FairMoney Enhances Operational Efficiency and Ensures Comprehensive Integration with Hevo Data

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Zero Missing Data and Enhanced Data Accuracy

Hevo significantly improved FairMoney’s data completeness and accuracy by minimizing the risk of missing data updates during ingestion jobs.

95% SLA for On-Time Data Availability

Despite 10X business and data volume growth over two years, Hevo ensured consistent, timely data delivery while improving SLA for data availability.

FairMoney, a leading African FinTech company, is rewriting the narrative for financial inclusion in emerging markets by building the leading mobile bank. Their mission goes beyond simply offering financial services; it's about rebuilding Africa's money story by providing digital financial services to both merchants and consumers. With over 10 million downloads, FairMoney stands as Nigeria's most downloaded FinTech app and is recognized as the nation's top digital bank, showcasing its growing influence in reshaping financial access.

Central to their success is the strategic use of data, which serves as FairMoney's secret weapon. By leveraging data, they can paint a more holistic picture of an individual's financial health and creditworthiness. Their centralized data platform processes information in near real-time, enabling rapid loan application decisions and further solidifying their impact in the financial sector.

Shubham Jain, Head of Data, FairMoney

Leading FairMoney's data initiatives is Shubham, the Head of Data, who oversees a strategically structured team to ensure well-informed and optimized data-driven decisions. His team consists of three key sub-units: 

  • Data Streaming/Platform Team, which manages real-time data flow for analysis and machine learning models 

  • Data Warehousing Team, which cleans, organizes, and prepares data for business use and efficient reporting of all relevant KPIs

  • Analytics Team, which analyzes data to create KPIs and lead data-driven decision-making

Data underpins almost every aspect of FairMoney’s business, from using data to improve collection efforts to analyzing performance metrics and identifying areas for improvement, such as staffing needs or targeted customer campaigns. Data is the backbone of every decision-making process across the entire company.

Navigating FairMoney's Data Expansion

FairMoney's journey over the past two years has been nothing short of remarkable. As their business boomed, the volume of data exploded and created some data management challenges:

  1. Managed vs. Unmanaged Data Sources: FairMoney dealt with a mix of managed, unmanaged, and third-party data sources, each with different delivery methods. Some sources provided Bin Log streaming capabilities, while others required direct database queries.

  2. Data Latency: Ensuring that all data was available in a timely manner was crucial for effective decision-making. Delays in data availability hindered their ability to respond quickly to changing business conditions.

  3. Data Quality and Governance: Maintaining data quality and enforcing data governance became increasingly challenging as the volume and variety of data grew, putting the accuracy and reliability of insights at risk.

FairMoney aimed to streamline its data integration processes, maintain high data quality, and ensure timely data availability for strategic decision-making.

Journey from Monolith to Microservices: FairMoney's Data Stack

FairMoney's data stack has undergone a significant evolution, mirroring its growth and increasing data complexity. Its journey began with a single, monolithic system - a large database holding all the events destined for the BigQuery data warehouse.

Over time, their data sources have diversified dramatically. They now leverage a diverse mix, encompassing internal data (application events, user behaviour), external databases (CRM systems, marketing platforms), file sources, and even dialer systems used for collections. 

This shift reflects their move towards a microservices architecture, where user actions trigger events that become a primary data source. Additionally, they leverage data from Google Analytics, Ads, and metasearches to understand marketing effectiveness, while clickstream data provides a deep dive into user journeys within the application.

FairMoney's Data Stack

FairMoney began its data integration journey with Fivetran. Initially, it proved to be a well-functioning solution, effectively moving data between various sources and the data warehouse. However, two years back in 2022, as FairMoney's data needs grew and became more complex, they encountered a few limitations with Fivetran’s support on a few specific issues. Additionally, Fivetran’s cost was a concern, and FairMoney’s team had to solution it with a built-in cost optimizer that allowed them to monitor and control the costs associated with data queries executed by the data integration tool.

Selecting the Right Data Integration Tool: Priorities and Solutions

This was when Fairmoney’s data team went on the lookout for a new tool that would solve their problems. They prioritized three key non-negotiables as they evaluated various tools. 

  1. Data completeness: This means the tool had to flawlessly capture and transfer all data without any loss or errors.

  2. Cost scalability: As their data volume grew, they needed a solution that wouldn't lead to a disproportionate increase in costs, putting pressure on their budget.

  3. Reliable customer support for addressing any technical issues that might arise. 

Positive user feedback and a successful Hevo Data proof-of-concept solidified their choice. Hevo's "plug-and-play" functionality eliminates the need for complex setups with production databases. Instead, FairMoney simply configures connection details and utilizes the necessary connectors, allowing for seamless data ingestion into its ecosystem. 

This streamlined data transformation frees up the data team to focus on enhancing KPIs and driving strategic analysis, facilitating FairMoney's continuous growth.

Completeness and accuracy are key data quality attributes. Hevo has significantly improved data completeness by minimizing the risk of missing data updates during ingestion jobs. This empowers us to make confident business decisions based on a holistic view of our information.

- Shubham Jain, Head of Data, FairMoney

Looking towards the future, FairMoney recognizes the potential of new technologies to further enhance its data stack. As they transition to a microservices architecture, real-time data integration becomes paramount. Furthermore, FairMoney maintains a forward-thinking approach, actively evaluating cutting-edge tools and frameworks within the data landscape. By embracing these advancements, FairMoney is well-positioned to unlock even greater value from their data and propel themselves towards continued success.

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