ASUG - Americas'​ SAP Users'​ Group’s Post

Facing challenges in material master data maintenance, a global company recognized a pivotal opportunity for business transformation. The goal was to automate new forms and systems while ensuring end-users felt confident using advanced technology solutions for master data authoring. The company partnered with Precisely to reimagine material master data processes and achieve broader transformation objectives. Discover how Precisely collaborated with this global company to drive data quality and efficiency in this ASUG case study: https://bit.ly/3YoIUiB

In an unpredictable market, businesses constantly seek out methods to maintain stability and growth. One such method revolves around the concept of Materials Master Data Management. This strategy involves creating and maintaining a single, authoritative source of truth for a company’s information assets. Understand the various benefits and how to get started: https://hubs.ly/Q02N_kBy0 #mdm #data #dataplatform #datamanagement #masterdata #referencedata

In an unpredictable market, businesses constantly seek out methods to maintain stability and growth. One such method revolves around the concept of Materials Master Data Management. This strategy involves creating and maintaining a single, authoritative source of truth for a company’s information assets. Understand the various benefits and how to get started: https://hubs.ly/Q02ySqTN0 #mdm #data #dataplatform #datamanagement #masterdata #referencedata

🔍 Struggling with low-quality material master data? Discover how it's impacting your organization's efficiency and what you can do about it! Unlocking operational excellence starts with understanding the impact of low-quality material master data on your organization. From increased costs to workflow disruptions, learn how effective MRO data management can revolutionize your efficiency and bottom line. Ready to optimize your processes and drive success? Read our blog to explore more! https://lnkd.in/dBuj7-xa #MasterDataManagement #MRODataManagement #MROInventoryManagement #MROMasterDataManagement #MROMaterialsInventory #MROSoftware #datacleansing #datascrubbing

This is a very concise and articulate post about how important a data warehouse is for accurate data analysis !

🏢 About Armstrong Transport Group: A leading non-asset-based logistics provider, Armstrong Transport Group connects shippers with a vast multi-modal carrier network, optimizing shipping costs and enhancing visibility. 🎯 Key Results: - 7500% reduction in data consolidation time—from 10 hours to 8 minutes! - Instant connectivity to any data source, even niche freight applications - Expanded data access from the finance department to 300+ users in Sigma by year-end. 💡 Challenges Overcome: - Outdated data infrastructure limited data access. - Marketing teams lacked insight into ad ROI due to integration challenges. 📊 Outcome: With Rivery, Snowflake and Sigma Computing Armstrong democratized data across their organization, automated data extraction, and enabled self-service analysis for 300+ users. Thank you, Armstrong, for partnering with Rivery to achieve these incredible results! https://hubs.ly/Q02y84j_0

𝗧𝗵𝗲 𝗙𝗼𝘂𝗿 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗛𝗶𝘀𝘁𝗼𝗿𝗶𝗰𝗮𝗹 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 In my previous post, I noted that historical analytics range from trivial, to complex enough to demand a data warehouse and copious caffeine. Given my compunction for categorisation, let me break down the four distinct types so I can dive deeper in future posts.   𝗦𝘁𝗮𝘁𝗶𝗰 𝗵𝗶𝘀𝘁𝗼𝗿𝘆 (𝘁𝗵𝗲 𝗲𝗮𝘀𝘆 𝗼𝗻𝗲) Think sales over time. Each transaction is recorded once and never changes. Dead simple to analyse because you're just examining final values. The historical "view" comes virtually free - it's just the data as initially recorded. Nothing to see here folks, move along.   𝗦𝗼𝘂𝗿𝗰𝗲-𝘁𝗿𝗮𝗰𝗸𝗲𝗱 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 This is where things get interesting. Consider accounts receivable, as we discussed previously. The outstanding invoice amount evolves as customers (hopefully) pay. Your ERP system tracks every transaction, so the history exists, but the calculations get spicy! Unless you enjoy watching queries run until the heat death of the universe, you'll want a proper data warehouse.   𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲-𝘁𝗿𝗮𝗰𝗸𝗲𝗱 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 Here's where a data warehouse truly earns its keep. Data changes state but the source system only knows about 𝙣𝙤𝙬. A classic example: support tickets moving through their lifecycle. Want to know how many tickets were active last Tuesday? Your source system shrugs, but your data warehouse can track that history. This is when data warehouse automation becomes invaluable. Note: The ticket 𝘢𝘨𝘪𝘯𝘨 chart above illustrates this concept in action (with concocted data ;)). 𝗦𝗼𝘂𝗿𝗰𝗲-𝗳𝗮𝗶𝗹! Finally, there's data that simply vanishes from the source. Cloud platforms are notorious for this - consider Azure providing only 6 months of cost data. Physical manufacturing equipment also often imposes data limits. If you need longer history, you better be capturing and archiving it yourself.   There you have it - four distinct flavours of historical analytics. Each one needing a different approach but all of them achievable with the right data warehouse automation tool.   Think I missed any? Hit me up in the comments.  

In a world that's becoming more and more about data, data governance is now starting to take center stage. I know many #Manufacturing companies who struggle with legacy data, new ways of working, and siloed data channels. Check out this recording (and transcript) of a dive into these issues and their solution with our trusted partner, STAEDEAN.

Once the master data is printed on labels or communicated to external parties, refactoring and communicating why it’s needed becomes extremely challenging. https://lnkd.in/gWqgwMu7

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