Matt Aslett's Analyst Perspectives

The market for data and analytics products is constantly evolving, with the emergence of new approaches to data persistence, data processing and analytics. This enables organizations to constantly adapt data analytics architecture in response to emerging functional capabilities and business requirements. It can, however, also be a challenge. Investments in data platforms cannot be constantly written-off as organizations adopt new products for new approaches. Too little change can lead to stagnation, but too much change can be chaotic, leading to silos of data and data integration complexity. This is one reason why there is growing interest in the concept of data fabric for managing and governing data across distributed environments. In addition to supporting hybrid and multi-cloud strategies, data fabric enables organizations to manage and generate insight from data spread across a combination of long-standing and new data platforms. Promethium focuses on automating data management and data governance across a distributed architecture with a combination of data fabric and self-service augmented analytics capabilities.

Promethium was founded in 2018 by chief executive officer Kaycee Lai, who had previously served as president of data catalog company Waterline Data. In that role, Lai saw first-hand that while data catalog products provided organizations with metadata-driven approaches to discovering, managing and governing data, there could still be considerable delays to generating meaningful insight from the data.

Typically, there are several manual steps between data discovery and data visualization, including data integration and transformation, data preparation, query validation and query processing. These can be time-consuming processes. For example, more than two-thirds (69%) of participants in our Analytics and Data Benchmark Research cite preparing data for analysis as one of the most time-consuming aspects of analytics initiatives. Promethium reduces delays associated with these manual steps by providing a data intelligence platform combining data fabric and self-service analytics functionality that would enable users to quickly access and analyze data in multiple data platforms without data movement or integration. Promethium’s offering combines functionality for connecting to data platforms, indexing, cataloging and orchestrating the data as well as augmented data preparation, query federation, visualization and natural language interpretation. Promethium features user interfaces targeted at the needs of data engineering teams as well as data analysts.

The company raised $26 million in Series A funding from Insight Partners, .406 Ventures and Zetta Venture Partners in February 2022, bringing its total funding to $34.5 million. It has used that to scale its data intelligence cloud service, with named customers including tire distributors ATD, bakery company Hostess, health savings provider HealthEquity and South Korean conglomerate CJ Group. Key target markets include retail and wholesale, food and beverage and healthcare.

Promethium’s combination of data fabric and augmented self-service analytics is designed to reduce the time required to deliver insights from data, accelerating business outcomes and improving productivity for both data teams and decision-makers. Data fabric is an approach to automating data management and data governance in a distributed architecture that comprises multiple on-premises and cloud data platforms. It is growing in popularity, given that data is increasingly spread across hybrid and multi-cloud architecture. I assert that by 2025, more than 6 in 10 organizations will adopt data fabric technologies to facilitate the management and processing of data across multiple data platforms and cloud environments.

While there are various (often highly vendor-centric) definitions of data fabric, key elements include a data catalog for metadata-driven data governance and agile self-service data integration. Promethium addresses both. Rather than migrate data from source data platforms into Promethium, the company offers connectors to multiple operational and analytic data platforms, both on-premises and in the cloud. Having connected to these data platforms, Promethium indexes the metadata and applies automated tagging and data matching to create a catalog of the connected data sources. Analysts can access the catalog via Promethium’s Data Explorer interface and perform ad hoc queries against the data using natural language search. As queries are run, Promethium automatically creates a Datamap. This is a data product that comprises the data, metadata and SQL generated by the natural language query. A Datamap can be published as a view, a table or as a dbt model to enable visualization of the data for analysis.

Promethium’s Storyteller Engine provides automatically generated data visualizations as well as natural language explanations. These visualizations and explanations are designed to accelerate understanding of the data to validate queries, and can be embedded into reports and dashboards. Promethium also integrates with existing data warehouse environments and business intelligence tools for further analysis. Data teams also use Data Explorer to create, validate and orchestrate data pipelines, including support for federated queries across multiple data sources.

Promethium has kept a relatively low profile since its formation, but has assembled a growing list of customers and a differentiated product that serves growing requirements for data analysts and data teams. Reducing friction between these two groups of users by encouraging collaboration provides an opportunity for the company, while there is the potential to drive greater adoption by serving the needs of chief data officers and data leaders by developing interfaces that provide higher-level views of data access and data usage within an organization. Additionally, reviewing data for quality issues is another time-consuming manual task that could be alleviated by the addition of data observability functionality to Promethium’s offering, further accelerating time to insight. Nevertheless, I recommend that organizations evaluate Promethium for approaches to improving productivity and accelerating business outcomes based on disparate and distributed data sources.

Regards,

Matt Aslett