How can data analysts work better with non-technical colleagues?

Successfully bridging the gap between data analysis and non-technical colleagues is pivotal for driving informed decision-making. Clear communication, visual storytelling, and a deep understanding of stakeholder needs are essential. Empathy and patience play a crucial role in fostering a collaborative environment. As a data analyst, I believe in empowering non-technical colleagues through training sessions and maintaining a continuous feedback loop.

My first principle is to approach all colleagues with empathy for their diverse backgrounds. I believe visualizing data is essential to communicate complex ideas. Specifically, interactive charts and dashboards. Also, using simpler and more relatable language are important while talking with non-technical team members.

As a Data Analyst, you are supposed to have a deep understanding of the business line that you support: the KPIs senior management is following, the ones linked to profitability, linked to cost and linked to risk & compliance. For every single request, a Data Analysts needs to have a good understanding of what the business leaders will do with that information. This helps to reframe the request, adjust the format and output to meet the requester demand.

Effective collaboration between data analysts and non-technical colleagues hinges on clear communication and bridging the gap. Data analysts should prioritize translating technical insights into understandable terms, avoiding jargon. Collaborative workshops can demystify data processes, fostering a shared understanding. Regularly share insights through visually appealing presentations or dashboards, making complex data accessible. Building relationships is key; understand non-technical colleagues' priorities and tailor data insights to address their specific needs. Actively seek feedback to refine analyses and ensure relevance. Encourage a culture of curiosity and learning, offering support for colleagues to develop basic data literacy.

In my experience when explaining your data analysis process to non-technical colleagues, it's key to simplify complex concepts. Use analogies and real-world examples to make abstract ideas more tangible. Break down your process into clear, concise steps, avoiding jargon. For instance, compare data cleaning to editing a draft document - it's about making sure the information is accurate and readable.

When explaining data analysis to non-technical colleagues, start by outlining your process: how you collected the data, the steps taken to clean it, and the analytical methods used. Be transparent about the tools and techniques chosen, emphasizing their relevance to the specific business context and goals. Discuss openly the limitations and assumptions inherent in your analysis, ensuring a balanced understanding of its scope and potential impact. This conversation is not just about showcasing expertise, but also about demystifying data analysis, fostering a shared knowledge base, and addressing any questions or misconceptions. #DataLiteracy #Teamwork #DataTransparency

Before jumping the gun, I believe we need to identify the right source of information required to fulfil the request, be crystal clear about it before starting the work and understand for which set of KPI we are building a data pipeline, regardless of the technology in use. Once this is done, we explain the chain of tasks to the requester (in their own language: let's not "talk SQL") for them to understand how easy / difficult it is, and perhaps, what else we can do at the same time, so we don't duplicate the work later on.

In my expertise, effective visualization is key. In addition to using charts, graphs, and tables, dashboards can be particularly powerful, offering an interactive and comprehensive view of various data trends. I like to highlight key findings by using distinct, color-blind friendly colors, clear labels, and annotations, ensuring that my main insights stand out and are easily digestible for my target audience. By transforming complex data into clear, engaging visuals, I can make my analysis more persuasive, and also enable non-technical peoples to grasp the value and impact of my findings. #DataVisualization #EffectiveCommunication #BusinessIntelligence

Effective communication is key for data analysts working with non-technical colleagues. Prioritize clarity in result visualization, using easily understandable charts and graphs. Tailor your message to the audience, emphasizing actionable insights rather than complex methodologies. Encourage collaboration by involving non-technical colleagues in the interpretation of visualized data, fostering a shared understanding and facilitating informed decision-making.

Before choosing visuals, it's important to understand why the stakeholder(s) are requesting a particular information: why it's important for them, and what do they plan to do with it. This helps us building the narrative, even before choosing the visual to illustrate it.

The art of translating raw data into a simple and easy to read visualiseation. Will do more to integrate the skilled and not so skilled, members of a team. The following feedback generated by a visualisation is highly valuable. Pictures before numbers!

Data analysts can enhance collaboration with non-technical colleagues by utilizing community-engaged participatory research methodologies. This methodology entails tight collaboration with colleagues to delineate research inquiries, collect and analyze data, and implement discoveries in a manner that is significant to all parties involved. By integrating a range of viewpoints, especially from those with non-technical expertise, analysts guarantee that their work remains pertinent, easily understood, and capable of being put into practice. This cooperation promotes mutual comprehension, improves the significance of the data analysis, and closes the divide between team members with technical and non-technical backgrounds.

Working with business leaders is an iterative process: I usually solicitude feedback every 3 days when I build dashboard and have a deep dive very week or two. Business leaders don't always know what they want: however, they recognise what they don't want and what is interesting in a dashboard.

We shouldn’t treat a given analysis as a one off. The follow-up is permanent as we help the business measuring its effectiveness across business/products/services lines, help to generate more revenues and / or improve customer experience.

ChatGPT's and LLMs are playing a pivotal role in transforming operations research and data science learning and understanding. Now, by seeking out training and courses specifically built for teaching non-technical users the right questions, they can harness the power of operations research and data science through AI, with no math or code. By enabling those without a background in data science or coding to access and leverage advanced analysis techniques, then also equipping them to use AI as a collaborator to aid in understanding, they can instantly simplify complex concepts, create captivating visualizations, and clean and prepare data– making data analytics accessible and actionable for a wider and non-technical audience.

Something I find interesting in my experience is that, "the business", " the non-tech stakeholders" are all people with questions and as many others have said, they each have their own contexts to have these questions answered. In data analytics, we need to understand, elicit and explain the big story that connects the threads of what each stakeholder is needing to know. When we use this power of connection, we not only answer questions, but bring people and strategies together. It's worth sitting back sometimes and considering the common threads too.

In my experience many non technical colleagues are hyper aware that they are “non technical”, dismissive of the expertise they do have, and nervous about talking with me before the conversation even begins. To overcome this when we first start working together I find it helpful to exhibit curiosity, be humble, and call out what I am learning from them about the business and their work. Part of this article mentions “educating them” but I prefer to think about learning together to answer a question we and the business as a whole care about. Framing a tractable question that is still valuable to the business is the hardest part of any analysis project and non technical partners are essential to that work.

It is always a good idea to work with non technical collegeagues. It can help us "Data Analyst" out in many ways: The whole idea of data analysis is to understand the business problem, do the analysis and present it in the form of visualization. We as a technical person often present data in a more complex way and the audience who is a layman doesn't understand it. So having non technical person gives us an idea how non technical person sees the visualization from their eyes. Non technical person can help us to understand how we can make people understand the solution in a more simple language. Non technical person gives us a edge in understanding the problems. It can help us understand the mindset of our audience.

1. Clear Communication 2. Tailored Presentations 3. Define Jargon 4. Storytelling with Data 5. Understand Business Needs 6. Collaborative Training 7. Visual Representations 8. Address Concerns Proactively 9. Establish Common Goals 10. Foster a Team Culture 11. Regular Check-ins 12. Feedback Mechanism