Data Import and Export Capabilities

📤 Designing Better Bulk Import UX (+ Figma Prototypes). With practical guidelines to design better bulk operations in complex digital products ↓ 🤔 Bulk operations are heavily underused in most products. ✅ Bulk means running repetitive tasks in large batches. ✅ It enables fast iterations/updates across large data sets. ✅ It also reduces time on task, human errors, manual actions. ✅ Many flavors: bulk import, edit, disable, move, update, archive. ✅ For import, first define core bulk attributes for mapping. ✅ For each attribute, we define a data profile, optional/required. ✅ Then, we study file types, encodings, max. file sizes, metadata. ✅ Usually we support Excel, CSV, copy/paste, preview, auto-fill. ✅ We write error messages for broken files, unmatched columns. ✅ Design 5 stages: pre-import, file upload, mapping, repair, import. 🚦 Pre-import: set up guardrails, give an example/Excel template. ✂️ File upload: support drag-and-drop, keyboard-only, copy/paste. 🚎 Mapping: map header columns, check values, add inline editing. 🧰 Repair: flag duplicates, allow users to see only rows with errors. 🗂️ Import: show a summary, support tags, labels or categories. Probably the most challenging part about bulk operations is helping users fix issues — be it columns mismatch, missing data, contradictory details — *within* the bulk feature itself. Many interfaces simply dismiss the file with generic and technical jargon — labelling it as "corrupt", "invalid", "incompatible" or "wrong". We can prepare users about the expectations on data format, sizes and attributes ahead of time. We could provide a sample template to consult or use. Map column headers but then allow users to fix errors. Look ahead: there might be duplicate records already, so before overwriting them, flag them and ask users to confirm how they'd like to manage them. And: once an import has completed, usually it’s very difficult to reverse the process — so help users categorize, tag or add some extra metadata (e.g. source) to tell the "new" data apart from the "old". You might not be able to reverse the process, but you can help users navigate around it if needed. Useful resources: Bulk Upload Feature UX, by Livinda Christy Monica https://lnkd.in/exMu32zd Making A Bulk of Payouts, by Divya Kotian Article: https://lnkd.in/ejjkkWTK Figma prototype: https://lnkd.in/e7UF2dVP Bulk Import UX For CSV & XLXS, by Yooshan Chandran Article: https://lnkd.in/ed5p8kbp Figma prototype: https://lnkd.in/eQnJwZBT Building a Seamless CSV Import, by Flatfile https://lnkd.in/e7BW6-gR #ux #design

5 ways to effectively export data from any finance or ERP system  (Ranked by cost effectiveness…) 1. Direct Database Queries Direct queries are great If you’re good with data, and know how your system’ structured. - Common mechanisms include SQL queries or ODBC - Great for specific, custom data exports. - Some technical skills required. [Note - You can modify your database with SQL queries, so be careful!] PRO Tip: Use the ‘From other sources’ facility in the Excel data tab to setup an ODBC connection. 2. Built-in Export Features The simplest and least technical option if your reporting is ad-hoc. - Common mechanisms include ‘Export to Excel’ or ‘Download’. - Great if the data’s already roughly in the format you want. - Accessible for all skill levels. PRO Tip: Data not quite in the format you want when exported? Use a simple bit of VBA or Python code, and set it to run as soon as the file’s downloaded to a folder. 3. Third-Party Data Connectors For the automation enthusiasts who prefer a no-code approach. - Platforms include Zapier, Make & Power Automate. - Great if your exporting data on a schedule. - Some technical skills required. [Note - Not every system has a connector (especially the more complex ones), so make sure you ‘try before you buy] 4. Marketplace Apps Sometimes it’s easier to use something pre-built. - Mechanisms include Power BI or Tableau connectors. - Great if you’re already using a BI or Data tool. - No technical skills required. PRO Tip: Try searching for ‘[Your system] + marketplace’. Xero, Quickbooks, Intacct, Dynamics (Appsource) all have them. 5. API Integration For when you need something totally bespoke. - Common API toolkits include .net, node.js, and PHP. - Great if you have a lot of complexity. - You’ll need a developer. PRO Tip: Have a look at the API documentation for your system. No point commissioning a developer if it doesn’t give access to the datapoints you want. Data export doesn’t have to be a mystery. There’s a solution for all sizes and budgets. BUT, cost effective does not equal time efficient. Balance your spend against time efficiencies wisely. P.S - Which of these are you using right now? Type 1-5. ------ Liked this? If you're a finance pro looking to win back your time, develop your tech skills, and stay ahead of the game. Subscribe to 'Framework Friday' at www.techforfinance.com

💡 Easily Load Data into Your Scratch Org or Sandbox I came across a helpful plugin by Fabien Taillon that simplifies data export/import for Salesforce: the Texei plugin. While the Salesforce CLI offers force:data:tree:export/import commands, they can struggle with org-specific IDs. The Texei plugin provides an easier solution. I tested it with a simple command, and it worked seamlessly: sfdx texei:data:export --objects Account,Contact,MyCustomObject__c --outputdir ./data --targetusername MyOrg It exports data in JSON format, and since it’s open source, you can modify it to use other formats if needed. It can even handle more complex exports/imports via a configuration file. For more details, read the articles (part 1 and 2) here: Easily Load Data into Your Scratch Org or Sandbox https://lnkd.in/gUKww5zV #Salesforce #DataManagement #SalesforceCLI #OpenSource

"We need to move Account data to the new Salesforce org." "Great! What about the related Contacts, Opportunities, Cases, and Campaign Members?" "Uh... we'll figure that out later." The Data Relationship Problem. Most data migration approaches: -Export Accounts to CSV -Import Accounts to new org -Realize all relationships are broken -Spend days and sometimes weeks manually rebuilding connections The uncomfortable truth: Salesforce Data Loader breaks relationships. Always. What enterprise teams know: Relational data migration isn't optional, it's essential. How Metazoa Snapshot's approach works differently: Snapshot preserves all relationships automatically: -Select parent objects with filtering -Choose related child objects -All internal relationships are maintained during migration - Hundreds of thousands of records moved efficiently via Bulk API Real example: Move Accounts? Snapshot automatically brings: -Related Contacts with proper Account references -Opportunities with correct Account/Contact links -Cases with maintained relationships -Campaign Members with preserved connections The counterintuitive insight: The more related data you have, the MORE you need automated relationship preservation. What Constant Contact discovered: Their custom object replication rules had to be manually generated for each org. Using Snapshot, they now automate rule generation, test in sandbox, and migrate to production seamlessly. Why this matters: While you're manually rebuilding broken relationships, enterprise teams are moving connected datasets that work immediately after migration. What's the most complex data relationship you've ever had to rebuild after a migration? Let's compare data migration war stories.