Here's how you can convey complex insights to non-technical stakeholders using creative storytelling.

-> Yes, different types of audiences have different levels of comfort with different aspects of Data charts and visualisation levels. -> For example, if you are creating a dashboard only for the Sales Team of an FMCG organisation, we have to keep in mind most of them have been going around Excel all their career, so it means these kinds of people are very comfortable with Data types like Table or Matrix in your Power BI. -> These charts are useful as you can add many KPIs just into a single chart and they can read and make sense of the tabular data -> In contrast with this if your audience is generic we might not be able to harness the utility of Table to that extent and have to create multiple chart to better explain the same data.

While conveying insights from a Data Science project, it is very important to know your audience. - Executives: They are business people, and they would rather be interested in the anticipated outcomes (ROI) from the project in simple language. Do not use technical jargon here. - Developers: These are more technical people (such as software engineers) where you can highlight key technical insights. - Infra Team: These people need to understand the scale and infrastructure required for your project.

It is critical that you set the stage before going deep. It is incorrect to assume that most people are aware of the context of your work, therefore always strive to define the background first, as well as the challenges you hope to address through your data science project. This allows individuals to relate to the value your idea would offer. Also, while you're doing it, try to portray the project's expected outcomes in a language that everyone can understand, so that stakeholders will be willing to invest in it. It is vital to get stakeholder buy-in.

Yes, keeping the concepts simple to have clear communication is key: -> This Can be done by addressing the question of what you want to convey -> Even depending on the Categorical or Qualitative data the kind of chart you pick up differs. -> Avoiding to not pick up a chart which makes it go into a 3D when you can convey the same message with a 2D chart is the right way to address it and keeping it simple and precise

Data Science projects can be complicated and involve a lot of technical jargon that may appear like Hebrew to someone who is unfamiliar with it. One effective method is to illustrate ideas using graphs, charts, infographics, or even basic powerpoint diagrams. It is critical that your audience understands the usefulness rather than the technicalities of things. Try to depict the big picture, but be prepared to go into the details if a question arises.

The most important skill is storytelling. This is the expertise that determines whether a data science project succeeds or fails. You must be proficient in connecting the dots. From the problem, to previous effort done to mitigate it, to current strategies to dealing with it, to your solution, and ultimately to the climax, which are the benefits that it delivers over the current approach. You can use simple infographics to describe the challenges and benefits, as well as the key performance indicators (KPIs) you want to improve.

Analogies help to describe complex concepts in simple words. It is critical that you plan your analogies ahead of time rather than making them up as you go; otherwise, you will not be able to present a suitable example. When explaining a complex idea to a non-technical individual, always try to use an analogy from everyday life that the person can readily relate to. This increases your audience's confidence in your proposed solution.

Learn about the teaching methods of great scientists. In particular, those who excelled in teaching and considered this work (or doing scientific journalism) a part of their legacy. For example: 1. Michael Faraday; 2. Richard Feynman; 3. Carl Sagan.