In a data-driven culture , in which the analytical perception of the context is the basis for any decision within the organization, there is constant interaction between the data area and different business sectors.Working with data is not just about interpreting graphs. It’s about using concrete information as the basis for any decision, from day-to-day operations to major strategic acquisitions.
The adoption of cutting-edge technology
combined with a cultural and organizational change,is necessary to break down barriers and, in fact, lead based on information, ensuring more accurate and informed decisions. It is about this transformation process and the importance of a close relationship between data and business that we will discuss in this article. The Evolution of Data The relationship between statistical analysis and decision-making has a long history.
Speed How quickly this data needs
From the first experiment recorded in 1663 to the term Business Intelligence in 1865, the use of this analytical information has grown exponentially. Since the 1920s, with the emergence of the first data storage method, the scenario has been marked by technological advances that culminated in the concept of Big Data. When we talk about Big Data, some aspects must be considered Volume the amount of data produced is gigantic and constantly growing to be processed Variety: Data comes in many forms, such as text, images, videos, and needs to be handled appropriately.Veracity: the reliability of data to extract accurate information.
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Value data must
Analyzed to add value to the business. An effective way to visualize data is through interactive dashboards and reports, which become essential tools for monitoring performance and supporting decision-making in real time. With the growing br lists importance of data use in today’s business context, data visualization and analysis is essential, as it is directly related to the organization’s ability to transform large volumes of information into actionable insights, enabling agile adaptation to market changes. However, for this approach to be truly effective, it is essential that the company is immersed in a data-driven culture.
The Evolution of Data Adopting
A data-driven culture goes beyond the use of new technologies. It requires a significant change in the way organizations operate and make decisions.Practical examples show that data areas are partners in various business functions, from marketing how to do sponsored ads on facebook and sales to operations and finance. Companies that adopt this approach are able to: Improve operational efficiency: optimizing processes based on historical and predictive data.
Personalize the customer experience:
using data to better understand customer behaviors and preferences. Innovate with confidence: testing and validating new ideas based on concrete data. Make strategic decisions: support investment, acquisition and expansion decisions with detailed data analysis. However, when using data, it is necessary to reflect on the responsibility of using it ethically. Data ethics ensures that decisions are fair, transparent and respectful of individuals’ privacy. This involves awareness of and mitigation of cognitive biases that can influence the interpretation of data and the resulting decisions.
Three common types of biases
to consider are Pattern bias: the tendency to rely on past patterns to predict the future, without considering changes in context. Short-term bias: the prioritization of quick fixes that may mask larger long-term problems. Winning bias: the manipulation or interpretation of data in a way that favors a particular narrative or outcome. Correlation and Causation Furthermore, it is essential to differentiate between correlation and causation.
Effect relationship between
When two variables can be numerically correlated without one causing or influencing the other directly, it is a correlation. Making decisions based on erroneous correlations can lead to incorrect conclusions and ineffective strategies. Causality, on the other hand, implies a cause-and- two variables.
Therefore, one variable directly influences or causes a change in the other. For example, if an increase in advertising leads directly to an increase in sales, this is a causal relationship. Knowing how to differentiate and identify causality allows you to understand the true relationships between.
Digital transformation requires
different factors and make informed decisions. companies to go beyond collecting and storing data, but to use it strategically and ethically to drive decisions and innovations. New call to action Understanding and applying the concepts taught by expert Julia Reksua, in the course Metrics for Digital Products, focused on developing skills for Digital Product Leadership , enables professionals to navigate the complex world of data.
transform product development
contributing to the success and competitiveness of their organizations. Tera students have access to the full content of this class. Learn more about how data cannd empower your team to deliver value quickly and efficiently. See you next time!
