Proactive Career Path Management (Talent Management)

Proactive Career Path Analysis
SAP Predictive Analysis for HR
Henner Schliebs, August 2013

© 2013 SAP AG. All rights reserved. 2Customer
How can we analyze the flow of people with
SAP Predictive Analysis?
1
Visually explore HR
data on employee
moves through the
company
Apply statistical
algorithm to identify
groups of employees
with similar patterns for
changing positions
Derive a model that
can predict the success
of a change of position
based on employee
attributes
Use this model to
predict the probability
of success for some
upcoming moves
2 3 4

© 2013 SAP AG. All rights reserved. 3
Our Source data is based
on Excel but could also
come via HANA or BO
Universe

Here we can preview
the data that is to be
imported

The imported data contains
information on position changes
as well as master data before
and after the move
List of dimensions
inside the dataset
Since Excel does not distinguish
between dimensions and measures,
we need to manually define our key
figures for the analysis

Our newly created
measures
Aggregation
behavior

Switch aggregation to “average”
to calculate average success
ratio of a position change

Let’s rename the
measures

Now we will filter out all employees who
did not have a performance rating after
their change of position

We repeat the previous steps to also
exclude employees who did not have a
performance rating before their move
(This is skipped here)

The two filters we
have just applied
Our source data has
some geographical
information that can be
leveraged for analysis

If you have additional data
like Region or City you can
create a navigation
hierarchy that can be
browsed visually

PA is quite smart and can
identify both ISO-coded
and explicitly named
geographical data

We have finished preparation of the data
and can now proceed to visual
exploration
We will start with an analysis of the
success ratio of position changes along
the country hierarchy we just created

It seems that there are
large differences in the
success ratio between
countries

Now, let’s look at how the success
probability might be linked with moves
across job functions
For this analysis, we will use a Heatmap
with the job functions before and after as
axes

Job functions that are
quite different seem to go
along with a low
probability of success…
…while moves inside a
job function work quite
well.

The heat map tells us something about
how well certain combinations of job
functions work together, but…
…it does not take into account how
frequent certain combinations appear in
the first place.
To analyze this we will look at a Treemap
which is basically a Heatmap with a
weighting factor.

The size of the rectangle
is now proportional to the
total number of moves for
this combination.
These combinations are
quite frequent and rarely
go well.

Let’s switch to a different view and look at
the question: How are success probability
and performance rating before the move
tied together?

For employees with low
performance ratings a
change of position has a
very high probability of
success.
Employees with high
performance ratings on
the other hand have it
much harder to perform
equally well in their new
role.

We see that there are some anomalies
but so far we have only explored the data
visually: Nothing has yet been proven.
Therefore we will now apply some
statistical algorithms to see which
patterns emerge scientifically.

1
Visually explore HR
data on employee
moves through the
company
Apply statistical
groups of employees
changing positions
Derive a model that
based on employee
attributes
Use this model to
of success for some
upcoming moves
2 3 4

Here you can see the
available algorithms that
can be applied to your
data.
In this area you can
combine individual
analyses to form a
comprehensive model for
understanding
relationships.

First we want to apply
some filters to the data

Let’s filter out the
employees without
performance ratings

We rename the filter so
we can keep track once
the model becomes
more complex

We configure the second filter to filter out
all dimensions that we are not going to
use in our first analyses – just to be more
convenient for us.

Here we will have only
the employees and only
the dimensions we are
interested in
Now we add a
classification algorithm
that will try to find
clusters of position
changes with similar
values in the dimensions

We’ve added the algorithm
twice to calculate two
different models on the same
data and compare them

We will use all the available
information to see what kind
of clusters the algorithm can
find
This first analysis will try to
categorize the available data
into five clusters…

…while our second analysis
will try to find ten clusters.

Let’s run the analysis!

Here we can see to
which cluster a record
was assigned by the
model…
The different
components of our
model can be viewed
along with their
intermediate results
…but we want to see a
summary of the models
first.

Here we see the
number of records that
were assigned to each
cluster in the 5-K
analysis
This chart shows how
homogenous (“dense”)
the clusters are and
how different from one
another
In this chart we can look at
individual dimensions and
check which dimension
values were how common in
each cluster
This chart shows a profile
diagram for each cluster
(the axes are the
dimensions that were put
into the analysis)

Example: In cluster 1 the
average time in position
before the move was 1.53
years
We can see that most
employees were assigned to
this cluster and that this
cluster is very heterogenous
– this is a strong indicator
that 5 clusters are not
enough to sufficiently
describe our data

Is the model with ten clusters
better than the one with five?
Let’s check…

Two of the ten clusters are
quite heterogenous – but not
as bad as in the previous
analysis.
Also: They are not as big as
before – that’s a big
improvement.

So we would prefer to use ten clusters.
But what describes these clusters?
Ordinarily one would use the charts on
this page or a custom visualization to find
out how the clusters are comprised.
We are not going to pursue this here but
are going to enhance our model with a
second type of analysis.

1
Visually explore HR
data on employee
moves through the
company
Apply statistical
groups of employees
changing positions
Derive a model that
based on employee
attributes
Use this model to
of success for some
upcoming moves
2 3 4

To answer the question:
“When is a move going to be
successful?” we will use a
decision tree.

We connect the decision tree
to the first filter since we
want to use additional
columns that were not
necessary for our previous
analyses.

From our visual exploration
we have formed the
hypothesis that the following
parameters affect the
probability of success:
Job Function before
Job Function after
Performance Rating before
Country AfterFrom our clustering we have
found that Time in Position is
important.
We will additionally add:
Job Level before
Job Level after
Change of Career Path Flag
Total Tenure
And we are going to model
whether a move will be
successful

We will save the decision
tree as a custom model so
we can apply it to a different
dataset later.

Run the analysis!

Here we can see whether
our models thinks that a
certain move will be
successful
But let’s look at the decision
tree directly

“Yes” = Position
Change will be
successful Green = relative
share with
successful moves
Dimension on which
decision for
categorization is made
Result 1: If low or medium
performance rating, very high
probability that any move will
lead to improvement

Result 2: For very good ratings
the move has a much higher
chance of being successful if
employee has been in his
previous position for a long
time (>3.7 years)

Result 3: Promotions for good
employees rarely go well when
they go along with a change of
job function – especially if the
employee was in R&D or
support before
Result 4: For more customer
oriented Job Functions
Promotions can go very well in
certain countries
Complex decision tree! Let’s
see how well it fits the data
overall.

This chart compares the
relative frequency of actually
successful moves against the
predicted success of a move
We can see that the model has
some trouble predicting
negative success where it has
an accuracy of about 55%
But if the model says a move is
going to be successful,
chances are quite high that it
will be so in reality as well

We have now created a prediction model
for success of a position change based on
historical data.
Now we will apply this model to some
upcoming moves and see what the model
predicts for these employees.

1
Visually explore HR
data on employee
moves through the
company
Apply statistical
groups of employees
changing positions
Derive a model that
based on employee
attributes
Use this model to
of success for some
upcoming moves
2 3 4

We add a new dataset into our
analysis that contains data on
the upcoming position changes
for seven employees

In the new dataset we have all
information that we need to
apply our decision tree

Here we have the predicted
results after applying our model
along with the predicted
probabilities for positive and
negative success
Based on the prediction – Mr.
Gonzales’ move will probably
lead to a lower performance
after the move
Mrs. Adams’ move on the other
hand will most probably go
well!

Here we can use visualizations
to compare the different
success probabilities for our
seven employees.
Last but not least, we can
share our models and
predictions with our colleagues
– directly from the application.

Select what you would like to
share – e. g. your datasets,
results, visualizations,…
And select how you would like
to share it.

Henner Schliebs
Analytics Product Marketing
@hschliebs
henner
henner.schliebs@sap.com
hschliebs
Flow of people blog: http://bit.ly/SAP-TA-blog
Talent Analytics Video: http://bit.ly/TA-YT

Proactive Career Path Management (Talent Management)

Recommended

Recommended

More Related Content

Similar to Proactive Career Path Management (Talent Management)

Similar to Proactive Career Path Management (Talent Management) (20)

More from Henner Schliebs

More from Henner Schliebs (11)

Recently uploaded

Recently uploaded (20)

Proactive Career Path Management (Talent Management)