How can you improve public policy and governance with data storytelling?

Clearly articulate the purpose of your data story. Understand whether it aims to inform, persuade, or drive specific actions in the context of public policy and governance. Align the narrative with organizational goals and objectives.

When communicating data effectively, storytelling takes center stage, transforming raw numbers into compelling narratives. Crafting an engaging story relies on its structure: a captivating beginning, relatable scenarios, and a meaningful conclusion. Using storytelling techniques like analogies and personal experiences brings life to data, making complex ideas accessible and captivating. It's crucial to use clear and simple language to avoid confusion. By weaving data seamlessly into a story that resonates emotionally, you give depth and importance to factual information. In the end, it's the emotional impact and clarity of the narrative that leaves a lasting impression, overshadowing the technical details.

Data Story telling in this domain shall focus on growth and inclusivity both simultaneously. Through data, one, we should identify high growth investments which will benefit economy in general. And second, by identifying inequalities of different kind, should focus on inclusive growth. Using concepts of basic data analytics we should tweak policy programs like Aspirational District Program. Using Operational Research we should optimize resource disbursements/locations etc. Using numerical and differential equation concepts, we should study our historical data and know what works for us. At the core of any research lies - *usefulness* - how useful is the analysis in making decisions.

Airbnb transformed the travel industry by identifying and acting on a clear purpose: to help people belong anywhere by renting out their spaces. Their data-driven approach in understanding market needs and user behavior underlines the significance of starting with a defined purpose to disrupt traditional sectors.

Something I've observed through my work as an analyst and a data scientist, the most important bit for storytelling is to have a well defined, clear and simplistic message that conveys your exact purpose. The 4 act story structure popular in literature is surprisingly helpful in achieving this. - Setup : What is the problem you've identified, and how is it relevant to the community -Confrontation : Tackling the challenge, the how. Ideally explained through visuals, backed by numbers/timelines -Climax : What findings and insights are you expecting, and the potential outcomes that can be derived -Conclusion : resolution of the initial defined problem

Identify the target audience, which may include policymakers, government officials, citizens, or advocacy groups. Tailor the narrative to resonate with the audience's knowledge level, interests, and concerns.

A common myth: Knowing your audience means just using demographic data. The truth is, effective data storytelling demands a deep understanding of audience values, concerns, and motivations beyond just age or location.

Having worked extensively in data science, I've seen firsthand the power of tailoring data storytelling to specific audiences. Whether it's policymakers or the general public, understanding their needs and preferences is crucial. By making data accessible and relatable, we can bridge the gap between complex information and real-world impact, driving better decision-making and governance for everyone.

Use visual aids such as charts, graphs, and maps to illustrate trends, comparisons, and relationships in the data, making abstract concepts tangible.

As an analyst working across technical and non technical lines, the power of tailoring conversations to the right audience is something I've learnt first hand. It isn't limited to just their skillset and technical ability, but also their own personal goals and motivations. An example would be a policy maker's goals and motivations would be far different from the public who is effected by the decisions. Bringing everyone to the same page through your research is highly dependent on how you communicate to each audience member. Some quick tips would be - Identify the audience's core need, and tailor your presentation towards answering it - Keep it simple, complexity prevents understanding. - Use relatable analogies to increase engagement.

Select relevant and reliable data sources that align with the policy issue at hand. Use data that supports your narrative and enhances the understanding of the problem or opportunity. Ensure data quality and accuracy to build trust in your story.

When selecting data for data storytelling, we should prioritize relevance to our narrative, accuracy, and credibility. It is always important to ensure data sources are reputable, diverse, and representative to provide a comprehensive view. We need to consider the audience's understanding and tailor the data to make complex information accessible. It is a best practice to always verify the timeliness of the data and its alignment with the specific policy or governance issue being addressed.

Unpopular opinion alert: Not all data is good data. Choose quality over quantity because irrelevant or poor-quality data can distort your story and mislead your audience, even if it seems counterintuitive in the data-rich digital age.

Be mindful when collecting data. More is not always good, and different doesn't always mean valuable. Ensure data variances between different samples are within tolerable limits (based on your own understanding of the subject and general research where available). Be careful to ensure collected data is representative of root cause (correlation isn't causation). Countless projects fail due to poor quality data, as insights aren't valuable enough or do not identify the main issues. Make sure transformation pipelines are built to be robust, and automated where possible for ease of updating data. Document your sources clearly, explain the reasons for your approach and data selection where applicable.

Adiciono a este tópico a importancia da simplicidade na escolha e apresenta??o de um dados ou uma narrativa completa. é fundamental que o seu público compreenda o que você apresenta e por isso deve-se observer o qu?o complexo s?o os seus dados e como facilitar ao máximo o entendimento.

Narrative is the true victor in conveying data, transforming mundane mathematics into insightful stories. The essence of a compelling narrative lies in its structure: a gripping hook, relatable scenarios, and a powerful conclusion. Use storytelling techniques like metaphors and personal anecdotes to breathe life into numbers, making complex concepts accessible and engaging. Remember, clear and simple language is key to avoid confusion. By weaving data into a coherent and emotionally resonant story, you transform bare facts into meaningful insights. In the end, it’s the story, enriched with clarity and emotion, that leaves a lasting impact, not the raw technical details.

Having a coherent narrative is crucial. Begin by identifying a particular business obstacle, issue, customer concern, or any pressing problem. It's important to recognize that you don't need to tackle every issue simultaneously. Leverage your existing resources and take incremental actions to create an impact, while also planning future steps. Adopting this flexible approach will significantly aid in achieving long-term success.

Un récit engageant doit d'abord commencer par une accroche. Sans elle, il n'y aura pas d'attention ! Commencez donc par énoncer un fait ou un chiffre qui fera réagir votre public ou posez leur une question qui les poussera à réfléchir. Par exemple, si vous souhaitez expliquer pourquoi le MLOps est nécessaire à mettre en place dans votre entreprise et ce que ?a co?tera, vous pouvez commencer par "85% des projets d'IA ne sont jamais mis en production" ou "pensez-vous que tous les modèles de valeur sont mis en production ?" Ou encore "savez-vous pourquoi le projet xxx a été arrêté il y a xx temps ?". Bref, l'accroche est vitale !

Data storytelling can transform public policy and governance by making complex information accessible and compelling. By weaving data into engaging narratives, policymakers can better understand societal needs, visualize potential impacts, and make informed decisions. This approach enhances transparency, fosters public trust, and encourages a data-driven culture within governance, ensuring that decisions are not just based on data, but are also communicated effectively to engage and inform the wider community.

Develop a compelling and coherent story structure. Clearly define the problem, present evidence, and propose solutions. Use storytelling elements like characters, conflict, and resolution to engage your audience emotionally. Emphasize the impact of policies on individuals or communities to make the data relatable.

Utilize data visualization techniques to make complex information more accessible and understandable. Choose appropriate charts, graphs, and maps to highlight key trends, patterns, and relationships in the data. Ensure visualizations align with the narrative and are easily interpretable.

Visuals can mislead when data is out of context. Imagine presenting climate data without time scales or geographic specificity; it becomes misleading. Effective visualization requires correct context and relevance to the audience's understanding.

Those are the gone days when storytelling was happening with long data tables which contains numbers or texts, in this data science era the visualisation of data is must. In fact if it’s not visualise then it’s even not considered data storytelling at an extent. While doing the visualisation, one needs to make sure that consistent colour panels are used as it’s make easier to viewers to compare the things. I do use R & SAS in for my data storytelling depending on my need selects the packages or process, my favourite are ggplot2 and proc template.

A picture (visual) is worth a thousand words, so goes the old saying. Every action you've taken from the objective, gathering, cleaning, processing to analysing could become irrelevant if the wrong visual is chosen. Your visual needs to be compelling, clear and drive the audience to action. Chose the right tool for the job and so the visual should represent the right category of data. For time series data - line charts, categories - column, bar or pie charts. For distribution within a numerical range - histogram etc. Make it easy to understand the message your visual is trying to convey within the shortest time possible of seeing it.

Moreover, the data to be visualized must be presented in a simple, clear and easily understandable way by the public (the people intended to see the graphs)

Gather feedback from a diverse group of stakeholders to ensure the story resonates with different perspectives. Test the effectiveness of your data story through pilot programs or focus groups. Incorporate feedback to refine the narrative and enhance its impact.

Data Storytelling is fundamentally about explaining key facts by creating a compelling narrative and leveraging data visualizations in order to further engage with the audience and enlighten them about what is important. When this is done correctly it can lead to meaningful change based on data-driven decisions. When done incorrectly, it can destroy trust and credibility or lead to bad decisions based on bad information. Testing, validating, and refining the data is critical. The visuals and overall narrative must also be adjusted based on the data analysis outcomes.

Make your story relatable. Jargon has no place in a compelling story. Replace technical terms with clear explanations and engaging visuals like infographics or interactive charts. Better yet: ask yourself, if this element is really needed to bring your point across. You may be inclined to provide details that you and I may find important, but remember that every additional piece of information (especially data) makes the story harder to follow for most recipients. Build something anyone can understand.

Fundamentally, data stories are all about engaging and helping users digest complex information through an engaging presentation that gives them a well rounded view of the facts and impacts. This is quite a challenge and usually requires a lot of revisions to get right, as getting it wrong can damage the trust in data driven decision making over intuitive thinking. Where possible, always engage stakeholders, both understanding what they need and want, as well as what they find as faults or challenges in the data. Do not be defensive, keep an open mind as the challenges raised are the potential reasons why users might disregard your analytics. Improve, update and refine until you believe it will have the observed impact.

Practice and feedback! Then more practice. Do not do all practice in a row, take time to breathe, to relax. Image possible questions. And remember: focus on efficiently conveying the message, so no unnecessary redundancies.

Ethical Considerations: Ensure that data collection and use adhere to ethical standards, respecting privacy and avoiding bias. Accessibility: Make the data story accessible to a broad audience, considering factors like language, literacy levels, and disabilities. Engagement Strategies: Implement strategies to actively engage stakeholders, such as town hall meetings, workshops, or interactive online platforms. Long-Term Impact: Consider the long-term implications of the proposed policies and governance changes, and communicate these effectively.

Cultivate a hermeneutic stance towards data storytelling, wherein policymakers recognize the inherent interpretive richness of narrative meaning-making processes. By embracing a hermeneutic approach, policymakers can cultivate empathy, foster dialogue across divergent perspectives, and co-create narratives that resonate with the complexity and ambiguity of lived experience.

Engajamento: O storytelling de dados pode envolver cidad?es e interessados no projeto, permitindo que eles aumentem a compreens?o dos desafios e participem ativamente no desenvolvimento e implementa??o das politicas. Transparência: Ao tornar os dados acessíveis, o storytelling de dados deve promover transparência e a presta??o de contas, permitindo que o público avalie o desempenho do governo e exija responsabilidade.

Ensure data security and privacy: Implement guidelines to protect sensitive data from security breaches and inappropriate use. This includes requirements for data encryption, anonymization, and data access controls. Comply with regulatory requirements: Reflect compliance requirements under laws like GDPR, CCPA, and other relevant regulations in your data governance policy. Account for future regulatory changes in the policy's scope. Regularly review and audit: Include procedures for regular reviews and audits to check compliance with the data governance policy and the organization's regulatory requirements.

Improving public policy and governance through data storytelling involves crafting persuasive narratives supported by data visualizations to effectively communicate complex issues, engage stakeholders, and drive evidence-based decision-making. Therefore, take some advice on properly "selling" your stories, understanding how to make an impactful presentation with the best oratory.