Realizing that data-driven decisions have grown rapidly in recent years as an essential segment of business intelligence, researchers have found a keen interest in delving deeper into this field. Data-driven decisions and digital asset management were addressed by expert researchers in a well-integrated theoretical framework of psychological, managerial and operational dimensions, evaluating the effect of these dimensions on decisions and deliberately analyzing the process of collecting, storing, processing, analyzing and using data. Data-driven decisions have significantly improved the quality of decisions by strengthening decision-making mechanisms and increasing their efficiency, replacing the intuition-based approach that suffers from exacerbations of structural and operational problems. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay Empirical examination of data-driven decisions and quantitative evaluation of the impact of such a tool have become inevitable. Recent evidence from both business and IT management literature and business-oriented non-academic papers has found growing interest in addressing digital asset management with a quantifiable approach. More specifically, the scrutiny of data-driven decisions and new digital asset management tools is actually a manifestation of collecting, managing, storing and processing data competently. Furthermore, the variable "not having staff trained on data" is consistent with what has been written and discussed in the literature as a potential cause of various aggravating factors of the difficulties of the market economy; this is largely due to decision makers and employees lacking data-oriented tools (Streifer and Goens, 2004). The efficient use of data and the latest digital asset management requires the use of these tools by companies and businesses intending to expand in the contemporary market. This is in line with what Leibowitz (2013) argues regarding the transformative role of statistical records in decision-making. This chapter attempts to discuss the empirical findings of the article, citing and integrating findings from the literature, ensuring that our findings are built on solid theoretical and empirical foundations. Quantitative data have been introduced at any essential stage of decision-making processes, demonstrating the effectiveness and reliability of such methods. The literature is full of these studies, such as that of Liebowitz (2013), who argues that pairing historical data with our current status quo offers humans psychological comfort in all types of decision-making situations. In fact, well-constructed models are analogous to the way our brains work. Therefore, using data-driven decision-making approaches is like emulating the way our human brain works. Therefore, this section aims to empirically explain the effect of data-driven decisions and the impact of digital asset management on company performance by meticulously extracting results from our regression models and statistical tests, organizing the concepts and ideas from the literature relating to our significant variables and drawing relevant policy implications. It is worth noting that the empirical approach adopted in this model is the Ordinary Least Square (OLS) model through STATA software. STATA was mainly used to run regressions, assuming the two models at hand and providing several statistical tests, such as T-test, robustness test and.