Our client, a fintech startup, provided online banking to the youth. They offer basic banking features like current accounts, money transfers, and bill payments. They also have some unique features like link-based payments, saving goals, and real-time transactions.
Being an online-only bank with a growing number of users and no physical locations, they collected a lot of data. However, they found it hard to make sense of this data since they didn’t have enough technical experts. Their small team, with just one data analyst and some support staff, had big goals but were struggling. They were manually building an analytics model but were struggling due the cumbersome data processing requirements and the great degree of manual manipulation needed.
The client requested from Datrics the following:
We unified their diverse data sources, streamlining the process without the necessity of establishing extensive infrastructure or spending weeks on building and deployment. This not only simplified the workflow but also enhanced efficiency and convenience.
The next phase involved category mapping and deployments, which paved the way for better data management. To visually represent the data, we employed Power BI, although it was primarily used for presentation purposes and not ideally suited for modeling tasks.
To address the modeling challenge, they utilized Datrics for data preparation, which proved to be a robust solution.
Additionally, we organized workshops that encompassed training and support to bolster their in-house analytics capabilities. Our experts guided them in understanding which analytics could significantly benefit their business, providing a pathway for them to extract meaningful insights from their data seamlessly.
Through this collaborative effort, we empowered them to leverage their data effectively, aligning it closely with their business objectives.
Within a month, the client's team successfully implemented calculations for key metrics, constructed reports, and automated the data preparation process for regular updates.
This optimization freed up the analyst's time and effort, enabling a sharper focus on achieving business goals rather than being entangled in data updates.
The team was fully onboarded and well-prepared to tackle subsequent projects.
The benefits were substantial:
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