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        David Menninger's Analyst Perspectives

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        Explorium Improves Machine Learning Models With External Data

        Today, organizations understand the importance of good external data that can be integrated with internal data to train machine learning models. Our Machine Learning Dynamic Insights research showed that external data adds a significant value in gaining competitive advantage, improving customer experience and increasing sales. But getting the right external data for a particular requirement is not always easy. Internal data is usually not enough to train different models because of its narrow scope of usage and lack of relevance. Manual data acquisition methods are resource-intensive and can take weeks or months to get the data ready to feed into models.

        Ventana_Research_DI_Machine_Learning_08_Benefits_of_External_Data IMAGE 1Explorium is an external data platform that enhances data discovery, validation and integration with external data sources to improve analyses including machine learning models. Its platform can analyze data models and search for relevant datasets in its library of external data signals. Analysts and data scientists can use the platform to enrich predictive models and enhance accuracy. Its data management platform enables data scientists and business analysts to explore and acquire third-party data from a variety of external data sources with its autoML engine. It can then integrate the data into production pipelines using recipes for real-time data enrichment.

        In mid-2021, Explorium raised $75 million in its series C funding to expand into new industries and grow the library of data sources. Series C came less than 12 months after Explorium’s series B funding of $31 million, bringing the total investment to $127 million. The company plans to bring external data to analytics processes and add new features to support more use cases.

        Organizations are always hunting to gather the best data to gain an analytical edge in the market. Good data is a must when building accurate machine learning models, but is sometimes hard to find for a given model requirement. Finding the right datasets is a complicated and resource-intensive process. Our research also showed some of the common challenges organizations face when applying machine learning, among which accessing and preparing data for training models tops the list. Data access and preparation is getting somewhat easier through automation, but is still a daunting and time-consuming process. Using an external data platform such as Explorium can enable organizations to integrate third-party data into analytics, business intelligence and machine learning models to increase operational efficiency.

        Ventana_Research_DI_Machine_Learning_02_challenges_applying_ML_200228 IMAGE 2Explorium offers a range of data sources and signals, and can enable business personnel to build data pipelines for business intelligence and analytical processes. It offers a searchable external data gallery that includes a variety of data such as company, people, geospatial and time-based data to generate datasets. Data scientists can also add custom code to incorporate domain knowledge and fine-tune artificial intelligence models.

        Organizations looking to incorporate external data in analytics and machine learning processes should consider Explorium for data enrichment. Its data management platform enables data scientists and business analysts to increase operational efficiency by accelerating machine learning models.

        Regards,

        David Menninger

        Authors:

        David Menninger
        Executive Director, Technology Research

        David Menninger leads technology software research and advisory for Ventana Research, now part of ISG. Building on over three decades of enterprise software leadership experience, he guides the team responsible for a wide range of technology-focused data and analytics topics, including AI for IT and AI-infused software.

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