Discover, Connect, and Solve: Exploring the Power of External Data

Table of Contents:

1. Introduction
2. The Explorium External Data Platform
3. The Shift in Focus to Finding the Right Data
4. Explorium: An External Data Platform
5. The Data Science Landscape
6. The Challenges with External Data Integration
7. The Solution: Explorium's Data Catalog
8. Use Cases for Explorium 8.1 Marketing Related Use Cases 8.2 Fintech Use Cases
9. Explorium's Customer Base
10. Product Demonstration: Enriching SMB Leads with External Data
11. Prioritizing SMB Leads Using Explorium
12. Use Case: Credit Risk for Small Businesses
13. Explorium's ML Engine for Credit Risk Prediction
14. Leveraging External Data for Accurate Predictions
15. Conclusion

Introduction

In today's data-driven world, finding the right data to feed into algorithms has become crucial. The focus has shifted from building better algorithms to uncovering the right external data sources. Explorium, an external data platform, aims to address this need by providing a vast and wide data catalog. This article will dive deep into the Explorium platform, its use cases, and the benefits it offers to data scientists and analytics teams.

The Explorium External Data Platform

Explorium, founded in 2017, is an external data platform that offers a comprehensive data catalog. It houses a wealth of external data, including information on people, businesses, locations, time, and events. This platform serves as a one-stop solution for discovering, connecting, and matching external data to internal data. By leveraging this external data, data scientists and analytics teams can enhance their advanced analytics and automated machine learning initiatives.

The Shift in Focus to Finding the Right Data

Traditionally, data science and advanced analytics focused heavily on algorithmic development. However, recent years have seen a shift in focus towards finding the right data. While internal data remains a significant source, incorporating external data can provide additional context and improve the accuracy of predictions. But finding and integrating external data can be a challenging and laborious process, given its fragmented nature.

Explorium: An External Data Platform

Explorium addresses the challenges associated with external data integration by offering a user-friendly platform with a vast data catalog. This platform simplifies the process of finding and accessing thousands of external data signals. By leveraging Explorium, data scientists gain access to an extensive range of data sources that provide context for various use cases. With Explorium, the process of finding and accessing external data signals is automated, enabling data scientists to focus on deriving insights.

The Data Science Landscape

Data scientists and analytics teams often rely on a mix of internal and external data. However, the external data space poses challenges due to its fragmentation. Explorium understands the need for a wider perspective and offers a solution that provides context for different use cases. By automating the process of finding and accessing external data signals, Explorium allows data scientists to work with a diverse set of attributes, including locations, time, and events, in addition to traditional data sources.

The Challenges with External Data Integration

Integrating external data into analytics workflows can be a complex and time-consuming process. Data teams face difficulties in evaluating various data vendors and struggle to find reliable external data sources. The fragmented nature of the external data ecosystem adds to the complexities. These challenges create a barrier to expanding the scope of analyses and deriving Meaningful insights from external data.

The Solution: Explorium's Data Catalog

Explorium's data catalog offers a solution to the challenges associated with integrating external data. With over 85 external data sources and 3,452 Relevant data signals, Explorium provides an extensive range of data for enrichment and analysis. The catalog includes online reviews, average ratings, industry information, and physical footprint data such as foot traffic. By leveraging these data signals, data scientists can gain valuable context about businesses and make informed decisions.

Use Cases for Explorium

Explorium caters to a wide range of industries and offers diverse use cases for data-driven insights. Marketing-related use cases include lead scoring, prioritization, and lifetime value prediction. In the fintech sector, Explorium assists with risk assessment, fraud detection, insurance claim predictions, and revenue forecasting. These use cases demonstrate the versatility of Explorium across different industries and highlight the importance of external data in improving analytics outcomes.

8.1 Marketing Related Use Cases

Explorium's data catalog proves invaluable for marketing-related use cases. By incorporating external data like online reviews and industry information, businesses can gain insights into brand popularity and industry trends. Lead scoring and prioritization become more accurate using a combination of internal and external data. Additionally, lifetime value prediction helps identify high-value customers and tailor marketing strategies accordingly.

8.2 Fintech Use Cases

In the fintech industry, risk assessment is a critical task. Explorium's external data platform offers an extensive array of relevant data signals, allowing data scientists to evaluate the credit risk of small businesses. With features like online reputation, industry classification, and physical footprint data, accurate credit risk predictions can be made. This helps financial institutions and online lenders make informed decisions and minimize defaults.

Explorium's Customer Base

Explorium has an impressive customer base spanning various industries. Businesses from marketing, fintech, and consumer packaged goods leverage Explorium's platform to enhance their data science initiatives. The flexibility of the platform allows it to cater to diverse requirements, making it a valuable asset for data-driven organizations.

Product Demonstration: Enriching SMB Leads with External Data

The Explorium platform offers a live product demonstration showcasing its ability to enrich SMB (Small and Medium-sized Business) leads. By integrating external data, analysts can prioritize leads for their field sales teams effectively. The demonstration provides insights into the benefits of leveraging Explorium for lead enrichment and advanced analytics.

Prioritizing SMB Leads Using Explorium

Using the Explorium platform, marketing analysts can prioritize SMB leads by adding context through external data sources. The data catalog offers features such as online reviews, average ratings, industry information, and physical footprint data. By utilizing this external data, marketers can make data-driven decisions, filter leads based on specific criteria, and prioritize leads with higher potential.

Use Case: Credit Risk for Small Businesses

Another compelling use case for Explorium is credit risk assessment for small businesses. Explorium's ML Engine enables data scientists to build and deploy models for predicting the likelihood of small businesses defaulting on loans. By combining internal data with external data signals such as online reputation, industry classification, and historical foot traffic, institutions can assess credit risk more accurately.

Explorium's ML Engine for Credit Risk Prediction

Explorium's ML Engine streamlines the process of building credit risk prediction models. Leveraging the platform's extensive data catalog and automated feature engineering, data scientists can discover relevant features and select top-performing ones. Machine learning models are then trained on these features, resulting in robust and accurate credit risk predictions.

Leveraging External Data for Accurate Predictions

Explorium's ML Engine demonstrates the power of external data for accurate predictions. By incorporating a wide range of external data signals, machine learning models can capture nuanced relationships and improve predictive performance. The integration of online reputation, physical footprint data, and other external sources enhances the credit risk prediction model's accuracy, enabling better decision-making in the lending industry.

Conclusion

In an era where data is abundant, finding the right data for analytics is essential. Explorium's external data platform offers a comprehensive solution for discovering, connecting, and matching internal and external data. By leveraging thousands of relevant external data signals, businesses across industries can enhance their data science initiatives, gain valuable insights, and make more informed decisions.

Highlights

• Explorium is an external data platform that provides a broad data catalog.
• The platform automates the process of finding and accessing external data.
• It caters to various industries with diverse use cases, including marketing and fintech.
• Explorium's ML Engine enables accurate credit risk predictions for small businesses.
• Leveraging external data enhances predictive models and improves decision-making.

FAQ

Q: How does Explorium automate the process of finding external data? A: Explorium's data catalog contains thousands of external data sources that are automatically ranked and scored. The platform simplifies data discovery by suggesting relevant data signals based on specified targets.

Q: Can Explorium be integrated with existing data science workflows? A: Yes, Explorium offers flexibility in integrating with existing workflows. The platform allows users to either deploy models on Explorium or consume the discovered features in their in-house models.

Q: Is Explorium suitable for businesses of all sizes? A: Yes, Explorium caters to businesses of all sizes, from small and medium-sized enterprises to large corporations. The platform's versatility allows organizations to leverage external data regardless of their scale.

Q: Can Explorium improve the accuracy of credit risk assessments? A: Yes, Explorium's ML Engine enables the integration of external data signals such as online reputation, industry information, and physical footprint data. By incorporating these signals, credit risk assessments for small businesses can be made more accurate.

Q: How does Explorium help in prioritizing leads for field sales teams? A: Explorium's platform provides context through external data, allowing marketing teams to prioritize leads effectively. By leveraging features like online reviews, industry information, and physical footprint data, analysts can filter leads based on specific criteria and focus on high-potential ones.

Resources:

• Explorium