The document discusses a project that aimed to predict employee absenteeism using predictive analytics. It describes some of the key factors for the project's success, including having an interdisciplinary team and creating a large number of potential predictor variables from available data. The results showed that previous absenteeism, job evaluation scores, and feeling supported by colleagues were good predictors of future absenteeism. The model performed well at an aggregated level and can now be used and maintained by the company itself.

2. Figures of recent studies in
Belgium show that burnout
represents a huge potential
cost to organizations and a
huge personal risk for
employees.
(Calculated on capacity of
the main room at DIS2017)

3. In this presentation we’ll
cover the 4 main reasons
for the success of the
project

4. This looks cheasy, but there is
much more to cracking the case
than having a superhero data
scientist. We had an excellent
team of sponsors, domain
experts, data experts, project
managers and a data scientist

5. Since burnout is not
registered in the data
(privacy reasons), we
predicted a proxy:
unplanned absenteeism
Within the whole
team, we created a
huge number of ideas
for potential
predictors. We were
later able to turn 85%
of ideas into data.

6. We benchmarked several
algorithms, some more
complex than others, but
we always focused on
presenting our results in a
way that business experts
understood technical
experts and vice versa
For this, we used for
example predictor insights
graphs (following slides)

7. 69% of employees had 0 to
4 days of absenteeism in
the last year
Starting to explain
predictor insights graphs

8. Those employees who
were absent 0 – 4 days in
the previous year, were on
average absent for 1.4 day
during the next quarter
As expected, previous
absenteeism is a good
predictor of future
absenteeism

9. We also add the overall
average number of days of
absenteeism during next
quarter

10. Low evaluation scores are
related to higher future
absenteeism

11. People who feel they have
a backup tend to be less
absent

12. The project was executed
in the environment of SD
Worx, so the scoring and
monitoring of this model
can be performed without
external intervention
We started the project
with a non-technical
training for the whole
team about projects in
predictive analytics and
ended the project with a
technical training for data
scientists – how they can
perform similar exercises
autonomously

13. We noticed there is a
lot of variation in
individual
absenteeism, but our
prediction works very
well when aggregated
SD Worx is able to roll
this out towards their
current clients
without external help