Managing a comprehensive view of customer data in Atlan
The Active Metadata Pioneers series features Atlan customers who have completed a thorough assessment of the Active Metadata Management market. Passing on what you’ve learned to another data leader is the true spirit of the Atlan community! They’re here to share their hard-earned insight into the evolving market, what makes up their modern data stack, innovative use cases for metadata, and more.
In this installment of the series, we meet Nevenka Perisic, Data Architect at Optimizely, a global digital experience leader helping over 10,000 businesses improve customer lifetime value, increase revenue and grow their brands through their flagship Optimizely One platform. Nevenka shares how Optimizely’s Data Services team transitioned from keeping data classified after several mergers to using Atlan as a holistic data management solution.
This interview has been edited for brevity and clarity.
Could you tell us a little about yourself, your background and what drew you to Data & Analytics?
I work as a Senior Data Architect at Optimizely, where I joined over a year ago. My goal is to bring data and its understanding closer to customers and help them see its business value.
While studying science in college, various projects exposed me to databases. This experience allowed me to develop essential skills as one opportunity led to another.
My training proved invaluable in helping me stay focused and structured in understanding data and appreciating the real information it provides, distinguishing between raw data and the value that can be derived from it.
Optimizely is a digital experience platform provider that aims to be a one stop shop for marketers. Our flagship product, Optimizely One, is the industry’s first operating system for marketers, enabling teams to connect every step of the marketing lifecycle through a single, unified workflow to better promote their products digitally. Many companies lack the skills, resources or time to do this, and that’s where Optimizely comes in. We take on this burden and offer customized solutions for each customer. Many big brands are already Optimizely customers and work closely with us to deliver value.
When I joined Optimizely, the company was going through significant changes, including multiple mergers, which created a challenge to bridge forces and identify where data was stored. I became part of Data Services, a newly established central function responsible for managing analytics and supporting our shared data platform at Snowflake. The team was small but grew as we recruited more members. We are now focused on building a modern data stack to connect teams across Optimizely that have traditionally operated in isolation.
Could you describe Optimizely’s journey with Atlan so far?
I was responsible for implementing the platform, coordinating with Atlan and the data engineering team to bring the Optimizely instance online. With Atlan’s help, I designed the initial implementation plan, outlined phases, user stories, and short- and long-term goals.
To engage the business, we needed something tangible, so we began integrating Atlan with key data sources. We quickly integrated all of our major systems and Atlan earned recognition as one of their fastest customers to complete the process. Native connectors like Salesforce have allowed us to take full advantage of Atlan’s features, and we’re working with Atlan to integrate systems without native connectors to expand access to features like dataline, consumption, and other metrics.
What are the use cases you have created with Atlan? Who has value in this?
We integrated data sources, glossaries and dictionaries and worked with power users who quickly recognized the value because they were already familiar with the software. We are now promoting this value to business users who still rely heavily on spreadsheets, a process we are actively implementing. I handled technical integration, made all data sources available, provided training so users could explore the data and identify useful features, and helped drive Atlan’s development to maximize value from the catalog.
I see Atlan not just as a data catalog, but as a collection of data artifacts. Our vision is to connect data flows, processes and data quality beyond the line provided by Atlan. Our goal is to offer a holistic solution for users who come to Atlan and say something like, “I need it for a specific project. Can I associate all these data artifacts with attributes associated with the right terms, glossaries, and data flows?“That’s our goal.
We’re still building it because it’s a huge undertaking, but we’ve made progress and users are starting to realize its value and actively engage with us.
What are you looking forward to achieving with Atlan in the future?
We want to ensure that everything our team needs to understand our customers is made available in Atlano as our data array matures.
We’re not there yet, so we’re limiting exposure for now because there’s still a lot of work to be done. We’ve made a lot of progress in Atlan, but we need to carefully consider how to expose meaningful data without creating an unusable swamp. Our intention is to provide reliable and trustworthy information within Atlan.
Photo by Davide Baraldi on Unsplash