Data is the New Oil - But How to Make Money out of it?

Data is the New Oil - But How to Make Money out of it?

In 2017, the Economist Magazine published a cover saying that data is "The World's most valuable resource".

In 2022, this holds true.
On one side, people are more willing to share their personal data by leaving reviews, marking locations, and buying online.

According to Klavi, an open finance startup in Brazil, 60% of people share their bank account when offered a benefit.

Who could imagine people sharing their most sensitive information - financial one?

From the other side, technology and cloud computing have allowed the processing of terabytes of data in a very fast way.

There are dozens of software that allow companies to track their user's journey and collect data, from clicking to time spent in a page.

Machine Learning models are spreading across different sectors.

At Wayfair, an online e-commerce, they use ML to optimize shipping, and they reduced their logistics cost by 7.5 percent, which in margin business is huge*.

Mckinsey estimates USD 1 trillion annual gains for the banking industry with better usage of historical data and sharpened analytics efforts.

About a third of the gains would come from reduced fraud losses and about 20% from better informed pricing and promotion.

What are the implications of this new context?

1) Data Infra

No matter a startup or a big company, companies need to create from day one data lakes consolidating structured and unstructured data from diverse sources: app, website, social media, IOT, external bureaus. That requires making sure that the accuracy and availability of the data is right.

2) Profit center

For many companies, data infrastructure is still a cost center. Companies need to start treating data as a corporate asset and profit center, by exploring daily opportunities data provides of cross-sell, retention, CAC reduction, CX improvement and product launch.

3) Data Culture
Amazon and Bridgewater are the benchmarks in using data all over the organization. They avoid the scenario with lots of people giving opinions in a meeting with no data. To drive culture change, besides having people with the right skills, a lot of training is necessary to embed the use of data in the analysis and decision making processes of the diverse areas.

And how can companies use data to generate value?

4) Personalization

Customers love when they open Spotify and see that their favorite songs are shown first. When I open my digital bank account, I still see offers that make no sense to me, such as personal loans (I have liquid investments) or auto-loans (I haven't had a car for 10 years). A lot needs to be done in terms of understanding the client deeply and personalizing offers.

5) Alternative Data for Credit

According to a Serasa Experian study from April 2022, Open Banking is expected to bring 4,6MM Brazilians to the formal credit market for the first time, with attractive rates. One of the most important variables in a credit model is disposable income.

Before open banking, financial institutions only had the information of Income (a rough estimation), which is misleading, since it does not incorporate cash expenses. Now, lenders have started to use variables such as Net Cash Income to be more assertive.

There are many other use cases for data, such as logistics, marketplace catalog, insurance pricing, ROI optimization in ads, airline tickets targeting. Start by making an assessment of your company's stage in data collection, systems and capabilities.

Then, incorporate agile methodologies to facilitate the discovery-prioritize-build of data products - similarly the way we do with consumer products.

The journey is not easy. But since data is the new oil - let's extract value from it!

Guilherme Tavares is a fintech, data and product enthusiast.
*Source: Mckinsey: Smart operators: How leading companies use machine intelligence

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