Promethium, CEO and Founder: Governance and Analytics Are Two Sides of the Same Coin

(US and Canada) Kaycee Lai, CEO and Founder of Promethium, speaks with Mark Johnson, Editorial Board Chair, CDO Magazine, and Regional VP, Fusion Alliance, about Promethium as a product and how it is the one-stop solution ideal for business people, data engineers, and data analysts. 

Promethium is a revolutionary melting pot of a product that brings together business people, data engineers, and data analysts, states Lai. They can find the required data instantly, but also can build data sets and answer analytical questions in minutes. With a connection to 200+ data sources, Prometheum provides a huge spectrum of data available at one’s fingertips. Once the connection is established, the entire organization gets a single logical view of everything in one platform across databases, data lakes, data warehouses, or Amazon S3 buckets.

Lai adds that the product is 100% auto-populated. Since no data has moved into Prometheum, no time is spent on moving and nothing is stored, thus fulfilling the implication of speed and security. He states that users tend to look for particular data by filtering the data source, so the product allows them to create and customize their view.

Speaking on the nature of data sources, he describes it as dynamic, noting that there is a scope of curation for data stewards. This aids in improving the governance of data by maintaining sync and providing the curator with real-time results that make things dynamic and trustworthy.

Promethium values automation accumulation of data into one place so that it becomes easier for the managers to endorse the product, given the level of visibility. Lai believes that in analytics, one cannot decide a product's relevance until they have worked with the data.

Highlighting the loops in data management is possible, wherein different tools are handled by a different team, be it discovery, governance or modeling. The challenge that arises is measuring the team's skills and integrating them. Promethium solves that problem by providing a well-integrated data set experience. It gives optimum information on context, relatable suggestions and dataset usage in one click, making it easy for both business users and data persons to look at it.

With this dynamic nature comes the storytelling aspect. Lai calls it the natural language generation, the correlation analysis, wherein the users proceed from one data set to another by following statistical trends. Promethium allows users to choose their dimensions, with a better understanding of data and knowing the implications.

The product briefs business people on using data management tools. If a business user has a query that requires a data set to be built, Promethium's reasoner will use global discovery and AI to determine the question's meaning and where the particular data can be found. The statistical analysis of data helps to validate trust and get the user something they want. Lai states that Promethium is simple enough for a nontechnical user but powerful enough for a data engineer.

He asserts that everything produced in Promethium becomes usable, reusable and editable objects. Sharing user success stories, Lai says that in the previous year Promethium answered 16,000 user questions, which also means 16,000 ETL jobs with advanced queries and new dashboards, which wouldn't be possible without Promethium.

The product has helped customers in figuring out if they wanted to make a quick adjustment in inventory levels and supply chain levels because of inclement weather. It helped to instantly get the visibility of data from various sources like Oracle, Microsoft SQL servers, Google big query, and the cloud bucket. 

Lai notes that they also have customers in retail who are e-commerce companies selling through many channels. In their case, the problem of keeping track of each selling outlet, pricing, discount levels and best-selling items was solved by Prometheus because it answered all of those questions in real-time with a click.

Moving on to the terms “data fabric” and “data mesh,”  he says that data fabric is a product and data mesh is a framework. Data fabric fits into the data mesh framework very well. 

Delving deeper into the topic of data fabric, he says that the name remains apt, as fabric implies flexibility and connection. The data fabric's purpose is to connect all data sources, regardless of type and location, for analytics. Secondly, it needs to provide visibility of data, regarding what is good, bad, or accessible.

Highlighting how Promethium delivers a value proposition, Lao points out that the product has been well received by everyone, especially the CDOs, who are the ones answering to the businesses and are under constant pressure to deliver meaningful insights apart from the governance aspect. That is where Promethium fits right in, by helping them build data insights from scratch, without the need of going through complex machinations. It sits proudly within the ecosystem, enabling its users — data engineers and data analysts — the people in charge of the complex discovery, the complex modeling, the ETL, and SQL.

Next, he uses an analogy to describe Promethium. He says that if dashboards were the final plate of food, then the data source would be the refrigerator where the food is stored and everything else in the middle — knives, pots and pans, the cookbook — is Promethium. He urges enthusiasts to read the book “97 Things Every Data Engineer Should Know” to see Promethium in action. Lai concludes by stating that Promethium has shown how active governance and fast agile self-service analytics can work together.