Looker Blog : Data Matters

Why We Built a Data Culture at Fivetran

Garegin Ordyan, Head of Analytics at Fivetran

Aug 9, 2019

At Fivetran, we build technology that centralizes data from different applications into data warehouses, so enabling organizations to be data-driven is an essential part of our mission.

But what does it mean to have a data-driven culture?

My teammates think of this in a few ways:

"A data-driven culture means that individuals are tuned in to start thinking about problems and their solutions with a data-focused mindset. Enabled by a data-driven culture, users across the organization can ask questions like: What can the data tell me about this problem?, What hypotheses can I test with the data we have available? and How will the changes I’m going to implement affect the data we’re collecting and other people who are leveraging this data?"

Christine Ndege, Solutions Engineer/Data Analyst at Fivetran

"Creating a data-driven culture is something that requires buy-in from across the organization. Not only does it mean making decisions based on evidence and analysis, but it also requires team members’ hard work to populate and provide the data behind this analysis, and thus is truly a whole team effort."

Ryan Muething, Data Analyst at Fivetran

To me, being data-driven has more than one advantage. Generally, when decisions are made based on facts and not best guesses, important discussions happen naturally. People ask: Is this data showing what we expected? If so, they know they’ve confirmed their suspicions. More often than not, however, the initial result is surprising and unexpected. It reveals things you weren’t aware of, both positive and negative.

Why a data-driven culture?

With numerous interpretations of what a data-driven culture is, you may be wondering why an organization would strive to be data-driven.

Think about the decisions teams within an organization make every day. Product teams are continuously iterating to deliver value to customers. Account reps are tracking their actions against quarterly targets. Marketing teams are building their go-to-market strategies. Finance teams are determining quarterly budgets — and the list goes on.

At any given time, all of these teams are leveraging data to measure, adjust, and deliver on their goals. While everyone works rapidly to help the organization succeed, the data they’re using can influence results.

The difference that having a data-driven culture makes is when everyone makes business decisions based on the same data, confidence in the decision-making process increases. Product teams can prioritize development based on the same data that marketing teams use to inform go-to-market strategies. Finance teams can be sure that their budgets are rooted in the same data that the sales teams use to forecast their pipelines — and so on.

Data-driven with Looker

At Fivetran, we build connectors that deliver ready-to-query data into cloud warehouses. To do this, we utilize Looker as our centralized data hub. All of our data, including (but not limited to) data from our product, sales, engineering, support, operations, and marketing departments, is centralized in a data warehouse and modeled for access in Looker. Many of us utilize scheduled reports in addition to daily queries to stay on top of alerts and changes.

The mission of my team is to ensure that our BI layer is a truly useful single source of truth for all of our teams. For instance, we use Looker to highlight progress towards company goals during every companywide meeting, allowing us to give teams across Fivetran a window into different departments' activities and successes.

Acknowledging challenges

Most of the time, the hardest part about continuing to build on our data culture is simply getting people started with Looker. Once people learn how to use it, our team doesn’t need to do much to keep their momentum going.

What is important, however, is the accuracy of the data and the data models. If folks start seeing that things are incorrect as they begin using Looker data regularly, they may start questioning how much they should trust the tool. It’s crucial that the quality of the information we provide via Looker is accurate and consistent with data from any other sources people may be using so that confidence and trust can continue to grow throughout users’ experiences with data and with Looker.

Building on the foundation

We’ve found that having a general understanding of what questions teams are attempting to answer is a good place to start when looking to encourage data-driven decision-making. Speaking to team members and understanding what they want to track helps our team deliver value to them.

A great way to continue building on this is to get people excited about finding answers and going deeper into the data. Instead of delivering explores and dashboards to answer questions for them, holding individuals accountable for meeting objectives and goals encourages them to be data-driven and track their progress more closely. In addition to this, providing regular opportunities for anyone to ask questions and learn in real-time helps to build on that excitement and trust in the platform. At Fivetran, we hold Looker office hours three times a week to help people get started, learn how to set up their first few dashboards, and do complex merged explores and offsets.

The point of establishing a strong data culture is to drive data usage, so making sure your data model is accurate and easy to understand is key. If the only people who can use Looker to build reports are folks with a background in SQL, the non-technical majority is not going to be able to utilize the explore functionalities at all, which denies people useful insights. To create a data culture that everyone can be a part of, make things simple and provide explores that aren’t visibly complicated — i.e., the data model behind the explores can be complicated, but it should remain hidden from the user. For the user, it should just work.

Join the conversation and share your own insights about data culture and data adoption in the Looker Community

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