4.1 Configuring Observability Sources
4.1.1 Observability Sources
4.1.2 Data Pipeline and Parser Configuration
4.1.3 Data Enrichment Techniques
4.2 Configuring RCABot and ML Models
7.2 Authentication and Security
7.3 Data Management and Data Model Handling
7.3.1 Storage
7.3.2 Retention
7.3.3 Export/Import
7.3.4 Working with Data Model
7.4 Control Center
7.4.1 License Entitlements
7.5 Platform Settings
7.5.1 Definitions
7.5.2 Preferences
7.5.3 About
Data Enrichment Techniques >
Data enrichment is a powerful process that takes your raw data and makes it even more valuable by adding relevant contextual information. It’s like enhancing your data’s context and depth.
To make data enrichment work, you need a unique key that exists in both your incoming raw data and the enrichment table, usually created manually. This key serves as the magic link that connects them. For example, it can transform IP addresses into branch names or decode postal codes into geographical information. In these cases, IP addresses/ postal codes will act as unique key. If the unique key present in raw data is also available in the addition source, the raw data is enriched with datasets available in the additional source. If the unique key is not present, raw data is not enriched. If there’s no match, the data remains unchanged and isn’t enriched. So, having that unique key in both places is essential for success.
Data enrichment is incredibly versatile. It can turn cryptic codes into easily understandable names. By using data enrichment, you’ll gain deeper insights and harness your data’s full potential. The extra context it provides makes data analysis a breeze. By applying data enrichment, users can gain deeper insights and make better use of their data. The enriched information provides additional context and enhances the understanding and analysis of the data.
A real-world example of data enrichment is upgrading geolocation data using an Enrichment Table. By linking a pincode (unique key) with values such as address, city name, and geo-IP, users can supercharge their geolocation data. When you provide a Key (i.e. pincode), the enrichment process fetches all the contextual data (i.e.ddress, city name, and geo-IP). This contextual data further helps in enabling a dynamic geographic map. A sample of geolocation data demonstrating the relationship between pincodes and geographical information is shown in the image below:
Here’s how data enrichment is done:
In essence, the key-value pair system enriches your data by adding contextual details based on the provided key (in this case, the pincode). It’s like giving your data a power-up, and it opens up exciting possibilities, like creating interactive maps with ease.
Performing data enrichment in vuSmartMaps™ is made simple with a clear step-by-step process. Let’s break it down:
The visual representation below illustrates the enrichment pipeline. Input data is transformed using configured enrichment settings, and the enriched output data is stored in an output stream. This allows users to enhance their data with additional context and insights for improved analysis and decision-making.
This simplified workflow guide ensures that you can easily enrich your data, making it more valuable and insightful for your analyses and decision-making processes.
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VuNet Systems is a next-gen visibility and analytics company that uses full-stack AI & Big Data analytics to accelerate digital transformation within an organisation. We provide deep observability into business journeys to reduce failures and enhance overall customer experience.