Ngày nay, các biểu đồ đẹp không còn là chương trình lớn; chúng là chương trình trước. Người ta ước tính rằng mỗi ngày chúng ta tạo ra một lượng dữ liệu đạt 2,5 quintillion byte (đó là đủ để giúp bạn dành cả cuộc đời của bạn để xem Netflix). Những công cụ này, chẳng hạn như Power BI và Tableau, có khả năng làm cho dữ liệu trông tuyệt vời, và điều gì xảy ra khi bạn thêm học máy vào hỗn hợp là hình ảnh phải làm điều gì đó ngoài việc chỉ nhìn tuyệt vời - hình ảnh phải biện minh cho lý do tại sao dự đoán là quan trọng và giành được sự tin tưởng của người dùng. Besides, more than 70 % of organizations report insufficient trust in analytics as an immense obstacle to AI and ML integration. It is not really only about big-time dashboards; it is about building experiences that people can comprehend and care about using. The ML-driven dashboards are not these general future predictors; they are specific advisors that clearly advise and explain why the sales should grow by next month, and what specific activities to undertake to monetize on that statement. When predictive analytics is accompanied by explorable, visual formats, which end users can access using a variety of devices, the dashboards foster trust and engagement amongst the end-users, whether they access desktops, smartphones, or wearable smart devices. The end goal is to turn moves of complex algorithms into open-book stories that motivate balanced decision-making with practical business outcomes, not just reports that are left untouched: The ML predictions must also not be put in the form of isolated numbers. In Power BI or Tableau, matching the forecast with background, historical trends, benchmarks of the relevant sphere, along with relevant KPIs, will give the user an idea of the significance of the estimates. To strike an example, a sales forecast is much more convincing when related to the annual cycles, the past campaign influences, and the market climate in a unified visual flow. Integrate Predictive Outputs with Contextual Storytelling: : Another feature that can help build trust is explainability that is integrated into the user experience in dashboards. This may contain feature significance graphs, model confidence bands, and scenario-based what-if analysis planes. Varying use of SHAP value summaries in Tableau to customize Power BI visuals facilitates the visualization presentation of XAI into overall BI tools so that non-technical users can identify the rationale behind the model outputs. Apply Explainable AI (XAI) Principles : There is a growing consumption of desktop, mobile, and embedded analytics experiences by users. The design uniformity (the same color schemes, symbolic signs, interaction patterns) allows for keeping the trust and familiarity. What that implies is that the ML insights need to be just as interpretable when looked at through a CEO's iPad dashboard as when looked at through a review tab of a sales manager or through a field engineer on his mobile app. Design for Cross-Platform Consistency : Dashboards should enable human-in-the-loop interaction, where ML suggestions are supplemented with expert commentary. For instance, an HR attrition model in Power BI can present both its prediction scores and an HR analyst’s qualitative assessment. This blend reduces “black box” skepticism by showing that AI augments rather than replaces human judgment. Blend Human Expertise with ML Recommendations Instead of having fixed images, interactive drill-downs enable the readers to drill down to find out the reasons behind the predictions. In Tableau, a forecasted spike can be clicked and might provide the background variables, comparisons against related historical events, and even connections to follow-on datasets. This dynamic changes the meaning of dashboards from a passive consumption context to an active decision-making context. Make Interactivity the Gateway to Deeper Insight: Conclusion Vấn đề không phải là làm cho thuật toán hoạt động, mà là làm việc; câu hỏi thực sự là làm thế nào để mọi người muốn tin vào nó và sẵn sàng sử dụng nó. Combining the attention-grabbing Space-Age looks of Power BI and Tableau with the geekier-than-ever predictions of ML and adding in a bit of explainability, consistency, and storytelling, you transform dashboards into “must-haves” as opposed to “mehs.” Since the most powerful ML-based dashboards are not just pretty graphs, but the type of data experience that causes people to nod, smile, and say, “Okay… now I get it.” That is when predictive power no longer becomes a boardroom buzz phrase, but one that can put you in a position to pay or collect the rent.
