Getting stakeholders on board is critical for any new project, and having supportive stakeholders ensures your department’s hard work is recognized. Data strategy is an increasingly vital function within any organization and can cover scaling analytics; creating strategies for current and future technologies such as AI, blockchain, and digital twins; and ensuring that the organization makes the best use of its data. A data strategy isn’t just about the data itself: It’s about communicating the value that can be generated through data from cradle to grave.   While emerging technology is always exciting and enticing, storytelling can effectively express a sound data strategy—determining and communicating what’s essential for your organization. Introduced technology should be relevant to the current and future data usage and the organization’s overall strategy, and should align with the organization’s analytics and technological maturity. Several common areas engaged in contemporary data strategy are the following:  
  • AI: allows machines to learn from data and apply that learning to perform tasks and execute processes using human-like thought patterns
  • Analytics: the use of technology to discover, interpret, and communicate patterns, relationships, and connections in data
  • Blockchain: a means to record digital transactional data and distribute it in a peer-to-peer network
  • Digital twin: a virtual replication of an object or system to help with decision making
  • Metaverse: the use of an immersive virtual world to facilitate connection in a digital reality


  Not everyone understands data, but everyone understands a story. Matthew Luhn, whose credits include the Toy Story films, Finding Nemo, and many more, suggests finding hooks that can be used to gain the attention of your audience.  
  1. The unusual: This is when you start your story with an unusual example to interest users in learning more about your project. For example, what if you can slice and dice data you have in a leadership meeting and get answers without waiting on the team to update visualizations? What if you create a data asset that’s accessible to all employees with the right security levels to enable better reporting self-service?
  1. The unexpected: This is when you surprise your users, sharing something with them that they aren’t expecting or accustomed to hearing. What if the data team can create a model for the business that tells it what to source from which country to which factory around the world?
  1. Conflict/action: This involves the identification of a conflict or challenge and can be couched as a call to action. If we know that the organization is struggling to hire data engineers, visualization specialists, and other data talents, for example, the upskilling of internal staff can offer training and resources through storytelling to get stalled projects up and running.
  Anyone who has attended a meeting and thought, “I can see that this is relevant to my area, but I didn’t really understand the content because it was too technical,” can appreciate that storytelling is an art. There are few best practices that can help communicate dense data, whether in a presentation, a proposal, or even an email to share data strategy with other stakeholders.  


  Make it personal and relatable: Make sure you know your audience before your presentation, project pitching, or meeting. You can use LinkedIn and other social media platforms to know them better. Ensure that you’re relatable when sharing information with them.   No matter how complex the data set is or how technical the information you’re trying to convey, remember you’re dealing with people, regardless of what level of the organization they’re in. Tell the data story in an authentic voice and with genuine interest in the key takeaways.  


  Particularly if your audience isn’t technical, be sure to include only enough technical information to make it understandable. On the other hand, if your audience is very technical, then take them with you on the journey by diving into the technical details.   If you’re sharing a forecasting model that’s created through the coding language Python, for example, and you know your audience members aren’t coders, avoid detailing lines of code and explaining the testing. Instead, explain how the forecasting was done covering criteria, confidence levels, and other relevant data. If you’re speaking to members of the finance data and analytics center of excellence, you might want to go through the robustness of the model and testing done. They would love to get technical.  


  Though most audiences won’t be interested to read the code or to look at the breakdown of numbers, the same information can often be conveyed in a visual format. Use graphs, figures, or even just pictures that you can speak to. A picture speaks a thousand words.   If you put lines of code on the screen, the nontechnical people will be switching off and not listening, and even the technical audience might get lost reviewing the code and stop listening to you. Include the positive impact. For example, how will this project support moving ahead of our competitors? Or how will it make the organization improve?   Data strategy is shaping the future of today’s organizations. Future technologies such as Web3 and the metaverse are developing as you read this article. Not everyone will be interested in these developing technologies, but we’ll all be operating within or alongside them. This is why storytelling is crucial. It helps land the right message and get the audience interested. Have you used storytelling in your data strategy? I would love to hear your thoughts and experiences.  

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