25% and 29.9%) functions. HR will have
static reports/interactive dashboards,
descriptive analytics, and prescriptive
analytics applications as the top three
analytics applications. Marketing and
operations can expect similar future
use patterns, with static reports/interactive dashboards, predictive analytics,
and big data analytics applications as
their top-three analytics applications.
These patterns suggest data-literacy
programs and associated analytics investments will need to be function- and
work-specific in the future.
Fourth, we found the functions are
changing at different rates in terms
of their expected future use of analytics applications for different dimensions of work. The finance function
will see the greatest point increases in
prescriptive analytics for implementing-related work. On the other hand,
the HR function will see the greatest
point increase in descriptive analytics for all dimensions of work. Both
marketing and operations functions
will experience the greatest point increases in predictive analytics across
all dimensions of work. Our data indicates across all dimensions of managerial work, the finance function lags
in today’s use of an analytics application nine times, while that lag for HR,
marketing, and operations occurs
four, one, and one time, respectively.
However, in the future, the finance
function will lag six times, while HR,
marketing, and operations will lag
four, five, and zero times, respectively.
These results suggest some functions
are more quick to embrace the capabilities of analytics to help them accomplish their work.
Finally, these survey results provide a benchmark for different functions in their current and future use
of different types of analytics applications. Our findings can be used as
a starting point for discussion within
organizations about their current
and anticipated future use of analytics to support different types of managerial work.
We thank Andrew A. Chien, Robert D.
Austin, and the anonymous reviewers
for providing very helpful comments that improved the quality of
1. Cappelli, P. Talent management for the 21st century.
Harvard Business Review 86, 3 (2008), 1–9.
2. Cappelli, P. and Tavis, A. The performance
management revolution. Harvard Business Review 94,
10 (2016), 58–67.
3. Collins, L., Fineman, D. R., and Tsuchida, A. People
analytics: Recalculating the route. Deloitte Insights
(Feb. 28, 2017), https://www2.deloitte.com/insights/
4. Davenport, T.H., Harris, J., and Shapiro, J. Competing
on talent analytics. Harvard Business Review 88, 10
5. Davenport, T.H. and Tay, A. Finance must ramp up
role as analytics leader. Argyle Executive Forum CFO,
Aug. 26, 2016, http://ww2.cfo.com/analytics/2016/08/
6. Drucker, P. Management: Tasks, Responsibilities,
Practices. Harper & Row, Heinemann, London, U.K., 1974.
7. Fayol, H. General and Industrial Management. Pitman,
London, U.K., 1949.
8. Horne, J.H. and Lupton, T. The work activities of
‘middle’ managers: An exploratory study. Journal of
Management Studies 2, 1 (Feb. 1965), 14–33.
9. Howson, C., Sallam, R.L., Richardson, J.L., Tapadinhas,
J., Idoine, C. J., and Woodward, A. Magic Quadrant
for Analytics and Business Intelligence Platforms.
Gartner, Stamford, C T, Feb. 26, 2018; https://www.
10. IDC. Executive Summary Data Growth, Business
Opportunities, and the I T Imperatives. Framingham,
MA, Apr. 2014; https://www.emc.com/leadership/
11. Iervolino, C. and Van Decker, J.E. The Future of
Financial Planning and Analysis. Gartner, Stamford,
CT, Nov. 1, 2017; https://www.gartner.com/
12. Kappelman, L., McLean, E., Johnson, V., and Gerhart,
N. The 2014 SIM I T key issues and trends study. MIS
Quarterly Executive 13, 4 (2014), 237–263.
13. Kelly, J. The study of executive behaviour by activity
sampling. Human Relations 17, 3 (Aug. 1964), 277–287.
14. Kluemper, D. H., Rosen, P. A., and Mossholder, K. W.
Social networking websites, personality ratings, and
the organizational context: More than meets the eye?
Journal of Applied Social Psychology 42, 5 (May 2012),
15. Logan, V. A. Information as a Second Language:
Enabling Data Literacy for Digital Society. Gartner,
Stamford, CT, Sept. 21, 2018; https://www.gartner.
16. Moorman, C. The CMO Survey. World Market Watch
LLC, Chapel Hill, NC, Feb. 2017; https://cmosurvey.
17. Richardson, J.L., Tapadinhas, J., Howson, C.,
Sallam, R. L., Idoine, C. J., and Woodward, A. Critical
Capabilities for Analytics and Business Intelligence
Platforms. Gartner, Stamford, CT, May 7, 2018; https://
18. Ward, P. T., McCreery, J.K., Ritzman, L.P., and Sharma,
D. Competitive priorities in operations management.
Decision Sciences 29, 4 (Sept. 1998), 1035–1046.
19. Wedel, M. and Kannan, R.K. Marketing analytics for
data-rich environments. Journal of Marketing 80, 6
(Nov. 2016), 97–121.
20. Xiao, L. and Ding, M. Just the faces: Exploring the
effects of facial features in print advertising. Marketing
Science 33, 3 (Feb. 2014), 338–352.
Vijay Khatri ( firstname.lastname@example.org) is a professor and
Arthur M. Weimer faculty fellow in the Operations and
Decision Technologies Department of Indiana University’s
Kelley School of Business, Bloomington, IN, USA.
Binny M. Samuel ( email@example.com) is an assistant
professor in the Operations, Business Analytics, and
Information Systems Department of the Lindner College
of Business at the University of Cincinnati, Cincinnati,
© 2019 ACM 0001-0782/19/04 $15.00
analytics applications is on the rise
within all four functions. As different
types of data become available for decision making (such as time-series scanner panel data, text-based user-generated content, social network data, and
consumer location data19) our findings
suggest that, in the future, managers
will need to be engaged in different
types of analytics applications as a core
aspect of their responsibility.
Second, in general, we found static
reports/interactive dashboards are
and will continue to be a frequently
used analytics application for all four
functions. Our survey findings sug-
gest managers need to continue to de-
velop competency in their use of static
reports/interactive dashboards, and
business intelligence initiatives. How-
ever, their use will decrease signifi-
cantly—in the range of 10 percentage
points to 20. 5 percentage points. This is
consistent with a Gartner report that
found spending on traditional busi-
ness intelligence has been decreasing
since 2015, with concomitant broad
and pervasive deployment of self-service
9 This decrease is happen-
ing as software for advanced analytics
applications is becoming easier to
use, and, with increased data literacy,
business functions are starting to dis-
cover how more-sophisticated analytics
applications can help managers ac-
complish their respective work. For ex-
ample, dedicated applications that can
predict financial riskiness of indi-
viduals based on their mouse move-
ments while completing loan appli-
cations are now being used by the
Third, we found the functions will
differ as to which will be most used in
the future. We found that the manage-
rial work in the finance function will
continue to rely on static reports/in-
teractive dashboards the most, with
projected future use at 41.7%, 49%, and
54.1% for planning-, implementing-,
and controlling-related work, respec-
tively. On the other hand, reliance on
static reports/interactive dashboards is
relatively lower for marketing (between
23.8% and 27.4%), HR (between 25%
and 28.5%), and operations (between