Here's how you can collaborate with cross-functional teams to gather and analyze data for decision-making.

Frequent and open communication is key. Use tools like Slack, Microsoft Teams, or regular meetings to keep everyone updated and engaged. Organize activities that help build relationships and trust among team members. This can include off-site events, casual lunches, or team-building exercises. Utilize collaboration tools like project management software (e.g., Asana, Trello) to streamline workflows and keep everyone on the same page. Provide teams with the necessary resources and documentation to support their work. This includes creating handbooks, guidelines, and other supporting materials. Implementing these strategies can help create a more cohesive and collaborative work environment.

I can share one example from one of my previous organizations. We were a huge team in our data management department and had different KPIs. However our activities were interrelated at times. In our goal setting meeting we were told to jot down our individual KPIs and common KPIs which aligned with different departments. We were told to prioritise all KPIs. After prioritisation it was very easy to map interlinked activities and their KPIs. In this way common organisational goals were defined and individual goals were aligned to them.

Collaborating with cross-functional teams for data gathering and analysis enables organizations to harness diverse expertise, cover a broader landscape of data, and make decisions that benefit a wider audience. By following the fundamentals of collaboration, leveraging cross-functional expertise, widening the scope of data gathering, and making inclusive and well-rounded decisions, organizations can achieve more effective and sustainable outcomes. This approach not only enhances the quality of decisions but also ensures that they are equitable, ethical, and beneficial to all stakeholders involved.

La comunicación es clave, sin embargo, no basta con hablar, sino que entender la información con la profundidad necesaria es fundamental. Como un rol de líder, es importante conocer las facultades y capacidades que tienen los distintos equipos involucrados, con tal de manejar estimaciones, desarrollos y entregables que deben generar cada una de las partes. Cuando entiendes el proceso y el trabajo que hacen los agentes involucrados, puedes intervenir y/o complementar de manera óptima.

I like survey 123 as a template to use in collaboration with field maps. Remember that the simpler you make it to use(i.e clear directions, drop downs when possible and only showing relevant questions) the better your data will be on the other side. Nothing worse than having to spend hours cleaning data that your boss wants ASAP

Not only having goals towards data analysis, the company must have data to analyse. For gathering data, the key is to know what data you want to collect. The collecting may take part within online forms, interviews and team participation. You can collect your teams metrics like resolution and response time.

Data analysis is incredibly satisfying. Recently, I conducted a market analysis with the commercial, marketing, and finance teams. We collected essential data, and by analyzing the average sales ticket and cross-referencing programs with specific products, we gained detailed insights. This analysis allowed us to refine strategies, better understand the ICP, and highlighted the importance of data collection and analysis for strategic vision.

Collect data from various sources relevant to your goals. Engage with team members from different functions to ensure all necessary data is included. This might involve customer data from marketing, financial data from accounting, and operational data from production teams.

Data analysis is one of the important activity. 4 W - what type of data, who is responsible for that data, why that data has to be included/excluded, where is the data gathered from and 1H - how are we going to analyse the data? ie by which technology is very crucial for data analysis.

In the world of clinical research if you are reviewing clinical trial data with cross functional team members, it is vital to understand different perspectives that are unique to each person based on their knowledge, experience and skills. You've defined data collection goals, review plans, you sliced and diced the data into metrics and put them in reports / graphs - now in a cross functional meeting or forum, the Data management representative will facilitate data analysis, closely collaborating with stakeholders - presenting data with visual aids be it Excel pivot charts or BI reports. The key here is communication! bringing different perspectives helps to interpret the data effectively.

With an excel/power bi/arc pro stack and some ai assistance you would be amazed at what you can pull from your data. At the end of the day though leaders might prefer to get their data in something simple like a lean format as opposed to the full tables and maps format I prefer. Remember your audience because busy people have short attention spans

Use appropriate analytical tools and techniques to process and interpret the data. Collaborate with data analysts, data scientists, and subject matter experts to derive meaningful insights. Ensure the analysis considers the perspectives and expertise of different team members.

It’s not enough to present the data; the team must understand its significance. This includes how it was collected, what it represents, and how it impacts the project or decision-making process.

Present the insights in a clear and actionable manner. Use data visualizations, reports, and presentations to effectively convey the findings. Tailor the communication style to the audience, ensuring that each functional team understands the implications for their area.

Decision implementing is one of the bravest yet simple actions. Discussion about the correct decision may take long but it has to be ended by a decision which is implemented. This is important because once the decision is implemented then only we will understand the practicality of that decision. A clear outline of plan to be followed along with clear RACI and a plan to revisit the decision after a fixed duration is important.

In this highly technological and innovative cutting edge, it is common to lean toward the profound and new but take care, not all findings point to a change to the latest. Sometimes you are doing the right thing maybe you need to add more or remove some to be optimal and your task is employing the wisdom not to default to believing change is imperative