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Here are a few things to focus on when creating a statistical analysis plan. Have the CRFs open. You have a 0% chance of not only accurately specifying but even just identifying all of the analyses you can and should be running if you don't know what data you are collecting. Of course it is important to keep in mind the goals of the study (it's why we list the objectives and endpoints in the early sections of the SAP) but it's equally important to focus on what data you will be working with. Speaking of data - think about what questions the analyses you design will answer based on the data, how it is collected, and how it is grouped. Work with your medical team to understand what subgroup analyses may be interesting for future studies or for other company needs. When you get to the later sections of the SAP where you begin to specify each of the analyses you plan to conduct - do not forget to specify and truly think about which analysis population those analyses should be run on. For some of your more complicated studies that have several different analysis populations it can be critical to think through what each analysis will be saying based on which population it is run on. Just some random SAP thoughts for you on a Friday. I hope these help. If you are currently working on an SAP and have a question or if you would just like some help, please feel free to message me. Happy Friday

Check out the new flexible modeling in #sapanalyticscloud! I am always happy to partner with Analysis Prime University to bring you some of the latest product updates! #sapbtp #dataanalytics #datamodeling SAP ASUG - Americas' SAP Users' Group

Discover the power of crafting Dimensional Views in our latest blog post, unlocking the potential to efficiently organize, analyze, and add context to your data for pattern discovery and geographical insights. Dive in now: https://lnkd.in/gye6gRU6 #SAPAnalytics #DataVisualization #DimensionalViews #SAPDatasphere #DataAnalysis

Revolutionize Your Data Collection: Discover ECS Team’s Game-Changing Questionnaire Strategy Need to collect feedback?? Questionnaire. ?? Need an employee survey?? Questionnaire. ?? Market research?? Questionnaire.? ?? Event planning? Questionnaire. ??? Questionnaires are everywhere ????. That is why it is very important to consider certain factors when building them, such as using different type of questions (checkbox, input text, etc.), labelling question (mandatory vs optional), multiple response options, logical flow (for example, with branching and skip logic), user-friendly design, and providing question context so users, if needed, can “expand” the question for additional information. Many questionnaires address subjects that might evolve in time (for instance, if having to do with Regulation). Therefore, it is also important to avoid the task of updating the questionnaire becoming something complex and tedious.? At #PwC ECS Team, we leveraged three great, well-known tools to fulfill each of these single requirements, so that we can have forms to collect CSRD Data and ease its reporting: #Excel: It is difficult to beat Excel for drafting structured data, which is the case for questionnaires. Here the Team defining the questions can quickly work with a well-known tool. #SAPBuildApps: We want the form to look good and appealing but also avoid having to code a web application from scratch. Here is the low-code solution SAP Build Apps leveraged. #Python: Some sort of “glue” is needed between the input (Excel) and the output (SAP Build Apps). We also want to avoid having to make changes in SAP Build Apps every time the Excel questions get updated. That is why a Python script is used to consolidate the Excel questions into the format required by the pre-defined SAP Build templates. After this automatic consolidation (which also expands the content), questions can be just uploaded into SAP Build. We'd love to hear your thoughts! Leave a comment below and share your experiences: How have outdated survey methods impacted your data collection efforts?? What innovative tools do you use to streamline your questionnaire process?? Engage with us and let's discuss how we can further revolutionize data collection together!

Sometimes in working with clients we get asked if Oracle Analytics Workbooks can be embedded in other applications, other web pages, etc. This video does a great job of showing how to easily embed a full Oracle Analytics workbook, with multiple pages, into another application or webpage. #oracleanalytics #OAC #visualizations #embeddedanalytics #ML #AI https://lnkd.in/ekasuRdd

New Feature in DataWeave 2.7.0: Mime Module ?? DataWeave 2.7.0 introduces a new module, dw::module::Mime, for working with MIME types. This module allows you to parse MIME types and create MIME-type objects from strings. Example: %dw 2.0 import * from dw::module::Mime output application/json --- fromString("application/json") Output: JSON { "success": true, "result": { "type": "application", "subtype": "json", "parameters": {} } } In this example, the code takes the string "application/json" and uses the fromString function from the Mime module to parse it. The output is a JSON object with the following properties: type: The main category of the MIME type subtype: The specific kind of data within the type parameters: Any additional parameters associated with the MIME type Ref: https://lnkd.in/dQTSRkww

#sapmm #TAANA analysis Analyzing Individual Tables 1.Using transaction TAANA you can access the screen Table Analysis: Administration. Choose Start of the navigation path Table Analysis Next navigation step Execute End of the navigation path. A dialog box appears. Enter the table name. Depending on this table, the system offers the analysis variants available for selection in the input help for the Analysis Variant field. 2.Select at least one analysis variant, or else create one. Note The system offers analysis variant DEFAULT for every table. This variant contains no fields. Therefore it determines the total number of entries in the client in which it is performed. 3.If an adequate analysis variant does not exist, the following options are available for creating analysis variants: 4.To create an ad hoc analysis variant specifically for a current table analyses, use Ad Hoc Variant. This variant is not saved on the database, and is available only for the table analysis that you are currently scheduling. For more information, see Creating an Ad Hoc Analysis Variant. Use the transaction TAANA_AV to create an analysis variant. It is saved to the database and you can use it multiple times. For more information, see Creating Analysis Variant. Select the desired type of process control and choose Continue. Recommendation To improve performance, we suggest analyzing large tables in the background, not online. 5.Specify a start time for background processing. Save your entries. When you perform the analysis online, it is started immediately. The table and analysis variant you selected for analysis are displayed on the left side of the screen. Note While the table analysis is being performed, you can Refresh the current data. 6.Double-click the table analysis on the left side of the screen to display it. The table analyses on the left side of the screen are sorted by table name, name, analysis variant, and time of analysis.

Implementing #GenAI for report auto-commenting in your company revolutionizes the reporting process by speeding it up and also generating new insights. Why? Auto commenting equips you to swiftly analyze, interpret, and foresee both qualitative and quantitative data simultaneously by automated commenting on reports and answering to user prompts. Learn more about PwC's Autocommenting with GenAI solution in SAP. #PwC_SAP

Does any of the following sound appealing?? ? Identified and consolidated duplicates? ?? Searching parts efficiently ? Avoiding false stock-outs ?? Increased maintenance productivity ?? Maximized ERP/EAM/CMMS functionality and reporting ?? Improved spend visibility ?? Optimized inventory ???? Assets managed efficiently If yes, submit a no cost - no obligation data evaluation today to get started on your data journey! Link: https://lnkd.in/gPRZqzTm Together, Let’s Make It Easy! #materialmasterdata #datagovernance #datacleansing #datamanagement #teamwork