## 3 Data Management Maturity Model
### Phase 4--Test
#### 3.3.1 Industry Survey
According to [PERSON]'s guidelines, a distinction can be made between very low (\(\mid\rho\mid\) \(<\) 0.10), low (\(\mid\rho\mid\) \(=\) 0.10-0.30), medium (\(\mid\rho\mid\) \(=\) 0.30-0.50), and high (\(\mid\rho\mid\) \(>\) 0.50) correlation. Statistical significance (\(p\)-value) can then be used to check how likely it is that a result is based on chance. The significance level was set at \(\alpha\) = 0.05. For this purpose, the \(p\)-value was calculated accordingly to determine the credibility of the statements. If the \(p\)-value was less than 0.05, significance was given. Selected correlations across the dimensions about investment in digitalization were determined. The correlations are shown in the Table 5 below. Correlations that are underlined represent a weak positive/negative linear relationship, and correlations marked in bold represent a strong one. All other correlations are medium correlations. Organizations that are increasingly investing in digitalization see a higher innovative power in their organization (\(\rho\) = 0.316, \(p\)-value = 0.174). However, with a \(p\)-value of 0.174, this correlation is not statistically significant, implying that although there may be a relationship, we cannot be confident in its existence without a larger sample or more rigorous analysis. In addition, investments are made in areas such as the implementation of data processes (\(\rho\) = 0.431, \(p\)-value = 0.058), data architecture (\(\rho\) = 0.423, \(p\)-value = 0.063), and automation of processes (\(\rho\) = 0.279, \(p\)-value = 0.234). The positive correlations between \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline N = 20 & \multirow{2}{*}{Spearman-Rho*} & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } & **Data** & **Future** & **Data-** & **New** & **Business** & **Automated** & **Invest** & **Employee** & **Gut** \\ N = 20 & & **Power** & **Processes** & **Competi-** & **License Organization** & **Models** & **Q14** & **Tolerance** & **Total** & **Feelings** \\  & **(Q29)** & **(Q22)** & **(Q23)** & **(Q24)** & **(Q24)** & **(Q24)** & **(Q24)** & **(Q24)** & **(Q24)** & **(Q24)** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Calculated Spearman-Rho correlations. investment in digitalization and both the automation of processes (\(\rho\) = 0.279) and data architecture (\(\rho\) = 0.423) show some potential relationships, though again, the \(p\)-values suggest these correlations are not strong enough to draw definitive conclusions. Furthermore, a weakly positive, linear correlation (\(\rho\) = 0.326, \(p\)-value = 0.161) between investment in digitalization and trained employees in the field of data and analytics skills was determined. Although the results calculated a positive correlation between the calculated variables x and y, some correlations are not statistically significant at the usual significance level of 0.05 due to the higher \(p\)-value. The most significant positive correlation (\(\rho\) = 0.523, \(p\)-value = 0.018) is between investment in digitalization and the development of new business models, suggesting a notable connection. This result indicates that organizations investing in digital technologies are more likely to explore and develop innovative business approaches. Moreover, the investment in digitalization is positively correlated with future competitiveness (\(\rho\) = 0.432, \(p\)-value = 0.057). The need for a data-literate organizations regarding digitalization is not yet recognizable due to a very weak correlation of 0.029 (\(p\)-value = 0.903). There is little evidence that changes in one variable are related to changes in the other. The high \(p\)-value of 0.903 indicates that the data do not provide sufficient evidence that true correlation exists. A negative correlation (\(\rho\) = \(-\)0.595, \(p\)-value = 0.006) exists between digital investments and decisions based on gut feelings.