How can you improve communication in data analysis projects?

It's about establishing a shared understanding among stakeholders regarding the project's purpose, key questions and expected outcomes. Clear documentation of the project plan, encompassing deliverables, timelines, data sources and methodologies, serves as a guiding document.

I can’t emphasize enough how important it is to take the team to sit down with the data and really explore it. Use this time to develop your understanding, draw up any questions you may have, and validate your understanding with the team collectively. Only when you really understand the data can you move forward and begin to look into stakeholder needs and problems to be solved.

Curiosity is fundamental when working with data, especially across organizations. Assumptions can be disastrous when you don't ask how the data is used and interpreted across the network. Even if the data seems pretty well documented, it's worth asking other key stakeholders about their understanding of the same data. Try to see the data and related verbiage from other perspectives and develop questions that could expose other possible interpretations. I can't stress enough that getting on the same page in terms of verbal aspects of the data is critical in preventing significant development risks.

For me, it's about the audience. Working in finance, yes- we're going to dive into the data and understand every element of it. From a market leader perspective, they need a high-level summary that informs business decisions. Gets back to the 5Ws: Who, What, Where, When and Why.

One thing I've found helpful when working on collaborative, data-oriented projects is to ensure everyone checks in frequently to avoid a separation and disparity in tone, voice, and presentation to have a seamless, uniform voice that best presents your data from multiple contributors.

In my experience tools and platforms are dictated by availability and training. You can use whats available to you (depends on good understanding of requirements) and you can get people to use what they're trained on or familiar with. A great example of this in the communication space is the preferences and familiarity/user base of Zoom or Teams in an organization. Oppositely, accessibility is a primary goal for file sharing and storage, and therefore system agnostic solutions can be less path-dependent choices later on.

I agree. Clear objextives, clean data, accountability/measure, simplified communication, transparancy, upate key stakeholders regularly. And of course team flexibility.

Simplifying our language is a fundamental and often overlooked skill in the data analysis space. As analysts, we can get deeply absorbed in the data stories. Yet, it's important to remember our audience might not share our data fluency. It's our responsibility to present findings in an easily understandable way for everyone involved, as these findings shape the decisions that drive the next best steps.

I've been a part of numerous projects where maintaining a 'jargon' list is helpful both for ensuring shared understanding of terminology but also a way of quickly integrating new team members. Instead of their having to navigate the languages of the six different disciplines in the team they can begin to see prior term alignment and get an understanding of the type of language they can expect to come across often.

Graphs and pictograms are fundamental to effective communication. We as experts often do not realize that our audiences may not know about the subject matter as much as we do, so visual elements are a great way of simplifying results. Moreover, a large quantity of information can be easily condensed into a graph/chart, thus saving you a lot of words. Senior management usually does not have the time to go through the entire analysis, but they sure look at all the visual elements to understand the analysis.

Promote a dynamic data analysis setting by cultivating teamwork and open dialogue. Embrace transparent documentation, employ visualizations for intricate data, host regular team gatherings for updates, and translate technical details into a language accessible to non-technical collaborators. Foster curiosity with feedback loops, ensuring everyone comprehends the analysis intricacies. Uphold project transparency through clear reporting, keeping the entire team engaged in the project's journey and discoveries.

I understand that making the effort to ask for and to use stakeholder/audience feedback can sometimes feel like extra work, especially since it's often not thought about until the reporting stage of data projects. I find it's easier in the long run if it's thought about from the very beginning. Input from folks who are not deep in the data can really make it so much easier to make smart decisions about goals, language, visuals, etc. After all, if we want all our analysis work to result in others in making new decisions and taking new actions, we'd be wise to do everything we can to make sure we are fully supporting them in doing so.

Be explicit about the assumptions made, and rationale behind them. It’s easy to assume that because something is quantified, and has data to back it up, that it is absolute truth. But data is a tool to inform business decision making, prioritization,and story telling. It can be misleading or manipulated (even unintentionally). Stating assumptions, testing them for robustness and representativeness, and being open about limitations of the model and analysis are critical to the outcomes we want data for (better, faster, more informed decision making). This in turn improves communication, culture around data, and results based on the data in question.