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