Promethium helps companies instantly know the value of their data and what questions their data can answer. This may sound easy but, with over 350 database, data warehouse, data lake vendors and an estimated 180 zettabytes of data by 2025, discovering one’s data, and understanding what it means becomes more complex every day. It’s not uncommon for enterprises to take up to 4 months to answer a question from one of their business units.

Promethium provides a single, AI-powered context solution that simplifies the data pipeline for BI by allowing users to simply ask a question. Promethium then automatically locates the data, provides instructions on how to assemble that data, reveals the quality of the data and even displays the SQL statement, dramatically reducing the time and costs associated with manual data analytics. This entire process now takes less than 4 minutes. Promethium achieves this in the following manner: 1. Connects to all data sources to abstract metadata. 2. Infers name, meaning, location and relationships of data across sources. 3. Leverages automation, machine learning and AI to automate the mapping of natural language to data and its intent.

Getting value from data is a fragmented task that spans multiple/separate teams who use multiple/separate tools. Unfortunately, these tools frequently do not integrate with each other, making it difficult and time-consuming for users to finally consume the data. For example, there are multiple data catalogs for each data type, different data prep tools for on-premise data sources and cloud data sources, and different ETL/ingestion tools. The list goes on, and integrating all of these tools often costs more than the cost of purchasing them. Promethium is a single solution that, while integrating with existing data catalogs, ingestion, prep and visualization tools, gives the user a single, seamless experience to ask a question, locate the data, see how the data should be assembled, see the SQL statement automatically created and finally see the execution of the query.

We provide value for customers no matter the size of their data estate because every company that relies on their data is trying to derive meaning from it as quickly and efficiently as possible. That being said, enterprise clients seem to benefit more due to the size, heterogeneous nature, complexity of their data infrastructure, as well as the necessity to comply with stringent governance regulations.

Yes. Promethium is a cloud-native data management solution that leverages AI and Machine Learning. It has an extensive API-layer allowing it to connect with existing / new data sources, BI tools and other data management solutions.

We offer a SaaS solution. Sign on, connect to the data sources you want us to contextualize, and that’s it. That is the fastest and easiest way. Promethium can be consumed in the public cloud or in users own virtual private cloud (VPC). A third way allows you to deploy Promethium on-premise in the customer’s own physical data center.

Any industry that uses data to derive business intelligence in the pursuit of competitive advantage. That being said many companies in banking / financial services, as well as insurance, gravitate to our solution.

Rather than starting from aggregating all of one’s data and trying to figure out what the data means, Promethium starts with the questions one wants to ask and maps the data to the question. One of our customers said, “If I don’t know what I am looking for, and I don’t know what I should be asking, giving me 1,000 options is not helpful.” Promethium’s AI/ML solution guides our customer’s to the few (less than 5) options that are best suited to their task. Our engine helps give context and provides guidance to apply the framework most relevant to your needs.

We have solved these types of challenges for enterprises worldwide at companies like Oracle, VMware, Google, and Electronic Arts. We understand how to build robust enterprise systems that scale seamlessly for the enterprise, allowing for fast and massive adoption. Furthermore, our expertise in data management, data analytics/BI, and data governance gives us a unique advantage of understanding how to address the pain points specifically related to all things data.

We don’t. We never touch your data. We only move the metadata, which is the data about the data, so your data is never compromised. We pull your metadata directly into a metadata index, allowing us to search for data as if we have the entire file. You can search by name, type, vendor, IP address, context, or even by asking a simple question. Lastly, we only move the tables related to answering your specific question. No more. No less, resulting in a highly efficient, fully automated process.

No. We actually work with any data source, automatically discovering where the relevant data is located across your infrastructure.

No. The whole point of being able to do ad-hoc analysis is that you want to answer questions that haven’t been asked or use data that has not been discovered. Promethium discovers the data and figures out how to assemble the data all in real-time AFTER the question has been asked. This avoids the painful task of identifying ALL of the data and relationships BEFORE the question has been asked.

No. We do it across multiple tables and across multiple vendors as well, eliminating the time-consuming and costly step of manually searching for your data’s location. At this point in the conversation, there is a long pause, followed by either an almost imperceptible head nod or grunt. The light is coming on, but the skepticism is not completely gone.

No. We automatically do that for you, providing step-by-step instructions on how to assemble the query, and with Promethium, you don’t have to know SQL code. What’s more, the directions can be easily attached to an email and sent to the relevant person in your enterprise to run the query.

If the results are wrong, you want to know specifically what to clean and where. Many solutions only tell you that in your five petabyte data warehouse, there is some bad data. That’s not very helpful. But since we are only answering a specific question, and know that to do that involves these four tables, and these six rows we can quickly and easily clean that data, which greatly increases the accuracy of the answer you are getting.