Today's CEOs and managers need information that helps them understand what the future holds. Accurate, up-to-date, and frequently collected data is essential . In such a fast-paced world, it's not enough to focus on the past, analyze metrics and KPIs based on historical data, generate statistics, and generate final reports to analyze user behavior or identify technical or critical events.
Consider, for example, the real-time management of connected industrial machinery for predictive maintenance , as applied in Industry 4.0, or financial and insurance transactions, where data analysis is used to identify fraud, or, again, marketing, where it's now the focus. It's necessary to anticipate consumer behavior by understanding their tastes.
A prime example is Spotify, which, with its preference-based song suggestion system, is one of the companies best known for having invested considerable resources in data-driven decision-making. This approach is applied throughout the organization, with internal teams of employees having been created entirely dedicated to developing a platform that automatically collects and analyzes data.
Recently, Spotify has also sought to pursue this direction from a technical and infrastructure chinese overseas europe database perspective, with the goal of enabling technicians to answer management's questions and ensuring that the technical infrastructure is capable of providing the data base upon which to base their thinking. Thus, the prioritization of technical improvement interventions is clearly understood.
Once you understand what a data-driven model is, the question arises: what data is needed? The preliminary work involves observing and understanding processes and behaviors and finding the best way to quantify and measure them, identifying what's important to each . For example: how many customers, when they purchase, how many transactions, how much they spend. But also how old they are, when their birthday is, what their personality is.