OUR 8-STEP DATA SCIENCE MODEL

OUR 8-STEP DATA SCIENCE MODEL

We have defined an 8-step Data Science model, providing a framework for managing your data science projects.

In this section, we explain how you can organize your work to help you use data analytics to streamline your Data Science initiatives.

This 8-step model works at every stage of your Data Science project cycle. It brings the right people into your organization to optimize your processes and technologies.

As a result, your organization achieves the agility and transparency to make the right data-driven decisions, at the right time. You can meet your data management and data governance requirements. The 8-step Data Science model also stimulates innovation across your enterprise, helping you develop new data-based products and services.

Let’s examine how it works. The 8-step Data Science model is cyclical in nature. Although sequential steps are defined, you must often iterate back to previous steps, for example, when you realize certain questions require clarification.

Some activities can also happen in parallel. For example, you should start gathering domain knowledge when you start qualifying the problem area. But, after exploring and understanding the data, you may need to move forward to the next step in order to interpret the intermediate outcomes and, possibly, adjust your initial questions.

It’s a harsh fact, but many data projects fail. To increase your chance of success, it is important to follow a proven methodology.

Our 8-step Data Science framework is a combination of existing methodologies and elements from the Design Thinking (problem solving) and Lean Startup (business model viability) methods. We have taken the breakthrough thinking element of Design Thinking and used the product testing element from Lean Start-up.

We are introducing a scientific approach to the world of innovation. We are combining an explorative approach to existing problems and, when we reveal new insights and information, your product ideas can further tested and validated.

These concepts are often criticized for oversimplifying the design process and trivializing the role of technical knowledge and skills. By combining this with a more scientific and technical approach from the existing BoK methodology, we are bringing together the robustness of technology, with the benefits of creativity.

As the volume of data continues to skyrocket, this is a much-needed capability. New business models and entire industries are now pivoting. As a result, creativity and technology must go hand-in-hand.

The 8-step Data Science model provides a practical and comprehensible framework for any data project, whether it is a BI-related project or an advanced-AI one. This framework is split into eight distinct steps:

Data Science framework for any data project

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