Wie kommunizieren Sie Ihre Analyseergebnisse und -empfehlungen an nicht-technische Stakeholder?

Another important aspect to consider is spending time upfront aligning on the problem statement and involving stakeholders in the problem-solving journey. Rather than delivering a big final recommendation at the end, breaking your project down into digestible chunks and mini-decisions can be more effective. This approach provides ample opportunity to build a relationship of trust with your stakeholders and gain a deep understanding of their communication styles and expectations. Ultimately, the best final recommendation meetings are those where there are no surprises.

When I share analytics findings with folks who aren't tech-savvy, I use a mix of storytelling and visuals to make the data easy to get and interesting. It's all about connecting the dots between what the data says and real-world choices or ideas that can boost the business. I keep things transparent and explain methods in a simple way, so everyone feels confident about the insights and what to do with them.

Simple: the audience doesn't not speak our technical language. It is on the analyst or the technical expert to translate and relate

Understand that non-technical stakeholders are a resource for you. Their business knowledge, domain expertise, deep experience and intuition are an asset. Use their needs, questions, appreciation and even skepticism to critically evaluate and continuously hone the quality of your output. The most common need in communicating with these audience is translating between the business and technical knowledge. Spend time with them understanding their pain points to first uncover insights that are most valuable to them and gradually foray out to related recommendations to create a win-win communication model.

1. Know Your Audience: Understand their goals, challenges, and preferred communication styles to tailor your message effectively. 2. Focus on Key Insights: Highlight the main outcomes and implications, using simple language and avoiding jargon. 3. Use Visuals and Narratives: Employ charts, graphs, and storytelling to make data relatable and engaging. 4. Anticipate Questions and Objections: Prepare for potential queries by considering different perspectives and being ready with clear explanations. 5. Follow Up and Measure Impact: Ensure understanding, agree on next steps, and measure the impact of your analytics on decisions and outcomes. This approach ensures that complex data is presented in a clear, engaging, and actionable way...

True. Narrative beats p-value. Meaning, technical fundamentals are not the win of the insights , but the expectations. Analysts have the responsibility to translate technical requirements into the business context. Remember a number is a conversation starter. Numbers bring people together.

Make sure to lead with your recommendations and action statements. Starting your presentation with the bottom line up front subconsciously instructs the audience to listen for the supporting facts and data that strengthen your recommendations. It allows them to build the case toward your ideas for themselves, as the rest of the information is presented. "This is what you should do. And now I'm going to tell you why..."

Lead people to the 'aha moments' when presenting analytics and data. In my days as an analyst, this was the biggest advice I took with me throughout my career. It's so much more impactful when people not only grasp the numbers but also connect the dots to those enlightening 'aha moments' that bring clarity and insight. When you focus on the key insight that led you to the results or main point you're sharing, others will do the same. This is why storytelling is so important even with data.

In my experience, aligning analytics communication with initial KPIs and goals co-developed with clients is crucial. It's not just about presenting data; it's about contextualizing insights to show how they directly impact or deviate from these predefined objectives. When discussing analytics with non-technical stakeholders, I focus on linking each finding back to a specific KPI. This approach makes the data relatable and actionable, providing a clear roadmap for decision-making. It's more than data; it's a strategic tool for validating our campaign's direction or making necessary pivots.

The most effective approach for presenting results is to emphasize the key recommendations and conclusions drawn from the data analysis. It's crucial to mention the methodology behind these conclusions, but there's no need to delve into exhaustive details, such as every outlier removal or data quality issue resolution, even if these tasks consumed a significant portion of your time in providing valuable insights.

It is critical to be mindful of how the visuals and narratives align with the overall message, reinforcing it rather than distracting from it. While visuals try to make the data digestible, sometimes we tend to oversimplify leading to misrepresentation of the data. Consider a product that sells exceptionally well in December due to the holiday season, dominating sales with 40% share, shown in a pie chart. However, for the rest of the year, sales are low, making its annual share only 10%. A time series graph would better capture this seasonal fluctuation helping make informed decisions.

While using visuals is great, it is essential to consider the layout out of your report. Ensure the most important insights/metrics are the first visuals to be seen by the audience. The report/dashboard should not be containing all sorts of information, with data presentation less is more! Tip: before starting to create a dashboard, start by preparing a mock layout of metrics on a paper as this will allow you to visualize the story you’re trying to narrate through the report.

Educating non-technical stakeholders on how to interpret and gain insights from data visualizations is crucial for effective communication. Features like Q&A in Power BI can greatly assist in this regard by allowing stakeholders to interact with the data and ask questions in plain language. This interactivity can enhance their understanding and help them derive meaningful insights. It's essential to include a brief tutorial or guidance on how to use such features and encourage stakeholders to explore the data actively. Combining visuals, interactivity, and clear explanations can empower non-technical stakeholders to make informed decisions based on data-driven insights.

Effective visual storytelling is important for communicating analytics insights. Analytics often involves complex data. Visual storytelling helps in breaking down this complexity, making it easier to understand. Visuals are more engaging than blocks of text or numbers. They can grab attention and maintain interest, which is vital in keeping your audience focused.

The visuals should be easy to understand. Complicated visuals though appealing can lead to non-technical audience loosing interest if it takes too long to understand them. Same goes for the techincally sound audience as well.

New analytics often represents a change in process, and change can often be viewed as a threat or a source of discomfort on the part of non-technical stakeholders. Its important to reinforce the potential value that Analytics can help to unlock, or how much easier Analytics can make those stakeholder's day-to-day, rather than articulating your project as a complete reinvention of internal processes. Also come prepared with concrete examples, such as the time to value analytics can bring versus existing manual processes.

- Simplify findings: Use plain language, avoiding jargon, and provide clear summaries of key insights. - Visual aids: Employ charts, graphs, and infographics to illustrate data trends and outcomes. - Focus on impact: Highlight how the findings affect business goals and decision-making. - Tell a story: Create a narrative that connects data to business objectives and real-world implications. - Anticipate questions: Prepare for potential queries by considering the stakeholders' perspectives and knowledge gaps. - Address objections: Acknowledge and respond to potential concerns with evidence-based counterarguments and alternative solutions. - Follow-up: Offer to clarify or provide additional details post-presentation.

One approach I have found beneficial is to engage in rigorous self-reflection by challenging my own assumptions. It is imperative to thoroughly question the analytics process to ensure that any potential unconscious biases are identified and mitigated. Strengthening my convictions empowers me to effectively address challenges that may arise during presentations. By engaging in this introspective process, I frequently anticipate and address questions and objections that non-technical stakeholders may raise. This proactive approach enables me to anticipate and effectively respond to their concerns, resulting in more impactful and insightful presentations.

Your audience will notice if you anticipate their needs and questions. If you plan ahead, you can address questions before they are asked. This will effectively mute your critics. They won't be able to object because you will have already addressed their objections.

In todays changing business world data leaders play a role as they seamlessly switch between being analysts, communicators and strategists. Their tasks are wide ranging. Maintaining the balance of energy to cater to the needs of various stakeholders is key, for sustained success. There are typically two types of stakeholders: 1. The Inquisitive Collaborator: This stakeholder is genuinely interested in utilizing data for insights and decision making. Are willing to explore viewpoints making collaboration both engaging and fruitful. 2. The Data Driven Decision Maker: This stakeholder usually has an objective in mind. Looks to data to support their existing beliefs. Their requests can be more demanding and require skillful management.

Analytics is a never-ending process, especially if you are working in a b2c Space, where analytics is the Heart of a Company. You need to make sure that the insights you draw out, & and after those insights the Changes that are implemented in the strategy or Product; have resulted in outcomes that will help you also improve the quality of insights & and also keep you motivated in terms of the impact of your insights.

Some ideas to generate insights on the impact of analytics on decision making, focusing on tangible results: ▸Come up with clearly defined key metrics, which should be linked to both benefits and costs : profits increase, impact on key business KPI's, report generation costs, etc. ▸Implement continuous monitoring mechanisms to track and analyze the performance of analytics initiatives, as per those metrics. ▸Establish feedback mechanisms to understand stakeholder experience with analytics offerings and make the necessary improvements.

If you do not follow up and measure impact then you are not closing the loop. Like I often tell my teams - 'If you do not measure it then it probably did not happen!' Its important to gather feedback, and what better way than the measure of impact brought about by your insights which you worked so hard to derive. On a lighter note - its an opportunity to earn some bragging rights...

It is important to establish KPIs to follow our project performance and identify trends. These KPIs must be established by identifying the main issues that need to be measured and how they can contribute to the optimal conclusion of the project.

After presenting your findings, it’s important to follow up with a plan to track progress and measure impact. Provide stakeholders with specific KPIs or dashboards to monitor real-time changes and ensure the success of any recommendations. Continuing this engagement demonstrates that you’re invested in the long-term success of the project, and allows stakeholders to see the practical outcomes of your analysis.

Generally, we believe that complexity is a sign of intelligence or sophistication. However, solving a business problem using a difficult or complex methodology does not necessarily mean that it is correct. In other words, the solution to the problem cannot be more complex than the problem itself. Using data storytelling techniques to present information can enhance people's ability to remember and comprehend it. Utilize your analytics findings to craft a narrative that highlights the key insights, and focus on two metrics at most at a given point in time, tying them in with the story.

I would recommend to build a simple google studio dashboard with quantitative & qualitative approach Add limited metrics in the dashboard: For example: 1. Pageviews 2. Bounce rate 3. Traffic sources 4. Goal completion(if u have added manually, if any) 5. Conversions (if you would like to show) If non-technical audience is interested for specific geographic, u have the ability to add filters as well while preparing the dashboard.

Effectively conveying analytics findings to non-technical stakeholders is pivotal for informed decision-making. Tailor your message to align with their knowledge levels and interests. Break down complex concepts into relatable terms, leverage visualizations, and prioritize key findings. Craft a compelling narrative, use plain language, and provide context to make insights meaningful. Encourage interactive exploration through dashboards, engage in two-way conversations, and use analogies for clarity. Highlight the business impact, and offer actionable recommendations. By adopting these strategies, we can bridge the gap between technical insights and actionable understanding.

Make your data interactive whenever possible, such as by offering live dashboards that stakeholders can explore on their own. Additionally, using analogies to explain complex concepts in familiar terms can help them understand difficult ideas more easily. These strategies can increase stakeholder engagement and comprehension, ultimately helping them to make more informed decisions based on your analysis.

Highlight Key Insights: Suppose your analysis reveals that particular marketing campaign led to significant increase in website traffic & conversions. You would emphasize this point as key insight, demonstrating effectiveness of campaign in achieving goals Provide Context: When presenting the results of customer satisfaction survey, you would explain how feedback aligns with company's focus on improving product quality & customer service Tell Story: Let's say you're analyzing social media engagement metrics. You could start by describing company's journey on social media, highlighting milestones & challenges along the way. Then you would weave in data analysis findings to show how different strategies impacted engagement levels over time