Data democratization is so ingrained in the company culture at Pangea that technical employees aren’t the only ones creating dashboards in Looker, a reporting and insights tool. In fact, analysts and senior management in every department at the money transfer platform have contributed to model formation.
When it comes to championing access, the fintech team is in good company. In 2017, Airbnb launched Data University in an effort to create more “citizen data scientists,” according to a Medium story. The 30-class curriculum is split into three stages based on difficulty.
But education is only one variable in the greater accessibility formula. The other is proper resources.
At Narrative Science, employees use the data storytelling company’s product Lexio to better serve customers. According to CEO Stuart B. Frankel, Lexio “allows anyone who needs access to information to ask questions and get answers in plain English.”
As a result, the account executive team has been able to identify which factors lead to bookings. But as exciting as data-driven insights might be, the tech professionals we spoke with stressed that data democratization is an ongoing process and a cultural shift, not a passing fad or a course to be checked off.
In a company-wide effort to democratize data at Pangea, leadership assigned product and engineering teams the responsibility of tool selection and development. Chandana Anand, a senior business manager, said that employees in marketing, customer experience and finance were then empowered to leverage and build upon the data models.
What were the initial steps you took to break down data silos across your organization?
Data silos rise in the absence of centralized data storage and reporting systems. When data is decentralized, teams tend to piece together information from different sources, which causes inconsistencies in metrics and leads to decision-making based on missed or incomplete information.
We evaluated our existing data and reporting solutions, interviewed teams across the organization and learned what worked and what did not work. Because of this, we realized the importance of centralized data early on. We implemented Snowflake, a cloud-based data warehouse solution, that we selected primarily for its performance, ease of management and ability to scale with the business.
We wanted to add a reporting layer on top of Snowflake that would empower teams by giving them access to near real-time data and allow them to derive meaningful insights from that data. The Looker platform that we implemented last year provided us exactly that.
Data silos rise in the absence of centralized data storage and reporting systems.’’
What are some of the tools your company uses to integrate your data and make it more user-friendly for non-technical employees?
Data is at the core of every decision we make at Pangea. When looking for a new reporting solution, a key criterion for us was ease-of-use for non-technical employees. The tech team does all of the technical heavy lifting by creating ELT data pipelines in Snowflake with DBT.
In Looker, after we initially set up LookML models with clear definitions for measures and dimensions, it becomes easy for users to create their own dashboards either by using the predefined data models or by leveraging existing dashboards built by someone else. Looker has since become the source of truth. Even non-technical employees have created several dashboards.
Tell us about a specific win one of your teams saw as a result of having better access to data.
As a digital remittance business, we are constantly evaluating ways in which we can better serve our customers by understanding customer behavior. We run several A/B tests that are designed to help drive product decisions. With the current reporting infrastructure, our product team has benefitted from being able to measure and drill into the results of A/B experiments and make informed product decisions.
We are also able to analyze UX funnels by various customer segments, identify points of friction and help make improvements to our app.
Narrative Science CEO Stuart B. Frankel is quite literally in the business of democratizing data. The company helps companies better understand their consumer data and insights using what Frankel calls “data storytelling technologies.” He said that giving a hundred percent of employees access to business data and the tools they need to use it has freed up analysts to focus on data discovery.
What were the initial steps you took to break down data silos across your organization?
We have always been transparent with all employees in terms of sharing data about our business. For example, we have always shared data around financial and operating performance. That said, simply sharing the underlying data isn’t enough. Because of the business that we are in, we have thought about this problem a lot.
We got rid of data silos at Narrative Science via our data storytelling technologies, Quill and Lexio. Data storytelling allows anyone who needs access to information to ask questions and get answers in plain English. If people can get most of the information they need on their own, data silos become virtually nonexistent.
Enabling employees to get value from data requires giving everyone the necessary tools and teaching them not only how to use the tools, but also what the information presented actually means. Since doing this ourselves, our employees have a fundamentally better understanding of our business. Our analysts also have more time to focus on complex analysis and data discovery because they are spending virtually no time explaining data to our employees.
Having information presented as stories bridges the gap between non-technical employees and our data.’’
What are some of the tools your company uses to integrate your data and make it more user-friendly for non-technical employees?
We mainly use Lexio, the data storytelling tool that we created, to integrate our data. Everyone in our company has access to Lexio, not just the executive team. That means our entire company has access to all of our data and stories about our company performance.
Having this information presented as stories bridges the gap between our non-technical employees and our data. They don’t have to understand data on a particularly deep level or be tech-savvy to get the most out of this platform. By making this information available in an easy-to-consume way, we’ve found that people are actually taking the time to read and understand what’s going on in our business. This promotes greater understanding and unity across the company.
Tell us about a specific win one of your teams saw as a result of having better access to data.
Before Lexio, we were primarily focused on the end result of bookings (booked customer contracts) versus the entire process of what factors lead to bookings. We did historical lookbacks before every planning year to see what our sales cycle, average sales price and new pipeline metrics looked like. But we didn’t have a true understanding of those metrics consistently throughout the year.
After implementing Lexio, we started to notice the little things required to accelerate our bookings. We could discuss which account executives had higher win rates, shorter sales cycles, higher ASPs or were accelerating new pipeline development on a daily or weekly cadence versus annually or semi-annually.
We also found that by focusing on those leading indicators, we created a culture of appreciation for the steps before the bookings themselves. For example, account executives who had a high velocity on a sales cycle and were bringing in more new pipelines were celebrated just as much as the AEs who closed the most in bookings alone.
