Data analysts and stakeholders don't always see eye to eye. How can you bridge the gap?

First Part of the Story: I worked on a project to evaluate the success of a marketing campaign for the introduction of a new product as a member of a team of data analysts. To learn about their objectives, difficulties, and expectations for the campaign, we had a meeting with the product manager and the marketing team. We questioned them on things like: -What are the campaign's key performance indicators (KPIs)? -Who are the product's target market categories and audience? -What are the campaign's primary platforms and channels? -How are the campaign results measured and monitored? This assisted my team in defining the goals, parameters, techniques, and instruments we would employ in our data

To bridge the gap between data analysts and stakeholders, it is important to go beyond understanding stakeholders' needs. Take the time to grasp their roles, pain points, concerns, and the organizational culture. By gaining insight into their perspectives and concerns, adapting your communication style to be empathetic and transparent, and advocating on their behalf, you can establish stronger rapport with them.

Story Part 1: I was part of a team of data analysts who worked on a project to analyze the effectiveness of a marketing campaign for a new product launch. We met with the marketing team and the product manager to understand their goals, challenges, and expectations for the campaign. We asked them questions such as: -What are the key performance indicators (KPIs) for the campaign? -Who are the target audience and segments for the product? -What are the main channels and platforms for the campaign? -How do they measure and track the campaign results? -What are the potential risks or issues that may affect the campaign? This helped my team define the scope, objectives, metrics, methods, and tools we would use in our Data Analysis steps.

One thing I have found helpful at work is to ensure that both parties have a clear understanding of the project's objectives, goals, and expected outcomes. Stay away from making assumptions without confirmation from stakeholders. Also, listen carefully to stakeholders' concerns, priorities, and expectations. Address their questions, ask clarifying questions, and incorporate their feedback into the analysis process.

Bridging the gap between tech and business folks is a real challenge. In my approach, I would initiate one-on-one conversations with key stakeholders to truly understand their pain points, goals, and preferences. Presenting data insights in a story format, linking numbers to real-world implications, adds a personal touch and enhances understanding. Additionally, I'd tailor visualizations and reports to suit the specific interests of each stakeholder, making the data more relatable and accessible.

To bridge the gap between data analysts and stakeholders, focus on building a strong relationship based on trust and communication. Engage stakeholders early and often to understand their needs and expectations, and involve them throughout the project lifecycle. Clearly define the objectives, scope, and deliverables of the project, and set realistic timelines and expectations. Keep stakeholders informed of progress, challenges, and changes, and be transparent about any risks or limitations. By fostering open communication and collaboration, you can ensure alignment and achieve successful outcomes that meet stakeholders' expectations.

Story Part 2: We agreed on the scope, timeline, deliverables & format of the data analysis project. We defined the objectives, criteria, metrics, and how we would report the results. Set realistic and achievable expectations and communicated any changes that may affect them. Tools and Processes: -Project charter: Outlines the project’s purpose, scope, objectives, deliverables, roles and responsibilities, assumptions, constraints, and risks. -Gantt chart: Visually shows the project’s tasks, dependencies, durations, and progress. -Data analysis plan: Describes the data sources, methods, techniques, and tools for the DA. -Data dashboard: Displays the key metrics and insights of the data analysis in an interactive and user-friendly way.

Most executives and managers typically focus on the return on investment. It is advantageous to quantify the benefits or savings from your data projects and present an estimated financial return.

Story Part 2: We reached a consensus over the data analysis project's deliverables, schedule, scope, and format. We established the goals, standards, measurements, and procedures for reporting the outcomes. Establish reasonable and attainable goals and let them know about any changes that might have an impact. Instruments and Procedures: -Project charter: Describes the goals, deliverables, roles and duties, assumptions, limitations, and hazards of the project. -Data analysis plan: Outlines the DA's tools, procedures, methods, and data sources. -Data dashboard: Provides an interactive, user-friendly display of the important metrics and insights gleaned from the data analysis.

Story Part 3: Involve them in the process We shared our progress, challenges, and insights with the marketing team and the product manager regularly and asked for their input and feedback. We used tools and techniques such as: -Data visualization (Tableau; Power BI): A technique that uses graphs, charts, maps, and other visual elements to present the data in a clear and engaging way. -Data storytelling (Juicebox; Nugit): A technique that uses narratives, context, and emotions to explain the data and its implications. -Data workshop: A session where we presented and discussed the data analysis results and recommendations with the stakeholders and solicited their feedback and suggestions.

Story Part 3: Give them a hand in the undertaking We routinely solicited feedback and input from the marketing team and the product manager, sharing with them our progress, difficulties, and insights. We employed methods and instruments like: -Data visualization (Tableau; Power BI): A method of presenting data in an understandable and captivating manner by utilizing graphs, charts, maps, and other visual components. -Data storytelling (Juicebox; Nugit): An approach that explains the data and its implications using stories, emotions, and context. -Data workshop: A meeting in which we asked the stakeholders for their opinions and suggestions and presented and discussed the findings and recommendations of the data analysis.

Based on my experience, the following points are very crucial: 1. It's essential to communicate your findings in simple, non-technical language. 2. Stakeholders evaluate your analyses based on how they impact end users, so addressing their concerns is important. 3. Express your thoughts openly, keeping in mind different perspectives. 4. Communication is the key.

I see this as a two way process. In addition to learning from stakeholders, one should seize this opportunity to educate stakeholders about the data analysis process, limitations, and assumptions. Collaboration and involvement in decision-making processes yield better outcomes and build stronger relationships.

Trust is important, trust with stakeholders is built through transparency, reliability, and consistency in data analysis processes and results.