Xceptor vs alteryx


  • Data Analytics Specialist
  • Alteryx vs. Tableau: Working Together
  • Our Partners
  • Alteryx and Tableau: Complementary Tools for Every Analyst
  • Alteryx and Tableau Comparison – What is the Best Solution for my Organization?
  • Robotic Process Automation (RPA)
  • Data Analytics Specialist

    This system is designed for use by BI analysts, but it works just as well regardless of whether or not you use SQL. A scalable and intuitive user interface UI makes working in the software fast and easy to learn, and flexible and diverse data discovery and management tools let you access dozens of data connectors and make edits to the incoming data.

    One major shortcoming with Alteryx is data visualization. Instead, Alteryx offers Analytic Templates for loading data into third-party visualization platforms. If you use Qlik or Tableau for data visualization, Alteryx also supports direct data integration. People know Tableau for offering some of the best data visualization tools on the market, and they use this system to reveal hidden insights and to tell stories with data.

    Using Tableau, you can create forecasts, spot trends and outliers, generate maps, and more. Similar to Alteryx, Tableau uses drag-and-drop functionality, but in Tableau, you apply visuals to data segments to see data in different ways instead of imposing various transformations on data to clean it.

    This means people with any amount of coding know-how can use Tableau, but the system also supports natural language in addition to custom SQL queries.

    For this, Tableau offers an Alteryx Starter Kit for Tableau, which makes it easier for you to prepare your data in Alteryx and then load it directly into Tableau for visualization. If you spend a lot of time writing code to prepare, cleanse, and analyze data, Alteryx will save you hours of writing SQL and R code.

    If you have no idea how to code, Alteryx empowers you to work in data using drag-and-drop features. Making a Tableau Data Extract compresses data to reduce storage requirements and to optimize it for visualization in Tableau. These extracts are columnar stores , which means they aggregate data into columns instead of rows. This improves performance in Tableau and speeds up load times, not to mention that it also eases file sharing and collaboration.

    Cleansing data for more insightful visualizations Using Alteryx with Tableau together goes beyond simply making data visualization easier. Clean data is valuable data. By corollary, data with missing values, structural errors, and other impurities is not.

    Even worse—impure data costs you money, both via opportunity cost and bad decisions. The second is that automation applied to an inefficient operation will magnify the inefficiency. The second is that visualization applied to messy data reveals poor insights. Cleansing your data in Alteryx before loading it into Tableau will save you time and make you more money. Not convinced Alteryx and Tableau are right for you? We can help. At TechnologyAdvice, we work with BI analysts every day. We know your pain points, and we want to help connect you with the right software.

    To get a free, personalized list of recommended business intelligence products, use our Product Selection Tool. Getting started is easy and takes less than five minutes. Get our best stuff. Sign up for our newsletter, and make your inbox a treasure trove of industry news and resources.

    Alteryx vs. Tableau: Working Together

    Alteryx and Tableau: Complementary Tools for Every Analyst December 30, by Paul Mathewson Alteryx and Tableau are powerful tools that can revolutionize data analytics and data consumption in any organization. Alteryx is a user-friendly ETL platform with a powerful suite of tools, including spatial and predictive analytics.

    Tableau is the best tool for sharing data in a dynamic visualization. The days of static cross-tabs are over. Consumers of reports want the ability to drill into the data.

    Alteryx and Tableau seamlessly bridge the gap between transforming raw data and a finished, dynamic report. In this blog, we will cover how to transform address fields in Alteryx for Tableau. Alteryx has many strengths. It is the best tool for analysts to use in regards to transforming, cleaning, calculating, joining and preparing data; however, it is not the best at displaying a final product.

    Tableau can be bogged down by very large data sets and is most efficient when these data sets are turned into a. Alteryx can power through any amount of data and export that final product as a.

    Preparing Address Files in Alteryx for Use in Tableau One example of using Alteryx as a tool for preparing data for visual analysis is with addresses.

    This is where Alteryx saves the day. Take, for example, a data set with addresses. Tableau will not recognize addresses as a geographical feature. Tableau will recognize and geocode some types of data including countries, states and cities. However, for more detailed points, one must use the latitude and longitude of a particular point for Tableau to recognize and plot these features on a map. How many of you keep the latitude and longitude of every single address on file?

    We will take this randomly generated customer sales list that includes addresses and sales: Say my end-user wants to view this data in two forms: Sales by State and Sales by Customer. I could produce a static cross-tab like this: Or I could put this data into Alteryx and utilize the address tools to format it for a dynamic analysis within Tableau.

    Alteryx Module: The. Saving as a Tableau Data Extract will increase performance in Tableau when I put this data into a dynamic dashboard. I will have two dashboards: One that displays Sales by State with states colored by the sum of sales and another that displays Sales by Customer with graduated symbols to reflect the total sales to that customer.

    The consumer of this dashboard will be able to select a state, and after doing so, a separate map of all customers in that state will populate the dashboard through an action command in Tableau. Tableau recognizes the new Lat and Lon field from Alteryx as a Measure.

    They should be dragged up to the Dimensions field instead so they operate as a discrete field. One view is made with State and Sales Tableau automatically recognized state and aggregated sales by state and another view with the Lat and Lon fields from Dimensions. These points are sized by sales. California is selected and the title of the Sales by Customer dashboard is automatically populated to reflect the selection: The consumer of this dashboard can then hover over each customer to activate the Tooltip in Tableau, displaying the information for that customer such as name, address and sales.

    This is just one example of utilizing Alteryx to prepare data for analysis in Tableau. Any module built in Alteryx can be put on a scheduler to run when the raw data is updated.

    After the module is built and the finished product exported as a. An analyst would use the. The dashboards would automatically update as the data is updated and send to the server from Alteryx, thus freeing up time for the analyst to analyze and not create new reports every time data is updated. The Latest.

    The days of static cross-tabs are over. Consumers of reports want the ability to drill into the data. Alteryx and Tableau seamlessly bridge the gap between transforming raw data and a finished, dynamic report. In this blog, we will cover how to transform address fields in Alteryx for Tableau. Alteryx has many strengths. It is the best tool for analysts to use in regards to transforming, cleaning, calculating, joining and preparing data; however, it is not the best at displaying a final product.

    Tableau can be bogged down by very large data sets and is most efficient when these data sets are turned into a. Alteryx can power through any amount of data and export that final product as a. Preparing Address Files in Alteryx for Use in Tableau One example of using Alteryx as a tool for preparing data for visual analysis is with addresses.

    This is where Alteryx saves the day. Take, for example, a data set with addresses. Tableau will not recognize addresses as a geographical feature. Tableau will recognize and geocode some types of data including countries, states and cities. However, for more detailed points, one must use the latitude and longitude of a particular point for Tableau to recognize and plot these features on a map.

    How many of you keep the latitude and longitude of every single address on file? We will take this randomly generated customer sales list that includes addresses and sales: Say my end-user wants to view this data in two forms: Sales by State and Sales by Customer.

    I could produce a static cross-tab like this: Or I could put this data into Alteryx and utilize the address tools to format it for a dynamic analysis within Tableau. This enables anyone to open the workflow and understand exactly what is occurring, without having to open any of the tool configurations.

    Weighted Average The Weighted Average tool calculates the weighted average of an incoming data field. There are several Alteryx tools that accomplish complex mathematical problems, with minimal effort on the data flow creator.

    Generally, Tableau Prep should be considered when the emphasis of analysis is on the organization of the data; if the data is well-organized, Tableau Prep may not be necessary for Tableau Desktop visualizations. If data reorganization at the source level will improve overall analysis, or improve the speed in which your results render, Tableau Prep is useful as a first step.

    In other words, sometimes, creating joins, calculations, aggregations in Tableau Desktop visualizations will perform nicely; other times, it is necessary to pre-scrub for optimization. Certain tasks, like pivoting, are more difficult if not impossible to accomplish in Tableau Desktop alone. Tableau Prep allows users to perform data preparation on files and against servers, and includes functionality such as: Joining Data Sets Create inner or outer joins based on data item names, or union data sets.

    Adding Calculations Users may enter a formula to create a new column or choose a commonly used function as a starting point for a custom calculation. A calculation in the source file could provide balance, based on object type, that would eliminate the need to create custom calculations when creating visualizations in Tableau Desktop.

    Creating Aggregations A new calculation may be created for a custom aggregation, by choosing an aggregation type and field. For example, this would be useful to have already aggregated in our data source if our end goal is to identify average test scores by demographic type by year.

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    Adding Pivots Pivoting allows a user to reorganize data to rows or columns in a manner that is best for analyzing a data set.

    For example, a data set that contains budget and actuals data with each month identified on a single row would be better analyzed if the account codes were broken out, so each month was identified on a new row.

    Notably, neither Tableau Prep nor Tableau Desktop allow users to perform the following Basic Joining and Data Preparation functions that exist in Alteryx: Sampling Sampling is related to a variety of tools, and it allows the data flow to only pull in a certain number, percentage, random sample.

    This is useful for very large data sets to understand trends and is also important for the beginning phases of predictive analytics.

    Tiles The Tile tool groups data sets by assigning a value tile based on ranges in the data. This tool is particularly helpful when looking to group-specific values, in order to provide a minimized result set. RegEx RegEx allows a user to parse out certain pieces of a string and provides a Boolean value if the components are found. This is very helpful in looking for data anomalies in a column and can allow for a corrective course of action when found.

    Alteryx and Tableau: Complementary Tools for Every Analyst

    Notably, anything preparation-related is occurring at the visualization level in Tableau Desktop: Joining Data Sets Create inner or outer joins based on data item names, or union data sets. Modifying Properties of Visualizations Users can change the colors, control the size of components of the visualizations, add tooltips, change the level of detail, and add labels to vastly change the appearance of the visualization.

    The level and depth of detail this provides allows users to hover and isolate specific data sets. Sort Data Create Data Groups and Data Sets Grouping and creating data sets is an efficient way of isolating certain data that is skewing a visualization. For example, in a data set containing student registration data across a year, we may notice that fall and winter are very low in numbers, where spring and summer are high.

    Creating Calculations There are three different types of calculations that are available to provide several ways to create calculations. Simple Calculations, such as determining balance from actuals and encumbrances from the budget, can be created by using the pre-defined function set or typing the functions, if known.

    Level of Detail and Table Calculations allow for more granularity in aggregation expressions and other kinds of calculations. A Level of Detail calculation might be used to aggregate the number of best sellers each author has written in the last five years. Basic Data Scrubbing and Joining — Comparison With complicated data sets that require additional effort to enable them to be analysis-ready, Alteryx has more robust capabilities than Tableau.

    The mathematical functions, documenting capability, and parsing functionality allow Alteryx users to create data flows that are complex in nature, but simple to build. Additionally, Tableau has two products that contribute to data cleansing and joining, while Alteryx can accomplish data cleaning in one, easy-to-follow application.

    Alteryx and Tableau Comparison – What is the Best Solution for my Organization?

    Winner — Alteryx Enhanced Analysis of Data Beyond creating an efficient, well-organized data source, a major of the power of tools like Alteryx and Tableau is taking the squeaky-clean data source to the next level by applying predictive components, trend lines, creating applications to further create efficiency, and generally providing different data perspectives that tell a compelling data story.

    This is a very sophisticated feature. Generate Rows Hypothetically, we have a data set containing account details for the sales team, with one sales team member assigned to each account. A new policy dictates that we have a primary and a secondary sales member assigned to each account. This tool allows the user to dictate the rules in which to replicate certain rows into the new data set, where we could tie the next sales representative to the account.

    In this case, developing a front-end application, where a user completes it per instruction, then that data is appended to a data source used for analysis, would provide a quick and easy way of getting exactly what we need. Predictive Modeling Using R to provide a means of statistical computing and graphics, Alteryx can use several different options of providing specific methodology that fit your needs.

    We can use tools like linear regression or decision trees to determine what future sales forecasts look like, based on past performance indicators. Geospatial Analysis Alteryx connects to geospatial entities such as US Census and TomTom to give exact attributes based on certain location characteristics. Users can view the data on the map inside of Alteryx Desktop or can view the raw data output.

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    It works primarily by illustrating stunning graphical representations of data. Some of the popular ones include: Geospatial Analysis Tableau Desktop provides a rich and easy-to-configure view into geospatial information that provides 16 levels of zoom, by integrating into R, GIS, or a custom geocoding data source.

    Forecasting and Trend Lines If a user has a data source from a relational database containing a date or integer and a measure, the exponential smoothing technique that creates forecasting shadows is an effective way of predicting future data based on historical patterns. For example, forecasting can add additional flavor to a sales report, so we can anticipate future sales for a given region. Trend lines are another visually pleasing method of displaying illustrating the trend of the underlying patterns.

    R is not integrated out-of-the-box, but may be added via scripting, and used in conjunction with the built-in visualization functionality of Tableau. Scatterplots and histograms are two very popular Tableau visualization types that are used in conjunction with statistical modeling principal and predictive analysis. Alteryx and Tableau both have comprehensive means of connecting and decoding geospatial data.

    While Alteryx can connect to a variety of geospatial tools and derive the exact node, Tableau provides an enhanced layer of geocoding by understanding regions and states and identifying the data on a map. On predictive analysis, Alteryx has dozens of tools that will provide exact statistical method of understanding data, but limited ways of viewing the output.

    Tableau can connect to R and provide a limited amount of statistical analysis with inherent functionality but can visually demonstrate these complex ideas in a way that Alteryx cannot. Alteryx receives bonus points for the application development, which Tableau does not have.

    Winner — It depends on what the user is trying to accomplish. Visualizing Data Most users will use the prior steps discussed — scrubbing, enhancing, joining data — to get to the end, which is enabling the data to be consumed easily. Alteryx Desktop: Alteryx provides an easy way, via the Browse tool, to view the output of data. This is particularly helpful when a user is in the cleaning phase of dealing with data, as the user can see the data, metadata, and quality of the data set, which could indicate to a user that further cleanup is needed.

    Other reporting options exist in Alteryx that allow users to view commonly used visualizations, such as charts and maps in the Browse output. Tableau Desktop: Tableau Desktop provides a wide variety of visualizations that can be customized to fit any data output need.

    A few examples of visualizations: Once the visualizations have been perfected, they can be added to a comprehensive dashboard. The views may interact, and filter together, as defined by the creator. Dashboards typically must be experienced real-time in order to see the speed and power in which they can consume, evaluate, and return results. Visualizing Data — Comparison While both products allow for the ability to view data, Alteryx is particularly good at viewing data as it flows through a potentially complex data stream, in order to get to a final output.

    Tableau is designed for stunning, powerful visualizations that allow users to interact with the output in an artful, meaningful way. Winner — Tableau Overall Conclusions Though there is overlap in functionality, most users can find benefit in using Alteryx and Tableau together because there are clear delineations in strengths and weaknesses in the products, depending on where the user is in the development cycle.

    It also provides means of data entry, from input via text file to application input.


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