Sie sind mit Datendiskrepanzen konfrontiert. Wie können Sie die Lücke zu nicht-technischen Stakeholdern schließen?
Wenn die Daten nicht zusammenpassen, ist es wichtig, die Verständnislücke mit nicht-technischen Teammitgliedern zu schließen. Hier sind ein paar Strategien:
- Beginnen Sie damit, komplexe Daten in verdauliche Teile zu vereinfachen.
- Verwenden Sie Analogien in Bezug auf ihr Fachwissen, um die Informationen zuordenbar zu machen.
- Bieten Sie visuelle Hilfsmittel wie Diagramme oder Grafiken an, um Ihre Punkte zu veranschaulichen.
Wie gehen Sie damit um, nicht-technischen Kollegen Datendiskrepanzen zu erklären?
Sie sind mit Datendiskrepanzen konfrontiert. Wie können Sie die Lücke zu nicht-technischen Stakeholdern schließen?
Wenn die Daten nicht zusammenpassen, ist es wichtig, die Verständnislücke mit nicht-technischen Teammitgliedern zu schließen. Hier sind ein paar Strategien:
- Beginnen Sie damit, komplexe Daten in verdauliche Teile zu vereinfachen.
- Verwenden Sie Analogien in Bezug auf ihr Fachwissen, um die Informationen zuordenbar zu machen.
- Bieten Sie visuelle Hilfsmittel wie Diagramme oder Grafiken an, um Ihre Punkte zu veranschaulichen.
Wie gehen Sie damit um, nicht-technischen Kollegen Datendiskrepanzen zu erklären?
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💥Data is meant for analytics and unless until simplified, it is always complex looking into computational models, either technical or non-technical. 💥Data visualization is of primary importance before getting it processed. 💥The non-technical stake holders can be provided with significant learning through layman approach which needs conceptual knowledge. 💥Correlating the dataset through practical case studies, natural processing and proper visualization in the primary phase makes the stake holders confident to handle the analysis. 💥Dataset must be played with fun to gather more & more learning leading to enriched learning. Creative Learning always brings in enthusiasm & passion and hence makes the mutual bonding strong enough.❣️
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To bridge the gap with non-technical stakeholders when faced with data discrepancies ... ✅ Translate tech into business: Use a universal semantic layer for all data and AI assets. This creates a common language that allows everyone to “speak data” without having to know the technical intricacies. ✅ Tell a data story: visualizations are your friend! Show the discrepancies and their impact using dashboards and reports. Make it clear how these issues affect the bottom line. ✅ Focus on solutions, not blame: Keep the discussion focused on how the problem can be fixed and avoided in the future. Nobody wins with finger-pointing!
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📝Break down complex data into easy-to-understand components. 🎨Use charts, graphs, and infographics to visualize discrepancies. 🔄Explain the issue using relatable real-world analogies. 💬Encourage dialogue by allowing stakeholders to ask clarifying questions. 📊Provide side-by-side comparisons of expected vs. actual data. 🎯Focus on business impact rather than technical details. 🚀Offer solutions and preventive measures to rebuild confidence.
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1. Speak Business , Not Bytes 💁♀️ Telling a stakeholder that “our ETL pipeline had a latency issue leading to an inconsistent aggregation” will get you blank stares. Instead, try: "It’s like when you check your bank balance at different times of the day—one shows pending transactions, the other doesn’t. Same data, different moments in time." 2.Make it a Team Sport 🙃 Instead of a blame game, make it a detective mission (Very Important) 🤌 - “Let’s define what ‘revenue’ actually means—do we count discounts?” - “Which number do you trust more, and why?” - “Let’s align on one single source of truth so we don’t have Groundhog Day meetings.”
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Simplify Communication Avoid technical jargon; use clear and concise language. Tailor your message to their level of understanding, focusing on the "why" behind the data discrepancies. Use Visual Aids Incorporate diagrams, flowcharts, and infographics to illustrate complex concepts. Visual tools can help stakeholders grasp the implications of data discrepancies more easily. Provide Real-World Examples Use analogies and relatable scenarios to explain technical issues. Share success stories or case studies that demonstrate how similar discrepancies were resolved in the past.
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Establish and enforce a data quality issue management workflow. Data issues/discrepancies should then be logged and triaged to the appropriate individual to help identify and remediate the issue. Technical stakeholders may need to come in to remediate identified discrepancies. This way discrepancies are tracked which makes it easier to identify and remediate similar future discrepancies.
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Addressing data discrepancies with non-technical stakeholders requires a clear and structured approach. It is essential to simplify communication: - Avoid technical jargon and use clear language. - Use metaphors or analogies to explain complex concepts. It is also important to visualize the data (a figure explains more than 100 words): - Present dashboards, graphs or infographics to highlight discrepancies. - Compare actual and expected data with practical examples. And finally, provide clear recommendations: - Present practical solutions based on the analyzed data. - Show the impact of possible corrections on their decisions. The goal is to transform a technical problem into an opportunity to improve understanding and trust in the data.
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Some thoughts… You have to explain in a way that non technical stakeholders can understand. There lies your expertise of how far you can see a case from an end user point of view. You will need to use examples from real world to explain the discrepancies and may need to use graphics for better understanding depending on the level of discrepancy and impact on business value. Explain how the discrepancy may impact the business value and stakeholders area of interest. Use simple explanations and graphics to convey that how by removing the discrepancy the data will yield to better results, reduced complexity and enhanced business performance.
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When explaining data discrepancies to non-technical colleagues, focus on simplifying the information. When the numbers don't look right, the trick is to keep it super simple for folks who aren't into that stuff. Think of it like explaining why your car's making a funny noise to someone who doesn't know anything about engines – you wouldn't use all the fancy terms, right? You'd just say, 'It's making a weird 'whirring' sound.' Same with data. You gotta find a way to explain it that clicks for them. Maybe use a real-world example they can relate to, or even draw a quick picture to show what's off. Basically, just talk to them like you're talking to a friend, not a computer.
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Maria Agrapidi
Business Intelligence Engineer | MSc Computer Science | BI & Data Analytics Certified
Effectively addressing data discrepancies with non-technical stakeholders requires a strategic approach. I translate complex findings into actionable insights using dashboards and reports to enhance understanding. In my experience, discrepancies often stem from how different systems structure and present data, rather than indicating errors. For example, sales data may exclude tax details, while financial reports provide a consolidated view that includes taxes, fees, and commissions. To bridge the gap, I analyze patterns across datasets and provide examples to illustrate these differences. By fostering transparency and aligning diverse perspectives, I turn data discrepancies into opportunities for clarity and improved decision-making.
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