What do you do if your innovative data science ideas are falling flat with non-technical stakeholders?

No matter how technically “cool” a project is, it needs to be able to solve a genuine business problem. If it’s not doing that, there is a fundamental flaw in the approach. If the innovation is really solving a good business problem, then being able to talk about it in layman terms and highlighting on the solved business use case helps. A thumb of rule which I always follow is that the end users and the customers might care about the “technological marvel” that I am creating, but they definitely care a lot more about how it helps them with their problems and how it adds real dollar value for them.

Too often data science projects fall flat because they are not contextualized in business needs, so business leaders don't understand the value and potential impact of the data science work. This is especially true when non-technical stakeholders are important in greenlighting the investment to launch or scale an innovative data science project. Binging non-technical stakeholders along in this context will mean building a narrative around a positive, transformative return on investment (ROI) on metrics that matter to them if they greenlight the data science project. Otherwise, you risk losing their attention (and resources) to other projects that have a more clear ROI, even if the other projects have a smaller impact for stakeholder needs.

There is a reason for that, any project or initiative specially data science and AI must result into a business impact or measurable value and simple to adopt, if not it will not succeed irrespective how great the innovation is. "Innovation must result into commercial value or business impact"

It is a common scenario that an idea is rejected, as it may not resonate with non-tech stakeholders, especially in the Data Science world. This could be mitigated by: 1. Avoiding excessive jargons - we should always formulate our strategy according to the target audience and convert into a real-time problem and not to show-off. 2. Charts, dashboards can create impactful message without telling much. 3. Simple use-case: developing on simple proof of concept can be more impactful than just speaking about it. 4. Communication with stakeholders right from the beginning of the project. This will generate traction and interest in them. The above may be limited or can be improved but this was definitely impactful in my day-to-day activities.

To effectively communicate and ensure my ideas are well-received, I would simplify my language, use relatable analogies, and focus on the practical benefits of my proposals. I'd employ visual aids like charts and graphs to clarify complex concepts and actively seek feedback to address stakeholders' concerns. Lastly, involving stakeholders in brainstorming and workshops would help increase their engagement and understanding of the project's value.

Less Jargons, Simplicity in thoughts can do wonders for you. 1. Use real-life data and make a story around how you plan to build a data science concept. 2. Define metrics and success criteria that would help achieve the business objectives. 3. Have engagement with existing customers to get their feedback onto any new concepts. This will solidify your ideas and get them to reality.

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For non-technical stakeholders, simplify complex concepts and highlight how your ideas can solve problems and improve outcomes. Work collaboratively to find common ground and build consensus.

Avoid using technical jargon that might confuse or alienate non-technical stakeholders. Use simple, straightforward language to explain concepts, methodologies, and results. Analogies or real-world examples can often be helpful in making complex ideas more accessible.

Storytelling is key to communicating data analysis/modeling achievements to the business. Learning to express the findings is as important as using the right model, cleaning data, or creating visuals.

Know the Product and Data Generation Processes: # Familiarize yourself with the product or service your organization offers. # Understand the user journey and how different actions generate data. # Shadow professionals who deal with the problem you’re trying to solve (e.g., account managers, customer success managers). Their practical experience will inform your data understanding, cleaning, and feature engineering.

Data science findings can be dense and confusing for non-technical audiences. That's where visualization becomes a superpower. Instead of relying solely on words, powerful charts, graphs, and infographics can transform complex data into clear, engaging stories. Imagine stakeholders seeing the impact of your data insights visually - it becomes an "aha moment" that fosters understanding and buy-in for your data science solutions.

Getting the feeling to build visuals to the business take practice, an understanding of the necessity of stakeholders, and most importantly time. There's always room to get feedback from what was built and adapt to achieve better results on the next update/delivery.

Utilize data visualization techniques to present your findings in a clear and compelling manner. Charts, graphs, infographics, and dashboards can help non-technical stakeholders grasp complex insights quickly. Visual representations often convey information more effectively than raw data or technical explanations.

Craft a compelling storyline. Make use of engaging visuals in your presentations: rather than use a monotonous presentation, why not adopt a gallery-walk approach of presentation? This strategy - gallery-walk strategy - gives the non-technical stakeholders a sentient walkthrough of your presentation.

Explain your data science output in terms of the business impact, how it will help the company achieve KRAs, create efficiencies in the business, or enhance customer experience. Connecting the insights from the algorithms to the business helps create strategy.

Connect your data science ideas to the interests and objectives of your non-technical audience. Explain how your innovations can contribute to achieving their business goals, improving efficiency, saving costs, or enhancing customer experience. Highlighting these connections makes your ideas more relevant and appealing.

It is helpful to connect your ideas to the stakeholders' existing priorities and objectives. Clearly show how your data insights can help them achieve their goals. Emphasize the potential cost savings, increased revenue, or improved efficiency your idea brings.

I focus on aligning our interests to ensure better buy-in. It's essential to demonstrate how these innovations can directly benefit their department or the company at large. For instance, if my idea can streamline operations, I emphasize the time and cost savings it will bring. If it's about enhancing the customer experience, I highlight how it could lead to increased customer satisfaction and retention. I make it my goal to make the connection between data science and tangible business value as clear as possible. This approach helps in bridging the gap between technical possibilities and practical business needs, making it easier for stakeholders to see the value and get on board with the idea.

Learn and Align — Keep the stakeholders updated. It is a best practice to communicate your understanding after every discussion. Even after translating the business understanding into technical requirements for the project, I ensured that we kept sharing our progress. We could greatly improve the final output by keeping the stakeholders involved throughout, checking their opinion on the analysis time and again, learning from their inputs/feedback and realigning if needed.

Consider starting with pilot projects or small-scale implementations to demonstrate the value of your data science ideas. This allows stakeholders to see tangible results and benefits firsthand. Successful pilot projects can build confidence and generate support for larger initiatives.

Starting with a pilot project is a smart approach. It allows you to demonstrate the value of your data science idea in a controlled environment. Visualize Results Effectively: # Create compelling visualizations that tell a story. Use dashboards or reports. # Highlight the impact of your data science work on the chosen metrics.

Take small bets. Start small, scale big. A complete overhaul is usually a big turnoff especially for non-technical stakeholders. It pays to first of all, propose a minimum viable product (MVP) to demonstrate the value of your idea before going all-in for the big kill.

Establish a feedback loop with non-technical stakeholders throughout the process. Regularly check in with them to gather input, address concerns, and refine your ideas accordingly. This collaborative approach ensures that your data science solutions align with their expectations and requirements.

When my data science ideas don't connect with non-technical stakeholders, I establish a feedback loop to refine my ideas and build trust. I encourage stakeholders to share their concerns and suggestions, which helps address issues and involves them in the process. This not only improves my proposals but also enhances stakeholder engagement and support for the initiatives. A strong feedback loop is key to building a collaborative relationship that benefits everyone involved.

Improved Data Quality: Feedback loops can help identify biases or errors in your data that you might have missed. Stakeholders familiar with the domain can provide valuable insights into the data's accuracy,relevance. Enhanced Model Performance: By incorporating stakeholder feedback on the desired outcomes and potential use cases, you can ensure your model is optimized for real-world applications Better Communication and Collaboration: Regularly soliciting feedback fosters open communication between data scientists and stakeholders. This transparency builds trust and fosters collaboration, leading to more successful projects. Alignment with Business Goals: Feedback loops help ensure that your data science projects are aligned with business

Sometimes depending on the application it's necessary to go into the architecture and create visualizations that helps non technical people understand deep learning (if applied), or further visualizations on the inner workings of the algorithms. Once the algorithm is displayed with its active functions then observers can understand the results.

Emphasize Business Benefits: Start by clearly outlining the direct benefits of your data science project to the business. This could include how the project will increase revenue, reduce costs, enhance customer satisfaction, or streamline operations. Be specific about the positive impact on key business metrics and objectives. Tell a Story: Frame your idea within a narrative that relates to a problem or goal that the audience cares about. Describe a common scenario they face, then show how your data science project can change that scenario for the better. Link to Existing Successes: Mention similar strategies or models that have been successful either within your organization or in other companies.

improve communication with non-technical stakeholders, simplify complex ideas, emphasize benefits over technical details, use visualizations and storytelling, seek feedback and build relationships, and offer training and collaboration opportunities.

1. Focus on Business Impact: Highlight the potential benefits of your data science ideas in terms of cost savings, revenue generation, efficiency improvements, or strategic decision-making. 2. Tell a Story: Frame your ideas within a compelling narrative that captures the stakeholders' attention and connects with their interests and goals. 3. Seek Feedback: Encourage open dialogue with stakeholders and listen to their feedback and concerns. Address their questions and incorporate their input into your presentations. 4.Build Relationships: Cultivate relationships with non-technical stakeholders by understanding their perspectives, collaborating on shared goals, and demonstrating the value of data science in achieving those goals.