What do you do if your data science work is not understood by business executives?

- Pick the problem that matters the most and consider going deep to explain the complexities. - Use slides or documents to explain the narrative - Share with other technical and non-technical folks in the company to get feedback, to make sure the document is self-sufficient and business friendly enough for folks to understand - Send out a pre-read prior to the meeting so folks can mull on it - Leverage the help of senior technical leadership to help make your case and build allies around your pain points

Management is always interested in business value of your work rather the technical quality of your work. Communicating your work to your seniors is an art. Their are multiple ways to achieve this objective. First is to explain your work in brief, clear and concise way. Second, is to make use of visualization tools to explain your work. Lastly, the most important is to educate them gradually in multiple short sessions.

Based on my experience, I can recommend these 3 points to know your audience: ??Know the business, it is important to know the processes, the problem and the stakeholders. ??The type of communication that best suits your audience, for example technical, business or functional. ??Practice, once you have a good project, it is time to practice your communication and the possible questions that your audience will ask.

Executives are not able to see the value of your work. It is not only about your DS work, it might be from your audience's side this time! -> Learn about your audience's backgrounds -> Get to know the business objectives before you present your work -> You should understand their success matrix -> In addition, you should be very familiar with their expectations Getting to know the audience can boost communication and the discussion with them, making your executives impressed even when your contribution is little. Alexsai

Adapting our level of comprehension to match our audience is pivotal in ensuring effective communication of our work. This approach is instrumental in fostering understanding and engagement among our audience. Determine the functions, duties, and key focuses of your audience, as well as their areas of concern, obstacles, and prospects. Grasp their methods for evaluating achievements and reaching decisions, and comprehend their perceptions and anticipations regarding data science. Customize your communication style, vocabulary, and presentation format to align with their specific requirements and assumptions, fostering meaningful connections and relevance.

Defining the problem you are trying to solve will define the expectations from your executives. But how to do that? -> Clearly define the problem -> If the problem is complicated, break it down into sub-problems -> Define how solving the problem is crucial to your executive and their business objectives! -> Transform your understanding into a story within your presentation Well-defined problems are more likely to be solved than not well-defined ones. You may solve part of that problem, that is something to show as well! Alexsai

Clearly articulate the problem you are addressing with your data science work and the value it brings to the organization. Frame your insights in terms of business impact, such as revenue growth, cost savings, or customer satisfaction improvements. Initiate the conversation with - a description of the problem statement, the proposed solution, and the impact on the business.

For a data scientist, it is important to understand the problem of the developed project, from my experience I advise you the following: ? Know the processes of the organization for which you are developing the project, this is going beyond just understanding the problem. ? Talk with the main stakeholders, to understand the real need, many times the solution to a problem is not a predictive model. ? Develop weekly deliverables, to demonstrate quick value, to start a predictive model pilot you do not need to have a very advanced version, many times the effort is to generate value before the technicality.

A problem, a solution, and a key, this is what you need to tell your story! -> Simplify the problem and break it down -> Solve each piece -> Display how you fixed each piece using simple and appealing visualizations -> Show the process if needed! Simple is a nice word, but making your story simple is not that simple. You might need to think "simple" to obtain that! Alexsai

?Make your data easy to understand: Simplify complex stuff! ? Show, don't tell: Use cool charts and graphs to tell your story visually. ? Add pictures or icons to make it fun! ? Say no to confusion: Keep it simple, avoid fancy words. ?Speak their language: Use everyday words and examples they can relate to. ?Share actionable insights and tips. ?Encourage questions and discussions to keep everyone engaged!

Simplify complex data science concepts and findings into easily understandable narratives and visuals. Use clear and concise language, avoid technical jargon, and leverage data visualization techniques to illustrate key insights and trends.

Translate complex technical concepts into simple, accessible language. Use visual aids such as charts, graphs, and dashboards to illustrate key insights and trends, making it easier for executives to grasp the information at a glance.

Keep an eye on the empty part of the glass! -> Ask new questions -> Challenge your executives with value -> Prove your hypothesis -> Try to convince if you think your work is valuable -> Lead by innovation, follow business ethics and values! You might contribute more to the company's values when you address questions as an executive :) Alexsai

- Offer to schedule follow-up meetings to dive deeper into specific aspects of the data science findings. - Provide examples of successful implementation from similar projects or industries to bolster confidence in your recommendations. - Highlight potential risks of not taking action and the consequences of inaction on business outcomes. - Encourage open dialogue and collaboration between data science teams and business stakeholders to ensure alignment and ongoing support for data-driven initiatives.

Clearly outline actionable recommendations or next steps based on your data science findings. Encourage executives to take specific actions or decisions based on the insights you have provided, emphasizing the potential impact on business outcomes.

Some other ways are: - Maximize Business Impact: Unleash the power of data science to skyrocket profits and efficiency. - Tailor to Your Needs: Customized strategies that tackle your business challenges head-on. - Translate technical jargon into layman's terms to enhance comprehension. - Utilize visual aids like charts and graphs to convey complex concepts more effectively. - Offer real-world examples or case studies to illustrate the practical implications of your findings. - Encourage open dialogue and questions to ensure clarity and alignment. - Provide follow-up support or training sessions to reinforce understanding and implementation. - Tailor your communication style to resonate with the audience's preferences and priorities.

These are some of the tips that I used as a Lead Data Scientist: 1. Utilize visualization tools like Tableau or Power BI for easy-to-digest data presentations. 2. Develop user-friendly web apps like Streamlit, dash, and gradio to showcase insights without technical complexities. 3. Craft compelling narratives using storytelling to convey key findings effectively. 4. Don't bombard the audience with too many high-level terms. Simplify technical jargon and focus on business impact for clear communication. 5. Collaborate with marketing and sales teams to align data insights with business goals. 6. Conduct training sessions to educate executives on data science basics.