As partes interessadas não técnicas querem mais análise de dados. Como você gerenciará suas solicitações de forma eficaz?

Most of the time data analysts tend to conceptualise users needs and plan ahead of time what the next data request may be. So, such times are usually very interesting because all you need to do is to create dashboards that can be interactively utilised. But it gets to a point when data requests become unpredictable. It can be frustrating when at times the actual Terms of References(ToR) are not well spelt out or when it appears that the entire concept is generally misunderstood by the customer/user. For me, I prioritise based on my clear understanding of the ToR as well as urgency.

É legal alinhar as expectativas e a viabilidade do que pode ser entregue. Procurar entender exatamente o que eles querem alcançar com os dados e, a partir disso, alinhar quais análises realmente fazem sentido para os objetivos deles. Também é interessante comunicar de maneira simples o que é possível dentro do prazo e dos recursos disponíveis – o que ajuda também a evitar frustrações.

Prioritizing data requests based on impact and feasibility is essential. I assess which analyses will provide the most significant insights and value to stakeholders while considering available resources. By focusing on high-impact projects first, I ensure timely delivery of critical information that enhances decision-making. For Instance: When faced with multiple requests, I collaborated with stakeholders to identify their top priorities. By delivering a high-impact analysis on customer behavior first, I was able to provide actionable insights that informed marketing strategies.

This issue is not unique to data; as it is all to common in IT within a larger company. Non-IT stakeholders, whilst they may be 'technical' in their own fields - think production, for example, they do not understand jargon from either 'techies' or those involved in extracting numerical data - both have to be explained in language that the audience can understand; be it graphical or otherwise.

Priorisierung von Daten ist in meiner Erfahrung entscheidend. Bei einer Flut von Anfragen ist es wichtig, sich auf Projekte zu konzentrieren, die den grössten Mehrwert bieten. Ich habe oft erlebt, dass Teams ins Stocken geraten, wenn sie versuchen, alles auf einmal zu erledigen. Eine klare Priorisierung, basierend auf Machbarkeit und Auswirkungen, steigert nicht nur die Effizienz, sondern stärkt auch das Vertrauen der Stakeholder.

The data team need to be able to talk in terms the business understands. What's the top 3, or top 5 things the business needs to operate better? Learn the key terms, key drivers, key risks, key stakeholders. Rocking up and talking "data" is no interest to most of them.

Clear communication is vital for managing expectations and conveying complex data concepts. I strive to translate findings into easily understandable language, using visual aids like charts and graphs to illustrate key points. Keeping stakeholders informed throughout the analysis process fosters trust and ensures alignment. For Instance: In a recent presentation, I simplified technical findings into straightforward insights, using visualizations to highlight trends. This approach helped non-technical stakeholders grasp the data's implications, leading to more informed discussions.

Meiner Meinung nach reichen 4 Kennzahlen den Stakeholdern zur Steuerung und alles was über vier Seiten anschaulicher Präsentation hinausgeht nimmt niemand mehr wirklich wahr. Klare Kante oder, wie ich zu sagen pflege “Die frühe Wahrheit” sollte gerade im Führungskreis Standard sein.

To communicate data analysis effectively with non-technical stakeholders, keep it simple, use visuals like charts, focus on key points, provide context, encourage questions, summarize key findings, and seek feedback for improvement. This approach ensures clear and concise communication that enhances understanding and decision-making.

1. Scope Management. The scope of analytical reporting should be established during the implementation phase of a project. While iterations are expected, they are more easily managed within a pre-defined scope. 2. Alignment with KPIs. Any request for more data should be guided by the essential metrics which are usually the KPIs, SLAs, OLAs of the process. If requests for more data persist outside of those guideposts, then they may need to be reconsidered. 3. Centralized reporting. The most frustrating "analysis-paralysis" scenario is when additional questions are being raised due to data discrepancies between systems. Agreeing on the single source/system/method of truth is essential to avoid the pitfalls of running endless data analysis.

Implementing training sessions can bridge the gap between data experts and non-technical stakeholders. By increasing data literacy, stakeholders can better understand data analysis and its relevance to their roles, empowering them to ask informed questions. For Instance: I organized a workshop on basic data analysis concepts for non-technical teams. This training not only improved their understanding but also encouraged them to engage more effectively in discussions about data-driven decisions.

Treating data analysis as an iterative process allows for continuous integration of stakeholder feedback. After presenting initial findings, I actively listen to their responses to refine the analysis, ensuring that final deliverables align with their needs. For Instance: After sharing preliminary results of a market analysis, I gathered feedback from stakeholders on their specific interests. This input guided further analysis, resulting in a final report that addressed their key concerns and questions.

Instead of overwhelming them with jargon, the team reframes data requests into actionable, bite-sized goals. Each iteration is presented as a storyboard, showing progress and sparking discussions. This process allows the stakeholders to see the evolution of their requests in real-time, ensuring alignment and adjustments along the way. This dynamic approach not only manages expectations but also fosters an environment where every iteration brings them closer to their goals, while subtly educating them on the complexities involved.

Utilizing technology can streamline the management of data requests. I advocate for data management tools that enable easy querying, reporting, and visualization, allowing non-technical personnel to run basic analyses independently. For Instance: By implementing a user-friendly data visualization tool, I empowered non-technical stakeholders to explore data on their own. This self-service capability reduced the burden on the data team and satisfied stakeholders' information needs more efficiently.

To meet their needs, streamline requests using intuitive dashboards, letting them explore data without deep dives into technicalities. Automated reports tailored to their preferences ensure timely insights. Foster collaboration through clear communication channels, translating data speak into actionable narratives. Integrate AI-driven tools to predict needs, enhancing decision-making. By balancing technology and transparency, you empower stakeholders, making data a shared language, fostering trust and driving strategic growth, all while keeping complexity at bay.

Diese Tools sind gerade auch in der Bearbeitung und Analyse von Ideen sehr wichtig, eröffnen dem Einzelnen die Möglichkeit 💡 Ansätze zu verifizieren und so Potentiale zu erschließen.