CEO and Co-Founder of Coalesce, Armon Petrossian, launched his company from stealth in January 2022 to solve the largest bottleneck in the analytic space: data transformations.
The 29-year-old entrepreneur focused on helping enterprises overcome the pressing challenge of converting raw data into a more suitable structure for consumption, a process that can take months or even years, to meet daily organizational and operational data-driven demands. The company is currently going head-to-head with dbt Labs and Matillion in the data transformation space.
M.R. Rangaswami: What are the core challenges you find that are associated with operationalizing data?
Armon Petrossian: Companies have been struggling with data transformation and optimization since the early days of data warehousing, and with the enormous growth of the cloud, that challenge has only increased. Data teams, in particular, are challenged with the everyday demands of the business and the shortage of skilled data engineers and analysts to combat the growing volumes and complexity of data.
We are on a mission to radically improve the analytics landscape by making enterprise-scale data transformations as efficient and universal as possible. We see the value of Coalesce’s technology as an inevitable catalyst to support the scalability and governance needed for cloud computing.
One of the most rewarding aspects of my role at Coalesce is seeing the impact our solution has on organizations that want to drive value out of their data. This is especially true for companies that deal with complex data sets and/or are in highly regulated industries.
One of our most recent customer success stories involves partnering with an organization that helps big restaurant brand clients leverage their customer data to show that the brand knows and understands its customers. Helping its numerous clients improve their digital marketing funnel and offering customers a frictionless experience every time they visit the store, whether in person or online, relies heavily on data. This requires having the ability to glean useful insight from data quickly and easily. Coalesce, alongside Snowflake’s Snowpark, was able to help their data science team complete a high-profile transformation in one month, whereas before, the entire team spent 6 months without much progress.
M.R.: What exactly is data transformation? Why does it play such a critical role in the future of data management and the analytics space?
Armon: It’s important to look at how we consume data to understand why data transformations are so important. Initially, organizations that were adopting cloud platforms like Snowflake hit a major hurdle which was getting access to data from their source systems. As that problem has been largely solved by companies like Fivetran, and getting access to different types of data has become much easier, transforming that data to create a cohesive view is the logical next step for businesses to accomplish. This becomes dramatically more difficult as you begin to integrate data from traditional on-premises platforms, like Teradata or Oracle, along with a variety of different web sources. For example, companies may look at vast amounts of historical data to understand how their production line performs in certain scenarios or look into demographic information to target the right potential customers. Whatever the reason, the analytics are only as good as their ability to curate data from various sources and transform it into a consumable format for the analytics and data science teams.
With Coalesce, the data can be organized in an easy-to-access and read fashion while using automation to streamline the process and limit the amount of time needed by highly skilled engineers. This ensures that companies are accessing high-quality data that is easy to use for a variety of purposes, an experience that is not guaranteed with existing tools. With our column-aware architecture, enterprises have the ability to efficiently and easily manage not only existing data but also new datasets as they grow and scale.
M.R.: What are your best practices for enterprises that are looking to keep up in today’s data-rich world?
Armon: My suggestions for best practices can be broken down into four areas:
i. Data-Competitive: Data competitiveness is key for every business, but given the enormous amounts of data being generated by modern enterprises, IT teams are falling behind in organizing and preparing data to be made available to business teams to help guide informed decisions.
ii. Embrace the Cloud: Managing hardware or technology on-premises is expensive, time-consuming and risky. In U.S. history, cars were not nearly as impactful to daily life as a form of transportation until the infrastructure of roads was built across the country. We’re now seeing a similar economic boom with the way the cloud allows access to data for organizations that would have never been able to achieve similar use cases or value previously.
iii. Evaluate Efficiency: IT teams finally understand how important efficiency can be to help deliver a continued competitive edge for enterprises. When applicable, data automation reduces time, effort, and cost while reducing tedious and repetitive work and allowing teams to focus on additional use cases with high-value data objectives.
iv. Strive for Scalability: With more data and the proliferation of the cloud, organizations are challenged with scaling IT systems while maintaining flexibility and control. Companies should look to implement processes that offer the speed and efficiency needed to achieve digital transformation at scale and to meet increasing business and customer demands.
M.R. Rangaswami is the Co-Founder of Sandhill.com