Data modelling and machine learning (ML) offers a tantalising possibility - that by gathering enough data inputs you can predict what will happen in the future based on current information. ML models are commonly used in the context of business decisions,
such as assessing investment outcomes or growth performance, where they can add significant value.
They’re rarely used in the realm of human experience for two main reasons. Firstly, humans are famously irrational and hard to predict using the few data points available. Secondly, the cost of traditional data modelling means that it only makes economic
sense in a business context.
But easy-access APIs are changing both of these to provide more data and to improve model accuracy. To see why, we’re going to talk about babies.
A £150k baby
At a recent developer meetup, I was fortunate enough to talk to a pair of entrepreneurs who have built a financial planning app for parents. The idea came after one of the pair had their first child and was quickly confronted with just how expensive parenthood
is – with a single child costing an average of £150k over their lifetime.
The platform helps parents predict child-related costs years in advance. The predictions are based on financial data and the budgeting and family lifecycle data on the platform, so they know what is going to happen in advance. This, in turn, helps reduce
debts from surprise costs while also enabling the platform to cross-sell different financial products that further help parents plan ahead. Crucially, all predictions are based on API data.
The API crystal ball
Data modelling is most effective when tracking repeatable systems. While humans themselves can be hard to predict, they do operate within plenty of repeatable systems. Think of a small business, with tax deadlines, busy and quiet seasons and staff holidays,
The platform applies this thinking to the lifecycle of a child, looking ahead to clothing costs, big ticket items such as prams and cots as well as childcare and education fees so parents can adjust their financial planning accordingly, while also matching
it with insights on the past spending of other parents.
While humans are theoretically capable of orchestrating detailed financial plans, the time and effort it would take to gather all the data, as well as process it, means it’s not really worth the trouble. Most would rather just take a conservative approach
to spending, but this might often mean scrimping unnecessarily, or worse, being surprised by unforeseen expenses. With APIs, however, relevant information can be accessed immediately. A financial planning app, for example, can quickly gather all the data a
user needs to make a decision and either present it for their review, or make the calculation for them.
Data solutions for human problems
The world is full of problems that could be solved with the right data. Most people just don’t have the time to do the research, testing and computation they would need to achieve this.
Plug-and-play APIs open up the field of app development by drastically reducing development costs, so enterprising developers can focus on solving specific issues. The wide array of financial data now available can transform the experience of users if it’s
combined in the right way to suit their needs.
In the case of the family planning platform, it is possible to bring together:
- Parental spending data
- Key milestones in a child’s lifecycle
- Average costs from other parents
- Live prices of relevant goods and services
For a parent getting little sleep for the first year of their child’s life, the app can do the financial thinking for them, with APIs doing the seamless legwork.
Closing the data gap
The goal is to close the data gap - a combined set of inputs that, combined with the right personalisation - give users the information they need to plan for their future, now.
By finding the right API data sources and matching them to a human experience, developers can close that data gap and provide apps that help us see the future and plan accordingly.