Thank you for Subscribing to CIO Applications Weekly Brief
Thank you for Subscribing to CIO Applications Weekly Brief
In an interview with CIO Applications, Nick Handel, CEO and Co-Founder at Transform, shares his insights into how their product makes data trustworthy and uncovers relevant insights and context to help all teams stay on the same page and make informed decisions.
What are the factors that led to the conception of Transform?
Between 2006 and 2014, the other two Co-Founders— Paul Yang and James Mayfield, were working on data infrastructure space at Facebook and struggled a lot with scaling storage and compute and data warehouses issues. Facebook was collecting enormous analytical applications for the datasets as they built out the infrastructure that unblocked new use cases for data. Later, both Mayfield and Yang joined Airbnb as a product manager and an engineer, respectively. I was working there as a data scientist at that time. They observed similar scaling challenges. They were building various tools and asked for my feedback as a data scientist on those tools. All three of us worked together on different parts of Airbnb’s data infrastructure. From there, we got the idea of building metrics—repository metrics— that can define the most critical metrics of a company and then serve those metrics to various applications. Metrics store offers specific tools to exist with consistent definitions of those metrics and with efficient compute and logic against the underlying data warehouse. Tools such as business intelligence, data visualization, and Excel have been around for decades, but still, there is an element missing to help companies and their workforce be data-informed and make data-informed decisions. We established the company with this idea to build easy-to-use, yet in-depth tooling.
We are building a generalized piece of infrastructure that any company with data storing in their data warehouse can benefit from. Businesses of all sizes from any industry that have challenges around metric consistency can leverage our product. We have customers across consumer internet businesses with physical stores, enterprises, shipping, and manufacturing. We can help organizations that collect data and then want to leverage those data sets for analytical applications to define metrics on top of the data sets.
We can help any company that collects data and then want to leverage those data sets for analytical applications to define metrics on top of the data sets
Business stakeholders want accurate metrics such as active customers, revenue, and acquisition to make informed decisions. That is why companies must ensure a consistent definition of all these metrics. Data analysts take raw data sets and ultimately turn them into useful metrics or time series of data or single data points that become the foundations for important decisions. But these metrics are prone to errors due to the involvement of the human element, and the process is also time-consuming. Secondly, data analysts often struggle with improving productivity. A data analyst becomes a gatekeeper for the data inside of a modern business. Today’s systems speak the language of data, which is SQL, and programming languages such as Python, which only data analysts understand. That is why business stakeholders need to go through data analysts to obtain the data sets they want.
We are trying to define a layer on top of logic that allows business stakeholders to express their data sets in metrics without the help of data analysts. They can ask to obtain answers for any question such as revenue by product category, week, or country. Even for the straightforward questions, the logic that gets expressed against the underlying data warehouse is complicated. We are building a layer that can translate business stakeholders’ simple requests into the language that is compatible with the underlying storage and compute engines for turning into data sets.
Could you elaborate on the features and functionalities of your platform?
We are bridging the gap between the data analyst and the business stakeholders or the data producer and the data consumer. Data analysts define metrics using a combination of structured abstractions to express complicated logic in a relatively simple format. Then, they manage and govern the definitions of the metrics using version control and various modern software engineering systems. These metrics get picked up by Transform that exposes a variety of interfaces for different consumers to use. We have a metrics catalog, which allows business stakeholders to come and look at all of the different metrics that are defined within Transform. They can see the definition, a chart of the data, metadata, ownership, tiering of the metric, and everything around governance. Data analysts can also create annotations and analyze that a particular event happened at this specific time, which is why there is a fluctuation in the metric.
Stakeholders can ask questions, and data analysts can answer those. Our aim is to build structured information around metrics, which has historically not existed in a formalized context in the data space. From data analysts, engineers to anyone trying to build different applications on top of metrics can utilize Transform. We have a Python interface, command-line interface, and all the generic interfaces that software engineers and data analysts consume. We enable data analysts to pull metrics into everything, including BI tools, forecasting platforms, anomaly detection for experimentation, executive reports, internal tools, and any applications where they want to consume metrics.
What does the future look like for Transform?
In June this year, we expanded our company into several markets but didn’t open access to the product yet. At present, we are still completing the foundational components of the product. We have worked with several partners and shaped our product based on their feedback to deliver more value to clients. Recently we have made a few new partnerships to broaden and generalize our product. These partnerships are proven to be valuable for enhancing the product’s capabilities and features. We have reached a point where many data teams are interested in the metric store. We are scaling our team, engineering capabilities, and product development to support our customers with a product that will immediately bring value to their organization.