How do we configure backup and restore procedures for critical data in Data Cloud?

To bring Apache Superset to the WCAG 2.2 AA compliance, you will want to consider both automated accessibility evaluation and manual usability testing because data visualization tools are not exactly easy to accessibility-wise. Begin with automatic scans with axe-core, Lighthouse, or Pa11y to identify low-hanging bugs such as the absence of ARIA labels, color contrast violations, and inadequate heading structures. Dynamically rendered components are also common in Superset dashboards and filters, and thus you will need to run these tests on load and once interacted with (the application of filters or a chart type change).
Then, keyboard navigation tests are required - all controls (filters, dropdowns, chart selectors, date pickers) are to be accessible and controllable only using the keyboard, with visible focus indicators. WCAG 2.2 puts an extra emphasis on the look of focus and drag gestures and therefore they should test whether the user can accomplish all the interactions without having to drag or hover.
And you will have to check if screen readers are compatible with such a tool as NVDA (Windows) or VoiceOver (Mac). Ensure that charts include descriptions that can be accessed (through ARIA or in embedded SVGs), and that tabular data are exportable to a readable format to allow assistive technology users. To create color and contrast, ensure that your themes are at the 4.5:1 minimum ratio and that color is not the sole form of passing information e.g. use colored lines with shape or text labels in legends.
Lastly, perform a manual review among test users who depend on assistive technology and also test against WCAG 2.2 AA success criteria, including recent capabilities such as 2.4.11 ( Focus Not Obscured ) and 3.2.6 ( Consistent Help ). The ideal practice is that you can add accessibility tests to your CI pipeline - meaning that any new Superset customization or any new Superset plugin will be automatically tested. It is not a single audit but a long-term practice to make your analytics platform actually inclusive.

The best way to reliably deploy these notifications and related features across engaged users in regions around the world is to create separate Metabase notification templates (for Slack or email alerts) for each supported language, one in English, one in Spanish, etc. and have each user routed to that version based on their resident language. You can keep track of that language in the identity provider (such as Okta, or Azure AD) you already have in use to manage the user context for your dashboard. You can maintain language preferences in your internal user DB, or even as group tags in Metabase if your teams are distributed in regions. From there, wherever possible just use your delivery option and send the appropriate localized message (SMTP, slack webhook, or API automation).

To track license utilization and right-size costs in Power BI, you can use the built-in Usage Metrics Reports, the Microsoft 365 Usage Analytics reports, and create a custom report from the Admin Monitoring workspace. These reports help identify which users are actively using Power BI and which licenses might be underutilized.

You should monitor KPIs that demonstrate quicker operations, fewer manual interventions, and consistent compliance performance in order to demonstrate that an NSDL upgrade (such as a new API, compliance rule, or transaction processing system) increased team productivity without raising risk. When it comes to productivity, if the upgrade improved your workflows, you should see a decrease in average processing time per transaction, manual reconciliation effort, and turnaround time for account or document adjustments. You can also track automation coverage (like percentage of NSDL-related operations now handled via scripts or APIs instead of manual uploads) and support ticket volume for data mismatches or delays — a downward trend means smoother processes.
On the risk side, focus on transaction error rate, compliance breach incidents, data rejection rates, and system uptime. If the update didn’t introduce new inconsistencies, downtime, or compliance issues, your risk posture is intact. You can even monitor audit exception counts and rollback frequency to confirm operational stability.
In short, if your team is processing NSDL tasks faster, making fewer manual corrections, and seeing no uptick in errors or compliance red flags, that’s solid evidence the update boosted productivity without adding risk — a clear win for both efficiency and reliability.

You should monitor KPIs that show system stability and deployment efficiency in order to demonstrate that a FluxCD update increased team productivity without creating new hazards. Measures like as deployment frequency, mean time to deploy (MTTD), and human intervention rate should all increase if the FluxCD update improved the efficiency of GitOps procedures. DevOps teams and developers experienced less friction as a result of the update when there were fewer instances of configuration drift and time spent troubleshooting sync issues.
For the risk side, focus on failed deployments, rollback frequency, cluster health metrics, and time to recovery (MTTR). Also monitor Flux reconciliation latency (how quickly the cluster reaches the desired state) and Git-to-cluster consistency rates. A stable update should maintain or improve these without increasing error counts or drift.
If, after the update, your team can deploy more often with fewer manual fixes, and your cluster metrics show steady or improved uptime and compliance, that’s solid evidence the FluxCD update actually boosted productivity without compromising reliability. In short, faster, cleaner deploys and stable environments = a successful update.