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The definitive set of standards for Anaplan model building.

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PLANS is the new standard for Anaplan modeling—“the way we model.” This covers more than just the formulas and includes and evolves existing best practices around user experience and data hubs. It is a set of rules on the structure and detailed design of Anaplan models. This set of rules will provide both a clear route to good model design for the individual Anaplanner and common guidance on which Anaplanners and reviewers can rely when passing models amongst themselves.  In defining the standard, everything we do will consider or be based around: Performance – Use the correct structures and formula to optimize the Hyperblock Logical – Build the models and formula more logically – See D.I.S.C.O. below Auditable – Break up the formula for better understanding, performance, and maintainability Necessary – Don’t duplicate expressions. Store and calculate data and attributes once and reference them many times. Don't have calculations on more dimensions than needed Sustainable – Build with the future in mind, thinking about process cycles and updates        The standards will be based around three axes: Performance - How do the structures and formula impact the performance of the system? Usability/Auditability - Is the user able to understand how to interact with the functionality? Sustainability - Can the solution be easily maintained by model builders and support? We will define the techniques to use that balance on the three areas to ensure the optimal design of Anaplan models and architecture.       D.I.S.C.O As part of model and module design, we recommend categorizing modules as follows: Data – Data hubs, transactional modules, source data; reference everywhere Inputs – Design for user entry, minimize the mix of calculations and outputs System – Time management, filters, list attributes modules, mappings, etc.; reference everywhere Calculations – Optimize for performance (turn summaries off, combine structures) Outputs -  Reporting modules, minimize data flow out   Why build this way?   Performance Fewer repeated calculations Optimized structures and formulas Logical Data and calculations reside in logical places Model data flows can be easily understood Auditable Model structure can be easily understood Simplified formula (no need for complex expressions) Necessary Formulas and structures are not repeated Data is stored and calculated once, referenced many times, leading to efficient calculations Sustainable Models can be adapted and maintained more easily Expansion and scaling simplified     Recommended Content: Performance Dimension Order Formula Optimization in Anaplan Formula Structure for Performance The Truth About Sparsity: Part 1 The Truth About Sparsity: Part 2 Data Hubs: Purpose and Peak Performance To Version or Not to Version? Logical Best Practices for Module Design Data Hubs: Purpose and Peak Performance Auditable Formula Structure for Performance Necessary Reduce Calculations for Better Performance Formula Optimization in Anaplan Sustainable Dynamic Cell Access Tips and Tricks Dynamic Cell Access - Learning App Personal Dashboards Tips and Tricks Time Range Application Ask Me Anything (AMA) sessions The Planual The Planual Rises
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The Planual provides a systematic set of standards for model building on the Anaplan platform. The rules in it are designed produce the most efficient, usable, and scalable Anaplan models, while dramatically increasing performance for models in all contexts. We highly recommend that all model builders familiarize themselves with these standards, and start incorporating them into their model-building practices. (The results will be significant!)
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What are the benefits and drawbacks of using Versions instead a General List
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What is Pre-Allocation in Lists? Pre-allocation in lists is a mechanism in Anaplan that adds a buffer to list lengths. It is not added by default for lists; it becomes enabled when a role is set on a list. Please follow 1.03-01 though. Only add roles when needed. When it is enabled, a 2 percent buffer is added to the list, and this includes all line items where the list is used. This means we create extra space (in memory) for each line item so that when a new list item is added, the line item does not need to be expanded or restructured. When the buffer is used up (the list has run out of free slots) another 2 percent buffer will be created and any line items using the list will be restructured. This buffer is not shown in the list settings in Anaplan, meaning if we had a list with 1,000 items, that’s what Anaplan would show as the size. But in the background, that list has an extra 20 hidden and unused items. Pre-allocation also applies to list deletions but allows for 10 percent of the list to be deleted before any line items using the list get restructured. The purpose of pre-allocation in lists is to avoid restructuring line items that use frequently updated lists. What Happens When We Restructure? Restructuring the model is an expensive task in terms of performance and time. The Anaplan Hyperblock gets its efficiency by holding your data and multi-dimensional structures in memory — memory being the fastest storage space for a computer. Creating the model structures in memory — building the Hyperblock — does take a significant time to complete. But once it's in memory, access is quick. The initial model opening is when we first build those structures in memory. Once in memory, any further model opens (by other users, for example) are quick. Restructuring is the process of having to rebuild some parts of the model in memory. In the case of adding an item to a list, that means any line item that uses that list as a dimension. When restructuring a line item, we have to recalculate it, and this is often where we see the performance hit. This is because line items have references, so there is a calculation chain from any line item changed by that restructuring. Pre-allocation is there to reduce this extra calculation caused by restructuring. An example of this was seen in a model that was adding to a list that contained trial products. These products would then have a set of forecasted data calculated from historical data from real products. The list of these new products was small and changed reasonably frequently; it contained around 100 items. Adding an item took around two seconds (except every third item took two minutes). This happened because of the difference between adding to the pre-allocated buffer and when it had to do the full calculation (and re-adjust the buffer). Without pre-allocation, every list addition would have taken two minutes. Fortunately, we managed to optimize that calculation down from two minutes to several seconds, so the difference between adding to the pre-allocation buffer and the full calculation was around five seconds, a much more acceptable difference. In summary, pre-allocation on lists can give us a great performance boost, but it works better with larger lists than small lists. Small, Frequently Updated Lists As we’ve seen, the pre-allocation buffer size is 2 percent, so on a large list — say one million items — we have a decent-sized buffer and can add many items. When we have a small list that is frequently used, then a performance characteristic that is seen is frequently changing calculation times. This is especially the case if that list is heavily used throughout the model. A list with 100 items will restructure and recalculate on every third addition. This will continue to be noticeable for quite some time. Doubling the list size is still just adding four unused items (2 percent of 200). When we have a small list that is frequently used, you will see the calculation times change from fast to slow while the buffer is frequently filled. In cases like this, it is very important to reduce and optimize the calculations as much as possible. What Can Be Done? There are a few options. You could always make the list bigger and increase the buffer so that it restructures less. How? Option 1: Create a subset of “active” items and ignoring the additional list items used to bulk out the list. The problem with this would be the size of any line items using that list would increase and so would their calculations. Changing from a 100-item list to a 10,000- or even 1,000-item list (enough to give us a bigger buffer) could greatly increase the model size. Option 2: Create a new list that is not used in any modules so we avoid any restructuring costs. This would work but it adds a lot of extra manual steps. You would have this new list used in a single data entry module, which means this data is unconnected with the rest of the model. Being connected is what gives us value. You would then need to create a manual process to push data from this unconnected module to one that is connected to the rest of the model (this way all the changes will happen at once). We do lose the real-time updates and benefits of Connected Planning, though. Option 3: Reduce the impact of restructuring by optimizing the model and the formulas. Our best option is optimizing calculations. If we have quick calculations, the difference between buffered and unbuffered list additions could be small. The best way to achieve this would be through a Model Optimization Success Accelerator. This is a tailored service delivered by Anaplan Professional Services experts who aim to improve model performance through calculation optimizations. Please discuss this service with your Anaplan Business Partner. You can also follow our best practice advice and reference the Planual to find ways you can optimize your own models.
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This article covers the necessary steps for you to migrate your Anaplan Connect (AC) 1.3.x.x script to Anaplan Connect 1.4.x. For additional details and examples, refer to the  latest Anaplan Connect User Guide. The changes are: New connectivity parameters. Replace reference to Anaplan Certificate with Certificate Authority (CA) certificates using new parameters. Optional Chunksize & Retry parameters. Changes to JDBC configuration. New Connectivity Parameters Add the following parameters to your Anaplan Connect 1.4.x integration scripts. These parameters provide connectivity to Anaplan and Anaplan authentication services. Note: Both of the urls listed below need to be whitelisted with your network team. -service "https://api.anaplan.com/" -auth "https://auth.anaplan.com" Certificate Changes As noted in our   Anaplan-Generated Certificates to Expire at the End of 2019 blog post, new and updated Anaplan integration options support Certificate Authority (CA) certificates for authentication. Basic Authentication is still available in Anaplan Connect 1.4.x, however, the use of certificates has changed. In Anaplan Connect 1.3.x.x, the script references the full path to the Anaplan certificate file. For example: -certificate "/Users/username/Documents/AnaplanConnect1.3/certificate.pem" In Anaplan Connect 1.4.x the CA certificate can be referenced via two different options. Examples of both options are included at the end of this article as well as in the Anaplan Connect 1.4.x download. Option 1: Direct Use of the Private Key with Anaplan Connect Use your Private Key with Anaplan Connect by providing to certificate, private key and optional private key passphrase.  For example: If your private key has been encrypted use the following: CertPath="FullPathToThePublicCertificate" PrivateKey="FullPathToThePrivateKey:Passphrase" If your private key has not been encrypted then the passphrase can be omitted, however the colon is still needed in the path of the private key. CertPath="FullPathToThePublicCertificate" PrivateKey="FullPathToThePrivateKey:" To pass these values to Anaplan Connect 1.4.x, use these command line parameters: -certificate {path to the certificate file} -privatekey {path to the private key file:}{passphrase} These parameters should be passed as part of the credentials in the script: Credentials="-certificate ${CertPath} -privatekey ${PrivateKey}" Option 2: Create a Java Keystore A Java Keystore (JKS) is a repository of security certificates and their private keys.  Refer to   this video   for a walkthrough of the process of getting the CA certificate into the key store. You can also refer to   Anaplan Connect User Guide   for steps to create the Java key store. Once you have imported the key into the JKS,   make note of this information : Path to the JKS (directory path on server where JKS is saved) The Password to the JKS The alias of the certificate within the JKS. For example: KeyStorePath ="/Users/username/Documents/AnaplanConnect1.4/my_keystore.jks" KeyStorePass ="your_password" KeyStoreAlias ="keyalias" To pass these values to Anaplan Connect 1.4.x, use these command line parameters: -keystore {KeystorePath} -keystorealias {KeystoreAlias} -keystorepass {KeystorePass} These parameters should be passed as part of the credentials in the script: Credentials="-keystore ${KeyStorePath} -keystorepass ${KeyStorePass} -keystorealias ${KeyStoreAlias}" Chunksize Anaplan Connect 1.4.x allows for custom chunk sizes on files being imported. The -chunksize parameter can be included in the call with the value being the size of the chunks in megabytes. The chunksize can be any whole number between 1 and 50. -chunksize {SizeInMBs} Retry Anaplan Connect 1.4.x allows for the client to retry requests to the server in the event that the server is busy. The -maxretrycount parameter defines the number of times the process retries the action before exiting. The -retrytimeout parameter is the time in seconds that the process waits before the next retry. -maxretrycount {MaxNumberOfRetries} -retrytimeout {TimeoutInSeconds} Changes to JDBC Configuration With Anaplan Connect 1.3.x.x the parameters and query for using JDBC are stored within the Anaplan Connect script itself. For example: Operation="-file Sample.csv' -jdbcurl 'jdbc:mysql://localhost:3306/mysql?useSSL=false' -jdbcuser 'root:Welcome1' -jdbcquery 'SELECT * FROM py_sales' -import 'Sample.csv' -execute" With Anaplan Connect 1.4.x. the parameters and query for using JDBC have been moved to a separate file. The name of that file is then added to the AnaplanClient call using the   -jdbcproperties   parameter. For example:  Operation="-auth 'https://auth.anaplan.com' -file 'Sample.csv'  -jdbcproperties 'jdbc_query.properties' -import 'Sample.csv' -execute " To run multiple JDBC calls in the same operation, a separate jdbcpropeties file will be needed for each query. Each set of calls in the operation should include then following parameters: -file, -jdbcproperties, -import, and -execute. In the code sample below each call is underlined separately.  For example: Operation="-auth 'https://auth.anaplan.com' -file 'SampleA.csv' -jdbcproperties 'SampleA.properties' -import 'SampleA Load' -execute -file 'SampleB.csv' -jdbcproperties 'SampleB.properties' -import 'SampleB Load' -execute" JDBC Properties File Below is an example of the JDBCProperties file. Refer to the   Anaplan Connect User Guide   for more details on the properties shown below. If the query statement is long, the statement can be broken up on multiple lines by using the \ character at the end of each line. No \ is needed on the last line of the statement. The \ must be at the end of the line and nothing can follow it. jdbc.connect.url=jdbc:mysql://localhost:3306/mysql?useSSL=false jdbc.username=root jdbc.password=Welcome1 jdbc.fetch.size=5 jdbc.isStoredProcedure=false jdbc.query=select * \ from mysql.py_sales \ where year = ? and month !=?; jdbc.params=2018,04 CA Certificate Examples Direct Use of the Private Key Anaplan Connect Windows BAT Script Example (with direct use of the private key) '@echo of rem This example lists files in a model set CertPath="C:\CertFile.pem" set PrivateKey="C:\PrivateKeyFile.pem:passphrase" set WorkspaceId="Enter WS ID Here" set ModelId="Enter Model ID here" set Operation=-service "https://api.anaplan.com" -auth "https://auth.anaplan.com" -workspace %WorkspaceId% -model %ModelId% -F set Credentials=-certificate %CertPath% -privatekey %PrivateKey% rem *** End of settings - Do not edit below this line *** setlocal enableextensions enabledelayedexpansion || exit /b 1 cd %~dp0 set Command=.\AnaplanClient.bat %Credentials% %Operation% @echo %Command% cmd /c %Command% pause Anaplan Connect Shell Script Example (with Direct Use of the Private Key) #!/bin/sh # This example lists files in a model set CertPath="/path/CertFile.pem" set PrivateKey="/path/PrivateKeyFile.pem:passphrase" WorkspaceId="Enter WS ID Here" ModelId="Enter Model Id Here" Operation="-service 'https://api.anaplan.com' -auth 'https://auth.anaplan.com' -workspace ${WorkspaceId} -model ${ModelId} -F" #________________ Do not edit below this line __________________ if [ "${PrivateKey}" ]; then     Credentials="-certificate ${CertPath} -privatekey ${PrivateKey}" fi echo cd "`dirname "$0"`" cd "`dirname "$0"`" if [ ! -f AnaplanClient.sh ]; then     echo "Please ensure this script is in the same directory as AnaplanClient.sh." >&2     exit 1 elif [ ! -x AnaplanClient.sh ]; then     echo "Please ensure you have executable permissions on AnaplanClient.sh." >&2     exit 1 fi Command="./AnaplanClient.sh ${Credentials} ${Operation}" /bin/echo "${Command}" exec /bin/sh -c "${Command}"  Using a Java Keystore (JKS) Anaplan Connect Windows BAT Script Example (Using a Java Keystore) @echo off rem This example lists files in a model set Keystore="C:\YourKeyStore.jks" set KeystoreAlias="alias1" set KeystorePassword="mypassword" set WorkspaceId="Enter WS ID Here" set ModelId="Enter Model ID here" set Operation=-service "https://api.anaplan.com" -auth "https://auth.anaplan.com" -workspace %WorkspaceId% -model %ModelId% -F set Credentials=-k %Keystore% -ka %KeystoreAlias% -kp %KeystorePassword% rem *** End of settings - Do not edit below this line *** setlocal enableextensions enabledelayedexpansion || exit /b 1 cd %~dp0 set Command=.\AnaplanClient.bat %Credentials% %Operation% @echo %Command% cmd /c %Command% pause Anaplan Connect Shell Script Example (Using a Java Keystore) #!/bin/sh #This example lists files in a model KeyStorePath="/path/YourKeyStore.jks" KeyStoreAlias="alias1" KeyStorePass="mypassword" WorkspaceId="Enter WS ID Here" ModelId="Enter Model Id Here" Operation="-service 'https://api.anaplan.com' -auth 'https://auth.anaplan.com' -workspace ${WorkspaceId} -model ${ModelId} -F" #________________ Do not edit below this line __________________ if [ "${KeyStorePath}" ]; then     Credentials="-keystore ${KeyStorePath} -keystorepass ${KeyStorePass} -keystorealias ${KeyStoreAlias}" fi echo cd "`dirname "$0"`" cd "`dirname "$0"`" if [ ! -f AnaplanClient.sh ]; then     echo "Please ensure this script is in the same directory as AnaplanClient.sh." >&2     exit 1 elif [ ! -x AnaplanClient.sh ]; then     echo "Please ensure you have executable permissions on AnaplanClient.sh." >&2     exit 1 fi Command="./AnaplanClient.sh ${Credentials} ${Operation}" /bin/echo "${Command}" exec /bin/sh -c "${Command}"   
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Note that this article uses a planning dashboard as an example, but many of these principles apply to other types of dashboards as well. Methodology User Stories Building a useful planning dashboard always starts with getting a set of very clear user stories, which describe how a user should interact with the system. The user stories need to identify the following: What the user wants to do. What data the user needs to see to perform this action. What data the user wants to change. How the user will check that changes made have taken effect. If one or more of the above is missing in a user story, ask the product owner to complete the description. Start the dashboard design, but use it to obtain the answers. It will likely change as more details arrive. Product Owners Versus Designers Modelers should align with product owners by defining concrete roles and responsibilities for each team member. Product owners should provide what data users are expecting to see and how they wish to interact with the data, not ask for specific designs (this is the role of the modelers/designers). Product owners are responsible for change management and should be extra careful when dashboard/navigation is significantly different than what is currently being used (i.e. Excel ® ). Pre-Demo Peer Review  Have a usability committee that: Is made up of modeling peers outside the project and/or project team members outside of modeling team. Will host a mandatory gate-check meeting to review models before demos to product owners or users. Committee is designed to ensure: Best design by challenging modelers. Consistency between models. The function is clear. Exceptions/calls to action are called out. The best first impression. Exception, Call to Action, Measure Impact Building a useful planning dashboard will be successful if the dashboard allows users to highlight and analyze exceptions (issues, alerts, warning), take action and plan to solve these, and always visually check the impact of the change against a target. Dashboard structure Example: A dashboard is built for these two user stories that compliment each other. Story 1: Review all of my accounts for a specific region, manually adjust the goals and enter comments. Story 2: Edit my account by assigning direct and overlay reps. The dashboard structure should be made of: Dashboard header: Short name describing the purpose of the dashboard at the top of the page in "Heading 1." Groupings: A collection of dashboard widgets. Call to action. Main grid(s). Info grid(s) : Specific to one item of the main grid. Info charts: Specific to one item of the main grid. Specific action buttons: Specific to one item of the main grid. Main charts: Covers more than one item of the main grid. Individual line items: Specific to one item of the main grid, usually used for commentaries. Light instructions. A dashboard can have more than one of these groupings, but all elements within a grouping need to answer the needs of the user story. Use best judgements to determine the number of groupings added to one dashboard. A maximum of two-to-three groupings is reasonable. Past this range, consider building a new dashboard. Avoid having a "does it all" dashboard, where users keep scrolling up and down to find each section. If users ask for a table of contents at the top of a dashboard, it's a sign that the dashboard has too much functionality and should be divided into multiple dashboards. Example:   General Guidelines  Call to Action Write a short sentence describing the task to be completed within this grouping. Use the Heading 2 format. Main Grid(s) The main grid is the central component of the dashboard, or of the grouping. It's where the user will spend most of their time. This main grid will display the KPIs needed for the task (usually in the columns) and will display one or more other dimension in the rows. Warning: Users may ask for 20+ KPIs and need these KPIs to be broken down by many dimensions, such as by product, actual/plan/variance, or by time. It's critical to have a main grid as simple and as decluttered as possible. Avoid the "data jungle" syndrome. Users are used to "data jungles" simply because that's what they are used to with Excel. Tips to avoid data jungle syndrome: Make a careful KPI election (KPIs are usually the line items of a module). Display the most important KPIs ONLY, which are those needed for decision making. Hide the others for now. A few criteria for electing a KPI in the main grid are: The KPI is meant to be compared across the dimension items in the rows, or across other KPIs. Viewing the KPI values for all of the rows is required to make the decision. The KPI is needed for sorting the rows (except on row name). A few criteria for not electing a KPI in the main grid are (besides not matching the above criteria) when we need these KPIs in more of a drill down mode; The KPI provides valid extra info, but just for the selected row of the Dashboard and does not need to be displayed for all rows. These "extra info" KPIs should be displayed in a different grid, which will be referred to as "info grid" in this document. Take advantage of the row/column sync functionality to provide a ton of data in your dashboard but only display data when requested or required. Design your main grid in such a way that it does not require the user to scroll left and right to view the KPIs: Efficiently select KPIs. Use the column header wrap. Set the column size accordingly. Vertical Scroll It is ok to have users scroll vertically on the main grid. Only display 15 to 20 rows at a time when there are numerous rows, as well as other groupings and action buttons, to display on the same dashboard. Use sorts and a filter to display relevant data. Sort Your Grid Always sort your rows. Obtain the default sort criteria via user stories. If no special sort criteria is called out, use the alphanumeric sort on the row name. This will require a specific line item. Train end users to use the sort functionality. Filter Your Grid Ask end users or product owners what criteria to use to display the most relevant rows. It could be: Those that make 80 percent of a total. Use the RankCumulate function. Those that have been modified lately. This requires a process to attach a last modified date to a list item, updated daily via a batch mode. When the main grid allows item creation, always display the newly created first. Status Flag. If end users need to apply their own filter values on some attributes of the list items, such as filter to show only those who belong to EMEA or those whose status is "in progress," build pre-set user-based filters. Use the new Users list. Create modules dimensioned by user with line items (formatted as lists) to hold the different criteria to be used. Create a module dimensioned by Users and the list to be filtered. In this module resolve the filter criteria from above against the list attributes to a single Boolean. Apply this filter in the target module.  Educate the users to use the refresh button, rather than create an "Open Dashboard" button. Color Code Your Grid Use colored cells to call attention to areas of a grid, such as green for positive and red for negative. Color code cells that specifically require data entry. Display the Full Details If a large grid is required, something like 5k lines and 100 columns, then: Make it available in a dedicated full-screen dashboard via a button available from the summary dashboard, such as an action button. Do not add such a grid to a dashboard where KPIs, charts, or multiple grids are used for planning.  These dashboards are usually needed for ad-hoc analysis and data discovery, or random verification of changes, and can create a highly cluttered dashboard. Main Charts The main chart goes hand-in-hand with the main grid. Use it to compare one or more of the KPIs of the main grid across the different rows. If the main grid contains hundreds or thousands of items, do not attempt to compare this in the main chart. Instead, identify the top 20 rows that really matter or that make most of the KPI value and compare these 20 rows for the selected KPI. Location: Directly below or to the right of main display grid; should be at least partially visible with no scrolling. Synchronization with a selection of KPI or row of the main display grid. Should be used for: Comparison between row values of the main display grid. Displaying difference when the user makes change/restatement or inputs data. In cases where a chart requires 2–3 additional modules to be created: Implement and test performance. If no performance issues are identified, keep the chart. If performance issues are identified, work with product owners to compromise. Info Grid(s) These are the grids that will provide more details for an item selected on the main grid. If territories are displayed as rows, use an info grid to display as many line items as necessary for this territory. Avoid cluttering your main grid by displaying all of these line items for all territories at once. This is not necessary and will create extra clutter and scrolling issues for end users. Location: Below or to the right of the main display grid. Synced to selection of list item in the main display grid. Should read vertically to display many metrics pertaining to list item selected. Info Charts Similar to info grids, an info chart is meant to compare one or more KPIs for a selected item in the rows of the main grid. These should be used for: Comparison of multiple KPIs for a single row. Comparison or display of KPIs that are not present on the main grid, but are on info grid(s). Comparing a single row's KPI(s) across time. Place it on the right of the main grid, above or below an info grid. Specific Action Buttons Location: Below main grid; Below the KPI that the action is related to, OR to the far left/right - similar to "checkout." Should be an action that is to be performed on the selected row of the main grid. Can be used for navigation as a drill down to a detailed view of a selected row/list item. Should NOT be used as lateral navigation between dashboards; Users should be trained to use the left panel for lateral navigation. Individual Line Items Serve as a call out of important KPIs or action opportunities (i.e., user setting container for explosion, Container Explosion status). If actions taken by users require additional collaboration with other users, it should be published outside the main grid (giving particular emphasis by publishing the individual line item/s). Light Instructions Call to action. Serves as a header for a grouping. Short sentence describing what the user should be performing within the grouping. Formatted in "Heading 2." Action Instructions. Directly located next to a drop-down, input field, or button where the function is not quite clear. No more than 5–6 words. Formatted in "instructions." Tooltips. Use Tooltips on modules and line items for more detailed instructions to avoid cluttering the dashboard.
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What happens to History when I delete a user from a workspace?
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How do we keep our users in the Anaplan platform to do their work which requires a high level of advanced customization, faster and more easily than their previous Excel environment? The solution is called “Smart Filters”. Check it out !
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Filters can be very useful in model building and are widely used, but they can come at the expense of performance—often very visible to users through their use on dashboards. Performance can also hit imports and exports, which in turn may lead to the blocking of other activity, causing a poor perception of the model. There are some very simple guidelines to design well-performing filters: Using a Single Boolean Filter on a Line Item That Does Not Have Time or Versions Applied and Does Not Have a Summary Is Fastest Try to create a Boolean line item that incorporates all the filter criteria you want to apply. This allows you to re-use the line item and combine a series of Boolean line items into a single Boolean for use in the filter. For example, you may want to filter on three data points: Volume, Product Category, and Active Status. Volume is numeric, Product Category is a list formatted line item matching a user selection, and Active Status is a Boolean. Create a line item called Filter with the following formula: Volume > Min Vol AND Product Cat = User Selection.Category AND Active Status Here’s a very simple example module to demonstrate this: A Filter line item is added to represent all the filters we need on the view. Only the Filter line needs to be dimensioned by Users. A User Selection module dimension only by Users is created to capture user-specific filter choices: Here’s the data before we apply the filter:  Here's the data with the filter applied: A best practice suggestion would be to create a filter module and line items for each filter part. You may want other filters and you can then combine each filter as needed from this system module. This should reduce repetition and give you control over the filters to ensure they can all be Boolean. What Can Make a Filter Slow? The Biggest Performance Hit for Filters Is When Nesting Dimensions on Rows. The performance loss is significantly increased by the number of nested dimensions and the number of levels they contain. With a flat list versus nested dimensions (filtering on the same number of items) the nested filter will be slower. This was tested with a 10,000,000 list versus 2 lists of 10 and 1,000,000 items as nested rows; the nested dimension filter was 40% slower. Filtering on Line Items With a Line Item Summary Will Be Slow. A numeric filter on 10,000,000 items can take less than a second, but with a summary will take at least five seconds. Multiple Filters Will Increase Time. This is especially significant if any of the preceding filters do not lower the load because they will take additional time to evaluate. If you do use multiple filter conditions, try to order them so the most effective filters are first. If a filter doesn’t often match on anything, evaluate whether it's even needed. Hidden Levels Act as a Filter. If you hide levels on a composite list, this acts like a filter before any other filter is applied. The hiding does take time to process and will impact more depending on the number of levels and the size of the list. Avoid Nested Rows for Export Views Using nested rows can be a useful way to filter a complex set of data for export, but, as discussed above, the filter performance here can be poor. The best way around this is to pivot the dimensions so there is only one dimension on rows and use the Tabular Multi Column export option with a Filter Row based on Boolean option. Some extra filter tips include the following:  Filter duration will affect saved views used in imports, so check the saved view open time to see the impact. This view open time will be on every use of the view, including imports or exports. If you need to filter on a specific list, create a subset of those items and create a new module dimensioned by the subset to view that data.
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Learn how to organize your model into logical parts to give you a  well-designed model that is easy to follow, understand and amend at a later date
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This post summarizes steps to convert your security certificate to PEM format and test it in a cURL command with Anaplan. The current production API version is v1.3. Using a certificate to authenticate will eliminate the need to update your script when you have to change your Anaplan password. To use a certificate for authentication with the API, it first has to be converted into a Base64 encoded string recognizable by Anaplan. Information on how to obtain a certificate can be found in Anapedia. This article assumes that you already have a valid certificate tied to your user name. Steps: 1.   To properly convert your Anaplan certificate to be usable with the API, first you will need openssl (https://www.openssl.org/). Once you have that, you will need to convert the certificate to PEM format. The PEM format uses the header and footer lines “-----BEGIN CERTIFICATE-----“, and “-----END CERTIFICATE-----“. 2.   If your certificate is not in PEM format, you can convert it to the PEM format using the following OpenSSL command. “certificate-(certnumber).cer” is name of source certificate, and “certtest.pem” is name of target PEM certificate. openssl x509 -inform der -in certificate-(certnumber).cer -out certtest.pem View the PEM file in a text editor. It should be a Base64 string starting with “-----BEGIN CERTIFICATE-----“, and ending with “-----END CERTIFICATE-----“. 3.   View the PEM file to find the CN (Common Name) using the following command: openssl x509 -text -in certtest.pem It should look something like "Subject: CN=(Anaplan login email)". Copy the Anaplan login email. 4.   Use a Base-64 encoder (e.g.   https://www.base64encode.org/   ) to encrypt the CN and PEM string, separated by a colon. For example, paste this in: (Anaplan login email):-----BEGIN CERTIFICATE-----(PEM certificate contents)-----END CERTIFICATE----- 5.   You now have the encrypted string necessary to authenticate API calls. For example, using cURL to GET a list of the Anaplan workspaces for the user that the certificate belongs to: curl -H "Authorization: AnaplanCertificate (encrypted string)" https://api.anaplan.com/1/3/workspaces
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The process of designing a model will help you: Understand the customer’s problem more completely. Bring to light any incorrect assumptions you may have made, allowing for correction before building begins. Provide the big-picture view for building. (If you were working on an assembly line building fenders, wouldn’t it be helpful to see what the entire car looked like?) Table of Contents:   Understand the Requirements and the Customer’s Technical Ecosystem when Designing a Model When you begin a project, gather information and requirements using a number of tools. These include: Statement of Work (SOW): Definition of the project scope and project objectives/high-level requirements. Project Manifesto: Goal of the project – big-picture view of what needs to be accomplished. IT ecosystem: Which systems will provide data to the model and which systems will receive data from the model? What is the Anaplan piece of the ecosystem? Current business process: If the current process isn’t working, it needs to be fixed before design can start. Business logic: What key pieces of business logic will be included in the model? Is a distributed model needed? High user concurrency. Security where the need is a separate model. Regional differences that are better handled by a separate model. Is the organization using ALM, requiring split or similar models to effectively manage development, testing, deployment, and maintenance of applications? (This functionality requires a premium subscription or above.) User stories: These have been written by the client—more specifically, by the subject matter experts (SMEs) who will be using the model. Why do this step? To solve a problem, you must completely understand the current situation. Performing this step provides this information and the first steps toward the solution. Results of this step: Understand the goal of the project. Know the organizational structure and reporting relationships (hierarchies). Know where data is coming from and have an idea of how much data clean-up might be needed. If any of the data is organized into categories (for example, product families) or what data relationships exist that need to be carried through to the model (for example, salespeople only sell certain products). What lists currently exist and where are they are housed. Know which systems the model will either import from or export to. Know what security measures are expected. Know what time and version settings are needed. Document the User Experience Front-to-back design has been identified as the preferred method for model design. This approach puts the focus on the end-user experience. We want that experience to align with the process so users can easily adapt to the model. During this step focus on: User roles. Who are the users? Identifying the business process that will be done in Anaplan. Reviewing and documenting the process for each role. The main steps. If available, utilize user stories to map the process. You can document this in any way that works for you. Here is a step-by-step process you can try: What are the start and end-points of the process? What is the result or output of the process? What does each role need to see/do in the process? What are the process inputs and where do they come from? What are the activities the user needs to engage in? Verb/object—approve request, enter sales amount, etc. Do not organize during this step. Use post-its to capture them. Take the activities from step 4 and put them in the correct sequence. Are there different roles for any of these activities? If no, continue with step 8. If yes, assign a role to each activity. Transcribe process using PowerPoint ®  or Lucid charts. If there are multiple roles, use swim lanes to identify the roles. Check with SMEs to ensure accuracy. Once the user process has been mapped out, do a high-level design of the dashboards. Include: Information needed. What data does the user need to see? What the user is expected to do or decisions that the user makes. Share the dashboards with the SMEs. Does the process flow align? Why do this step?  This is probably the most important step in the model-design process. It may seem as though it is too early to think about the user experience, but ultimately the information or data that the user needs to make a good business decision is what drives the entire structure of the model. On some projects, you may be working with a project manager or a business consultant to flesh out the business process for the user. You may have user stories, or it may be that you are working on design earlier in the process and the user stories haven’t been written. In any case, identify the user roles, the business process that will be completed in Anaplan, and create a high-level design of the dashboards. Verify those dashboards with the users to ensure that you have the correct starting point for the next step. Results of this step: List of user roles. Process steps for each user role. High-level dashboard design for each user role. Use the Designed Dashboards to Determine What Output Modules are Necessary Here are some questions to help you think through the definition of your output modules: What information (and in what format) does the user need to make a decision? If the dashboard is for reporting purposes, what information is required? If the module is to be used to add data, what data will be added and how will it be used? Are there modules that will serve to move data to another system? What data and in what format is necessary? Why do this step? These modules are necessary for supporting the dashboards or exporting to another system. This is what should guide your design—all of the inputs and drivers added to the design are added with the purpose of providing these output modules with the information needed for the dashboards or export. Results of this step: List of outputs and desired format needed for each dashboard. Determine What Modules are Needed to Transform Inputs to the Data Needed for Outputs Typically, the data at the input stage requires some transformation. This is where business rules, logic, and/or formulas come into play: Some modules will be used to translate data from the data hub. Data is imported into the data hub without properties, and modules are used to import the properties. Reconciliation of items takes place before importing the data into the spoke model. These are driver modules that include business logic, rules.  Why do this step?  Your model must translate data from the input to what is needed for the output.  Results of this step: Business rules/calculations needed. Create a Model Schema You can whiteboard your schema, but at some point in your design process, your schema must be captured in an electronic format. It is one of the required pieces of documentation for the project and is also used during the Model Design Check-in, where a peer checks over your model and provides feedback.  Identify the inputs, outputs, and drivers for each functional area. Identify the lists used in each functional area. Show the data flow between the functional areas. Identify time and versions where appropriate. Why do this step?   It is required as part of The Anaplan Way process. You will build your model design skills by participating in a Model Design Check-in, which allows you to talk through the tougher parts of design with a peer. More importantly, designing your model using a schema means that you must think through all of the information you have about the current situation, how it all ties together, and how you will get to that experience that meets the exact needs of the end-user without fuss or bother.  Result of this step: A model schema that provides the big-picture view of the solution. It should include imports from other systems or flat files, the modules or functional areas that are needed to take the data from current state to what is needed to support the dashboards that were identified in Step 2. Time and versions should be noted where required. Include the lists that will be used in the functional areas/modules.  Your schema will be used to communicate your design to the customer, model builders, and others. While you do not need to include calculations and business logic in the schema, it is important that you understand the state of the data going into a module, the changes or calculations that are performed in the module and the state of the data leaving the module, so that you can effectively explain the schema to others. For more information, check out 351 Schemas. This 10-to-15-minute course provides basic information about creating a model schema. Verify That the Schema Aligns with Basic Design Principles When your schema is complete, give it a final check to ensure: It is simple. “Any intelligent fool can make things bigger, more complex, and more violent. It takes a touch of genius — and a lot of courage to move in the opposite direction.”  ― Ernst F. Schumacher “Design should be easy in the sense that every step should be obviously and clearly identifiable. Simplify elements to make change simple so you can manage the technical risk.” — Kent Beck The model aligns with the manifesto. The business process is defined and works well within the model.
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Learn how small changes can lead to dramtic improvements in model calculations
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Overview The following is a g uide for the new  Statistical Forecasting Calculation Engine Models (monthly and weekly).  It includes enablement videos, practice data import exercise, model documentation, and specific steps when using the model for implementations .  1. Enablement Videos & Practice Exercise # Item Details Link 1a Intro and Overview Video Model overview and review of new key features. Video Below 1b Initial Model & Data Import Steps Steps on how to setup model, product hierarchy, customer list and multi-level forecast analysis.  Video Below  1c Practice Exercise—Import data to setup stat forecast Two sets of load files included to practice setup for single level product set or multi-level product set w/ customers, product and brand level.  Start on "Initial App Setup" dashboard and load   either Single OR Multi Level files   into model, and use Import video as guide if needed.  .Zip File Attached  2. Documentation  # Item Details Link 2a Lucidchart Process Maps Lucidchart Process Map document includes High-Level process flow for end-user navigation and detailed tabs for each section.  **Details & links also on "Training & Enablement" dashboard. Process Maps  2b High-Level Process Map PDF High-level process map PDF format. Attached 2c Forecast Methods PDFs High-level version with forecast algorithms list and overview. Detailed version which includes a slide for each forecast method, m ethod overview, advantages/disadvantages, equation and graph example output.  **These slides are also included on "Forecast Methods Overview & Formulas" dashboard.   Attached 3. Implementation Specifics # Item Details 3a Training & Enablement Dashboard Training & Enablement dashboard contains details on process map navigation.  3b Initial Model Setup  Initial Setup: current model staged with chocolate data from data hub, execute CLEAR MODEL action prior to loading customer-specific data. 3c Changing Model Time Scale— align Native & Dynamic Time Settings If a Time Settings change is required, need to review Initial App Setup dashboard to align Native Time with Dynamic Time setup in model.   3d Monthly Update Process After initial setup, use Monthly Data History Upload dashboard to update prior period actuals and settings . 3e Single Level vs. Multi-Level Forecast Setup Two implementation options & when to use:  Single Level Forecast:  Forecast at one level of product hierarchy (i.e. all stat forecasts calculated at Item level). Most use cases will leverage single level forecast setup. Multi-Level Forecast : Ability to forecast at different levels of the product hierarchy (i.e. Top Item | Customers, Item and Brand level can all have stat forecast generated). This requires a complex forecast reconciliation process, review "Multi-Level Forecast Overview" dashboard if this process is needed.   3f Troubleshooting Tips Follow troubleshooting tips on Training & Enablement dashboard if having issues with stat forecast generating before reaching out for support.  3g Model Notes & Documentation Module Notes—includes DISCO classification and module purpose.   3h "Do Not Modify" Items Module notes contain DO NOT MODIFY for items that should not be changed during the implementation process.  3i User Roles & Selective Access Demo, Demand Planner, Demand Planning Manager ro les can be adjusted  After Selective Access process run on Flat List Management dashboard; then users can be given access to certain product groups/brands etc. 3j Batch Processing Details on daily batch processing and how to prepare a roadmap of your batch processes – files, queries, import actions/processes in Anaplan (see attachment). 4. Videos Intro & Model Intro and Overview Video. Data Import and Setup Steps.  5. Model Download Links Monthly Statistical Forecasting Calculation Engine Weekly Statistical Forecasting Calculation Engine
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Imagine This Scenario: You are in the middle of making changes in your development model and have been doing so for the last few weeks. The changes are not complete and are not ready to synchronize. However, you just received a request for an urgent fix from the user community that is critical for the forthcoming monthly submission. What do you do? What you don’t want to do is take the model out of deployed mode! You also don’t want to lose all the development work you have been doing.  Don’t worry! Following the procedure below will ensure you can apply the hotfix quickly and keep your development work. The following diagram illustrates the procedure: It’s a Two-Stage Process: Stage 1: Roll the development model back to a version that doesn’t contain any changes (is the same as production), and apply the hotfix to that version. Add a new revision tag to the development model as a temporary placeholder. (Note the History ID of the last structural change as you'll need it later.) On the development model, use History to restore to a point where development and production were identical (before any changes were made in development). Apply the hotfix. Save a new revision of the development model. Sync the development model with the production model. Production now has its hotfix. Stage 2: Restore the changes to development and apply the hotfix. On the development model, use the History ID from Stage 1 – Step 1 to restore to the version containing all of the development work (minus the hotfix). Reapply the hotfix to this version of development. Create a new revision of the development model. Development is now back to where it was, with the hotfix now applied. When your development work is complete, you can promote the new version to production using ALM best practice. Additional Resources: The procedure is documented in the Fixing Production Issues Anapedia article.
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You may have heard about a model called a data hub, but perhaps you aren’t confident that you understand the fundamentals, primary functions, or considerations when architecting one. There are three main advantages to incorporating a data hub: Single source of truth: Stores all transactional data from the source system. Data validations: Ensures all data is correct and valid before the data gets to the spoke model(s). Performance: It is always faster to load data from a model rather than a file. Additionally, the administrator can ensure the correct granularity of data in the spoke model(s) when using a data hub. For example, the source system may only contain transactional data at the daily level, but the planners may need the data aggregated to the month. The data hub can summarize the data and export only the data needed. The following information is designed to further define a data hub and support you in your journey of building your own. Table of Contents Definition of the Data Hub First, we need to define what a data hub is. This can be split into four sections: Use cases: The data hub should be the first model built, whether you have a single use or multiple use cases. The data should be automatically refreshed on a schedule, whether it is nightly, weekly, monthly, etc., from the source system—often an Enterprise Data Warehouse (EDW). All modules and views that create hierarchies or lists should be stored in the data hub, which enables your models in having one version of truth, as well as reducing the duplication of data. Model connectivity: Anaplan Connect , one of our 3 rd party vendors (Informatica Cloud, Dell Boomi, Mulesoft, or SnapLogic), or our REST API can be used to automate the loading of data to the data hub from the source system, as well as transferring data from the data hub to the spoke model(s). Additionally, transitional data should not be loaded directly into the spoke module, especially if there is a large volume of data. Functions: Often, simple ETL (Extract, Transform, and Load) functions can be utilized within your data hub to transform the data for the spoke model(s). This is helpful when consolidating data from multiple sources where you have different “codes” and need a mapping module to ensure the correct data gets mapped correctly. Team: The management of the data hub should have a designated team of experts who understand what data is stored in the data hub (to ensure duplication doesn’t happen), as well as the how and when the data gets loaded. Anaplan Architecture with a Data Hub There are several ways your Anaplan architecture could look, depending on the number of workspaces you currently have and the type of security your company requires. The following are illustrations of common architectures. Master Hub Model: Across Workspaces The most common, and recommended, architecture is when the data hub is in its own workspace. Not only does this have the advantage of not interfering with the other models, but it also adds an additional security layer, with a segregation of duties. In this view, the Anaplan Workspace Admin(s) can limit the access to the data hub workspace to only the people who require it. Master Hub Model: Within a Workspace The simplest depiction is where your data hub is within the same workspace as your spoke models. While this can be accomplished, it is not best practice as there is no segregation of duties and there is a possibility, upon heavy loads from the source system, of performance issues. Additionally, when adding users, the Anaplan Workspace Administrator (Admin) would need to ensure users don’t have access to the data hub, as well as any users of the data hub not having access to the spoke models Multiple Data Hubs Finally, the data hub doesn’t necessarily have to be the only model in the workspace. You can have additional data hubs, if needed. Factors to Consider When Implementing a Data Hub There are six main elements to think about when architecting a data hub: User stories. Source systems. Lists. Modules. Data validation. Exporting data to spoke model(s). User Stories One of the cornerstones of The Anaplan Way is data (process, model, and deployment being the others), which is critical to all implementations. You will need to know what data is needed for a certain use case. Consider the following, common, data questions that need to be answered in order to be successful: What granularity of the data is needed? How much history is needed? How much history do you have? Does the source system only have transactional data, but the use case needs the data at the month level? Can the source system do the aggregation for you? After all data questions have been answered, shift your focus to the source system and consider the following: Consider the source system. Where is the data coming from? What is the source system, and is it a trusted environment? Is it Excel? Typically, you should stay away from Excel as the “source” because Excel cannot be audited. Define the data source owners. Who has access to this data? Who is preparing it? Are they part of the project? These are often-overlooked questions that are critical to success. Ideally, the data source owners need to be part of the project from the start to understand the file specifications and prepare the initial load of the data, as well as towards the end of the project to do a final load of the data. Define file specifications. How many files will be needed? Typically, you will need master data, as well as transactional data. Instead of having one file with all of this data, determine if the data can be split between different files (one for transactional, one for the unique members of the master data). It will be better for Anaplan (for performance reasons) to split these to reduce warnings during the data load process. Analyze the data. Understand what makes each record unique (date/period and transactional amounts should not be part of this), and make sure the data owners don’t give you everything (Select * From Employee) when you only need five columns. Remember, it is better to ask for additional columns midway through the project than getting all columns in the beginning and only using a select amount. Consider custom codes in the source system. Find more on this in the transactional lists section. This is a great trick for transactional data. After you have analyzed the data to understand what makes each record/row unique, concatenate the “codes” of the metadata into one transactional code, but remember, you will need to be under the 60-character threshold. Define the schedule. When is the data available? Is the data on a certain schedule? What is the schedule required with this use case? Determine the ETL medium to be used. Will Anaplan Connect be sufficient, will one of our 3 rd parties be used, or will a more custom application be needed, such as REST API? Does your company already have this experience inhouse, or will training be required? These will need to be factored into all data stories. Transactional Usually, the largest lists are those containing transactional data. There can be millions of transactional ID’s with several list properties defined. First, properties should not be defined on a transactional list (or any list, except for Display Name, as they do get accounted for in the workspace memory). Secondly, instead of loading metadata to list properties (Cost Center and Account as properties), try to figure out a way to incorporate them into the code. If the transactional data is defining a transactional amount at the intersection of Cost Center and Account for a particular month, attempt to use the code of the Cost Center and the code of the Account concatenated together (0100_57000). Not only will this decrease your list size, but it will also create a healthier model. In the below example, the model builder did not create a custom code, but rather used a combination of properties to make the record unique, which included the date/period, as well as the transactional amount. Notice the original number of records vs. the number of records after a custom code was created. By incorporating the date/time period, as well as the transactional amount, it inflated the list size exponentially based on the number of years that were loaded. Doing this not only caused the model to be bigger, but also caused poor model opening performance.  See the Appendix for a simple worked example to explain further. Learn more about sparsity in the two-part series The Truth about Sparsity: Part 1 and The Truth About Sparsity: Part 2 . Flat Lists Similar to transactional lists, flat lists are not part of a hierarchy and are a series of records grouped in a list, like Products, Companies, Cost Centers, or Employees. These are your “legends” or “anchor” for all metadata about this unique record. Again, the only property that should be defined is a Display Name, if needed. It is best practice, from a model builders’ perspective, to suffix the name with “Flat” or “- Flat”. This helps identify whether the list is part of a hierarchy or flat list (Employee – Flat, Cost Center – Flat, Product – Flat). These lists can be used for data validation, which will be described later in this article. Modules Ideally, you should have three types of modules in the data hub: Transactional: A Transactional module will store the transactional data by the time series, whether that be by day, week, month, quarter, or year. The only data, or line items, should be transactional data. No other line items should be defined. Additionally, to keep the size down, make sure the summaries on the line items are turned off, or None, as there is no reason to sum the data within the module. System: System (SYS) modules, or the “S” in DISCO , do not have time associated with them and should only be dimensionalized by the same list (Employee Flat, Cost Center Flat, Product Flat). These modules store the metadata or attributes about the list item that doesn’t change over time, for example the employee’s start date. Another example of a SYS module would be any kind of mapping that is required, whether it be SYS Time Filter module or a mapping from one source system to another. Export modules: If the data from the source system is being loaded at a lower granularity than needed in the spoke model(s), export modules can aggregate the data to the specified need (month, quarter, or year level), which will lead to more efficient data load performance to the spoke model(s). Additionally, it is better to only load the granularity of data needed instead of loading all data to the spoke model, but only using a portion of it. Loading Data vs. Using Formula’s in SYS Modules If you can devise a custom code where all of the attributes of the data are accounted for, you can greatly increase the performance of your data load, especially on very large data volumes. It is actually faster to use formulas to derive the data from the custom code than it is to load the data. Why? A couple of reasons. First, when data is loaded, the load is triggering the change log, and every change is being recorded in the model history . Second, loading data to another module is an additional action. If you didn’t need that action, you would save processing time. In the example below, the exact same data was loaded four different ways: Import Properties to a List: A list was created with all attributes, including the transactional data, and was loaded to list properties (not best practice and against DISCO). Import to List, Attribute, and Trans: A list was created, the transactional data was loaded to a transactional module, and all of the attributes were loaded to a SYS Attribute module. Import to List, Trans, Calculate Attribute: A list was created, the transactional data was loaded to a transactional module, but the SYS Attribute model was calculated using two different methods: One Line Item: Using FINDITEM() with several functions parsing out the information from within the FINDITEM(). For example, FINDITEM(Cost Center, RIGHT(LEFT(Trans Details.Code, '2nd Group’), 3)). Multiple Line Items: Parsing of the member spread across multiple line items and using FINDITEM() with only the list and code as the parameter. First, you do the parsing to get the correct piece of the code (one line item), and then the FINDITEM() of that code (2 nd line item). Load Performance Notice, the best performing data load was the last one, Import to List, Trans, Calculate Attribute (multiple line items), where the parsing out of the data was spread over multiple line items. This is due to the fact that the data load was able to take advantage of Anaplan’s multithreading capabilities. The worst performing data load occurred when data was loaded to the Attribute module because, due to the sheer size of the data, a save had to be performed. Exporting to Spoke Models One of the most important concepts to remember when exporting data is to use a view from a module. Lists should not be exported because you lose control over what you export. It is either all or nothing. By using views, you can employ a filter (should always be a Boolean) to render exactly which data needs to be exported. If you need more than one filter, combine both into one line item and use that line as the filter. You will have much better performance if you are only using one Boolean line item as a filter vs. having multiple filters defined. Another important concept to remember is to only export detailed information, as there is no reason to export parent information (quarter, year, etc.). Not only will you get warnings when exporting parent information, but the performance of the export will decrease because the system will have to create a debug log. The goal is to make sure a debug log is not created, all green checks, so if there ever is an issue, you will know it truly is an issue that needs attention. Line items in the data hub formatted as text should not be exported as text, but actually as list formatted line items in the spoke model (text->list formatted line item). The goal is to reduce the number of text formatted line items in the spoke model. Some say they need to do validation in the spoke model, therefore they need to import the data as text. Actually, this is false, because the validation should have already been done in the data hub, so there should be no need to do the validation again. Lastly, you should think about what really needs to be exported. Do you really need to export historical data that hasn’t been changed? Instead, just export the newly loaded data, or delta data. This can be accomplished by using one of two methods: From the source system, request IT to only send the updated information, not the full load every time. Additionally, request IT to create a column in the source file with a hardcoded value of “TRUE.” This will tell Anaplan which row is new or has been updated and can be used as a filter for an export. Just know, before the import of the source data gets loaded, make sure the first action within the process clears out the previous true records (set this up via a view using a filter where the view only shows members with a value of true). Utilize the current period function to only export the current period data. In the SYS Time Filter module, create a line item named Current Period with the formula CURRENTPERIODSTART(). In the export views, filter the data on this line item. Tips and Tricks A few of tips and tricks to be aware of include the following: Hierarchies should not be in the data hub. Analytical modules should not be in the data hub. Do not delete and reload lists. Data Validations Model Why should hierarchies not be in the data hub? To answer that question, you need to understand why hierarchies are used in the first place. Essentially, hierarchies are only needed to aggregate data for analytical purposes, and since users will not normally login to the data hub, the lists essentially take up space. With that said, it is perfectly okay to create the hierarchies for testing purposes to ensure your actions from the meta modules are building the hierarchies correctly, but as soon as the actions are working correctly and have been verified, you can remove the list structures from the data hub. A case can be made that certain implementations may need the hierarchies created in the data hub for validation purposes of several sources. If this is the case in your implementation, just be sure to only use the hierarchies for validation purposes. In addition to the above, there are two more reasons to not have hierarchies built in the data hub—cluttered data, and spoke models that pull data from the lists.   Data hubs need to be clean and clutter free to ensure optimal performance, which also makes it easier for the administrators to understand exactly what data is stored in the data hub. Additionally, when you have lists—especially hierarchical lists—spoke model builders will sometimes build their lists from the lists within the data hub instead of from a view. It is best practice to always build lists from views from within a module so the action can benefit from filters (there are no filters when importing from lists). Analytical modules should not be in the data hub since end users don’t normally access the data hub. There really isn’t a reason to have products by versions by time in the data hub, that belongs to the spoke model. Remember, the data hub should only be used to store data from the source system(s). Within your nightly data load process, do not delete and reload data, including the list structures. If you have a proper code, you shouldn’t need to do this. Additionally, not only does this impact the overall performance of the process (adding an additional action to delete the list, which then deletes all data associated with that list), but the process is essentially filling up the change log with the exact same data that it had before the delete. When a certain threshold is surpassed, the model will require a save, thus taking up even more time. Ultimately, you are forcing the model to re-aggregate all of the data, instead of just the new data. Lastly, if you know you will have to do a lot of transformations on your data (consolidating multiple source systems or your data is not clean), think about creating a Data Validations model.  This model’s sole purpose would be to clean the data and then feed the data to the data hub, thus keeping the transformations to a minimum in the data hub as well as keeping the data hub clean. Worked Example Use Case: Transaction Data is by Store and SKU and Month Bad Way The code for the Transaction list is a three-part code Store_SKU_Month Attributes for Store, SKU and Month are imported as Text and matched against the Store list, SKU list and Time period respectively An additional line item is needed for the Store and SKU code (for export). This is the screenshot of the bad way: Notice the repetition of the attributes. STR07 and SKU031 are repeated each month. Good Way Two data files Unique combinations of Store and SKU (two-part code) Store SKU code by month for the quantity. The transaction details are stored in a module dimensioned by Transactions The Store and SKU attributes are calculated using the “_” delimiter The quantity is stored in a module dimensioned Transactions and by month The additional line item is needed for the Store and SKU code (for export). This is a subsidiary view in the module as it is not dimensionalized by Time. These are the screenshots of the good way: Below lists out the breakdown of the model in terms of List size, Line items and the associated member usage of the various structures. The main reasons for the improvement are because lists themselves account for approximately 500b for each member and also there is repetition of the attributes per “month” in the transaction data (as mentioned above). Hopefully, this article has shed some light on data hubs, how they should be used, and what you can do to ensure they perform at their peak level. Remember, analyze the data to understand what makes the row unique and use that as the code. Every list should have a code—every list!
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Overview Imports are blocking operations: To maintain a consistent view of the data, the model is locked during the import, and concurrent imports run by end-users will need to run one after the other and will block the model for everyone else. Exports are blocking for data entry while the export data is retrieved, and then the model is released. During the blocking phase, users can still navigate within the model. Rule #1  Carefully Decide If You Let End-Users Import (And Export) During Business Hours Imports executed by end-users should be carefully considered, and if possible, executed once or twice a day. Customers more easily accept model updates at scheduled hours for a predefined time—even if it takes 10+ minutes—and are frustrated when these imports are run randomly during business hours. Your first optimization is to adjust the process and run these imports by an administrator at a scheduled time, and then let the user based know about the schedule. Rule #2 Use a Saved View The first part of any import (or export) is retrieving the data. The time it takes to open the view directly affects the time of the import or export. Always import from a saved view—NEVER from the default view. And use the naming convention for easy maintenance. Ensure the view is using optimized filters with a single Boolean value per axis. Hide the line items that are not needed for import; do not bring extra columns that are not needed. If you have done all of the above, and the view is still taking time to complete, consider using the Tabular Multi Column export and filter "in the way out." This has been proven to improve some sluggish exports.  Rule #3 Mapping Objective = Zero Errors or Warning Make sure your import executes with no errors or warnings as every error takes processing time. The time to import into a medium-to-large list (>50k) is significantly reduced if no errors are to be processed. In the import definition, always map all displayed line items (source→target) or use the "ignore" setting. Don't leave any line item unmapped. Rule #4 Watch the Formulas Recalculated During the Import If your end-users encounter poor performance when clicking a button that triggers an import or a process, it is likely due to the recalculations that are triggered by the import, especially if the action creates or moves items within a hierarchy. You will likely need the help of the Anaplan Model Optimization team to identify what formulas are triggered after the import is done and to get a performance check on these formulas to identify which one takes most of the time. Usually, those fetching many cells such as SUM, LOOUKP, ANY, or FINDITEM are likely to be responsible for the performance impact. Speak to your Business Partner for more details on the Model Optimization services available to you. To solve such situations, you will need to challenge the need for recalculating the formula identified each time a user calls the action. Often, for actions such as creations, moves, assignments done in WFP or Territory Planning, many calculations used for reporting are triggered in real-time after the hierarchy is modified by the import, and are not necessarily needed by users until later in the process. The recommendation is to challenge your customer and see if these formulas can be calculated only once a day, instead of each time a user runs the action. If this is acceptable, you'll need to rearchitect your modules and/or formulas so that these heavy formulas get to run through a different process run daily by an administrator and not by each end-users. If not, you will need to look at the formulas more closely to see what improvements can be made. Remember, breaking formulas up often helps performance. Rule #5 Don't Import List Properties Importing list properties takes more time than importing these as a module line item. Review your model list impacted by imports, and look to replace list properties with module line items when possible. Use a system module to hold these for the key hierarchies, as per D.I.S.C.O. Rule #6 Get Your Data Hub Hub and spoke: Setup a data hub model, which will feed the other production models used by stakeholders. Performance benefits: It will prevent production models to be blocked by a large import from an external data source. But since data hub to production model imports will still be blocking operations, carefully filter what you import, and use the best practices rules listed above. All import, mapping/transformation modules required to prepare the data to be loaded into planning modules can now be located in a dedicated data hub model and not in the planning model. This model will then be smaller and will work more efficiently. Try and keep the transaction data history in the data hub with a specific analysis dashboard made available for end users; often, the detail is not needed for planning purposes, and holding this data in the planning model has a negative impact on size, model opening times, and performance. As a reminder of the other benefits of a data hub not linked to performance: Better structure, easier maintenance: data hub helps keep all the data organized in a central location. Better governance: Whenever possible put this Data Hub on a different workspace. That will ease the separation of duties between production models and meta-data management, at least on actual data and production lists. IT departments will love the idea to own the data hub and have no one else be an administrator in the workspace. Lower implementation costs: A data hub is a way to reduce the implementation time of new projects. Assuming IT can load the data needed by the new project in the data hub, then business users do not have to integrate with complex source systems but with the Anaplan data hub instead. Rule #7 Incremental Import/Export This can be the magic bullet in some cases. If you export on a frequent basis (daily or more) from an Anaplan model into a reporting system, or write back to the source system, or simply transfer data from one Anaplan model to another, you have ways to only import/export the data that have changed since the last export. Use a Boolean line item to identify records that have changed and only import those.
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You can interact with the data in your models using Anaplan's RESTful API. This enables you to securely import and export data, as well as run actions through any programmatic way you desire. The API can be leveraged in any custom integration, allowing for a wide range of integration solutions to be implemented. Completing an integration using the Anaplan API is a technical process that will require significant action by an individual with programming experience. Visit the links below to learn more: API Documentation Anaplan API Guide You can also view demonstration videos to understand how to implement APIs in your custom Integration client. The below videos show step-by-step guides of sequencing API calls and exporting data from Anaplan, importing data into Anaplan, and running delete actions and Anaplan processes. API sequence for uploading a file to Anaplan and running an import action is as follows: API sequence for running an export action and downloading a file from Anaplan is as follows: API sequence for running an Anaplan process and a delete action is as follows:
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Summary This article describes the technique to dynamically filter specific levels of a hierarchy on a dashboard and provides a method to select and visualize hierarchies on a dashboard. Details This article explains how to configure the calculation of the level of a list in a hierarchy in order to apply specific calculations (custom summary) or filters by level on a dashboard. In this example, we have an organized hierarchy of 4 levels (Org L1 to Org L4). For each item in the hierarchy, we want to calculate a module value that returns the associated level that is to be displayed on a dashboard. Notes and Platform Context The technique addresses a specific limitation within dashboards where a composite hierarchy's list level cannot be selected if the list is synchronized to module objects on the dashboard. The technique uses a static module based on the levels of the composite structure used for filtering of the object on a dashboard. The technique is based on utilizing the Summary Method "Ratio" on line items corresponding to the list levels of the composite hierarchy to define the values of the filtering line items. Note that this method is not a formula calculation, but rather a use of the Summary Method Ratio on each line item applied to the composite hierarchy. Example List In this example, a four-level list composite hierarchy list is used. The hierarchy in this example has asymmetrical leaf items per parent: Defining the Level of Each List In order to calculate the level of each item in each of the lists L1 - L4, we need to create a module that calculates the associated level of each member by this technique: 1) Create as many line items as levels of hierarchy, plus one technical line item. 2) Configure the settings in the blueprint of the line items of this filtering module, per this example and table: Line Item Formula Applies to Summary Summary method Setting Ratio Technical line item* 1 (empty) Formula   Level or L4 (lowest level) 4 Org L4 Ratio* L3 / Technical L3 3 Org L3 Ratio L2 / Technical L2 2 Org L2 Ratio L1 / Technical L1 1 Org L1 Ratio L1 / Technical                     When applying these settings, the filtering module looks like this: *Note the Technical line item Summary method is using Formula. Alternatively, The Minimum Summary Method can be used but will return an error when a level of the hierarchy does not have any children and the level calculated is blank. The filtering module with Summary method applied results: Use the line item at the lowest level—Level (or L4) (LOWEST)—as the basis of filters or calculations. Applying a Filter on Specific Levels in Case of Synchronization When synchronization is enabled, the option “Select levels to show” is not available. Instead, a filter based on the level calculated can be used to show only specific levels. In the example, we apply a filter which matches any of the level 4 and 1: The following filtered dashboard result is achieved by using the composite hierarchy as a page selector:
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Little and Often Would you spend weeks on your budget submission spreadsheet or your college thesis without once saving it? Probably not. The same should apply to making developments and setting revision tags. Anaplan recommends that during the development cycle, you set revision tags at least once per day. We also advise testing the revision tags against a dummy model if possible. The recommended procedure is as follows: After a successful sync to your production model, create a dummy model using the ‘Create from Revision’ feature. This will create a small test model with no production list items. At the end of each day (as a minimum), set a revision tag and attempt to synchronize the test model to this revision tag. The whole process should only take a couple of minutes. Repeat step 2 until you are ready to promote the changes to your production model. Why Do We Recommend This? There are a very small number of cases where combinations of structural changes cause a synchronization error (99 percent of synchronizations are successful). The Anaplan team is actively working to provide a resolution within the product, but in most cases, splitting changes between revision tags allows the synchronization to complete. In order to understand the issue when a synchronization fails, our support team needs to analyze the structural changes between the revisions. Setting revision tags frequently provides the following benefits: The number of changes between revisions is reduced, resulting in easier and faster issue diagnosis.  It provides an early warning of any problems so that someone can investigate them before they become critical. The last successful revision tag allows you to promote some, if not most, of the changes if appropriate. In some cases, a synchronization may fail initially, but when applying the changes in sequence the synchronization completes. Using the example from above: Synchronizations to the test model for R1, R2, and R3 were all successful, but R3 fails when trying to synchronize to production. Since the test model successfully synchronized from R2 and then R3, you can repeat this process for the production model. The new comparison report provides clear visibility of the changes between revision tags.
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