The data solutions industry has become more and more sophisticated over time, and even though that’s exciting, it’s also created a problem for many data professionals: They’re stuck working with cutting edge tech on legacy systems.
That’s like souping up a car into a race car without replacing the engine, or buying high-priced running shorts and running shoes for a race, but not properly stretching and conditioning beforehand.
More and more tech companies are realizing that modernizing their tech stack is pivotal for growth and success, and that’s exactly what’s happening at Ontra. The legal technology company that offers experienced corporate attorneys for negotiating and managing routine legal work recently upgraded its data warehouse solution with the implementation of Snowflake.
The result? Streamlined improvements across the board, such as the company’s new self-service dashboards reducing ad-hoc requests, and the ability to quickly revamp various machine learning processes.
“Most importantly, Snowflake can scale compute power to drive very complex use cases,” Charles Chi, Ontra’s director of data infrastructure, said. “It can grow with our needs over time.”
Built In San Francisco sat down with Ontra to learn more about why they decided to modernize their data stack, what tools and technologies have been pivotal in this process, and the overall benefits of data modernization.
What initially spurred your team's decision to modernize your data stack, and what are the main focuses and/or goals of your data modernization strategy?
Ontra’s priority is to democratize data insights. My data infrastructure team works with teams across the company to develop analytics and machine learning initiatives to boost efficiency with productive, self-service tooling and frameworks.
The catalyst for the expansion of our data layer is the speed at which we’re growing. We’ve seen huge increases in demand from external customers and all internal stakeholders. This required growth and optimization of our data warehousing solution, data replication and ETL pipelines. It required cloud resource orchestration and management, workflow and scripts monitoring and alerting, and stakeholder-facing dashboards. Modernizing any technology stack is an evolving and iterative process, and we’re always looking for ways to get better.
As we build our DataOps and MLOps solutions, we’re focused on helping our developers work in tighter iteration cycles, which leads to faster insights and quicker time to production. We also strive to provide easy monitoring and alerting, which leads to less time spent on troubleshooting problems. These principles direct our efforts to help us all become more proactive and more prepared should problems arise.
What tools or technologies have been most central to your data modernization efforts?
Our most important upgrade was the implementation of Snowflake as our data warehouse solution. It is the crucial junction where all of our data lives, whether that is application data or data from vendor tools and services. It’s also where we turn raw data into publishable assets. That all feeds into our business intelligence dashboards to provide insights to multiple business functions across the organization to facilitate data-driven decision-making. In addition, we use our data warehouse to store derivative datasets for our machine learning team.
The tools Snowflake provides along with those we’ve developed in-house allow us to easily update access roles and lock down sensitive data.’’
I’ve observed and played a role in data warehousing initiatives throughout my career, and Snowflake has been exciting in a variety of aspects. It’s a very cost-effective solution for us to isolate and monitor warehouse compute needs. As a legal technology company, security is very important, and the tools Snowflake provides along with those we’ve developed in-house allow us to easily update access roles and lock down sensitive data. Most importantly, Snowflake can scale compute power to drive very complex use cases. It can grow with our needs over time.
What benefits has your team, or the company as a whole, experienced as a result of your data modernization efforts?
We’re seeing improvements across the board with new self-service dashboards reducing ad-hoc requests, and with developers using the tooling that our platform teams provide to increase productivity. Moreover, we’re able to quickly revamp various machine learning processes centered around building datasets for training models and introducing a data-driven way to ship ML predictions to our attorney end users.
Our efforts are also paying out at the highest levels. I’ve heard customer success stories about data insights helping our account management team provide real value to our customers. Ontra prides itself on being a data-driven technology company and it shows up in the conversations, collaboration and interest from both our customers and colleagues.
