Writing Annual Reports

Peer review at this journal took 38 working days. One change cut it to a fraction of that, and the reviews got better. Not because of AI. Not because of some clever algorithm. Because someone finally treated reviewers like professionals doing skilled work. Our colleagues at The journal Biology Open (Daniel Gorelick, Alejandra Clark) just published results from scaling up something they call "Fast & Fair" peer review. The idea is almost embarrassingly simple: → Contract reviewers in advance → Ask them to respond to an invite within 1 working day → Pay them per manuscript, but only if they deliver on time AND the review is actually useful That's it. No payment for just saying yes. No payment for a late or lazy report. Compensation tied to performance, the way it works in basically every other expert profession. The results, comparing paid review against their conventional process: 📉 Time to first decision: 37.7 days → 5.5 days ✅ Invitations accepted: 23% → 67% 🔕 Reviewers who ghosted: 39% → 13% 📝 Completion once accepted: 87% → 98% Now here's the part that should end the debate. The usual counter-argument is that paying reviewers, and rushing them, produces sloppy science. It didn't. Editors rated the paid reviews slightly higher in quality, with fewer useless reports. Acceptance rates barely moved (59% vs 61%), so the bar didn't drop either. Faster. Higher quality. Same editorial rigor. We have spent years pretending that the slowness of peer review is some noble feature of careful science. It isn't. Most of the delay has nothing to do with the thinking. It's the months spent chasing people who never reply. Researchers donate billions of pounds in unpaid labor to a publishing system that turns around and charges them to read it. Then we act surprised when nobody answers the review invite. You get the behavior you pay for. Or in this case, the behavior you've refused to pay for. Pay reviewers. Set deadlines. Hold the quality bar. It works. Link to article: https://lnkd.in/etyPgXPY

📚 Key Test Documentation Types 1. Test Plan Purpose: Outlines the overall strategy and scope of testing. Includes: Objectives Scope (in-scope and out-of-scope) Resources (testers, tools) Test environment Deliverables Risk and mitigation plan Example: "Regression testing will be performed on modules A and B by using manual TC" 2. Test Strategy Purpose: High-level document describing the overall test approach. Includes: Testing types (manual, automation, performance) Tools and technologies Entry/Exit criteria Defect management process 3. Test Scenario Purpose: Describes a high-level idea of what to test. Example: "Verify that a registered user can log in successfully." 4. Test Case Purpose: Detailed instructions for executing a test. Includes: Test Case ID Description Preconditions Test Steps Expected Results Actual Results Status (Pass/Fail) 5. Traceability Matrix (RTM) Purpose: Ensures every requirement is covered by test cases. Format: Requirement ID Requirement Description Test Case IDs REQ_001 Login functionality TC_001, TC_002 6. Test Data Purpose: Input data used for executing test cases. Example: Username: testuser, Password: Password123 7. Test Summary Report Purpose: Summary of all testing activities and outcomes. Includes: Total test cases executed Passed/Failed count Defects raised/resolved Testing coverage Final recommendation (Go/No-Go) 8. Defect/Bug Report Purpose: Details of defects found during testing. Includes: Bug ID Summary Severity / Priority Steps to Reproduce Status (Open, In Progress, Closed) Screenshots (optional) Here's a set of downloadable, editable templates for essential software testing documentation. These are useful for manual QA, automation testers, or even team leads preparing structured reports. 📄 1. Test Plan Template File Type: Excel / Word Key Sections: Project Overview Test Objectives Scope (In/Out) Resources & Roles Test Environment Schedule & Milestones Risks & Mitigation Entry/Exit Criteria 🔗 Download Test Plan Template (Google Docs) 📄 2. Test Case Template File Type: Excel Columns Included: Test Case ID Module Name Description Preconditions Test Steps Expected Result Actual Result Status (Pass/Fail) Comments 🔗 Download Test Case Template (Google Sheets) 📄 3. Requirement Traceability Matrix (RTM) File Type: Excel Key Fields: Requirement ID Requirement Description Test Case ID Status (Covered/Not Covered) 🔗 Download RTM Template (Google Sheets) 📄 4. Bug Report Template File Type: Excel Columns: Bug ID Summary Severity Priority Steps to Reproduce Actual vs. Expected Result Status Reported By 🔗 Download Bug Report Template (Google Sheets) 📄 5. Test Summary Report File Type: Word or Excel Includes: Project Name Total Test Cases Execution Status (Pass/Fail) Bug Summary Test Coverage Final Remarks / Sign-off 🔗 Download Test Summary Template (Google Docs) #QA

Your research findings are useless if they don't drive decisions. After watching countless brilliant insights disappear into the void, I developed 5 practical templates I use to transform research into action: 1. Decision-Driven Journey Map Standard journey maps look nice but often collect dust. My Decision-Driven Journey Map directly connects user pain points to specific product decisions with clear ownership. Key components: - User journey stages with actions - Pain points with severity ratings (1-5) - Required product decisions for each pain - Decision owner assignment - Implementation timeline This structure creates immediate accountability and turns abstract user problems into concrete action items. 2. Stakeholder Belief Audit Workshop Many product decisions happen based on untested assumptions. This workshop template helps you document and systematically test stakeholder beliefs about users. The four-step process: - Document stakeholder beliefs + confidence level - Prioritize which beliefs to test (impact vs. confidence) - Select appropriate testing methods - Create an action plan with owners and timelines When stakeholders participate in this process, they're far more likely to act on the results. 3. Insight-Action Workshop Guide Research without decisions is just expensive trivia. This workshop template provides a structured 90-minute framework to turn insights into product decisions. Workshop flow: - Research recap (15min) - Insight mapping (15min) - Decision matrix (15min) - Action planning (30min) - Wrap-up and commitments (15min) The decision matrix helps prioritize actions based on user value and implementation effort, ensuring resources are allocated effectively. 4. Five-Minute Video Insights Stakeholders rarely read full research reports. These bite-sized video templates drive decisions better than documents by making insights impossible to ignore. Video structure: - 30 sec: Key finding - 3 min: Supporting user clips - 1 min: Implications - 30 sec: Recommended next steps Pro tip: Create a library of these videos organized by product area for easy reference during planning sessions. 5. Progressive Disclosure Testing Protocol Standard usability testing tries to cover too much. This protocol focuses on how users process information over time to reveal deeper UX issues. Testing phases: - First 5-second impression - Initial scanning behavior - First meaningful action - Information discovery pattern - Task completion approach This approach reveals how users actually build mental models of your product, leading to more impactful interface decisions. Stop letting your hard-earned research insights collect dust. I’m dropping the first 3 templates below, & I’d love to hear which decision-making hurdle is currently blocking your research from making an impact! (The data in the templates is just an example, let me know in the comments or message me if you’d like the blank versions).

What do reviewers notice that authors miss? During my recent experience as an external reviewer for two different international publishers: -Elsevier  -Virtus Interpress I reviewed two academic papers published in: -Journal of Accounting Education -Journal of Governance and Regulation Despite the differences in journals and contexts, the review comments revolved around almost the same core themes, Points I am sharing here with any researcher aiming to publish in a high-ranked journal: 1️⃣ The title is not a marketing façade The title must accurately reflect the core substance of the research. In one of the papers, the work was rich and important, yet the title was misaligned with the content, this is a fundamental concern for any reviewer, regardless of the paper’s overall quality. 2️⃣ The research gap… or nothing Without a clear research gap, there is no real scientific contribution. A gap is not a rephrasing of what already exists, but a logical justification of what has not yet been addressed. 3️⃣ Methodology is not a formal procedure Methodology must be: -Aligned with the research question -Justifiable -Replicable 4️⃣ Statistical analysis: quality over quantity The issue is not “how many statistical tests were used,” but rather: -Is the analysis robust?  -Has it undergone sensitivity testing? In my review, I focused particularly on: Sensitivity analysis The quality of results, not merely their statistical significance This aligns with what I teach and deliver in Systematic Review & Meta-Analysis courses, where we use critical appraisal tools such as JBI to assess methodological quality not just form. 5️⃣ References are not academic decoration Do not include references that are not actually used in the paper. A reviewer immediately notices an inflated reference list with no analytical function. 6️⃣ Artificial intelligence: an enhancement tool, not a substitute for the researcher I explicitly stated to the publisher that AI was used only to improve academic writing quality. AI can: -Improve phrasing -Enhance clarity But it is not the author, nor the source of ideas or methodology. Conclusion Peer review is not about fault-finding; it is a test of the quality of research thinking, from the title to the final reference. If you are a researcher, always ask yourself: Would my paper convince an editor… before it convinces me?

Your feelings do not make a decent review decision. Throughout my career, I have read reviewer comments like "the framing is not robust," or "the insight is not needed," offered without a shred of evidence. Subjectivity in review is unavoidable. The absence of evidence is not. We ask authors to substantiate every claim. We should hold ourselves to the same standard. Three commitments are overdue: 1. Evidence. If you say a paper does not contribute, name the work it duplicates. If the framing is weak, say which premise fails. If the insight is not needed, show what already covers it. 2. Paradigm respect. Judge the paper on its own terms. A qualitative study should not be measured by statistical generalisability. A quantitative study should not be asked for interpretive depth it never promised. Reviewing is not paradigm enforcement. 3. Honesty about our limits. If a paper sits outside your expertise, decline. If you can speak to some sections but not others, tell the editor exactly which. Editors need calibrated reviews, not confident ones. Reviewers too often hold authority without accountability, and the cost falls hardest on early career and Global South scholars. Peer review deserves the rigour, and the humility, we demand of the work it judges. Who is with me?

Most FP&A teams still build their P&L templates the same way they did ten years ago: Copying, pasting, and dragging formulas down a 200-line sheet. But if your model still depends on manual rows, you’re missing one of Excel’s biggest upgrades — dynamic arrays + structured tables. Here’s how to build your own mini P&L template that updates automatically as new accounts or cost centers appear. Step 1: Use a structured data table Your raw data might look like this: Account Department Month Amount Convert this into a Table (Ctrl + T). Tables auto-expand when new rows are added — no range adjustments needed. Step 2: Create a dynamic account list Use: =UNIQUE(Table1[Account]) This gives you a real-time list of accounts — instantly updating as your chart of accounts changes. Step 3: Build a mini P&L view Across months, use: =SUMIFS(Table1[Amount], Table1[Account], A2, Table1[Month], B$1) Wrap it in BYCOL or LET if you’re on Microsoft 365 for cleaner, more scalable logic. Key advantages: • No manual refresh or new row insertions • Fully scalable — data grows, model adjusts • Easy to audit — readable formulas and clear structure FP&A insight: Once you build one of these, you’ll never go back. It’s the difference between “reporting” and “designing a system that reports for you.” 💭 Question for you: What part of your current P&L build is the most time-consuming — mapping accounts, managing versions, or updating ranges? 📈 Promo: If you’re working on improving your FP&A model design, I share Excel workflows built specifically for finance pros — from dynamic P&Ls to driver-based forecasts. Follow me for weekly, practical breakdowns you can plug directly into your workbooks.

Let’s face it: lengthy UX research reports often go unread. We need to present findings in a way that’s effective, engaging, and persuasive, making it easier for stakeholders to understand and act on our insights. Here’s how I structure my reports to ensure they make an impact: 1. Start with the essentials: ↳ Outline the background and goals. ↳ Reflect on the current state, highlight who conducted the research, and define the scope — not just for the present activities but also any future research. 2. Document your methods: ↳ Explain how we uncovered the insights and why we chose specific methods. ↳ Build trust by showcasing the statistical significance of findings, including sample sizes, confidence levels, and margin of error. 3. Visualize the findings: ↳ Instead of dense text, use video snippets, verbatim quotes, and photos to bring the user’s journey to life. ↳ Existing artefacts like journey maps help visualize user struggles and successes. 4. Focus on key insights: ↳ Highlight 3-5 striking, easy-to-remember insights. ↳ Connect these findings to risks, costs, and missed opportunities, showing stakeholders the value and business impact. 5. Provide actionable steps: ↳ Wrap up with a clear overview of the next steps, short-term objectives, and long-term strategy. ↳ Link insights back into the product development process ↳ Where needed, mention further research opportunities. A concise, visually engaging report builds trust, showcases the validity of our research, and demonstrates the value it brings. The goal is to make it easy to read, easy to follow, and ultimately, useful. What do you think? How do you structure your reports to make them more impactful?

Recently, I was tasked with creating a Power BI report template that the business could use for all their reporting needs. The goal was to provide clear, intuitive data storytelling while enabling stakeholders to compare performance across time and easily see which filters were applied to their analysis. Business Need 📌 Year-over-Year analysis for performance comparison 📌 Easy-to-understand data storytelling 📌 Clear visibility of applied filters 📌 Intuitive and user-friendly UI My Approach Using a sample sales dataset from Kaggle, I built the report with these core principles in mind, focusing on dynamic visuals and an interactive user experience. Key Features ✅ Dynamic Year-over-Year Analysis – Custom labels enhance data storytelling, making trends and comparisons more meaningful. ✅ Interactive Filtering – A pop-up filter panel visually displays active filters, improving usability and navigation. ✅ Custom SVG Sparkline Charts – Embedded within KPI visuals to provide quick trend insights at a glance. ✅ Bookmark Navigation – Enables users to seamlessly switch between different chart views for deeper data exploration. The Result The final report empowers business users with an intuitive, interactive experience, making data-driven decisions easier and more insightful. By combining dynamic analysis, interactive filtering, and effective storytelling, this report serves as a scalable template for all future reporting needs. Have you worked on similar Power BI solutions? I'd love to hear about your experience! 🔍📊

The AICPA released two Peer Reviewer Alerts, demonstrating their commitment to improving SOC 2 quality. These are good changes and send the right signals to the market. In addition, the AICPA's Peer Review Board's Enhanced Oversight Program is currently increasing scrutiny over peer reviews of CPA firms with SOC 2 practices. In an enhanced oversight, a subject matter expert reviews the work performed by a peer reviewer to assess the appropriateness of the peer reviewer’s conclusion on a specific engagement.  👉 February 2026 Alert (Summary): -Recent peer reviews show that some engagement partners aren't sufficiently involved throughout SOC engagements, risking non-compliance with attestation and quality management standards. Peer Reviewers should critically assess whether partner involvement is thoroughly and appropriately documented to ensure professional standards are met. -To evaluate partner involvement, peer reviewers should check documentation and interview the partner to ensure oversight was continuous, not just at the end. Reviewers must use professional judgment to assess if the partner's hours and their percentage of total engagement hours were reasonable given the project's complexity. Additionally, if an Engagement Quality Review (EQR) was conducted, verify that the Engagement Quality Reviewer remained independent and did not sign the report on behalf of the firm. 👉 May 2026 Alert (Summary) -Recent findings highlight a risk where CPA firms leveraging third-party SOC 2 technology platforms may rely too heavily on those tools, failing to comply with professional standards. Peer reviewers should look for indicators that SOC 2 engagements are not tailored to the client, responsive to risks, or supported by sufficient evidence. -If timelines seem unreasonable, reports lack customization, or other risk indicators are present, the Peer Reviewer should determine if there is an elevated risk that the SOC 2 engagements do not comply with professional standards. -If an elevated risk is identified, the review team must develop a response to address it. For example, they may need to select and compare roughly five SOC 2 reports, ideally from different partners and prior years, and review targeted areas requiring engagement-specific judgment. Through this process, reviewers should check for signs of a generic approach, such as identical risk assessments, control designs with no linkage to client-specific risks, identical sample sizes regardless of population sizes, or identical testing procedures that ignore variations in client size, industry, or complexity. -If reports are substantially identical, it is likely that the SOC 2 engagements are not designed to address the facts and circumstances at that particular entity and, thus, are non-conforming. -Review teams should consult with their administering entity or the AICPA when determining if this risk exists for a particular peer review client and when determining an appropriate response.

On peer review as a virtuous learning cycle. One of the hardest lessons I learned as an early-career academic was the art of virtuous reviewing. Early in my career, I treated reviewing as pointing out flaws - really, as gatekeeping. Then. I attended a session held by Detmar Straub at The PhD Project. At the time, Det was not yet the EIC of MIS Quarterly. But. He foreshadowed what he espoused as EIC. Good peer review is a virtuous cycle. What does that mean? Good academic peer review should do more than identify flaws. It should evoke double-loop learning. Det argued that means the process should not only help authors fix problems in a paper. It should also help them rethink assumptions, sharpen theory, improve methods, and clarify the contribution. But. He placed the onus for virtuous cycles not only on editors and reviewers. Det asserted to the audience, that authors have an obligation to learn from review. Not every comment will be right. Not every suggestion should be followed. But every serious reviewer comment deserves reflection. From him, I learned that the author’s job is to ask: What is this feedback revealing about how the work is being read, understood, and evaluated? To make that possible, Det asserted that reviewers have an obligation to offer constructive feedback. The goal is not to prove the author wrong. The goal is to help the work become stronger. Good reviews explain what is unclear, why it matters, and what kind of revision would improve the paper. This assertion changed my view of reviewing. I pivoted from gatekeeping to building. While sometimes negative, I tried to make sure that every review included content that authors could use to improve their work. Det also changed my understanding of editors. He explained that editors have a responsibility to protect and encourage this learning cycle. They set the tone. They should guide reviewers toward developmental feedback and help authors see the path forward, especially when reviews are mixed, difficult, or blunt. When you step back and look at the big picture, what Detmar taught in the early 2000s remains relevant today. A bad peer review means you submitted an idea and were told it was wrong. Good peer review means you submitted an idea, can clearly see what we learned from the review package, and can see how I can improve my work and how I can possibly move the conversation forward. That is when peer review becomes a virtuous cycle. And that. Is when you as an author and a reviewer truly become part of the editorial process. Best of luck! #academicjourney