Here's how you can convey data mining insights to non-technical stakeholders effectively.

In my experience, when in meetings with non-technical stakeholders, I avoided using technical terms and instead described the solutions in simpler and clearer words. If the meetings were held virtually, I always had my camera on and shared my screen to be able to build simple diagrams from scratch so that the stakeholders could easily understand, be involved, and ask questions. Additionally, having built simple and effective demos along the way kept them engaged and further clarified the solution architecture.

Always backup the presentation with facts / numbers to keep the non technical / senior leadership folks engaged . For example instead of just explaining a data trend, demonstrate its benefits / value / effect on market, etc. Steer the presentation towards how the data presented can be used for future actions/ new possibilities / opportunities to make the impact on audience.

The outcome of data mining activity should always be non techincal even when there's lot technical going in the backend. It's sole objective should be story telling to different business functions whether their collective effort and actions is creating the right impact in the ORG journey to achieve its overall goals. It should clearly articulate facts around existing business processes and areas of improvement looking at the data points collected.

I convey data mining insights to non-technical stakeholders by using clear visuals, real-world examples, and focusing on actionable outcomes rather than technical details.

The basics of any presentation is simplified concepts with facts, numbers and data. Visual representation of data through charts and graphs always makes communication better and address the concerns of non technical Stakeholders. We have to ensure that the end goal of the data mining concept deck should show the right relevance to the stake holders and they should be able to match their end goals. This could additionally be extended by offering a follow up support.

When presenting complex data mining concepts, simplifying without losing meaning is crucial. Replace jargon with plain language to make the ideas accessible. For instance, instead of saying "support vector machines," explain it as a technique that classifies customers based on their buying habits. This method allows stakeholders to easily understand how the insights directly relate to improving business strategies and outcomes. Keeping explanations clear and relevant ensures that the audience can see the value in the data being shared.

Conveying data mining insights to non-technical stakeholders requires clarity and relatability. Craft a relatable narrative that captures their interest. Use straightforward language and engaging visuals, like infographics, to simplify complex data. Highlight practical implications aligned with organizational goals and incorporate relevant examples and analogies. Provide a concise summary for easy reference and invite questions to foster discussion. This approach transforms data into meaningful insights, enabling stakeholders to make well-informed decisions.

When you boil down most of these tools, they're really simple for lay-people to understand. "KNN" - it's just a way of saying "Hey, all of the things next to this object are oranges, so it's most likely that this is an orange, too." "SVM" - Anything above this line is X, anything below the line is Y. Even moving into modern AI concepts like LLM's can be dumbed down in a way for lay-people to understand. ChatGPT can be, pretty accurately, described as a sophisticated version of the text suggestion tool on your phone that suggests words when you're typing... except it's been trained on the entire internet of text, and just happens to be useful enough to produce value to a business.

Visual aids are powerful tools for sharing data mining insights. Turning complex numbers into graphs, charts, or infographics makes the data more digestible and engaging. The right visualizations can reveal patterns, trends, and anomalies that may otherwise go unnoticed. By presenting your findings visually, you make it easier for stakeholders to grasp the key points, fostering better understanding and support. After all, a well-chosen visual can communicate insights more effectively than words alone.

Humans tend to process / remember visuals better than words. If your model is helping solve 80% of a business problem by only doing 20% of the work, show it in a chart. However using a classic pareto chart (though it is very intuitive for you) could be complex for the audience. Stick to the basics and try this instead, show a stacked bar chart with 2 bars, showing that 20% of one bar is solving 80% of the other bar, with a simple dotted line connecting them. Additionally as per experience people tend to understand basic line, pie, bar charts and waterfalls much easier. Take it like a "joke" if you have to explain it, it is not good enough. Avoid using scatter plots (unless the trend is super clear), box plots, sankey, radars etc...

While visualizations undoubtedly play a pivotal role in communicating data-driven insights, it's essential to acknowledge their limitations. Overly complex or poorly designed visuals can cloud information, leading to confusion, and even worse, misinterpretation of analytical results. Again, know your audience because visualizations alone might not suffice for some stakeholders that require detailed numerical analysis or explanations to fully grasp the implications. Therefore, it's crucial to strike a balance between visual appeal and clarity. Supplement visuals with concise explanations and offer access to underlying data for those who seek further exploration.

Visual aids are powerful tools for presenting data mining insights effectively. By converting raw data into graphs, charts, or infographics, you make complex information more accessible and engaging. Select visualizations that highlight key patterns, trends, and outliers to ensure clarity in your message. A well-chosen visual can simplify data interpretation, making it easier for stakeholders to understand and act on the insights. In many cases, these visuals are crucial to gaining their support and driving business decisions.

A great example of this is a pair of line charts overlaid on top of each other showing the average number home runs per game in MLB each year, and the number of viewers of the World Series, stretched out from 1973 to 2023. The subtext of the conversation is around "Moneyball" and the data-driven approach to picking players for teams based on quantitative reasons - which yields greater numbers of home runs than it did before those strategies were adopted. What the graph shows is a steady increase in homeruns - and a steady decrease in viewers. The story you can tell with this data is that while home runs increased, the sport became less exciting, because stealing bases became less common... and people stopped watching as much.

What I have understood in my experiences working with Product, Engineering and Data leaders is that everyone wants you to align the insights with the broader goals of the organization. The story should be built around what the org is try to achieve, and then the relevant metrics and data should be identifies. Post that, it's about showing this data in a story format.

When you are presenting your findings, always think the audience. What is in it for them to listen to you? Or why would they even care about these findings, then tailor the message accordingly. When you decide on the final message, ask yourself "so what?" Then answer, then ask again "so what?" then iterate your answer... If you do this enough times, you will find yourself relating your insight to the end business goal... If you can not relate it to a business goal, or you can not create a business recommendation or action based on the insight, you might as well thinking about to drop presenting it altogether.

Understand the business in a bigger aspect like what are their KPIs? what challenges are they facing? what market opportunities are present for the business. A business may have strategic/organizational goal to achieve in a long term. Data can be linked to understand how tactical/ departmental goal can help to achieve bigger aim for a company. For example, "Goal may be to reduce customer churn by 20% for a year. Data analysis can be used to reveal key factors contributing to churn, and addressing these could help meet/exceed the target. Aligning insights with stakeholder goals makes the message more compelling, showing how the data can lead to actionable, business-driven outcomes.

To make your data mining insights resonate, directly connect them to the business goals of your stakeholders. Show how the data addresses their challenges or uncovers new opportunities. For example, if a pattern reveals a market opportunity, explain it in terms of revenue growth or gaining a competitive edge. By aligning your insights with their objectives, you make the information more relevant, actionable, and compelling, increasing the likelihood of stakeholder engagement and support.

Storytelling is the art that backs up the data mining science to make it more understandable by the audience... Say that you have done a great segmentation exercise ending up in 5 key segments that define how your customers behave... Look at those customer segments and their metrics again and again and again until you find the persona. Name that persona, 2-3 words max, find a visual representing that so that it sticks, and explains itself already... A long tenured, high value customer base could be named as "oldies but goldies" Or a group of customers always buying the newest collection first could be "trend setters" , "fashionistas" Don't try to make it perfect, make it memorable.

Story telling is very important to make non technical audiences sensitise about data analysis outcomes. A good contextual story is easy to consume and help in decision making.

While storytelling is a powerful tool for conveying complex data insights, mastering this skill can enhance presentations. Follow this structure in the narration: 1. Establish the context. What business concern prompted this analysis? 2. Middle: Present the road of discovery. What unexpected patterns have emerged? 3. End: Provide insights and actionable recommendations.

Narratives offer a compelling way to present complex data mining insights. By weaving a story that traces the path from data collection to the conclusion, you provide context and make the information more engaging and memorable. For instance, instead of just showing analysis results, craft a story about how customer feedback was mined to enhance a product. This approach allows stakeholders to connect emotionally with the data, making the insights more relatable and impactful, ultimately increasing retention and understanding.

After sharing your data mining insights, offering follow-up support is crucial to ensure stakeholders fully grasp and apply the information. Be available to answer questions, provide supplementary materials, or host workshops tailored to their roles. This continued support helps stakeholders confidently leverage the insights for informed decision-making, reinforcing the practical value of your data mining efforts and fostering ongoing collaboration.