Personalized Homepage
Private Beta Feature
Interested in trying this feature? The functionality detailed in this article is in private beta testing. To request exclusive access, please sign up for the waitlist.
The personalized homepage serves as a dynamic hub within the GoodData Cloud ecosystem, providing a seamless interface for data exploration, user interaction, and advanced analytics. Utilizing machine learning algorithms and user behavior analytics, it tailors its offerings to individual user needs, ensuring relevant, timely, and actionable insights are always at the forefront. Designed for optimal user engagement and data navigation, the personalized homepage integrates a range of specialized features to enhance the depth and breadth of your analytical capabilities.
Features of the personalized homepage:
Have a question about your data? Ask our Generative Chatbot! This feature allows you to interact with your data in a conversational manner, quickly retrieving specific metrics or gaining insights from vast datasets. You can even get visualization suggestions. It’s like having a data scientist ready to assist at any moment. Learn more about the Generative Chatbot.
Our recommendation engine proactively suggests content based on your behavior, trends in the data, and the preferences of similar users. Whether it’s a visualization you haven’t noticed before or a dataset that’s particularly relevant, these recommendations help ensure you’re always at the forefront of your analytics journey. Dive deeper into Recommendations.
Stay informed without the hassle of deep diving into data. Our Automated Data Stories feature generates easy-to-understand, human-readable narratives for your alerts. This means when an alert is triggered, you get an instant, clear description of the event, tailored to your configured settings. It’s like having a personal analyst summarizing your most critical data changes. Explore Automated Data Stories for streamlined, context-aware insights.
Easily access and manage all your reports, dashboards, metrics, and more with the Analytics Catalog. It’s a curated space that streamlines your analytics access, allowing for quick navigation and organization of your most important content. With integrated search and categorization features, finding the data you need has never been easier. Explore the Analytics Catalog in detail.
Recommendations
In the realm of analytics, users are often inundated with an overwhelming amount of data. This overabundance can lead to analysis paralysis, where users struggle to determine which metrics, visualizations, or dashboards are most relevant to their current objectives. Further, as organizations and their datasets grow, locating pertinent analytics becomes increasingly challenging, time-consuming, and can hinder productivity.
To combat these challenges, GoodData’s personalized homepage introduces the recommendations feature. By analyzing user behavior, data relevancy, and other contextual parameters, Recommendations proactively offers users the most pertinent analytics based on their unique needs and recent activities. Instead of manually sifting through vast amounts of data, users are presented with suggestions tailored to their workflow, allowing for more focused and efficient decision-making.
Overview
User-Centric Suggestions:
The Recommendations engine considers individual user behavior and interaction history with the platform. This means the more you engage with GoodData, the better Recommendations becomes at predicting what you might need next.
Context-Aware Analytics:
Beyond user behavior, Recommendations factors in organizational context. For instance, if there’s an ongoing campaign or quarterly review, relevant dashboards or metrics might be prioritized in your recommendations.
Continuous Learning:
Recommendations isn’t static. As workflows evolve, so does the engine. It adapts and refines its suggestions based on changing patterns, ensuring users always have the most relevant insights at their fingertips.
Seamless Integration:
Recommendations are seamlessly integrated into the personalized homepage. Users don’t have to venture into a separate module or interface. Right from the homepage, they can quickly access and act on the suggestions provided.
Make the Most of Your Recommendations
To optimize the utility of Recommendations, regularly engage with the analytics presented. This continuous interaction will fine-tune the system’s understanding of your needs. Also, encourage team members to actively use GoodData, as broader organizational engagement can provide a richer context for more accurate recommendations.
Automated Data Stories
In the fast-paced world of data analytics, staying on top of real-time events without getting bogged down in details is crucial. Automated Data Stories address this need by providing instant, human-readable descriptions of events as they happen, especially when an alert is triggered. This feature simplifies understanding the context behind each alert, ensuring that users can quickly grasp the significance of data changes without extensive analysis.
Overview
Contextual Understanding Without Effort:
Automated Data Stories eliminate the need for users to manually study the context surrounding their alerts. When an alert is triggered, the system generates an easy-to-understand narrative explaining the underlying event.
Personalized Alert Narratives:
Each alert narrative is customized. To make use of this feature, users must set up their own alerts, see Use Alerts to Set Up Data-Driven Workflows. Once configured, the system provides a tailored description specifically relevant to the user’s configured parameters.
Human-Readable Event Descriptions:
The core of Automated Data Stories lies in translating complex data points into clear, concise narratives. This makes interpreting and responding to data-driven events simpler and more intuitive for all users.
Usage in the Demo Environment
To test this feature you need to integrate your demo environment with OpenAI using your own OpenAI API token, see Start Using the Demo Environment.
Analytics Catalog
As organizations scale their analytics operations, they accumulate a vast array of visualizations, dashboards, and metrics. Discovering, revisiting, or sharing these analytical objects can become a daunting task. In the absence of a structured system, valuable analytical outputs risk getting lost, underutilized, or outdated, leading to redundant efforts and inconsistencies in decision-making.
Nestled within the Explore Homepage, the Analytics Catalogue is a centralized hub where users can swiftly search, view, and organize all analytical assets. By streamlining access and enhancing discoverability, it ensures that analytics are promptly leveraged and shared across the organization.
Overview
Streamlined Search:
Harness the power of a sophisticated search mechanism to quickly find specific visualization or dashboards. The smart search intuitively prioritizes relevant results, minimizing the time spent searching.
Dynamic Recommendations:
Based on user interactions and popular trends within the organization, the Analytics Catalogue offers tailored recommendations. This proactive approach ensures that users are always presented with pertinent and actionable insights.
Clear Metadata Annotations:
Each analytical asset is supplemented with descriptive metadata. Understand the context, origin, last updated timestamp, and other crucial details associated with each analytical object to bolster data literacy and trust.
Categorized Views:
Navigate with ease through structured categories. Whether it’s a recent dashboard or a frequently accessed report, the Analytics Catalogue organizes assets in a user-friendly manner to accelerate discovery.
Collaborative Interaction:
Leverage community-driven analytics by adding comments, sharing visualizations, or even upvoting valuable dashboards. By fostering collaboration, the Analytics Catalogue amplifies the collective intelligence of the organization.
Maximize Benefits with the Analytics Catalogue
To optimize your experience with the Analytics Catalogue, ensure that your analytical assets have clear naming conventions and detailed descriptions. Engage actively with the platform, contributing comments, and annotations. This not only enhances the catalogue’s recommendation engine but also supports a collaborative analytics ecosystem.