How do you overcome technical jargon or complexity barriers in data communication? Can you share examples of how you have simplified complex data insights to make them more understandable and relatable to stakeholders?
I completely agree with Utpal. It's crucial to tailor the message to individual stakeholders. There's a risk in not fully grasping the level of knowledge or exposure they might have to the technical aspect of the solution being discussed.
What I find helpful in these cases is to structure the communication in layers. Start with a high-level executive summary, then dissect the different technical aspects of the solutions, layer by layer, using diagrams, graphs, and visualizations.
Given the amount of knowledge out there and the time and interest people are devoting to learn about artificial intelligence and data analytics, I'm often surprised by the level of understanding some senior leaders have about technology or data architecture.
So, while you're tailoring your communication to a senior audience, it's helpful to structure those communications in a layered format. This allows those who are more technically oriented or curious to go deeper and understand the more technical aspects of the discussion.
2. Which brings me to my second point: storytelling. By framing data insights within a narrative, I can connect with stakeholders on an emotional level, making the data more memorable and impactful. Instead of just presenting raw numbers, I provide a particular example (ex: an impact to a customer, how an issue could have been avoided by an employee ant the repercussions it had instead, etc.). Not everyone likes these type of stories, but in general I think that this approach can turn complex data into a compelling story that resonates with the audience.
Agree with George. Simple visual aids and storytelling are the key tools, accompanied by value metrics relatable to in different parts of the business.
For example, when presenting findings from a customer churn analysis, instead of diving into the intricacies of machine learning algorithms, I focused on telling a story. This narrative immediately resonates with stakeholders, painting a vivid picture they can relate to.
Another technique I employ is the use of analogies. Visual aids are also invaluable.
Ultimately, the key is to empathize with your audience. Put yourself in their shoes, understand their priorities, and frame your insights in a way that speaks directly to their goals and challenges. By doing so, you transform data from mere numbers into a powerful tool for decision-making.
Remember, our job isn't just to analyze data – it's to make that data meaningful and actionable for everyone in the organization, regardless of their technical background
The second approach is during meetings or decision-making processes where data jargon and complexities often arise. We decode these complexities using different kinds of pictorial representations. We use architecture diagrams and other visual aids when presenting to stakeholders. Business stakeholders are primarily interested in the impact on their business, the value, and the return on investment we are bringing to the table.
At the same time, technical stakeholders are expecting more technical details. So, we aim to take the discussion to a level where it is understandable by both parties. We use flow diagrams, block diagrams, charts, histograms, and other visual aids to represent the data so that it's clear how everything is connected.
I've noticed that over the last year or so, communication has become much easier because many people have started to understand these jargons and complexities. They have already entered into that arena.