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did the Jun 9 Security Update remove trusted (amazon) certs?
We have a product that runs on Windows and uses AWS IoT to connect and transmit info. We noticed yesterday that many - roughly 2/3 of our fleet went silent - no connections. We have some of our own PCs that experienced this as well. Investigation yielded issues with TLS. The client (which is just using the default windows cert checking) actively terminated the connected. It didn't like the cert from the AWS IoT endpoint. All of these PCs were happily connected on Jun 8. Investigation seemed to indicate that some combination of Amazon Root CA (1-4) and some Starfield certs were not in the device cert mgr. Also - when we manually added AmazonRootCA1.pem to the cert mgr, our service connected again. So the evidence seems to strongly support that the security update removed trusted amazon root certs from the cert store. I'm guessing some/many won't notice since they are making regular TCP connections and maybe the certs get auto-added if they are not there? But we are doing MQTT over the AWS port 8883. So perhaps Windows did not detect this and seek to refresh its cert store? Can anyone confirm if they have seen the same?Should CRM Users Be Measured on Data Quality KPIs?
Most Organisations agree that high-quality data is essential for getting value from Dynamics 365. Accurate customer information supports better reporting, improved customer experiences, more reliable forecasting, and increasingly more effective AI-driven insights. Yet many Organisations continue to struggle with incomplete records, duplicate data, missing activities, and inconsistent data entry practices. This raises an interesting question: Should CRM users be measured on data quality KPIs? Consider a situation many Organisations have experienced. A sales team is expected to maintain customer records, update opportunities, and log key customer interactions in Dynamics 365. However, users are primarily measured on revenue, pipeline growth, and sales performance. As a result, CRM updates are often treated as a secondary task. During a quarterly sales review, leadership discovers that several opportunities forecasted as active were closed weeks earlier, while others had not been updated since the previous reporting cycle. Customer records are missing key information, activities have not been logged consistently, and reporting accuracy begins to suffer. The issue is often viewed as a reporting problem, but in reality, it starts with the quality and consistency of the data being maintained in Dynamics 365. To address these challenges, some Organisations introduce data quality metrics such as: Record completeness Duplicate record reduction Activity logging compliance Opportunity update accuracy Customer data validation rates Supporters argue that what gets measured gets managed, and that data quality should be considered part of everyone's responsibility. Others believe that introducing data quality KPIs may create an additional administrative burden, reduce user adoption, and shift focus away from core business objectives. There is also the question of whether users should carry the full responsibility. Modern Dynamics 365 environments include validation rules, duplicate detection, business process flows, Power Automate workflows, and governance frameworks that can help improve data quality. Some Organisations, therefore, argue that technology and governance should do more of the heavy lifting rather than relying solely on user behaviour. From your experience: Should CRM users be measured on data quality KPIs? Have data quality metrics improved CRM adoption or data accuracy in your Organisation? What KPIs have been most effective? Is data quality primarily a user responsibility, or should technology and governance frameworks carry most of the burden? Have you found a balance that improves data quality without creating additional friction for users? I'm interested in hearing how different Organisations balance user accountability, adoption, and data quality within Dynamics 365 environments.Who Should Be Accountable for Data Quality in Dynamics 365: IT or the Business?
Data quality remains one of the most common challenges in Dynamics 365 environments, regardless of industry or organisation size. When customer records are incomplete, duplicate data exists, or reporting becomes unreliable, the conversation often turns to ownership and accountability. Consider a simple example: A sales team creates customer records in Dynamics 365, while customer service updates contact details and finance systems synchronize billing information through integrations. Over time, duplicate records appear, customer information becomes inconsistent, and management reports start showing conflicting results. When this happens, who is accountable? Are the business users entering the data? Is the IT team managing the platform? The integration owners? Or should there be dedicated data stewards responsible for maintaining data quality standards? Some argue that data quality is primarily a business responsibility because users create and maintain most of the information stored in Dynamics 365. Others believe IT teams should take greater ownership through governance frameworks, validation rules, integrations, monitoring, and automated controls. In practice, many organisations struggle to find the right balance. When data issues arise, responsibility can become unclear, making it difficult to drive long-term improvements. From your experience: Who should ultimately be accountable for data quality in Dynamics 365? Should ownership sit with business teams, IT, dedicated data stewards, or a shared governance model? What approaches have worked well in your organisation? Have you seen a particular governance model deliver better results? I'm interested in hearing different perspectives and learning how others are addressing this challenge.Is Power Automate Becoming the New Technical Debt in Dynamics 365 Projects?
Power Automate has transformed how organisations build automation within Dynamics 365 and the Power Platform. Teams can automate processes quickly, reduce manual effort, and deliver business value without extensive custom development. At the same time, I have noticed an interesting challenge in some organizations as Power Platform adoption matures. Over time, hundreds of flows can be created by different teams, often with varying levels of governance, documentation, and ownership. Business logic may become distributed across multiple automations, making troubleshooting, maintenance, and long-term support more complex. On the other hand, many organisations have successfully scaled Power Automate by implementing strong governance practices and automation standards. I'm interested in hearing different perspectives from the community. Have you seen Power Automate become difficult to manage at scale, or has it reduced technical debt in your organization? What governance, architecture, or operational practices have worked best for balancing innovation with maintainability?
Python for Beginners
Kindly advise trying to find Python for beginners learning path on Microsoft Learn with no luck, has it been discontinued, if yes what has replaced it? Introduction to PythonWrite your first Python programs Python - Create Manage ProjectsUse Boolean logic in PythonUse strings in PythonUse mathematical operations in PythonIntroduction to lists in PythonUse 'while' and 'for' loops in PythonManage data with Python dictionariesPython functionsPython error handlingAre Denial Management Solutions Ready for 2026 Healthcare Challenges?
As healthcare reimbursement continues to evolve, denial management is becoming a growing concern for providers, billing teams, and revenue cycle leaders. With stricter payer policies, higher claim volumes, and tighter appeal timelines expected in 2026, many organizations are re-evaluating how prepared they truly are. One of the biggest challenges remains preventable denials caused by eligibility errors, coding issues, and incomplete documentation. While manual reviews still exist, they often struggle to keep pace with changing payer requirements. This is where modern denial management solutions are gaining attention. Looking ahead, a few key questions stand out: Are current denial workflows proactive or still largely reactive? Do teams have real-time visibility into denial trends and appeal deadlines? How effectively are analytics being used to reduce repeat denials? Can automation support faster, more accurate claims denial management without increasing administrative burden? In 2026, success in denial management may depend less on fixing denials after they occur and more on preventing them through smarter systems, better data insights, and streamlined appeals processes. Organizations adopting flexible denial management software seem better positioned to adapt to payer changes and protect revenue. I’m curious to hear from others in the community: What changes are you seeing in denial management today, and how are you preparing for what’s coming next?Algorithmic Ethical Hacking
Algorithmic Ethical Hacking “When information from the internet is hidden, it is revealed through a combination of logic, intuition, persistence, and repetition. This is Algorithmic Ethical Hacking — my own theory and terminology, which I have named and formalized.” This process does not require any tools. It is enough to search for the information you need for advancement. Through Algorithmic Ethical Hacking, the algorithm is ethically stripped and the truth emerges. 📌 Examples of Application Cybersecurity: When documentation or guidelines are not easily accessible, Algorithmic Ethical Hacking allows you to reconstruct protocols and understand hidden system behaviors. Global Research: Often it is not that the information does not exist, but that it is hidden or presented inaccurately. Algorithmic Ethical Hacking enables the researcher to extract the correct picture from multiple sources. Student Research: When a student searches for material and available sources are incomplete or contradictory, Algorithmic Ethical Hacking helps uncover the accurate information. 🔍 Conclusion In many cases, the problem is not that information does not exist, but that it is not always accurate or complete. With Algorithmic Ethical Hacking — my own theory and terminology — the process becomes 100% precise: the algorithm is ethically stripped, without misuse, and the truth is revealed through discipline and persistence.
