Analytics / Alteryx training for
TD practitioners
Almaty
Course objectives
1
Discuss how TD engagements are changing with more data
and more tools available for us to do the work
2
Recognize how and when Alteryx should be applied on TD
engagements
3
Become proficient in Alteryx basics and know how to build your
knowledge further
4
Understand why we need to be thoughtful about how we work
with data if we want to benefit from analytics
Page 2
Course agenda
Duration
Analytics in TD
13:30 – 13:45
Introduction to Alteryx
13:45 – 14:15
Case Study 1 – Part 1: Alteryx basics (hands on)
Break
Case Study 1 – Part 1: Alteryx basics – introduction
Case Study 1 – Part 2: Alteryx macros – introduction
Unguided case study
14:14 – 15:00
15:15 – 15:30
15:30 – 16:30
16:30 – 17:00
17:00 – 17:45
Page 3
TD Analytics
Page 4
TD Future Practitioner Capabilities
TD practitioners will need to be familiar with various tools which will be critical to handle and prepare data
1
2
Data mindset
Data transformation
Deal
Deal
adjustments
adjustments
Trust
Discovery
Translate
Provide confidence
that data will be secure
and protected
Describe
What is the right data
to request in order to
address the client
questions
Unlock
the value
in data
System
reconciliation
Databook(s)
Extracts to client
Carve out
statements
Dashboards
Due diligence report
Analytics as a
Service – access
to clients & bidders
Analyse &
Understand
What insights does
the data provide?
CARVEx
Empower
Deliver
The most appropriate
way of delivering our
insights to the client
Page 5
Due diligence database
Data visualization / output
Be the conduit
between the client Insights
requirements and the
analytics required
Discover
What value exists in
both client and external
data
Carve out
adjustments
Accounting
Accounting
adjustments
adjustments
3
Ensure that these
insights are being
delivered to the client
and internal team
Actions
Practitioners need to be familiar
with more efficient ways of
handling and preparing data.
Practitioners will have the ability to
deliver the insights to clients in the
most effective way.
Tools, capabilities, and client value-add
Data processing
What
►
Repeatable data cleansing workflows
►
10x efficiency gains on large datasets
►
A must to use whenever large, but not
perfectly clean datasets are received
►
What
►
Interactive data visualizations (desktop &
hosted environments)
►
Faster deep-dives, more flexibility in analysis
►
Advisable to use to make data exploration,
Q&A sessions, reporting more effective
Partial automation of previously manual tasks
Sales pitch to clients
►
We can handle all the data you throw at us
very efficiently (with seamless updates, raw
formats, direct connections to your systems)
►
We can be the “data stewards” for all
involved stakeholders, so you do not need to
provide data twice; we will transparently
handle any adjustments to data made
Page 6
Data visualizations
Sales pitch to clients
►
We can get to insights faster and thus
address critical issues early in the process
►
We can easily perform deep-dives and
connect financial, commercial and
operational KPI developments into one equity
story
►
We have solutions that allow sharing data
with bidders in a controlled fashion (you see
the data, but you cannot get it)
Success factors in using analytics on engagements
1
Instead of (or in combination with) standard TD work, not on top of it
2
Planned and integrated into scope and DD approach, reflected in the
information requests issued
3
Differentiation between exploratory analysis and ground work vs.
deliverables that convey specific insights or results
4
Storyboarding with the senior part of the team is crucial; pen & paper
first, software second
5
Early delivery to clients, iterations based on their feedback
6
Patience and resilience when execution does not go smoothly the
first time it is tried
Page 7
A data-minded team involves everyone
“Successfully applying analytics requires all team members to
be involved. It is not just hands-on super users”
Consultants and Seniors
►
►
Take the time to get proficient with the tools. Practice, practice, practice.
Follow the latest developments, learn best practices. Share lessons learned!
Managers and Senior Managers
►
►
Embed analytics: in scoping, information requests, storyboarding.
Give your teams space to try out new approaches and occasionally fail.
Partners and Directors
►
►
Understand the possibilities. Challenge teams to adopt analytics on projects.
Market our capabilities aggressively. Today, we can still surprise our clients!
Allow for some learning curve.
Innovation means failing from time-to-time.
Page 8
What are those latest analytics tools and
approaches?
1
Visualization tools: Spotfire / Tableau / PowerBI
2
Data processing: Alteryx / PowerQuery
3
Dynamic databooks
4
DAS solutions: CARVEx, DAS Suite (Capital Edge)
5
Deliverables: EY Synapse, Digital Direct
6
Social Media Analytics: Crimson Hexagon
Page 9
Examples from the field
Page 10
Introduction – why Alteryx?
Page 11
We need to stop being “databook-centric” and start being
“data-centric”
Databook approach
Data centric approach
Ending point
Starting point
Ending point
Starting point
Support services
Currency: KD000
Sales sector phones
Sales sector Co-Op
Head Office
Branch Customer Service (Satellite)
Advertising Department
Maintenance Section of mobile phones
Grocery Sector
pronto wash
Salwa Co-op
Sultan / Arabia
Zain Support
Total
KPIs:
Number of support service outlets
Average revenue per support service
FY15A
74
2
138
364
0
1
579
FY16A
78
1
162
317
0
383
133
1,075
6
97
7
154
YTD17A
26
1
79
48
168
32
354
6
59
Source: XXX
Ref: - Ref: - Ref: Fonz dealers v s. franchises - Section PL - Profit and Loss Analy sis
Databook version
Data visualization
Machine learning
Maximizing flexibility and modern
technology that saves time
Minimizing the time to get to nicely
formatting schedules
Data centric approach
Issues with the databook approach
►
Human errors
►
Version control issues
►
No reusability
Page 12
►
Efficiency in repetitive processes
►
Faster, deeper insights
►
Platform for innovation
Diligence then… vs. diligence now
Databook approach
►
This went up… This went
down…
►
This went round and
round…
Data centric approach
Conclusion / Point of view
Conclusion / Point of view
We recommend that you consider this in the context
of your valuation…
and seek appropriate SPA protection…
Using a data centric approach allows detailed
analytics which enables providing much more
focussed advice.
Page 13
5 tenets of being data-centric
1
Obtain as much data as you can, not as little as you need
2
Differentiate between presentation of data and storage of data
3
Avoid manual changes of source data, have an audit trail
4
Store data in computer friendly way, not human friendly way
5
Leverage technology to do the heavy lifting for you
Page 14
Core idea of being data centric: flat files and hierarchies
Flat file – one observation per row
Enrichment of dataset through hierarchies
Time
Date
(Calendar)
Legal
Entity
Account
Number
Account
Name
Amount
ABC
ABC
ABC
123
Entity
Information
Accounts
Data type legend
ABC
String
123
Double
Note: This is an illustrative selection of mappings.
Page 15
Date
Definitions
Date
(Calendar)
Fiscal
Year
Fiscal
Month
YTD vs.
YTG
ABC
ABC
ABC
Legal Entity Business
Unit
Country
ABC
ABC
ABC
Account
Number
IS / BS / CF
Roll-ups
ABC
ABC
ABC
Account
Number
Statutory /
Conso.
Reported /
Adjusted
ABC
ABC
ABC
Region
ABC
Net Debt /
NWC
ABC
Alteryx will be your best
friend in getting your data
flattened
Page 16
Alteryx graphical user interface
1
►1
Tool palette
►2
Workflow canvas
3
►3
Configuration window
►4
Results window
2
4
Page 17
Workflow basics
Each tool has input and/or
output „ports“.
Always need to start with
an input of data. The
selected file name is
displayed.
Page 18
You may or may not
need to connect to all
ports of a tool
Connect tools by clicking
on a port and dragging it
to the port of the next
tool.
Selecting the data source file
Input Data
1
►
Drag an INPUT DATA tool onto the
canvas
►
2
Select the arrow next to the
“Connect a File or Database” drop
down
►
►
You must close the source file (e.g. from Excel)
before you can input it to
a workflow
Page 19
Select File Browse and navigate to
the source data file and select open
Notice the other default input
options Alteryx provides
Data field types and column selection
Select Tool
►
It is important to define the “type” of each field
accurately as it affects which functions or
actions can be performed on that field.
►
Most common types include:
Double – used for numbers with decimals
V_String – used for text/ non-number content
Date – recognizes several formats such as
xxmmyyyy
►
It is good practice to use a SELECT TOOL
immediately after importing data (and after
performing certain workflows steps) to check
the data type for each field.
►
Columns can be removed from the dataset with
this tool by unchecking the box on the left.
Page 20
Data field types (1 of 2)
Select Tool
TYPE
DESCRIPTION
EXAMPLE
Bool
Boolean: The type of an expression with two possible values: True or
False
0=False; -1=True
Note: any value other than 0 would indicate
the value is True.
Byte
Number: A byte field is a positive whole number than falls within the
range, 0 thru 255
0, 1, 2, 3....253, 254, 255
Int16
Number: 2Byte: Twice Exponential to the Byte, or 216
–32,768 to 32,767
Int32
Number: 4Byte: Four Times Exponential to the Byte, or 232
–2,147,483,648 to 2,147,483,647
Int64
Number: 8Byte: Eight Times Exponential to the Byte, or -263 to +263
–9,223,372,036,854,775,808 to
9,223,372,036,854,775,807
Fixed
Decimal
Number: The specification of width of field and then to decimal
threshold. The first number is the total width of number, the second
number is to the decimal level of precision. The decimal point is
included in the character width.
"7.2" => 1234.56 "8.2" => -1234.56
Float
Number: A single-precision floating point number is a 32-bit
approximation of a real number.
+/- 3.4E +/- 38 (7 digits) where 38 is the
exponent and 7 digits references seven
digits of accuracy.
Double
Number: A double-precision floating point number is a 64-bit
approximation of a real number.
+/- 1.7E +/- 308 (15 digits) where 308 is the
exponent and 15 digits references fifteen
digits of accuracy.
String
Character: Fixed Length String. The length must be at least as large
as the largest character value contained in the field. Limited to 8192
characters.
Any string whose length does not vary
much from value to value.
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Data field types (2 of 2)
Select Tool
TYPE
DESCRIPTION
EXAMPLE
V_String
Character: Variable Length. Length of field will adjust to accommodate If the string greater than 16 characters and
the entire string within the field.
varies in length from value to value
WString
Any string whose length does not vary
Character: Wide String will accept unicode characters. Limited to 8192
much from value to value. Æ.ç.ß..Ð.Ñ...
characters.
Any string that contains unicode characters
V_WString
Character: Variable Length Wide String
If the string greater than 16 characters and
varies in length from value to value. If the
string contains unicode and is longer than
16 characters, use V_WString, such as a
"Notes" or "Address" field.
Date
Character: A 10 character String in "yyyy-mm-dd" format
December 2, 2005 = 2005-12-02
Time
Character: A 8 character String in "hh:mm:ss" format
2:47 and 53 seconds, pm = 14:47:53
DateTime
Character: A 19 character String in "yyyy-mm-dd hh:mm:ss" format
2005-12-02 14:47:53
Blob
Blob: Binary Large Object: A large block of data stored in a database.
A BLOB has no structure which can be interpreted by the database
management system but is known only by its size and location.
an image or sound file
SpatialObj
Blob: The spatial object associated with a data record. There can be
multiple spatial object fields contained within a table.
A spatial object can consist of a point, line,
polyline, or polygon.
Page 22
Filtering and selecting certain
rows from the dataset
Select Records
You can select which rows of data move forward in your
workflow by defining the row numbers you want using the
SELECT RECORDS tool 1 or creating a filter on the
contents of a column using the FILTER tool 2 .
2
1
When you create a filter, you can use the true results or
the false results (or both 3 ) as your output to continue on
with in the workflow.
3
Page 23
Filter
Tool Audit & Input/Output Anchors
Selecting the green input and output anchors
after running a workflow will help a user audit
the changes caused by a particular tool
Selecting the input and output anchor will
populate the results window with the data for
the corresponding point in the workflow.
Highlighting the workflow link between icons
will describe the errors, warnings and
messages caused by each icon in the
workflow
Page 24
Formula
Formula Tool
Create or update fields using one or more expressions
to perform a broad variety of calculations and/or
operations
►
1
Output Field
►
►
2
3
►
2
Select the appropriate field type of the new
field. If an existing field was picked above, the
Type is for reference only.
Expression
►
Page 25
3
This is the field the formula will be applied to.
Either choose a field listed in the drop down
to edit or add a new field. To add a new field,
type the new field name in the box.
Type
►
1
The expression box is for reference only. It
will populate with the expression built in the
Expression Box below.
Un-pivoting data (Transpose)
Transpose
Data that has multiple columns of data such as a trial
balance with a column for each reporting entity or
month, is already partially summarized. The types of
analyses and visualizations you can do with this data
is limited. Often, data is most useful to us if we have a
single value per record (row).
1
►
From the Key Fields Section, select the field(s) to
pivot the table around. This field Name will remain
on the Horizontal axis, with its value replicated
vertically for each data field selected (step2).
These columns typically contain categorical or
descriptive contents.
►
2
From the Data Fields Section, select all the fields
to carry through the analysis. These are the
columns with values in them.
1
2
2
1
1
Page 26
2
4
Combining Multiple Datasets
A key feature of Alteryx is the ability to “join” different datasets into one larger combined dataset. There are
different tools depending on what you need to achieve. The key ones are:
►
Join: similar to a vlookup / index match in Excel. The join tool is used where you have 2 tables with one or
two fields in common, and you want to see all fields in the one table.
►
Unions: when you have the same structure of data across multiple tables you can use the union tool to
combine it into 1 large table to see all rows in the one table. E.g. combining 12 monthly reports into 1.
►
Append: when you want to add (or append) values or additional data to each row of your data
Example Join Inputs:
Join
"Left" Data
Year Value 1
2001
X1
2002
X2
2003
X3
2004
X4
2005
X5
"Right" Data
Year Value 2
2003
Y1
2004
Y2
2005
Y3
2006
Y4
2007
Y5
Page 27
Example Join Outputs
Year
2001
2002
L: Joined Data
Value 1 Value 2
X1
X2
Year
2003
2004
2005
J: Joined Data
Value 1 Value 2
X3
Y1
X4
Y2
X5
Y3
Year
2006
2007
R: Joined Data
Value 1 Value 2
Y4
Y5
All values in the Left table not in the
Right table
All values in both the Left table and
the Right table.
Note: rows will duplicate if there are
multiple potential joins (i.e. more
than one row per year)
All values in the Right table but not
in the Left table
Combining Multiple Datasets
Example Union Inputs:
Union
Month
Jan
Feb
Mar
Apr
May
Jun
Dataset 1
Value1 Value2
100
35
200
40
300
45
400
50
500
55
600
60
Dataset 2
Month Value1 Value2
Jul
700
65
Aug
800
70
Sep
900
75
Oct
1000
80
Nov
1100
85
Dec
1200
90
Example Union Output
"Unioned" Dataset
Month Value1 Value1
Jan
100
35
Feb
200
40
Mar
300
45
Apr
400
50
May
500
55
Jun
600
60
Jul
700
65
Aug
800
70
Sep
900
75
Oct
1000
80
Nov
1100
85
Dec
1200
90
All data from dataset A
All data from dataset B
Note: Dataset A and Dataset
B need to have the same
structure (fieldnames and
datatypes) as each other
Example: Joins
All combinations
generated
Append
Example: Joins
Page 28
Case Study 1 – Trial Balance Data
Transformation
Page 29
Preview of case study data
►
►
►
What information does this file contain?
What is it missing?
How does its current layout inhibit you from analyzing it?
Page 30
Preview of intended results
►
►
►
One type of data in a given field (column)
One numerical value per record (row)
A separate field for each categorical, mapping, or otherwise
descriptive element
Page 31
Case study 1: Trial balance preparation
►
Objective:
►
►
Transform a transaction dataset into a flattened file by building a
workflow in Alteryx to enable use in databooks, other analytic and
visualization tools and the TS diligence dashboards.
Three-step approach:
1. Use Alteryx to transform data from one tab into a flat file (1 hour)
2. Leverage the workflow from step 1 to process an entire dataset
(45 min)
3. Add meaningful hierarchies (45 min)
►
Instructions:
►
Page 32
Individually complete this exercise by following the steps detailed
in the following slides. Your table facilitators will be there to help
you along the way
Case study 1 - step 1 (of 15): loading data
Purpose: Load data from 1 tab (Apr14) into Alteryx for
further processing.
►
Open Alteryx and select File  New Workflow.
►
Drag the Input Data tool
►
In the configuration window, select the dropdown in the
Connect a File or Database bar, and navigate to the
source file “Alteryx Raw Data – ProjectTraining – V1.xslx”.
►
In the Choose Table or Specific Query window, select the
tab “Apr 14” tab.
►
Select option “First row contains data”. This will force
Alteryx to give standard column names instead of relying
what it found in the first row of data. Consider this as a
best practice unless you are sure that the labels in the
first row of data are static across dataset.
►
Run the workflow to see a current preview of the data.
►
You should see the data from tab Apr14 in the results
panel.
onto the workflow canvas.
Note: You cannot have the file open in Excel as Alteryx
needs exclusive access to read the data. Save a copy of the
file for Excel viewing instead.
Page 33
Case study 1 – step 2 (of 15): Excluding
irrelevant headers and setting the right headers
Purpose: We will exclude irrelevant header data and ensure
that column headers of the dataset are appropriate.
Specify the data range to pull from your input.
►
Select the Preparation palette and choose the Select
Records tool, used to select and deselect rows. Enter the
desired row range for your data.
►
In the case of Project Training, the trial balance information,
including row headers, begins on row 7. Enter the range 7+ to
ensure no data is missed on future tabs.
►
Run the workflow to check your work.
►
With only one tab of data to input, it may not take long to run
the workflow. But later, when we add multiple tabs, using an
unlimited range may slow down the workflow. There are
several ways to speed up workflows but the easiest is to limit
the number of rows as shown in the range at right.
Set the column labels from dataset.
►
Select the Developer palette and choose the Dynamic
Rename tool and connect the workflow to the L input.
►
Page 34
Select “Take Field Names from First Row of Data” in the
Rename Mode menu. This will make Row 1 our new column
headers.
Case study 1 - step 3 (of 15): creating account
name and account number columns
Purpose: We will separate columns for account name and
account number. This will be helpful later and is generally
good practice.
►
Reviewing your dataset, you should note that your
account column has both names and numbers. To
separate these into separate columns, select the
Parse palette and choose the Text to Columns tool.
This tool works very similar to Excel.
►
In Field to Split menu, select Financial Row. Noticing
in your dataset that “-” is used to separate the account
number from the account name, use “-” as the
delimiter.
►
Input 2 as the number of columns to split.

Run the workflow
, and look to the Results pane in
the bottom right of the workflow. You should see two
new columns on the right containing the account
number and account names separately. Notice, the
column headers are derived from the original column
name or the “Output Root Name” of the tool
configuration. We will rename columns later.
Page 35
Case study 1 – step 4 (of 15): removing
subtotals and other irrelevant rows
Purpose: The dataset includes subtotals, grand totals and
other rows that are not necessary in the flat file. We will
remove them from the dataset based on a rule we infer for
the data.
Clean up the non-data rows in the dataset.
►
We can see that the first three rows do not contain TB
account data, and that later rows are empty, have
subtotals, etc. We need to filter out these rows.
►
Reviewing your data, notice that all TB accounts have
a “0” in the number. Therefore, if we filter out rows that
do not contain a 0, we will remove all irrelevant rows.
►
Select the Filter tool under the Preparation palette. It
will query records based on an expression to split data
into two streams, True (records that satisfy the
expression) and False (those that do not).
►
Use a Custom Filter and express the condition that we
want to achieve (ie. Field “Financial Row1” should
contain a “0”). The condition leverages a function
called Contains() – there are many other functions
that you will learn over time that are helpful in similar
situations.
Page 36
Case study 1 – step 4 (of 15): removing
subtotals and other irrelevant rows
(cont’d)
►
The tool has two outputs, “True” and “False”
►
Be sure to connect the next step in the workflow
to the “True” function, as that is the data we want
to continue working on.
►
Note that the false output can be just as useful as
the true output – in circumstances in which you
want to split your dataset and perform two
different workflows on two different types of data.
Note: You do not always want to simply filter
subtotals out of your data. In practice, you may
want to keep them in your flat file to be able to
validate that line items actually add up to
subtotals (i.e. check that the dataset internal
integrity is working)
Page 37
Case study 1 – step 5 (of 15): cleaning up and
relabelling data
Purpose: Clean up the data for empty values, whitespace
in front of / at the end of account names. Relabel columns
to appropriate names.
Rename columns:
►
Use a Select tool to (i) deselect “Financial Row” (it is
no longer required), (ii) rename “Financial Row 1” and
“Financial Row 2” as “Account number” and “Account
name”, respectively, and (iii) highlight and move
Account number and Account name to the top of the
list.
(continued on next page)
Note: There are several fields that do not have
headers indicating they do not contain data (e.g.
Field 26). While we could deselect them at this
stage, we would risk our workflow not be
reusable for other tabs (in case they have a
varying number of columns). We will instead filter
out these fields later, after the “flattening” step.
Page 38
Case study 1 – step 5 (of 15): cleaning up and
relabelling data
(cont’d)
Clean up your data:
►
Next, go to the Preparation palette and select the
Data Cleansing which can automatically perform
common data cleansing with a simple check of a box
such as remove nulls, eliminate extra white space,
clear numbers from a string entry.
►
Ensure all desired fields under the “Select Fields to
Cleanse” menu are selected. Here we can select all fields.
►
All “String” fields with [null] will be made blank and all
double fields with [null] will be 0.
Page 39
Case study 1 – step 6 (of 15): flattening the
dataset
Purpose: Reshape the file from having 1 row / multiplecolumn form (a “wide” form) to a flattened format that is
more flexible.
►
Each row of data contains multiple datapoints, one
amount for each entitiy. Analyzing the data later will be
more powerful if each row only contains one datapoint
so we must “unpivot” the data by transposing those
columns into rows.
►
Select the Transpose tool under the Transform
palette.
►
The “Key Fields” are the columns are data you do not
want transformed from a horizontal to a vertical axis.
In our case, select Account number and Account
name.
►
The “Data Fields” are the columns that contain data
for which we want a single row each. This will create a
column for the entity and a column for the values.
►
Run the workflow
Page 40
to see the results.
Case study 1 – step 7 (of 15): adding the month
and year to the dataset
Purpose: Add a column with an indication which
month the data refers to. We will pick the month from
cell A3 of the dataset.
►
Return to your data input file (‘Alteryx Raw DataProject Training V1’) at the beginning of the workflow.
Create a new branch by adding another Select
Records tool. See illustration at upper right.
►
Enter 4 into the Range in the configuration panel to
pick up the third row of data only. In the results output
you should see Apr 2014 in the first column.
►
Use Text to Columns to separate month and year.
►
Select field F1 as your column, and use a space as
the delimiter. The result should give the month and
year in separate columns. You can also choose a
specific output root name to identify new columns
easily.
►
Use the Select tool to (i) rename the two output
columns as “Month” and Training Inc2 as “Year” and
(ii) deselect all other fields
►
Click run and your results should look like the this
(continued on next page)
Page 41
Case study 1 – step 7 (of 15): adding the month
and year to the dataset
(cont’d):
►
Now that we’ve identified and extracted the month and
year information from the source file, we need to add it
to the TB information itself
►
Select the Join palette and choose the Append Fields
tool. This tool will append the fields from a source
input to every record of a target input. The souce (S)
should always contain fewer records than the Target
(T).
►
Connect the TB account work stream to the T input
and the dates work steam to the S input.
►
Run the workstream and notice in the results that
every row now has the month and year column.
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Case study 1 – step 8 (of 15): final clean up of the
dataset
Purpose: Final clean up of the data: making sure we do
not have irrelevant entries and adjusting data types.
►
It is very important to check that our data types are
correct in the final dataset. Use a Select tool and
observe that the Value field is currently V_String
type. This is obviously not what we want – change it to
Double type instead. Note that Value field was
created automatically in Step 6 – Alteryx chose the
datatype for it based on values that were found.
►
Additionally, let’s rename Name field to Entity.
►
Finally, run another Data Cleansing Tool on the
dataset, as this will ensure that any empty values
become zeros in the Value field.
(continued on the next page)
Page 43
Case study 1 – step 8 (of 15): final clean up of the
dataset
(con’t)
►
If we run the Alteryx workflow and review the results,
you’ll see rows containing “Fields” and “N/A” in the
Entity column. This is due to the extra columns not
containing data imported from the tabs of the original
source file.
►
As the dataset is now in a flat format, we no longer
need to worry about identifying such “columns”
manually, but rather we can filter them out based on
Name field. Observe that we are interested only in
rows where Name column is in format “EntityXX”.
►
Use a Filter tool and a Contains() or StartsWith()
function to filter the relevant entries.
Congratulations! Take a break, and we will
move on to learn how to apply your workflow
across multiple tabs of datafile at once.
Page 44
Note: In this step, we are again
filtering out subtotals. If you wanted
to check if the “Total” column
actually is the sum of all entities,
can you figure out a way to do it in
Alteryx? (see optional step 8b for a
solution on that)
Case study 1 – optional step 8b (of 15): checking
that subtotals are right
►
1
2
3
4
In some cases, you will want to check that your
dataset’s line items add up to subtotals. The general
idea is as follows:
►
►
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2
Separate your dataset into two tables: line items
and subtotals. In our example, you can do by
using Filter tool from step #8 and further filtering
its “False” output to include only rows that have
entity name “Total”.
1
4
Aggregate both datasets to the same level of
information using a Summarize tool. In this case,
we want to Group data by Account Number,
Month and Year, and Sum by Value
Then, join both datasets to have the “calculated”
total value (from line-items dataset) and the
original total value (from totals dataset) match on
a row-by-row. Use a Join tool for that, and join
on common dimensions: Account Number,
Month and Year.
Finally, use a Filter Tool to find if there are rows
where calculated total value is not equal to
original total value.
2
2
3
3
Case Study 1 Part 2: Batch Macros
Page 46
Introduction to Alteryx Macros
Full workflow
►
►
►
Alteryx allows packaging your workflows into
macros which make your workflow accessible
as a new “tool” in a workflow. This generally
allows you saving commonly recurring
workflows to avoid designing them over and
over.
In order to convert your workflow to a macro
you need to specify inputs/outputs (check out
Interface palette), change workflow type to
macro under Workflow Configuration and
save it.
Repetitive steps
Convert to a macro
New workflow
There are three types of macros:
►
Standard macros: a simple packaged up
workflow
►
Batch macros: provided a list of values,
run a workflow with each of the values as
an input, and provide an output as a union
of individual iterations.
►
Iterative macros: run a workflow until a
specified condition is met.
Page 47
Convert to a macro
Applying a macro solution to process data
across multiple tabs
Workflow that processes one tab
►
We can leverage the batch macro functionality
to have our one-tab workflow to be used to
process multiple tabs at once.
►
The steps are as follows:
►
Modify the one-tab workflow to become a
macro by i) adding an explicit Macro
Output Tool, ii) adding a Control
Parameter Tool which will allow us
dynamically change the parameters of the
Input Data tool. We will configure it so
that Input Data tool does not refer to a
fixed worksheet, but rather uses the value
of the worksheet we provide to the macro.
►
Save the workflow as a macro
►
Design another workflow that uses the
macro as a tool and passes it a list of tab
names
Page 48
Macro that accepts a tab name and produces output
Pass tab names, get output for all of them at once
A new workflow that uses macro
Case study 1 – step 9 (of 15): converting the
workflow to a batch macro
Copy paste our final workflow (as seen in the
picture right) into a new workflow and create
a batch macro.
►
Search for the Control Parameter and
connect it to your Input tool. Alteryx will
automatically connect an Action tool.
►
Go into the configurations of the Action tool
(left side of your Canvas) and select “File
value” and “Replace a specific string”. We
want to be replacing just the “default” tab
name, so let’s enter “Apr 2014” as the value.
►
Search for a Macro-output tool and connect it
to your Append tool at the end of the workflow.
►
Now save your workflow. You will notice it is
saved with an extension of “yxmc”, not the
usual “yxmd”. This workflow is your batch
macro, and it now can be used as a new tool in
your workflows.
Page 49
Case study 1 – step 10 (of 15): testing the batch
macro
Let’s now test the macro by providing tab
names manually
►
Open a new workflow and right click on your
Canvas. Choose “insert – Macro” and look up
your previously saved batch macro.
►
Create a Text Input tool and manually give the
field name “SheetNames” and enter value “Apr
2014”.
►
Connect the Text Input tool and adjust the
Macro configuration to use the SheetNames
field.
►
Run the workflow and you should see results
for Apr 2014.
►
Repeat the same for Jun 2014.
►
Now try passing two rows of data: Apr 2014
and Jun 2014. What happens?
(continued on the next page)
Page 50
Case study 1 – step 10 (of 15): testing the batch
macro
(cont.)
►
When you run the macro with both Apr 2014 and
Jun 2014 as inputs, you should be getting an error
“The Field schema for the output changed
between iterations”
►
This error means that the macro did not produce
identical datasets with each of two inputs. In this
case, the column names are the same, but the
issue is the data types!
►
To test it, add a Select Tool after the macro and
run it with “Apr 2014” as input and “Jun 2014” as
input separately. You will see that The size
parameter of Entity field changes from 10 to 11
characters. This is because it is an automatically
generated field (step #6) and Alteryx tries to
optimize its size.
►
To fix it, go back to the original workflow, and add
a Select Tool just before the Macro Output Tool,
and set Entity field type to be V_String and size to
be 255 characters. Don’t forget to save the
workflow after you make the changes.
►
Now you should be able to run the workflow with
both “Apr 2014” and “Jun 2014” passed
simultaneously.
Page 51
Case study 1 – step 11 (of 15): running the batch
macro across all tabs in a dataset
Providing tab names manually is not the best
solution. Let’s use a better way to do it.
►
Instead of relying on Text Input tool, let’s
leverage functionality in the Data Input Tool.
►
Add an Input tool and look up our source data.
However, instead of choosing a tab like we did
before, choose “list of sheet names”.
►
Clean up the list of sheet names by applying a
Select tool to define the data type (V-strings) and
a Filter tool to filter out the one unnecessary data
point (Sheet1). Now connect these three tools to
your batch macro.
►
Click onto your batch macro and choose “Sheet
Names (V-Strings)”. Now run the workflow and
check the result. (use Browse Tool to see the
entire result).
Less than 10 seconds for processing the
entire file into a organized flat format!
Woohoo!
Page 52
Case Study 1 Part 3: Adding Hierarchies
Page 53
Case study 1 – step 12 (of 15): adding a fiscal year
rollup and EY formatted month labels
Purpose: The original data has calendar years in it.
However, we want to present data on a fiscal year basis,
which starts in July. Additionally, we want to have EYformatted month labels in the dataset.
To add a fiscal year rollup and a EY formatted month
labels, we need a “hierarchy” file (also known as a
“mapping table”). Such a hierarchy file is provided to you in
file Date Mapping.xlsx.
►
Use the Input Data tool to add in the Date
Mapping.xlsx file from your training materials.
►
Add a Select Tool and observe that Year field of
Double datatype. Convert it to V_String as we don’t
want to treat is as a number.
►
Add the Join tool to join the two datasets. The TB
accounts should be the left input and the date
mapping data should be the right input. In the “Join by
specific fields”, select “Month” and “Year”. In the Field
list, you can deselect “Right Month” and “Right Year”.
►
When we add a connector on the output side of the
Join tool, we will connect to the “J“ output. Click the
icon to read more about the types of join options.
Page 54
Case study 1 – step 13 (of 15): adding a P&L
hierarchies
Purpose: The original data has only account-level
information in it. We want to have aggregation levels that
allow us to get total revenue, total EBITDA, and etc.
Use the separately created mapping file to add the
financial statement hierarchy mapping.
►
Next, use the Input tool to add in the Alteryx trainingMapping. Use the Select tool to ensure the data types
are properly categorized (Categorize all as V-String for
this exercise).
►
Choose the Join tool to combine the trial balance
work stream with the mapping file. Choose Join by
Specific Fields. The left and right columns should be
mapped using the columns labeled “account number”.
►
Within the fields list of the Join tool, you can deselect
Right Account Number as otherwise it will appear in
the data twice.
Page 55
Case study 1 – step 14 (of 15): checking if we
dropped any data
Purpose: When we use mapping tables, it is important to
make sure we do not lose any data due to “missed joins”
Remember that Join Tool has three outputs:
►
L output, which represents rows from L input which did
not join
►
J output, which represents joined rows
►
R output, which represents rows from R input which
did not join.
Check the L and R outputs of the Join tool from the
previous step. What do you see? What does it mean? Is
the workflow correct?
Answer: in this case, the workflow is almost correct. The L
output mainly represents accounts ending with “00” in the
end, which are subtotal accounts are should be excluded.
However, there are a few accounts that are missing in the
mapping file and thus excluded. Can you think of a way to
identify them?
R output represents unused accounts.
Page 56
Such “dropped” data in Join Tools
is one of the most common
gotchas for beginner Alteryx users.
Make sure you always check if you
have L or R outputs and you are
comfortable with that!
Case study 1 – step 15 (of 15): reordering
columns and exporting the data to Excel
Purpose: Final clean up of the data and exporting back to
excel
►
Use a Select Tool to rename columns as you see fit,
and reorganize their order accordingly.
►
Then, export the data using the Output tool from the
In/Out palette and define where you want to save the
output file. Be sure to save it in the file format you
intend to use in Spotfire (or other tool) such as Excel
or CSV.
►
Note that the “Output Options” allow you to define the
behaviour of Alteryx if specified output file or sheet
already exists.
Page 57
Case study 1: Debrief
►
Discuss:
►
►
►
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Page 58
What were some benefits gained by processing this dataset in
Alteryx instead of Excel?
What in this workflow could have been done in a different way?
Are there any hidden assumptions in the workflows that one needs
to be aware of?
Give some additional examples where you think data
transformation in Alteryx will be helpful going forward.
Quality and Risk Management considerations for
Alteryx
►
►
As you work with more detailed data, understanding of its basis (i.e.
Quality of Financial Information work) becomes increasingly
important. Do that upfront!
Alteryx workflows should be reviewed in the team
►
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►
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Page 59
Make sure workflows are clearly documented using comment tools to help
the reader
Any potential issues (e.g. “unjoined” rows) should be flagged in the
workflows using message tools that raise errors when unexpected outputs
are generated
If you are the reviewer who is not technically strong in Alteryx, make sure
you understand workflow purpose, any assumptions made, and check
inputs and outputs.
It’s always possible to take the output of Alteryx and reconcile it back to
“ground truth” using usual TD methods
Always consult prior to sharing Alteryx workflows with your
Engagement Partner and Quality Leader. It represents an additional
revenue opportunity as well as requires special considerations for
Channel 1 clients.
Unguided case study
Page 60
Unguided case study: from raw data to a
databook
►
Objective:
►
►
►
Datasets:
►
►
►
►
Transform a transaction dataset into a flattened file by building a
workflow in Alteryx to enable use in databooks, other analytic and
visualization tools and the TS diligence dashboards.
Build a dynamic databook showing Lead PL with ability to slice by
legal entity / business unit.
Data delivered in excel spreadsheets: management accounts per
year
Multiple business units (A, B, C,…) that each have several legal
entities (1010, 1011, 1012, …)
Mapping files provided
Time: 1 hour
Page 61