of your answer, it indicates that the conversation was not effective in your audience. Recall that the way you deliver the message is important to an audience that lacks a technological context.

The main skills are beyond technical competence in this process. To present your results in such a way that you can answer all business questions and turn them into actionable actions, you need strong business knowledge.

In addition to the instruments required to visualise data, soft skills such as presentation and communication skills are important to assist you in this phase of the project life cycle combined with a flair for reporting and writing skills.

Sometimes, the stage of implementation is ignored and unplanned but it is necessary to realize business value. Deployment relies on a job model of applications that can be supported and implemented regularly. Once the models are deployed, the performance is tracked as model accuracy degrades due to organizational change and time. Models are also restrained when this occurs

We need more success stories – better resources but also proven business leaders able to understand and appreciate how a radically revolutionary collection of technology, processes and stuff can be generated in a company. For a number of companies, particularly non-digital indigenous firms, it will be difficult, as they have no luxury to start with or legacy systems. There will be plenty of hard work – but the future is truly exciting and the war has already taken place.