This document provides an overview and agenda for a one-day data analysis training. The training will cover foundational concepts of data analysis including data preparation, visualization, and effective data presentation. It will include exercises in data gathering, graph types, pivot tables, and developing data stories. The goal is to help participants turn data into meaningful insights through analysis and visualization.

1. DATA ANALYSIS TRAINING
“In God we trust. All others must bring data.”
W. Edwards Deming

2. TODAY IN A
GLANCE Foundation of Data analysis
10:00 – 10:30
Data Preparation exercise
Types of graphs 10:30 – 11:00
COKE BREAK 11:00 – 12:00
Data Visualization 12:00 - 12:30
Visualization exercise 12:30 – 13:00
Story building
13:00 – 14:00
5 steps for successful data presentation
Data presentations exercise 14:00 – 14: 30
Group Presentations 14:30 – 15:00
Wrap up 15:00 – 15:30

3. HUMANS ARE PATTERN-SEEKING STORY-TELLING ANIMALS

4. WHAT IS THIS?
• This is not an excel training, you should have been part of it before coming here
• This is a training for basic analysis of data and presenting it effectively
“The goal is to turn data into information, and information into insight.”
Carly Fiorina

5. WHAT DO YOU SEE?

8. FOUNDATION
OF DATA
ANALYSIS Data Story
Visual

9. Data
Meaning
facts and statistics collected together for reference or analysis.
"there is very little data available"
Origin
Mid 17th century (as a term in philosophy): from Latin, plural of
datum.
Source: Oxford Dictionary
“Data are becoming the new raw material of business.”
Craig Mundie

10. Data
Quantitative Qualitative
Interview
Observation
Feedback
“Not everything that can be counted counts, and not everything that counts can be counted.”
Albert Einstein

11. Data
Identify
What data
Source
Acquir
e
Extract
Measure
Prepar
e
Validate
Organize
Explore
The Data preparation process
The quality of your analysis depends on the
answers that you are giving with it!
Choose your data in such a way, that it is
able to give you these answers
Gather data so you can analyse all main
attributes of your process
Think about all possible reasons that my
relate to your problem, and try to measure
them
It’s better get more data, then less data
Majority of what we do is recorded in the
SAP/BW/SSF and other systems. Make
sure you know where to get the data
from:
Where is the most relevant data stored
at
If you have no access to the report,
check with your KU / BSS lead who
may provide it to you
If your data is not placed in any report,
it might still be in a SAP database
table. Check with your KU

12. Data
Identify
What data
Source
Acquir
e
Extract
Measure
Prepar
e
Validate
Organize
Explore
The Data preparation process
For data which is already available in the
system, we can directly extract and use it. In
that case we should not forget to:
Check the time period for which we need
it
Make sure the data is correct and will not
change in the future
Update the data in case it becomes
irrelevant
Data which is important for our analysis and is
not available in the system we need to
manually measure it. In that case we should
always:
Make a correct plan for measurement –
who/when/how
Assign 2 different people to measure the
same data to assure correctness
Control if the data is really measured

13. Data
Identify
What data
Source
Acquir
e
Extract
Measure
Prepar
e
Validate
Organize
Explore
The Data preparation process
The fact that something is in our system or is
measured by us does not mean it is true.
Always validate your data!
Simple ways for validating are:
 Several measurements of the same data
 Missing data entries
 Non-logical negative values – time, money
etc.
 Extreme or inadequate values
 Dates in the future or too much time in the
past
Very often when extracted, the data first
needs to be cleaned, converted to the
right format and organized in a way
suitable for analysis. This might require
anything from the following:
 Removing/hiding sets of data
 Converting dates, numbers, currencies,
percentages, etc. into the right format
 Re-structuring of the layout
 Include calculated rows/columns
 Combine data from different sources

14. Data
Double check your data
Know your data – when and where it comes from
Specify the source of information in your graphs
Specify the time frame for which the data is
taken
“With data collection, ‘the sooner the better’ is always the best answer.
Marissa Mayer
Don’t forget, always to:

15. PRACTICAL EXERCISE PART 1 – DATA GATHERING
Analyze the root causes and propose improvements for Italy
FTRR for March 2017
Tasks to do:
• Identify what data is needed
• Understand which report is needed and extract the data to be analyzed
• Ensure data is complete and clean
• How reliable is our source – when was data updated, how it is measured/calculated?
• Do we double check?
• Is data reconciled?
• Is all data available in one report. How do you link data from other reports?

16. Data
Identify
What data
Source
Acquir
e
Extract
Measure
Prepar
e
Validate
Organize
Explore
The Data preparation process
Tables are the right choice if the audience
wants to look up individual values of a data
set. They allow to pick up individual points
with great precision.
Graphs, reveal meaningful relationships
between the data. Using graphs
enables you to see trends, patterns and
exceptions

17. TYPES OF TABLES
A table that consists of
columns and rows only,
and does not provide
any summarized data.
Each row is usually a
separate event or
observation.
Used typically for data
entry and storage
Flat table
Used to present data that
summarizes the
occurrence of an event
based on certain category
Used for final presentation
of data
Summary table
A dynamic summary
table built on the basis
of a flat table, which can
be modified by the type
and category for
summarizing
Used for analysis and
data presentation
Pivot table
Customer Name 1 Document
Type
Assignment
3400007023 ADM LTD WHOLESALE RV 3500568891
3400007023 ADM LTD WHOLESALE RV 3500570220
3400007023 ADM LTD WHOLESALE AB SSF 8001100435
3400009023 BWG FOODS LTD RF 34002000
3400009023 BWG FOODS LTD RF 34002000
3400009023 BWG FOODS LTD RF 34002000
3400009023 BWG FOODS LTD RF 34002000
3400009023 BWG FOODS LTD RG 341123
3400009023 BWG FOODS LTD RG 341123
3400009023 BWG FOODS LTD RG 341126
3400009023 BWG FOODS LTD RG 341126
Name 1 Invoice LTA Grand Total
ADM LTD
WHOLESALE 591.96
- 2
130.55
BWG FOODS
WHOLESALE LTD 3 398 830.99
941
322.81
4 398
885.46
LONDIS - 28 385.22 8.64
- 28
376.58
BWG FOODS LTD - 105 141.48 - 105 141.48
SPAR (TOPAZ) - 30.48
-
30.48
XL ATHY ROAD
COFFEE 1 217.70
1
960.79
LONDIS 1 206.30
1
226.30
BWG FOODS LTD
C/BILLING 5 378.60
- 4
633.37 745.23
3 738 753.44
871
766.84
4 666
931.00
Years (All)
Quarters (All)
Posting Date (All)
Count of Amount in doc.
curr.
Column
Labels
Row Labels AB D5RDRF RG RV RWRXZR
Grand
Total
ADM LTD WHOLESALE 1 2 3
BWG FOODS LTD 4 6 10
BWG FOODS LTD
C/BILLING 1 8 9
BWG FOODS WHOLESALE
LTD 60 1 276 5 20 3 365
BWG FOODS/LONDIS BILL
TO AC 196 3 723 1 2 2 927
LONDIS 2 1 4 2 53 62
LONDIS 06841 2 2
LYONS MATT LYONS 2 2
SPAR (TOPAZ) 1 1
SPAR CANTILLON 1 1
XL ATHY ROAD COFFEE 4 6 10
Grand Total 257 6 1 6 11
102
3 8 75 5 1392
Data

18. 5
THINGS TO
REMEMBER ABOUT
THE PIVOT TABLES
1. Select your raw data and press the button
2. All your columns should be labeled in order to make a Pivot
table
3. Don’t forget to check if your results are counted, summed up
aggregated in another way
4. If you make modifications to your source data, don’t forget to
refresh your pivot
5. For better visualization you can make pivot charts, by clicking
the button
Here you can find more about
building PivotTables in Excel

19. COKE BREAK

20. “86% STRUGGLE TO TURN VAST VOLUMES OF DATA INTO VALUABLE
INSIGHT.”
(source: CIMA Survey, 2013)

21. FOUNDATION
OF DATA
ANALYSIS Data Story
Visual

22. WHY WE NEED TO
VISUALIZE?
VISUALS are processed
60,000X FASTER in the brain than text
(3M Corporation, 2001)
90% of the information
transformed to the brain is
VISUAL
(Hyerle, 2000)
Visual stories bring
clarity and efficiency.
They excite and influence
your audience.
VISUAL TEXTUAL
A two dimensional
figure with four
straight, equal sides
connecting at four
right angles
It takes LESS THAN 1/10 OF A SECOND for
the brain to INTERPRET visual information
(Semetko, H. & Scammell, M., 2012)0,1
SEC

23. TYPES OF GRAPHS

24. Data
D a t a a n a l y s i s W h a t t o d o ?
Trends
Patterns &
Relationships
Deviations
“Information is the oil of the 21st century, and analytics is the combustion engine.”
Peter Sondergaard

25. Measurement
absolutes
Context
target, average, forecast
Trends
Patterns &
Relationships
Deviations

26. COLUMN CHART
BAR CHART
The Column Chart very effectively shows the comparison of one or
more series of data points.
The Column Chart very effectively
shows the comparison of one or
more series of data points
Clustered column chart Clustered bar chart
0%
20%
40%
60%
80%
100%
First Time Right Rate
0%
100%
First Time Right Rate per type of invoice
First Time Right Rate - FI invoices
First Time Right Rate - Rebate invoices
First Time Right Rate - PO invoices
Austria
Bosnia-Herz.
BSS
0 50 100 150 200
First Time Right Rate per type of invoice
PO invoices
Rebate invoices
FI invoices

27. Range, Distribution
high, low, shape
Trends
Patterns &
Relationships
Deviations

28. HISTOGRAM
&
PARETO CHART
Histogram is only used to plot the frequency of score occurrences in
a continuous data set that has been divided into classes, called bins.
It represents the distribution of your data.
The size of the bin depends on the number of times when the
category on the X axis has occurred.
Very effective for showing
estimates as to where or when
values are concentrated, if there
are repeating patterns or any
outliers.
0
100
200
300
400
500
600
700
800
900
Below 10 Between 10
and 100
Between 100
and 1000
Between 1000
and 10000
Between
10000 and
100000
Between
100000 and
1000000
Above
1000000
Histogram of invoices amount (in EUR) for Ireland

29. PARETO CHART
Pareto chart is a histogram where the bins are not ordered in their
natural sequence, but based on the frequency of their occurrence.
The aggregation line on the top represents the sum of the
occurrences of the particular bin and all the preceding ones
A tool specifically used to
represent the main contributors to
a certain effect
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0
100
200
300
400
500
600
700
800
900
Between 1000
and 10000
Between 100
and 1000
Between 10
and 100
Between
10000 and
100000
Below 10 Between
100000 and
1000000
Above
1000000
Pareto chart for invoices amount in Ireland

30. Ranking
big, medium, small
Hierarchical
category, subcategories
Categorical Comparison
proportionTrends
Patterns &
Relationships
Deviations

31. PIE CHART In a Pie chart the different values are represented as part of the
whole, all categories of adding up to 100%.
Very effective for comparison,
when one of your categories is
much bigger than the rest
Sunburst/doughnut chart Treemap chart
FI
invoice
s
9%
Rebate invoices
18%
PO invoices
73%
Share of Not FTRR invoices per type -
Bulgaria
FI invoices
9% Rebate
invoices
18%
PO invoices
73%
Share of Not FTRR invoices per type - Bulgaria

32. Direction
up, down or flat
Rate of Change
linear, exponential
Optima
highs – lows
Trends
Patterns &
Relationships
Deviations
Fluctuation
seasonal, rhythm
Significance
signal – noise
Intersection
overlap, crossover

33. LINE CHART In a Line Chart, the vertical axis (Y-axis) always displays numeric
values and the horizontal axis (X-axis) displays time or other category
Especially effective in displaying trends
0
100
200
1.дек
2.дек
3.дек
5.дек
6.дек
7.дек
8.дек
9.дек
12.дек
13.дек
14.дек
15.дек
16.дек
17.дек
19.дек
20.дек
21.дек
22.дек
23.дек
27.дек
28.дек
29.дек
30.дек
Number of invoices per day
You can additionally add trendline and deviation. Look for trends, cycling, patterns and
outliers

34. Boundaries
highs – lows
Correlation
weak – strong
Exceptions
outliers
Trends
Patterns &
Relationship
s
Deviations
Clusters
bunching – gaps
Intersection
overlap – crossover
Association
variables - values

35. SCATTER PLOT Scatter plots are similar to line graphs in that they use horizontal and
vertical axes to plot data points. However, they show how much one
variable is affected by another.
Very useful for observing how
the values of two series
compares over time or other
category.
It quickly shows trends,
dependencies and outliers
0
5
10
15
20
25
30
0 10000 20000 30000 40000 50000 60000
Disputes Resolution time vs. its amount, per country
Resolution time - CH (in hours) Resolution time - IT (in hours)
Linear (Resolution time - CH (in hours)) Linear (Resolution time - IT (in hours))

36. Data
The analysis is a game of trial and error
Try different tables and graphs until you find the best
When you find peculiarity, explore it
Look in the specifics, but do not forget the big picture
Don’t forget, always to:
“If we have data, let’s look at data. If all we have are opinions, let’s go with mine.”
Jim Barksdale

37. PRACTICAL EXERCISE PART 1I – ANALYSIS
Analyze the root causes and propose improvements for
Italy FTRR for March 2017
Tasks to do:
• Identify the main drivers of the Not FTRR /
• Prepare different types of graphs to represent the behaviour in terms of biggest
contributors and root causes
• Discover and present deviations, trends and patterns for your data. What do you
think is the reason for those?

38. FOUNDATION
OF DATA
ANALYSIS Data Story
Visual

39. HOW MANY “5”
DO YOU SEE? 3346366328560568320549638463460345820834
0137430545049673406734029750272057206974
6039763047320457204693746039763097630976
3209673046973069736039760752323597207326
093276039673
3346366328560568320549638463460345820834
0137430545049673406734029750272057206974
6039763047320457204693746039763097630976
3209673046973069736039760752323597207326
093276039673
Visual

40. GRABBING ATTENTION
Line length Line width Orientation Shape
2D position Size Additions Enclosure
Intensity Hue
Blinking
Flicker
Direction
Curvature /
form
Motion

41. #1
ONLY DISPLAY
INFORMATION THAT IS
RELEVANT TO YOUR
MESSAGE
# 2
USE VISUAL DIFFERENCES TO
AMPLIFY YOUR MESSAGE
# 3
BE CONSISTENT IN YOUR
VISUALISATION

42. # 4
USE PROPORTIONS
CORRECTLY
# 5
USE THE TITLE TO ANNOUNCE
THE MAIN OUTCOME OF YOUR
GRAPH
# 6
PROVIDE ADDITIONAL DETAILS
TO ENHANCE YOUR MESSAGE
0
10
20
30
40
50
60
70
Jan Feb Mar Apr May Jun Jul Aug Sep
Growing Sales, with some bumps on the road
2 FTEs leaving
Unusualy cold
weather
+ 5pp vs. LY
# 7 ALWAYS STATE THE SOURCE OF THE INFORMATION

43. DATA VISUALIZATION IN EXCEL
• How to make graphs
• How to make a combined graph
• How to change the type of graph and edit the legend
• How to change colors and width of line

44. PRACTICAL EXERCISE PART I1I – VISUALIZE YOUR ANALYSIS
Analyze the root causes and propose improvements
for Italy FTRR for March 2017
Tasks to do:
• Visualize your analysis from Part II
• Polish your graphs in order to better represent your idea

45. FOUNDATION
OF DATA
ANALYSIS Data Story
Visual

46. Story
I think people have begun t o f o r g e t h o w p o w e r f u l h u m a n
s t o rie s are, exchanging their sense of empathy for a fetishistic
fascination with data, networks, patterns, and total information...
Really, t h e d a t a is just p a r t o f t h e story . The human stuff is
the main stuff, and the data should enrich it.
Jonathan Harris

47. Story
Meaning
A narrative (or story) is any account of connected events, presented to
a reader or listener in a sequence of written or spoken words, or in a
sequence of (moving) pictures
Origin
Derived from the Latin verb narrare, "to tell"
Source: Oxford Dictionary

48. logos | reason
ethos | credible
pathos | emotional
THE ART OF
PERSUASION

49. A SIMPLE STORYLINE
The most basic story structure
is familiar to us all. It consists
of a beginning, a middle and
an end.
The middle (discovery)
To make your message compelling, make the middle about
discovery. Explain a conflict, the hurdles that had to be overcome
and potentially the wrong turns that were taken in arriving at the
real proposed solution and call to action
The end (conclusion)
Effective Data Storytelling must go
beyond simple display of data but
answer the so-what-question. This
means including a conclusion,
recommendation, forecast or next
step suggestions. You have to present
your opinion and a message
The beginning
(context)
To allow readers of a report to grasp its
full meaning and insights, they need to
have a point of reference. That
reference is the context in which
reported events took place or the
assumptions on which forecasts are
based.
“Numbers have an important story to tell. They rely on you to give them a voice.”
Stephen Few

50. A SIMPLE STORYLINE
Context
General Comparisons
Trends
Dependencies
Conclusion
Next
steps
Make sure you highlight your key point at the front of your story, e.g. in the headline or graph title
Overview first, then zoom &
filter, details on demand
Ben Schniederman

51. THE 5 STEPS FOR SUCCESSFUL DATA
PRESENTATION

52. IDENTIFY YOUR
AUDIENCE
Who am I reporting to?
How do they like to receive the information?
Is there just one group or different audiences?
Where and when can I communicate with them?
1

53. ESTABLISH AN
OBJECTIVE AND
STORY
What problems are we trying to solve?
What do they already know?
What business decisions do my audience need to make?
Am I recommending a decision or providing the facts?
2

54. DECIDE WHAT
DATA WILL HELP YOU
TELL THE STORY
What data does the company have available to investigate the story?
Do I need new data?
What type of analysis can I use to surface the insights?
What kind of data has to be presented to persuade the counterpart to
take action?
3

55. CREATE A
STORYBOARD
What is the best way to bring my story to life for my audience?
What visualizations should I use?
How to make sure that the story flow is logical and understandable for
my audience?
4

56. CONTINUOUSLY
IMPROVE
Did my audience understand everything?
Did I give them sufficient information?
Was the decision successful?
Is there anything new to add in the future?
5

57. PRACTICAL EXERCISE PART 1II - DATA ANALYSIS
PRESENTATION
Analyze the root causes and propose improvements for Italy FTRR for
March 2017
Each group to make an elevator speech
• Based on real data and visual graphs
• Build a Story line
• Drive decision making and reach an agreement
• Explain why the counterpart must take action

58. WHAT’S WRONG WITH THIS GRAPH?

59. WHAT’S WRONG WITH THIS GRAPH?

60. … at least 5 things
Uneven time periods for the X axis
Y axis does not start from 0, leading
to false disproportion
Over usage of the Y axis
values
No conclusion in the title
Source of information not
announced
WHAT’S WRONG WITH THIS GRAPH?