Analyst Perspectives

The phrase ‘big data’ may have largely gone out of fashion, but the concept of storing and processing all relevant data continues to be important for enterprises seeking to be more data-driven. Doing so requires analytic data platforms capable of storing and processing data in multiple formats and data models. This will be an important focus for the forthcoming Data Platforms Buyer’s Guide 2024. 

It also requires the ability to integrate data from multiple data sources through flexible and agile data pipelines, as highlighted in the Data Pipelines Buyer’s Guide 2023. Those data sources include numerous enterprise applications but also external data from partners, customers and independent data providers. Vendors such as Explorium are helping enterprises discover, ingest and integrate external data to improve analytics initiatives, including the training of artificial intelligence and machine learning models.

Explorium was founded in 2017 to create a platform designed to help customers improve use of external data to enhance and accelerate business decision-making. Customers can use the company’s data platform to explore and acquire third-party data from Explorium’s library of external data sources and integrate it into production pipelines. Explorium’s platform can also be used by analysts and data scientists to analyze, enrich and enhance the accuracy of predictive and generative AI models.

More than one-half (56%) of participants in our Analytics and Data Benchmark Research incorporate three or more external sources in data processes. Popular external data sources include location, economic, social media, demographic, government, market and weather data. The importance of external data is well understood, particularly by sales and marketing professionals who use it to complement internal data to enhance segmentation and personalization efforts, amongst other things. However, many enterprises struggle to find external data that can be trusted and matched with internal application data and integrated with internal machine learning models.

Explorium argues that its ExplorAI platform provides users with more leads as well as better conversion rates and improved pipelines. The company’s target industries include manufacturing, consumer package goods and cloud-native application providers, with key use cases including the identification of prospective customers, sales lead scoring and qualification, data enrichment, the analysis of addressable markets and supply chain analysis. Explorium has raised $127 million in funding, including a $75 million Series C round provided in May 2021.

Explorium’s ExplorAI provides users with a searchable catalog of external data as well as generative AI capabilities to facilitate the identification of important data, attributes and trends with which to drive business decision-making. The catalog provides a variety of bundles of data related to:

• Businesses, including key financial metrics, contracts, spending intent, web traffic trends and workforce demographics.
• Individuals, including consumer preferences and spending habits, education and employment information and validation of contact information.
• And locations, such as national and regional demographics, property ownership and values, transportation and footfall and geographic data.

The data bundles are available via an application programming interface, while ExplorAI also provides data integration capabilities. These enable customers to upload data from internal data platforms and applications and export data along with machine learning features generated in ExplorAI to production data pipelines, applications and analytics and predictive modeling tools.

ExplorAI also provides data enrichment functionality supported by generative AI capabilities to create data signals that are tailored to customer requirements. In the case of prospective prospecting, for example, ExplorAI can be used to examine an enterprise's internal data to determine the characteristics of good prospects and then use a combination of keyword-based filtering, ideal customer profile analysis and custom scores and prioritization to identify “look-alikes” from a combination of internal and external data.

Our research has consistently shown that accessing and preparing data is one of the most time-consuming aspects of analytics and one of the most significant challenges enterprises face when making use of machine learning and AI. External data platforms can assist with accessing, matching, merging and integrating internal data sources, while AI can make it easier to work with external data by automating data enrichment, discovery and utilization. I assert that by 2027, three-quarters of all data processes will use AI and ML to accelerate the realization of value from the data. 

Enterprises attempting to be more data-driven should integrate all relevant data into analytics and AI initiatives, including internal and external data. Those evaluating approaches to streamlining the incorporation of external data in analytics and machine learning processes should consider Explorium and the role ExplorAI can play in facilitating data enrichment and enabling business analysts and data scientists to improve operational efficiency by accelerating the development of analytics projects and machine learning models.

Regards,

Matt Aslett