Three steps to have a data-driven product — startup

Neubank, Rappi, and Loft are startups categorized today as unicorn startups; that is, they are companies that valued at more than 1,000 million dollars and have not yet gone public. This business model is characterized by its rapid growth and use of new technologies. Also, experiencing rapid economic and financial growth. Hence, a unicorn company is an innovative and successful business undertaken by entrepreneurs who take significant risks.

One of the fundamental aspects that unites these three startups, and that makes them so promising is their DNA of constant innovation. In these, whenever possible, data and figures reign; then intuition. Each decision is supported by figures, and only if there is no information is intuition trusted.

Businesses must change the way they interact with data over time to make smart use of their information to enable financing rounds, make better product decisions, and closely monitor cash flow.

So you must be wondering how to take my startup to the next level? It’s about having and using data in a data-driven environment. Next, I’ll show you how to transform your startup and your team into a successful data organization.

1. Don’t just collect data; monetize it.

The relevant data is swimming in an ocean of irrelevant information that we don’t need, making the task of analyzing it almost impossible. This means tackling the root of the problem and creating data management processes where data is cleaned, structured, and enriched in the desired format for better decision-making in less time.

2. Emphasize the visual

Nate Silver, the founder of the FiveThirtyEight survey aggregator, is a pioneer in data visualization. FiveThirtyEight used statistical models to predict the 2008 presidential election’s outcome and has demonstrated the emotional appeal of data visualization.

Data visualization changes the way we relate to data, thus allowing everyone in the organization to understand what is happening and why it is happening. These kinds of processes manage to respond to problems more quickly and accurately.

Plus, it lets you spot trends immediately, track goal achievement, quickly identify outliers, and compare the performance of different categories, products, brands, and more.

Analytic Board

3. Automate the product

To be more precise, let’s think of an API (application programming interface) as a software USB port, an interface for transferring data. Once the code is configured to port data into a predictive model, the visualization of that model can be automated and capitalized as an income source.

En este equipo trabajamos todos los días para que los datos puedan hablar y cuenten su historia para que sean escuchados e interpretados de la mejor manera