Click and drag a tool from the Tool Palette into the workflow canvas to begin building a workflow. Tools are linked by clicking output anchors and dragging a downstream path to the next tool's input anchors. See Tool Palette, Build Workflows.
To show deprecated tools on the palette, right-click the palette and select Show Deprecated Tools.
The Favorites category includes the most common tools used in workflow creation. You can add a tool to the Favorites by clicking on the gray star in the top right of the tool icon on the Tool Palette. A Favorite tool is indicated by a yellow star.
Browse: The Browse tool offers complete views of underlying data within the Alteryx workflow. A browser can be outputted via a Browse tool to view the resulting data anywhere within the workflow stream.
Input Data: The Input Data tool can be the starting point for any project in Alteryx. Every project must have an input and output. The input tool opens the source data to be used in the analysis. The input tool reads information from the following file formats: CSV, MDB, DBF, XLS, MID/MIF, SHP, TAB, GEO, SZ, YXDB, SDF, FLAT, OleDB, Oracle Spatial.
Output Data: The Output tool is used anytime results are needed to be output to a file from the analysis. Every project must have an input and output. The Output tool opens the data results derived to from the analysis. The Output tool will write the results of the analysis to the same variety of formats specified for the input tool.
Text
Input:
The Text Input tool makes it possible for the user to manually type text
to create small data files for input. It is useful for creating Lookup
tables on the fly, for example.
Data Cleansing: The Data Cleansing tool can fix common data quality issues using a variety of parameters.
Filter:The Filter tool queries records in your file to meet specified criteria. The tool creates two outputs, True and False. True is where the data met the specified criteria, False is where it does not.
Formula: The Formula tool is a
powerful processor of data and formulas. Use it to add a field to an input
table, to create new data fields based on an expression or by assigning
a data relationship, or to update an existing field based on these same
premises.
Sample: The Sample tool extracts a specified
portion of the records in the data stream.
Select: The
Select tool is a multi-function utility that allows for selected fields
to be carried through downstream, renaming fields, reordering field position
in the file, changing the field type, and loading/saving field configurations.
Sort: The
Sort tool arranges the records in a table in alphanumeric order, based
on the values of the specified data fields. more.
Join: The Join tool combines two inputs based on a commonality between the two tables. Its function is like a SQL join but gives the option of creating 3 outputs resulting from the join.
Union: The
Union tool appends multiple data streams into one unified steam. The tool
accepts multiple inputs based on either field name or record position,
creating a stacked output table. The user then has complete control to
how these fields stack or match up.
Summarize: The
Summarize tool can conduct a host of Summary Processes within the input
table, including: grouping, summing, count, spatial object processing,
string concatenation, and much
Comment: The Comment tool adds annotation to the project workspace. This is useful to jot down notes, explain processes to share or reference later.
Each workflow must contain inputs and outputs. Both the Input and Output tool have different configuration properties, depending on the file type. The Browse tool offers a temporary view of what the data looks like in table, map, or report format. Click each tool to find out more.
Browse: The Browse tool offers complete views of underlying
data within the Alteryx workflow. A browser can be outputted via a Browse
tool to view the resulting data anywhere within the workflow stream.
Date
Time Now: This Macro will return a single record: the Date
and Time at the workflow runtime, and convert the value into the string
format of the user's choosing.
Directory: The Directory tool returns all the files in a specified
directory. Along with file names, other pertinent information about each
file is returned, including file size, creation date, last modified, and
much more.
Input Data: The Input Data tool can be the starting point for any project in Alteryx. Every project must have an input and output. The input tool opens the source data to be used in the analysis. The input tool reads information from the following file formats: CSV, MDB, DBF, XLS, MID/MIF, SHP, TAB, GEO, SZ, YXDB, SDF, FLAT, OleDB, Oracle Spatial.
Map Input: Manually
draw or select map objects (points, lines, and polygons) to be stored
in the workflow.
Output
Data: The output tool is used
anytime results are needed to be output to a file from the analysis. Every
project must have an input and output. The output tool opens the data
results derived to from the analysis. The Output tool will write the results
of the analysis to the same variety of formats specified for the input
tool.
Text Input: The Text Input tool makes it possible for the user to manually type text to create small data files for input. It is useful for creating Lookup tables on the fly, for example.
XDF
Input: This tool enables access to an XDF format file (the format
used by Revolution R Enterprise's RevoScaleR system to scale predictive
analytics to millions of records) for either: (1) using the XDF file as
input to a predictive analytics tool or (2) reading the file into an Alteryx
data stream for further data hygiene or blending activities.
XDF
Output: This tool reads an Alteryx data stream into an XDF
format file, the file format used by Revolution R Enterprise's RevoScaleR
system to scale predictive analytics to millions of records. By default,
the new XDF files is stored as a temporary file, with the option of writing
it to disk as a permanent file, which can be accessed in Alteryx using
the XDF Input tool.
The Preparation category includes tools that prepare data for downstream analysis.
Auto Field Strings: The Auto Field tool reads through an
input file and sets the field type to the smallest possible size relative
to the data contained within the column.
Create Samples: This tool splits the input records into two or three random samples. In the tool you specify the percentage of records that are in the estimation and validation samples. If the total is less than 100%, the remaining records fall in the holdout sample.
Data Cleansing: The Data Cleansing tool can fix common data quality issues using a variety of parameters.
Date Filter:
The Date Filter macro is designed to allow a user to easily filter
data based on a date criteria using a calendar based interface.
Filter: The Filter
tool queries records in your file to meet specified criteria. The tool
creates two outputs, True and False. True is where the data met the specified
criteria, False is where it does not.
Formula:
The formula tool is a powerful processor of data and formulas. Use it
to add a field to an input table, to create new data fields based on an
expression or by assigning a data relationship, or to update an existing
field based on these same premises.
Generate Rows:
The Generate Rows tool will create new rows of data, at the record level.
This tool is useful to create a sequence of numbers, transactions, or
dates.
Imputation: The Imputation tool updates specific values in a numeric data field with another selected value. It is useful for replacing NULL values.
Multi-Field Binning: The Multi-Field Binning tool groups multiple numeric fields into tiles or bins, especially for use in predictive analysis.
Multi-Field Formula: The Multi-Field Formula tool makes it easy to execute a single function on multiple fields.
Multi
-Row Formula: The Multi-Row Formula
tool takes the concept of the Formula Tool a step further, allowing the
user to utilize row data as part of the formula creation. This tool is
useful for parsing complex data, and creating running totals, averages,
percentages and other mathematical calculations.
Oversample
Field: This tool will sample incoming data so that there is
equal representation of data values so they can be used effectively in
a predictive model.
Random % Sample: This Macro will
return an expected number of records resulting in a random sample of the
incoming data stream.
Record ID: The Record ID tool
creates a new column in the data and assigns a unique identifier, that increases sequentially, for each record in the data.
Sample: The Sample tool extracts a specified
portion of the records in the data stream.
Select: The
Select tool is a multi-function utility that allows for selected fields
to be carried through downstream, renaming fields, reordering field position
in the file, changing the field type, and loading/saving field configurations.
Select Records:The Select Records tool selects specific records and/or ranges of records including discontinuous ranges. It is useful for troubleshooting and sampling.
Sort: The
Sort tool arranges the records in a table in alphanumeric order, based
on the values of the specified data fields.
Tile:
The tile tool assigns a value (tile) based on ranges in the data.
Unique: The
Unique Tool distinguishes whether a data record is unique or a duplicate
by grouping on one or more specified fields, then sorting on those fields.
The first record in each group is sent to the Unique output stream while
the remaining records are sent to the Duplicate output stream.
The Join category includes tools that Join two or more streams of data by appending data to wide or long schemas.
Append Fields:
The Append Fields tool will Append the fields of one small input (Source)
to every record of another larger input (Target ). The result is a Cartesian
Join where all records from both inputs are compared.
Find Replace:
The Find and Replace tool searches for data in one field from the input
table and replaces it with a specified field from a different data table.
Fuzzy Match: The Fuzzy Matching
tool can be used to identify non-identical duplicates of a database by
specifying parameters to match on. Values need not be exact to find a
match, they just need to fall within the user specified or prefabricated
parameters set forth in the configuration properties.
Join:The Join tool combines two inputs based
on a commonality between the two tables. Its function is like a SQL join
but gives the option of creating 3 outputs resulting from the join.
Join
Multiple: The Join Multiple tool
combines two or more inputs based on a commonality between the input tables.
Only the joined records are outputted through the tool, resulting in a
wide (columned) file.
Make Group: The
Make Group tool takes data relationships and assembles the data into groups
based on those relationships.
Union: The Union tool appends multiple
data streams into one unified steam. The tool accepts multiple inputs
based on either field name or record position, creating a stacked output
table. The user then has complete control to how these fields stack or
match up.
The Parse tools separate data values into a standard table schema.
DateTime:
The Date Time tool standardizes and formats date/time data so that it
can be used in expressions and functions from the Formula
or Filter tools.
RegEx:
The
Regular Expression tool is a robust data parser. There are four types
of output methods that determine the type of parsing the tool will do.
These methods are explained in the Configuration Properties.
Text to Columns:
The text to columns tool takes the
text in one column and splits the string value into separate, multiple
fields based on a single or multiple delimiter (s).
XML Parse:The XML parse tool will read in a chunk
of Extensible Markup Language and parse it into individual fields.
Tools that summarize data are in the transform category.
Arrange: The Arrange tool allows
you to manually transpose and rearrange your data fields for presentation
purposes. Data is transformed so that each record is turned into multiple
records and columns can be created by using field description data or
manually created.
Count Records: This Macro returns
a count of how many records are going through the tool.
Cross
Tab: The CrossTab pivots
the orientation of the data table. It transforms the data so vertical
data fields can be viewed on a horizontal axis, summarizing data where
specified.
Running Total: The Running Total
tool calculates a cumulative sum, per record, in a file.
Summarize: The Summarize tool can conduct
a host of Summary Processes, including: grouping, summing, count, spatial
object processing, string concatenation, and much more.
Transpose: The Transpose tool pivots the
orientation of the data table. It transforms the data so you may view
Horizontal data fields on a vertical axis.
Weighted
Average: This Macro will calculate the weighted average of
an incoming data field. A weighted average is similar to a common average,
but instead of all records contributing equally to the average, the concept
of weight means some records contribute more than others.
The In-Database tool category consists of tools that function like many of the Favorites. This category includes tools for connecting to a database and blending and viewing data, as well as tools for bringing other data into an In-Database workflow and writing data directly to a database.
Browse In-DB: Review your data at any point in an In-DB workflow. Note:
Each Browse In-DB triggers a database query and can impact performance.
Connect In-DB: Establish a database connection for an In-DB workflow.
Data Stream In: Bring data from a standard workflow into an In-DB workflow.
Data Stream Out: Stream data from an In-DB workflow to a standard workflow,
with an option to sort the records.
Dynamic Input In-DB: Take In-DB Connection Name and Query fields from a standard data stream and input them into an In-DB data stream.
Dynamic Output In-DB: Output information about the In-DB workflow to a standard workflow for Predictive In-DB.
Filter In-DB: Filter In-DB records with a Basic filter or with a Custom
expression using the database’s native language (e.g., SQL).
Formula In-DB: Create or update fields in an In-DB data stream with an
expression using the database’s native language (e.g., SQL).
Join In-DB: Combine two In-DB data streams based on common fields by
performing an inner or outer join.
Macro Input In-DB: Create an In-DB input connection on a macro and populate it with placeholder values.
Macro Output In-DB: Create an In-DB output connection on a macro.
Sample In-DB: Limit the In-DB data stream to a number or percentage of
records.
Select In-DB: Select, deselect, reorder, and rename fields in an In-DB
workflow.
Summarize In-DB: Summarize In-DB data by grouping, summing, counting, counting
distinct fields, and more. The output contains only the result
of the calculation(s).
Union In-DB: Combine two or more In-DB data streams with similar structures
based on field names or positions. In the output, each column
will contain the data from each input.
Write Data In-DB: Use an In-DB data stream to create or update a table directly
in the database.
The Reporting category includes tools that aid in data presentation and organization.
Charting: The Charting tool allows the user to display data in various chart
types.
Email: Allows
you to select from fields inputted to e-mail to recipients instead of
having to use a batch e-mail as before. Automatically detects SMTP address,
and will allow attachments or even e-mail generated reports.
Image: The
Image Tool allows the user to add graphics to reports.
Layout:The
Layout Tool enables the user to arrange Reporting Snippets.
Map Legend Builder:
This macro takes the components output
from the Legend
Splitter macro and builds them back into a legend table. If
you add a Legend Builder tool immediately after a Legend
Splitter tool, the resulting legend will be the same as the legend
output originally from the Map
tool. The purpose of the two macros is that you can change the data between
them and therefore creating a custom legend
Map Legend Splitter: This
macro will take a legend from the Map
tool and split it into its component parts. Once split, the legend can
be customized by the use of other tools. Be sure to use the Legend Builder macro to easily build
the legend again.
Overlay: This tool arranges reporting snippets on top of one another for output via the Render tool.
Render:The
Render tool transforms report Snippets into presentation-quality reports
in PDF, HTML, XLSX, DOCX, RTF and Portfolio Composer (*.pcxml) formats.
Report Footer:
This macro will allow a user to easily setup and put a footer onto their
report.
Report Header: This
macro will allow a user to easily setup and put a header onto their report.
Report Map: The Map Tool enables the user
to create a map image from the Alteryx GUI. The tool accepts multiple
spatial inputs, allows for layering these inputs, and supports thematic
map creation. Other cartographic features can be included such as a legend,
scale and reference layers.
Report Text: The
Text tool allows the user to add text to reports and documents.
Table:The
Table tool allows the user to create basic data tables and pivot tables
from their input data.
Documentation tools improve workflow presentation, annotation, and tool organization.
Comment: The Text Comment tool adds annotation to the project
workspace. This is useful to jot down notes, explain processes to share
or reference later.
Explorer Box:The Explorer Box is populated with
a web page or file location of the user's specification.
Tool Container: The Tool Container allows the user to
organize tools in a workflow. Tools can be placed inside the container
to isolate a process. The container can then be collapsed, expanded or
disabled.
The tools contained within the Spatial category offer a large array of spatial data manipulation, processing, and object editing tools. Click on each tool to find out more.
Buffer:The Buffer tool will take any polygon
or polyline spatial object and expand or contract its extents by the user
specified value.
Create Points:The Create Points
tool creates a point-type spatial object by specifying input fields containing
the X coordinate (Longitude ) and the Y coordinate (Latitude ).
Distance:The Distance tool calculates
the ellipsoidal direct point-to-point, point-to-edge, or the drive distance
between two sets of spatial objects.
Find
Nearest:The
Find Nearest Points tool identifies the shortest distance between points
or polygons in one file and the points, polygons, or lines in a second
file.
Generalize:The
Generalize tool will decrease the number of nodes that make up a polygon
or polyline, making a simpler rendition of the original spatial object.
Heat Map: The Heat Map tool generates polygons representing different levels of "heat" (e.g. demand) in a given area, based on individual records (e.g. customers).
Make
Grid: The Make Grid tool takes
a spatial object and creates a grid. The resulting grid is either a single
grid, bound to the extent of the input spatial objects, or individual
grids that dissect each input polygon.
Non Overlapping Drivetime:This
Macro will create drivetime trade areas, that do not overlap, for a point
file. This macro requires licensed installation of Alteryx Drivetime to
run successfully.
Poly-Build:The
PolyBuild tool takes a group of spatial point objects and draws a polygon
or polyline in a specific order to represent that group of points.
Poly-Split:The
PolySplit tool takes polygon or polyline objects and splits them into
their component point, line, or region objects.
Smooth:The
Smooth tool takes a polygon or polyline object and adds nodes to smooth
sharp angles into curves along the lines that make up the object.
Spatial Info:The
Spatial Info tool extracts tabular information about the spatial object.
Attributes such as: area, spatial object, number of parts, number of points,
and centroid Latitude/Longitude coordinates can be appended.
Spatial
Match: The Spatial Match tool establishes
the spatial relationship (contains, intersects, touches, etc) between
two sets of spatial objects. The tool accepts a set of spatial objects
from the Left Input (Targets) and a set of spatial objects from the Right
Input (Universe ). At least one input stream should contain Polygon type
spatial objects.
Spatial Process:
The Spatial Process tool performs high-level
spatial object editing from a simple, single tool. You can combine multiple
objects or cut the spatial objects of the input table.
Trade Area:The Trade Area tool creates regions
around specified point objects in the input file. Trade Areas are created
one of two ways: either by defining a radius around a point, or by a drivetime.
Drive time trade area creation is only an option if a licensed installation
of Alteryx Drivetime is detected.
Interface tools are used to author apps and macros. These tools make it easy to design user interface elements and update workflow tools at runtime based on user specifications.
Action: Update values of development tools
with the values from the interface questions at runtime.
Check
Box: Display a check box option in an app.
Condition:
Test for the presence of user selections. The state is either true or
false.
Control
Parameter: Control Parameter input for a Batch Macro.
Date:
Display a calendar in app.
Drop
Down: Display a single selection list in an app.
Error
Message: Throw an Error message.
File
Browse: Display a File Browse control in an app. This tool
can be used to read an input or write an output.
Folder
Browse: Display a Folder Browse control in an app. This Interface
tool is not supported for running apps in the Alteryx Analytics Gallery
List
Box: Display a multi-selection check box list in an app.
Macro
Input: Input for a Macro.
Macro
Output: Output of a Macro.
Map:
Display an interactive map for the user to draw or select map objects
in an app.
Numeric
Up Down: Display a numeric control in an app.
Radio
Button: Display a mutually exclusive option in an app.
Text
Box: Display a free form text box in an app.
Tree:
Display an organized, hierarchal data structure in an app.
This tool category contains tools for both better understanding the data to be used in a predictive analytics project, and tools for doing specialized data sampling tasks for predictive analytics.
Association
Analysis: This tool allows a user to determine which fields in a database
have a bivariate association with one another.
Contingency
Table: Create a contingency table
based on selected fields, to list all combinations of the field values
with frequency and percent columns.
Distribution
Analysis: The Distribution Analysis
macro allows you to fit one or more distributions to the input data and
compare them based on a number of Goodness-of-Fit* statistics. Based
on the statistical significance (p-values) of the results of these tests,
the user can determine which distribution best represents the data.
Field
Summary: This tool provides
the user a summary report of descriptive statistics for the selected data
fields. This information provides the user a concise summary of the data
fields to give the user a greater understanding of the data being analyzed.
Also provided are “Remarks” which provide suggestions on best practices
of managing the particular data field.
Frequency
Table: Produce a frequency analysis
for selected fields - output includes a summary of the selected field(s)
with frequency counts and percentages for each value in a field.
Heat
Plot:
Uses a heat plot color map to show the joint distribution of two variables
that are either continuous numeric variables or ordered categories.
Histogram: Provides
a histogram plot for a numeric field. Optionally, it provides a smoothed
empirical density plot. Frequencies are displayed when a density plot
is not selected, and probabilities when this option is selected. The number
of breaks can be set by the user, or determined automatically using the
method of Sturges.
Importance Weights: This tool provides methods for selecting a set of variables to use in a predictive model based on how strongly related each possible predictor is to the target variable.
Pearson Correlation: The Pearson
Correlation tool measures the linear dependence between two variables
as well as the covariance. This tool replaces the now deprecated Pearson
Correlation Coefficient macro.
Plot of Means: The Plot of Means
tool takes a numeric or binary categorical field (with the binary categorical
field converted into a set of zero and one values) as a response field
along with a categorical field and plots the mean of the response field
for each of the categories (levels) of the categorical field.
Scatterplot: This tool makes
enhanced scatterplots, with options to include boxplots in the margins,
a linear regression line, a smooth curve via non-parametric regression,
a smoothed conditional spread, outlier identification, and a regression
line.
Spearman Correlation:
Spearman's rank correlation coefficient assesses how well an arbitrary
monotonic function could describe the relationship between two variables,
without making any other assumptions about the particular nature of the
relationship between the variables.
Violin Plot: A
violin plot shows the distribution of a single numeric variable, and conveys
the density of the distribution. In addition to concisely showing the
nature of the distribution of a numeric variable, violin plots are an
excellent way of visualizing the relationship between a numeric and categorical
variable by creating a separate violin plot for each value of the categorical
variable.
This tool category includes tools for general predictive modeling for both classification and regression models, as well as tools for model comparison and for hypothesis testing relevant for predictive modeling.
Boosted Model:This
tool provides generalized boosted regression models based on the gradient
boosting methods of Friedman. It works by serially adding simple decision
tree models to a model ensemble so as to minimize an appropriate loss
function.
Count
Regression: Estimate regression
models for count data (e.g., the number of store visits a customer makes
in a year), using Poisson regression, quasi-Poisson regression, or negative
binomial regression. The R functions used to accomplish this are glm()
(from the R stats package) and glm.nb() (from the MASS package).
Cross-Validation: This tool compares the performance of one or more Alteryx-generated predictive models using the process of cross-validation. It supports all classification and regression models with the exception of Naive Bayes.
DataRobot Automodel Tool: The DataRobot Automodel Tool uploads data to the DataRobot machine learning platform.
DataRobot Predict Tool: The DataRobot Predict tool scores data using models generated with the DataRobot machine learning platform.
Decision
Tree: A decision tree learning
model is a class of statistical methods that predict a target variable
using one or more variables that are expected to have an influence on
the target variable, and are often called predictor variables.
Forest
Model: A forest learning model is a class of machine learning
methods that predict a target variable using one or more variables that
are expected to have an influence on the target variable, and are often
called predictor variables.
Gamma Regression: Relate
a Gamma distributed, strictly positive variable of interest (target variable)
to one or more variables (predictor variables) that are expected to have
an influence on the target variable.
Lift
Chart: This tool produces two very commonly used charts of
this type, the cumulative captured response chart (also called a gains
chart) and the incremental response rate chart.
Linear Regression:
A linear regression (also called a linear model or a least-squares regression)
is a statistical method that relates a variable of interest (a target
variable) to one or more variables that are expected to have an influence
on the target variable, and are often called predictor variables.
Logistic
Regression: A logistic regression model is a class of statistical
methods that relates a binary (e.g., yes/no) variable of interest (a target
variable) to one or more variables that are expected to have an influence
on the target variable, and are often called predictor variables.
Model Coefficients: Extract the model coefficients from a standard Alteryx Count, Gamma, Linear, or Logistic Regression model for use in customized reports or downstream calculations.
Model Comparison: Compare the performance of one or more different predictive models based on the use of a validation (or test) data set.
Naive Bayes Classifier: The Naive Bayes
Classifier tool creates a binomial or multinomial probabilistic classification
model of the relationship between a set of predictor variables and a categorical
target variable.
Nested
Test: A nested hypothesis test is used to examine whether two models,
one of which contains a subset of the variables contained in the other,
are statistically equivalent in terms of their predictive capability.
Network Analysis: The Network Analysis tool creates an interactive visualization of a network, along with summary statistics and distribution of node centrality measures.
Neural Network: This tool allows
a user to create a feedforward perceptron neural network model with a
single hidden layer.
Score:
The Score macro takes as inputs an R model object
produced by the Logistic Regression, Decision Tree, Forest
Model, or Linear Regression macro and a data
stream that is consistent with the model object (in terms of field names
and the field types) and outputs the data stream with a one (for a model
with a continuous target) or two or more (for a model with a categorical
target) "Score" (fitted value) field(s) appended to the data
stream.
Spline
Model: Predict a variable of interest (target variable) based on one
or more predictor variables using the two-step approach of Friedman's
multivariate adaptive regression (MARS) algorithm. Step 1 selects
the most relevant variables for predicting the target variable and creates
a piecewise linear function to approximate the relationship between the
target and predictor variables. Step 2 smooths out the piecewise function,
which minimizes the chance of overfitting the model to the estimation
data. The Spine model is useful for a multitude of classification and
regression problems and can automatically select the most appropriate
model with minimal input from the user.
Stepwise:
The Alteryx R-based stepwise regression tool makes
use of both backward variable selection and mixed backward and forward
variable selection.
Support
Vector Machine: Support Vector Machines (SVM), or Support Vector
Networks (SVN), are popular supervised learning algorithms used for classification
problems, and are meant to accommodate instances where the data (i.e.,
observations) are considered linearly non-separable.
Survival Analysis: Generate a survival model that can be used by the Survival Score tool to estimate relative risk and restricted mean survival time.
Survival Score:
This tool provides both the estimated relative risk and restricted mean survival time based on a Cox proportional hazards model, which can be estimated using the Survival Analysis tool.
Test of Means:
Compares the difference in mean values (using a Welch two sample t-test)
for a numeric response field between a control group and one or more treatment
groups.
Variance Inflation Factors: Produce a coefficient summary report that includes either the variance inflation factor or a generalized version of the VIF (GVIF) for all variables except the model intercept (which always has a VIF or GVIF that equals one).
These tools assist in carrying out A/B testing (also known as test and learn) experiments, such as examining the effect of a new marketing communications campaign on sales, or the effect of changing store staffing levels.
AB Analysis: Determine which
group is the best fit for AB testing.
AB Controls: Match one to ten
control units (e.g., stores, customers, etc.) to each member of a set
of previously selected test units, on the basis of seasonal patterns and
growth trends for a key performance indicator, along with other user specified
criteria.
AB Treatments:
Determine which group is the best fit for AB testing.
AB
Trend: Create measures of trend and seasonal patterns that can be
used in helping to match treatment to control units (e.g., stores or customers)
for A/B testing. The trend measure is based on period to period percentage
changes in the rolling average (taken over a one year period) in a performance
measure of interest. The same measure is used to assess seasonal effects.
In particular, the percentage of the total level of the measure in each
reporting period is used to assess seasonal patterns.
The time series tool category contains a number of regular (in terms of the data time interval, such as monthly), univariate times series plotting, and forecasting tools.
ARIMA:
This tool estimates a univariate time series forecasting model using an
autoregressive integrated moving average (or ARIMA) method.
ETS: This tool estimates
a univariate time series forecasting model using an exponential smoothing
method.
TS_Compare: This macro compares
one or more univariate time series models created with either the ETS
or ARIMA macros.
TS Covariate Forecast: The TS
Covariate Forecast tool provides forecasts from an ARIMA model estimated
using covariates for a user-specified number of future periods. In addition,
upper and lower confidence interval bounds are provided for two different
(user-specified) percentage confidence levels. For each confidence level,
the expected probability that the true value will fall within the provided
bounds corresponds to the confidence level percentage. In addition to
the model, the covariate values for the forecast horizon must also be
provided.
TS Filler: The
Time Series Filler macro allows a user to take a data stream of time series
data and “fill in” any gaps in the series.
TS
Forecast: The TS Forecast tool provides forecasts from either
an ARIMA or ETS model for a user specified
number of future periods.
TS Forecast Factory: This tool provides forecasts from groups of either ARIMA or ETS models for a user-specified number of future periods.
TS Model Factory: This tool estimates time series forecasting models for multiple groups at once using the autoregressive moving average (ARIMA) method or the exponential smoothing (ETS) method.
TS Plot:
This tool provides a number of different univariate time series plots
that are useful in both better understanding the time series data and
determining how to proceed in developing a forecasting model.
The predictive grouping tool category contains tools to group either records or fields into a smaller number of groups.
Append Cluster: The Append Cluster
tool appends the cluster assignments from a K-Centroids
Cluster Analysis tool to a data stream.
Find
Nearest Neighbors: Find the selected
number of nearest neighbors in the "data" stream that corresponds
to each record in the "query" stream based on their Euclidean
distance.
K-Centroids Cluster Analysis:
K-Centroids represent a class of algorithms for doing what is known as
partitioning cluster analysis. These methods work by taking the records
in a database and dividing (partitioning) them into the “best” K groups
based on some criteria.
K-Centroids
Diagnostics: The K-Centroids Diagnostic tool is designed to
allow the user to make an assessment of the appropriate number of clusters
to specify given the data and the selected clustering algorithm (K-Means,
K-Medians, or Neural Gas). The tool is graphical, and is based on calculating
two different statistics over bootstrap replicate samples of the original
data for a range of clustering solution that differ in the number of clusters
specified.
MB Affinity: The MB Affinity macro takes "transaction" data and constructs a matrix where each row is a transaction and the columns are the set of "items" that could appear in the transaction.
MB Inspect:
Step 2 of a Market Basket Analysis: Take the output of the MB
Rules tool, and provide a listing and analysis of those rules that
can be filtered on several criteria in order to reduce the number or returned
rules or itemsets to a manageable number.
MB Rules: Step 1 of a Market Basket
Analysis: Take transaction data and create either a set of association
rules or frequent itemsets. A summary report of both the transaction data
and the rules/itemsets is produced, along with a model object that can
be further investigated in a MB Inspect
tool.
Multidimensional Scaling: Multidimensional Scaling (abbreviated MDS) is a method of separating univariate data based upon variance. Conceptually, MDS takes the dissimilarities, or distances, between items described in the data and generates a map between the items. The number of dimensions in this map are often provided prior to generation by the analyst. Usually, the highest variance dimension corresponds to the largest distances being described in the data. The map solution relies on univariate data, so the rotation and orientation of the map dimensions is not significant. MDS uses dimensional analysis similar to Principle Components.
Principal Components: tool that
allows the dimensions (the number of numeric fields) in a database to
be reduced. It does this by transforming the original set of fields into
a smaller set that accounts for most of the variance (i.e., information)
in the data. The new fields are called factors, or principal components.
This category includes tools that can assist with determining the best course of action or outcome for a particular situation or set of scenarios. It can help augment the output of predictive models by prescribing an optimal action.
Optimization: The Optimization tool can solve linear programming (LP), mixed integer linear programming (MILP), and quadratic programming (QP) optimization problems using matrix, manual, and file input modes.
Simulation Sampling: The Simulation Sampling tool samples data parametrically from a distribution, from input data, or as a combination best fitting to a distribution. Data can also be "drawn" if you are unsure of the parameters of a distribution and lacking data.
Simulation Scoring: The Simulation Scoring tool samples from an approximation of a model object error distribution. Whereas standard scoring attempts to predict the mean predicted value, Simulation Scoring also considers the error distribution to provide a range of possible values.
Simulation Summary: The Simulation Summary tool visualizes simulated distributions and results from operations on those distributions. It also provides visual and quantitative analyses of input versus output variables.
Tools in the Connectors category are used to retrieve data or push data to the cloud or internet/intranet environment.
Adobe Analytics: The Adobe Analytics tool authenticates to the Adobe Analytics report suites (to which you have access) and generates ad hoc reports based on multiple parameters via the Adobe Analytics Reporting API.
Alteryx Web Data Connector for Tableau: Enable an analytic app to be used as a data source in Tableau.
Amazon
S3 Download Tool:The Amazon S3
Download tool will retrieve data stored in the cloud where it is hosted
by Amazon Simple Storage Service.
Amazon S3 Upload Tool:The
Amazon S3 Upload tool will transfer data from Alteryx to the cloud where
it is hosted by Amazon Simple Storage Service.
Azure ML Text Analytics: This tool uses the Text Analytics API from the Cortana Analytics Gallery to perform sentiment analysis and/or key phrase extraction.
Download Tool: The Download tool
will retrieve data from a specified URL to be used in downstream processing
or to be saved to a file.
Foursquare
Search: Search Foursquare
Venues by location with an option to filter by a search term.
Google
Analytics: The Google Analytics (“GA”) tool returns statistics derived from the data collected by a Google Analytics tracking code. You use the Core Reporting API to query for dimensions and metrics to build customized reports.
Google Sheets Input: Download data from a Google Sheets spreadsheet directly into your Alteryx workflow.
Google Sheets Output: Publish data from an Alteryx workflow to a Google Sheets spreadsheet.
Marketo Append: The
Marketo Append tool retrieves Marketo records and appends them to the
records of an incoming data stream.
Marketo Input: The
Marketo Input Tool reads Marketo records for a specified date range.
Marketo Output: Data
is written back to Marketo using an 'Upsert' operation.
Mongo Input: Reads data stored
in MongoDB databases.
Mongo Output: Writes data
to MongoDB databases.
Publish to Power BI: The Publish to Power BI tool uses the Power BI REST API to upload a data table from your Alteryx workflow to the Power BI web application.
Publish to Tableau Server: Publish a data stream in Alteryx to an instance of Tableau as a Tableau data source (.tde) file.
Salesforce Input: The Salesforce Input tool allows you to read and query tables from Salesforce.com into Alteryx.
SalesForce
Output: The Salesforce Output tool allows you to write to Salesforce.com
tables from Alteryx.
Salesforce Wave Output: Publish data from an Alteryx workflow as a dataset in Wave Analytics.
SharePoint List Input:The
SharePoint Input tool reads lists from Sharepoint to be used as a data
input in a workflow.
SharePoint
List Output:The SharePoint output
tool writes the content of a data stream to a Sharepoint list.
Twitter
Search: Search tweets of the last 7 days by given search terms
with location and user relationship as optional properties.
The tools contained within the Address category include the ability to Standardize mailing lists and geocode to the 9-digit ZIP Code level. These tools require a special license and are US Data-specific. Click on each tool to find out more.
CASS: The CASS tool takes the input address file and checks it against the USPS Coding Accuracy Support System.
Parse Address Tool: The Parse Address tool breaks down a street address into its component parts, such as street number, directional (S, NW, and so on), street name, and suffix (ST, RD, BLVD).
Street Geocode: Geocoding associates geographic coordinates with input addresses, letting you pinpoint locations and carry out geography-based analyses.
US Geocoder: This Macro will utilize many methods to geocode a customer file. This macro requires licensed installations of Alteryx Geocoder, CASS, and the ZIP + 4 coder to run successfully.
US Zip +4 Coder: The ZIP + 4 Coder associates geographic coordinates with input ZIP9 (also known as ZIP+4) codes in an address file, enabling the user to carry out geography-based analyses.
The tools contained within the Demographic Analysis category offer the ability to extract data utilizing the Allocate Engine within Alteryx. You must have a license for an installed Allocate dataset to use these tools.
Allocate Append: The Allocate Append Data tool lets you append demographic fields from an existing Allocate installation.
Allocate Input: The Allocate Input tool allows the user to pick geographies and data variables from any Allocate dataset installed on the user's system.
Allocate MetaInfo: The Allocate MetaInfo tool returns pertinent information about installed Allocate datasets.
Allocate Report: The Allocate Report tool allows the user to retrieve and run any pre-formatted or custom report associated with Allocate.
The tools within the Behavior Analysis category offer the ability to extract data utilizing the Solocast Engine within Alteryx. In addition to the tools mentioned in this category, users can leverage the information generated from the Behavior Analysis tools, using the Summarize and the Browse Data tools.
Behavior
MetaInfo: The Behavior MetaInfo tool returns pertinent information
about installed Behavior Analysis data sets.
Cluster Code: The Cluster Code
tool will append a Cluster Code field to a stream of records using a Cluster
Level ID, such as a Block Group Key.
Compare Behavior: The Compare
Behavior tool analyses 2 Profile sets, comparing one against the other.
Think of it as building a sentence: "Analyze'this/these'Using 'this/these'."
Create Behavior Profile: The Create
Behavior Profile tool takes an incoming data stream and constructs a Behavior
Profile from its contents. A Profile can be built via different modes
including: Spatial Object, Known Geography Key, Combine
Profiles, Cluster Code, and Cluster Level ID.
Behavior Detail Fields: The Behavior
Detail Fields tool returns detailed field information at the Cluster or Group
level specific to the Profile.
Behavior Profile Set Input: The Behavior
Profile Set Input tool allows you to select a specific type of dataset known
as a Profile Set to use as an input in your workflow. Profile Sets are
composed of Profiles. A Profile is an object, whether it be a geography,
a customer database, or a product within a syndicated product file - that
has been assigned segmentation codes. Segmentation codes are assigned
based on the Block Group assignment of the object.
Behavior Profile Set Output: The
Behavior Profile Set Output tool takes an incoming data stream containing
a Profile or collection of Profiles and writes out a Profile Set *.scd
file.
Profile Rank Report:The Profile
Rank Report tool takes two Profile inputs (a Geography and a Product profile)
and generates a rank report.
Profile
Comparison Report:The Profile Comparison Report tool accepts two Profile
inputs and generates a comparison report.
Profile
Detail Report: The Profile Detail Report tool accepts a Profile input
and generates a detailed report.
Calgary is a list count data retrieval engine designed to perform analyses on large scale databases containing millions of records.
Calgary Input:The Calgary Input tool enables users
to query a Calgary database.
Calgary Join:The Calgary Join tool provides users
with the ability to take an input file and perform joins against a Calgary
database where an input record matches a Calgary database record based
on specific join criteria.
Calgary Loader:The Calgary Loader enables users to
create a Calgary database (*.cydb) from any type of Input file. Each field
contained in the Input file can be indexed to maximize the Calgary database
performance.
Calgary Cross Count:The Calgary CrossCount tool enables
users to aggregate data across multiple Calgary database fields to return
a count per record group.
Calgary Cross Count Append:The
Calgary CrossCount Append tool provides users with the ability to take
an input file and append counts to records that join to a Calgary database
where an input record matches a Calgary database record based on specific
join criteria.
The Developer category includes specialized tools specific to Macro and Analytic App creation as well as running external programs.
API
Output: This tool has no configuration. See the API help for
more information.
Base 64 Encoder: The Base 64 Encoder macro issues a base 64 encode string for a specified string field.
Blob Convert:
The Blob Convert tool will take different
data types and either convert them to a Binary Large Object (Blob) or
take a Blob and convert it to a different data type.
Blob
Input: The Blob input tool will
read a Binary Large Object such as an image or media file, by browsing
directly to a file or passing a list of files to read.
Blob Output: The Blob Output tool writes
out each record into its own file.
Block Until Done:The
Block Until Done tool stops downstream processing until all records come
through. This tool makes it possible to overwrite an input file.
Detour: The Detour tool is useful in constructing
Analytic App or macro workflows, where the developer can prompt a user to bypass a process in a workflow.
Detour End:
The Detour End tool will unify the
data processes from a resulting Detour upstream into a single stream for
further analysis in Analytic App and Macro workflows.
Dynamic Input: The
dynamic input tool allows the user to read from an input database at runtime
and dynamically choose what records to read in. Alteryx does not input
the entire database table content, instead it filters the data and only
returns the user specified criteria and joins it to the data coming into
the tool.
Dynamic Rename:The Dynamic Rename tool allows
the user to quickly rename any or all fields within an input stream by
employing the use of different methods. Additionally, dynamic or unknown
fields can be renamed at runtime.
Dynamic Replace:The
Dynamic replace tool allows the user to quickly replace data values on
a series of fields. Say you have a hundred different income fields and
instead of the actual value in each field, you want to represent the number
with a code of A, B, C, D, etc. that represents a range. The Dynamic Replace
tool can easily perform this task.
Dynamic Select:
The Dynamic Select tool allows fields
to be selected either by field type or via a formula. Additionally dynamic
or unknown fields will also be selected by field type or via formula at
runtime.
Field Info: The Field Info tool
allows the user to see in tabular form, the name of fields within a datastream
as well as the field order, field type and field size.
JSON
Parse: The JSON Parse tool separates
Java Script Object Notation text into a table schema for the purpose of
downstream processing.
Message:The Message tools allows the user to
report messages about the process to the Results window.
R:The R tool is a code editor for users
of R, an open-source code base used for statistical and predictive analysis.
Run Command:
The Run Command tool allows the user
to run external command programs within Alteryx. This tool can be used
as an Input, Output or as a pass through, intermediary tool.
Test: The Test tool verifies data or processes in a workflow. Since the Test tool accepts multiple inputs, with a single Test tool you can create multiple tests and test multiple sets of data and processes.
Throttle: The Throttle tool slows down the speed of the downstream tool by limiting the number of records that are passed through.
IMPORTANT! The Laboratory category contains tools that are not for production use. They may have documented known issues, may not be feature complete, and are subject to change. Your feedback when using these tools is appreciated. Submit feedback to the Alteryx Community.
Basic Data Profile: The Basic Data Profile tool outputs basic metadata such as data type, min, max, average, number of missing values, etc.
Charting: The Charting tool produces simple line and bar charts for use with Alteryx reports.
JSON
Build: The JSON Build tool takes
the table schema of the JSON
Parse tool and builds it back into properly formatted Java Script
Object Notation.
Make
Columns: The Make Columns tool
takes rows of data and arranges them by wrapping records into multiple
columns. The user can specify how many columns to create and whether they
want records to layout horizontally or vertically.
Transpose In-DB: Pivot the orientation of a data table in an In-DB workflow. It transforms the data so you may view horizontal data fields on a vertical axis.
Generic Tool: The Generic or Unknown
tool is not visible in the toolbox because it is not a tool you would
normally use. Instead it is a tool Alteryx uses to capture as much information
about the incoming tool as possible to help you to continue with the creation
of your new workflow. You will see the Generic tool on the workflow canvas
when Alteryx is looking for a Macro that it cannot find.
As improvements to Alteryx are implemented, some tools become obsolete. These tools are classified as Deprecated Tools. Workflows created with these tools in previous versions will still be able to function.
Alteryx recommends updating workflows to use applicable new replacement tools as no resources will be allocated to support the older tools. Alteryx may remove deprecated tools after two major releases.
To show deprecated tools on the palette, right-click the palette and select Show Deprecated Tools. Deprecated tools are marked with a "Deprecated" banner.
In/Out
In-Database
Data Investigation
Prescriptive
Calgary
Preparation
Reporting
Predictive
Connectors
Developer
Join
Documentation
AB Testing
Address
Laboratory
Parse
Time Series
Demographic Analysis
Unknown
Transform
Interface
Predictive Grouping
Behavior Analysis
Related Topics