Building and Scaling a Data Governance Program

Contentsquare is a company that lives and breathes data. With over 1,000 customers and over 1,600 employees, their digital experience analytics platform provides rich context and insights into behavior, feelings, and intent at each touchpoint in a customer journey.

With $1.4B in funding, and now in its Series F round, Contentsquare’s meteoric growth has been enabled by data. And at the center of making that data accessible, understandable, and trustworthy are their Data Governance team. Composed of Kenza Zanzouri, Data Governance Strategist, and Otavio Leite Bastos, Global Data Governance Lead, Contentsquare’s team led an?Atlan Masterclass?to share how Data Governance is capable of accelerating critical business outcomes.

In this week’s newsletter, we spotlight how Contentsquare leveraged collaboration, data quality, and data cataloging together.?You can read the full story here →

? Spotlight: Contentsquare’s?Guide to Data Governance

Contentsquare’s standards are high. They expect data governance to act as an accelerator, not a hindrance, to their Analytics and Business Intelligence responsibilities.

“Data Governance can help us have a faster time to value,” Otavio explained. “From the time you commit to an analytics project until you deliver it, you can make it faster. If dashboards go live faster, and if analysis can be faster, then decision-making is faster. We can also promote faster onboarding for new data professionals, and speed up remediations of any data downtime that we might have.”

With ambitions as high as Contentsquare’s, their team is mindful of striking a delicate balance between being a business enabler, and building a strong analytics foundation to avoid the pitfalls that a governance function is expected to address.

Contentsquare’s team understood the potential consequences if their commitment to data governance was insufficient, or went unsponsored altogether.

“We might have two different people calculating the same metric using two different methods or formulas, leading to different decisions. We can also have data flows that are poorly known, so we don’t master our data pipelines. Being compliant with data protection guidelines and regulations would be very challenging,” Otavio shared. “And data reliability would be at risk. So you would have data consumers calculating and re-calculating metrics to compare with dashboards, not trusting the dashboards that are live.”

Data Governance at Contentsquare

Contentsquare’s data team consists of 10 people, reporting to the CIO, and segmented into three teams. Data Analytics consists of a Lead and four Analysts, Data Engineering consists of a Lead with two Engineers, and Data Governance consists of a Lead and a Strategist.

“We are the heads of KPI standards. For any formula, for any method of calculating metrics, it’s up to us to orchestrate and lead people to identify the standards for the most important metrics at the company. Second is data quality, testing data to see if it’s behaving correctly, and as expected,” Otavio shared. Also crucial for the Data Governance team is data protection, ensuring that best practices are perfectly executed, and regulations are adhered to.

Living at the center of each of the Data Governance team’s responsibilities is Atlan, advancing their charter of accessibility, understandability, and trustworthiness. “It’s our home for every KPI and dashboard that we have at the company,” Otavio explained. “Our purpose is to make data accessible. Data must be simple, understandable. And data must be trustworthy.”

Contentsquare’s Modern Data Stack

Contentsquare’s data stack includes source systems like Salesforce, Workday, and Hubspot flowing through Matillion, their integration layer. Snowflake serves as their data storage layer, with a data lake containing raw data, a data warehouse with transformed data, and data marts directly connected to Tableau and Google Data Studio for visualization and analysis. Finally, Monte Carlo serves as Contentsquare’s data quality tool.

Contextualizing and activating this data stack, and serving at the center of their data governance function, is Atlan.?

“Data domain owners can both read data from Atlan and write data to Atlan. Same for KPI owners, who can read any information, but can also enrich Atlan with documentation,” Otavio explained. “We have a data catalog. We have a glossary that’s home for our most important KPIs and metrics. And we can catalog every dashboard that we have at the company, and do automatic data lineage, which is very nice.”?

And to ease collaboration across a spectrum of users, Contentsquare has integrated Atlan with Slack, enabling seamless communication about their data assets.

Treating Data Output as a Data Product

Contentsquare views data governance as a way to bring clarity to a complex, difficult-to-understand landscape of assets, ownership, and context. “You might find in any company across the globe that there are dashboards, KPI owners, data fields, data health checks, data consumers, and not everything is connected. They’re flowing in an ocean, and no one understands their meaning or how they’re related,” Otavio explained.

But with a well-defined governance program, these dots are connected, expressing meaningful links between Contentsquare’s data assets, technology, and people, avoiding inefficiencies across their data operations.

The first step toward launching a governance program was to better define ownership and responsibility using a Data Product Tree, and to ruthlessly prioritize by defining the most important metrics to their business.

You can read more about how Contentsquare executed data governance and data quality →

?? From Our Reading List

• How to Identify Your Business-Critical Data?by Mikkel Dengs?e
• Black Swans & People Analytics?by Cole Napper
• Creating Harmony: Collaborative Database Schema Design for Better Data Teamwork?by Greg Meyer
• A Decade In Data Engineering - What Has Changed??by Seattle Data Guy
• How Scale Kills Data Teams?by Chad Sanderson

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