Data analytics in startups is usually a task delayed to later stages of the product, which is plausible considering startups are focused on constantly delivering a product and may have little or no data to analysis. Although the startup may use a commercial data analytics framework, sooner or later, the data captured by the startup itself becomes a valuable source of insight for strategy and decision-making. To spark a successful data analytics initiative, two components are required. The first component is a motivated and collaborative startup team. Startup management should be motivated by the ease of an exploratory analysis and its results, as well as the reduced amount of work required from them to engage in this task. The second component is technical but cannot occur without the first because, although it requires skills, the analysis team needs to ensure collaboration and support from the startup team to answer questions regarding the company and the data. This initial analytics project yielded useful results for a startup who wants to see who are its main users based on their own data, but segmentation is just one of the many possibilities of data analytics. By focusing on developing flexible code built for change through frameworks like extraction-transformation-loading, a foundation has been created for further data analytics projects such as predictive analytics.

Data analytics in startups is usually a task delayed to later stages of the product, which is plausible considering startups are focused on constantly delivering a product and may have little or no data to analysis. Although the startup may use a commercial data analytics framework, sooner or later, the data captured by the startup itself becomes a valuable source of insight for strategy and decision-making. To spark a successful data analytics initiative, two components are required. The first component is a motivated and collaborative startup team. Startup management should be motivated by the ease of an exploratory analysis and its results, as well as the reduced amount of work required from them to engage in this task. The second component is technical but cannot occur without the first because, although it requires skills, the analysis team needs to ensure collaboration and support from the startup team to answer questions regarding the company and the data. This initial analytics project yielded useful results for a startup who wants to see who are its main users based on their own data, but segmentation is just one of the many possibilities of data analytics. By focusing on developing flexible code built for change through frameworks like extraction-transformation-loading, a foundation has been created for further data analytics projects such as predictive analytics.

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