use of various analytics applications for three dimensions of managerial work—planning, implementing, and controlling—then compare the current and future use of various analytics applications for each function separately.

Our overall results are outlined in the figure here as different dimensions of managerial work along the y-axis— planning, implementing, and controlling—and business functions—finance, marketing, HR, and operations—along the x-axis. For planning-related managerial work, static reports/interactive dashboards are currently, as in Figure 1a, the most commonly used analytics applications. The current use of static reports/ interactive dashboards applications was 56.1%, 44.8%, 35.0%, and 36.4% for planning-related work in finance, marketing, HR, and operations, respectively. On the other hand, the current least-used analytics application was prescriptive analytics— 4.9%, 5.7%, and 7.9% for planning-related work in finance, marketing and operations functions, respectively— with the exception of the HR function where the least-used analytics application for planning-related work was big data analytics ( 5.5%). With respect to current implementing- and controlling-related managerial work, static reports/ interactive dashboards were again the most frequently used, and prescriptive analytics was least-frequently used. Static reports/interactive dashboards and prescriptive analytics are generally the most- and least-frequently used, respectively, analytics applications for managerial work.

The prominence of static reports/ interactive dashboards is not surprising considering it was the traditional view of analytics in organizations in the 1990s, and organizations have since then invested in various business-intelligence and reporting initiatives. 17 Static reports/interactive dashboards tend to have less rigorous technical ( mathematical) sophistication, requiring little training in use and adoption.

In contrast, the lower current use of
more advanced prescriptive analytics
can potentially be attributed to the
mathematical skills (such as linear
programming) required to understand
and use them. Among other analyt-
ics applications, the survey results
suggest big data analytics is the most
frequently used in the marketing func-
small- (22%), medium- (38%), and
large-size (36%) organizations.

Survey Findings

We first explore the current and future
of total work experience. Respondents
worked in organizations that, on aver-
age, employed 900 full time equivalent
employees. As reported in Table 1, the
respondents were distributed among

Table 2. Current vs. future use of analytics applications in finance management.

Type of Analytics Applications Current Future Difference P la n nin g

None 13.5% 6.0% - 7. 5** Static reports/interactive dashboards 56.1% 41.7% - 14. 4** Descriptiveanalytics 8.1% 15.5% 7. 4** Predictive analytics 12.1% 14.7% 2. 6 Prescriptiveanalytics 4.9% 11.9% 7.0** Big data analytics 5.4% 10.3% 4. 9**

Imp lem en tin g

None 7.4% 3.7% - 3. 7 Static reports/interactive dashboards 63.0% 49.0% - 14.0* Descriptive analytics 11.6% 16.2% 4. 6* Predictive analytics 6.9% 11.2% 4. 3** Prescriptiveanalytics 5.1% 10.4% 5. 3** Big data analytics 6.0% 9.5% 3. 5*

C o nt ro l lin g

None 8.7% 4.4% - 4. 3* Static reports/interactive dashboards 64.4% 54.1% - 10. 3* Descriptive analytics 10.0% 12.2% 2. 2 Predictive analytics 8.7% 12.2% 3. 5 Prescriptive analytics 3.7% 8.7% 5.0** Big data analytics 4.6% 8.3% 3. 7*

Note * indicates significant Chi-square difference at the p ≤ . 10 alpha level ** indicates significant Chi-square difference at the p ≤ .05 alpha level

Table 3. Current vs. future use of analytics applications in marketing.

Type of Analytics Applications Current Future Difference Pl an ni ng

None 6.8% 2.3% - 4. 5** Static reports/interactive dashboards 44.8% 24.3% - 20. 5** Descriptive analytics 10.9% 14.7% 3. 8* Predictive analytics 9.4% 20.2% 10. 8** Prescriptive analytics 5.7% 14.7% 9.0** Big data analytics 22.4% 23.9% 1. 5

Im p leme nti ng

None 9.8% 4.0% - 5. 8** Static reports/interactive dashboards 42.4% 27.4% - 15.0** Descriptive analytics 14.1% 14.9% 0.8 Predictive analytics 10.9% 20.4% 9. 5** Prescriptive analytics 7.1% 10.0% 2. 9 Big data analytics 15.8% 23.4% 7. 6**

C ont r o lli ng

None 8.1% 1.4% - 6. 7** Static reports/interactive dashboards 42.3% 23.8% - 18. 5** Descriptive analytics 15.4% 13.3% - 2. 1 Predictive analytics 8.1% 24.5% 16. 4** Prescriptive analytics 7.3% 11.9% 4. 6* Big data analytics 18.7% 25.2% 6. 5**

Note * indicates significant Chi-square difference at the p ≤ . 10 alpha level ** indicates significant Chi-square difference at the p ≤ .05 alpha level