Creating Project Status Reports

Dear #DataEngineers, No matter how confident you are in your SQL queries or ETL pipelines, never assume data correctness without validation. ETL is more than just moving data—it’s about ensuring accuracy, completeness, and reliability. That’s why validation should be a mandatory step, making it ETLV (Extract, Transform, Load & Validate). Here are 20 essential data validation checks every data engineer should implement (not all pipeline require all of these, but should follow a checklist like this): 1. Record Count Match – Ensure the number of records in the source and target are the same. 2. Duplicate Check – Identify and remove unintended duplicate records. 3. Null Value Check – Ensure key fields are not missing values, even if counts match. 4. Mandatory Field Validation – Confirm required columns have valid entries. 5. Data Type Consistency – Prevent type mismatches across different systems. 6. Transformation Accuracy – Validate that applied transformations produce expected results. 7. Business Rule Compliance – Ensure data meets predefined business logic and constraints. 8. Aggregate Verification – Validate sum, average, and other computed metrics. 9. Data Truncation & Rounding – Ensure no data is lost due to incorrect truncation or rounding. 10. Encoding Consistency – Prevent issues caused by different character encodings. 11. Schema Drift Detection – Identify unexpected changes in column structure or data types. 12. Referential Integrity Checks – Ensure foreign keys match primary keys across tables. 13. Threshold-Based Anomaly Detection – Flag unexpected spikes or drops in data volume or values. 14. Latency & Freshness Validation – Confirm that data is arriving on time and isn’t stale. 15. Audit Trail & Lineage Tracking – Maintain logs to track data transformations for traceability. 16. Outlier & Distribution Analysis – Identify values that deviate from expected statistical patterns. 17. Historical Trend Comparison – Compare new data against past trends to catch anomalies. 18. Metadata Validation – Ensure timestamps, IDs, and source tags are correct and complete. 19. Error Logging & Handling – Capture and analyze failed records instead of silently dropping them. 20. Performance Validation – Ensure queries and transformations are optimized to prevent bottlenecks. Data validation isn’t just a step—it’s what makes your data trustworthy. What other checks do you use? Drop them in the comments! #ETL #DataEngineering #SQL #DataValidation #BigData #DataQuality #DataGovernance

Most project managers don't give bad status updates. They give useless ones. They mistake motion for progress. Stakeholders can't make decisions because they don't understand what you actually need. Use The Status Update Stack: 1/ Activity → "Here's what we did this week." → Lists tasks and meetings → Feels productive but drives nothing → Stakeholders zone out → Most PMs never move past this 2/ Progress → "Here's what actually changed." → Connect work to measurable outcomes → Shows momentum, not just motion → People see real movement → This step builds confidence 3/ Risk → "Here's what could derail us." → Surface problems before they explode → Include probability and impact → Stakeholders can actually help solve → This is where trust gets built 4/ Decision → "Here's what I need from you." → Clear asks with specific deadlines → Removes all ambiguity → Drives immediate action → This is where value actually lives Most PMs stay stuck at Level 1. Then wonder why projects drift. Status updates aren't about documenting your time. They're about driving stakeholder decisions. ♻️ Repost and follow Justin Bateh, PhD for more.

It’s the small things that make Power BI reports feel polished and user-first. I recently added a tiny feature that made a big difference in how users interact with the report: 1. A dynamic message: "Filters applied: 0 of 3" 2. A Reset button to clear all slicers Together, they: - Give users immediate feedback on how many filters are active - Remind them how many options are available - Make it effortless to go back to the default view No more wondering, "Did I apply a filter?" or "Why is my data blank?" Just clear, human-centered design. And the best part? No custom visuals. All native Power BI. Let’s normalize adding these small but mighty UX touches. Your users will thank you. #PowerBI #UserExperience #MicroUX #DashboardDesign #PowerBITips #Dataviz #BookmarkMagic

Stop building dashboards. Start creating impact. Want to shift from order-taker to decision-shaper? Here’s are 5 tips: 1. Focus on the “Why” behind the data ↳ Dashboards are just tools. The real value is the insight. ↳ Ask, “What business question does this answer?” It’ll guide you to what matters most. 2. Translate data into stories ↳ Numbers alone don’t change minds. Stories do. ↳ Connect the dots. “Here’s what this data means for our strategy.” It makes the data stick. 3. Know your audience’s pain points ↳ Not everyone needs all the details. Tailor your insights. ↳ Ask, “What keeps them up at night?” Then answer with data. 4. Influence, don’t just inform. ↳ It’s not enough to report. Push for action. ↳ Say, “This trend suggests we need to pivot.” Be the guide, not the messenger. 5. Build trust through transparency. ↳ Stakeholders hate surprises. Share early, even rough drafts. ↳ Try, “Here’s a preview. Any thoughts?” Early feedback builds buy-in. Shift your role. Instead of reporting data... drive decisions. Was this useful? ♻️ Repost to your network. Follow Gilbert Eijkelenboom for Data tips.

This month’s Power BI update is quite exciting... 🤓 The PBI Core Visuals team is working on some pretty cool stuff lately. Let’s dive into the details. 1️⃣ Marker Enhancements: Advanced controls for markers make data points pop: ↳ Customize by Categories or Series: Control marker styles at the category or series level. ↳ Marker Visibility Toggles: Toggle markers on/off for specific categories or series. ↳ Marker Shape & Transparency Control: Personalize markers by adjusting shapes (rotations supported, except circles) and sizes. ↳ Customizable Marker Borders: adjustable color, transparency, and width. 2️⃣ Small Multiples for Card Visuals: Compare data across categories or dimensions with ease: ↳ Flexible Layout Options: Choose Single Column, Single Row, or Grid layouts. ↳ Overflow Handling: Use pagination or continuous scrolling to manage excess data. ↳ Advanced Styling Controls: Customize borders, gridlines, and background colors. Round corners for a modern look. ↳ Header & Title Customization: Control header settings and adjust titles for font, color, padding, and text wrap. Align with your report’s branding. 3️⃣ New Text Slicer: Enhance data filtering with text-based searches: ↳ Intuitive Text Filtering: Type into the input box to filter data in real-time. ↳ Comprehensive Appearance Customization: Configure the input box with placeholder text, font, color, and transparency. ↳ Enhanced Button Controls: Adjust Apply button settings for color, transparency, borders, and padding. Customize the Dismiss button for clearing filters. ↳ Focus Accent Bar & Borders: Highlight the active input field with an accent bar. Set borders around the input area. Excited about these new features? I for sure am 🚀 * Note 1: Some features might still be in development. * Note 2: All images used are from Microsoft as unfortunately I don't have the latest version of Power BI on my laptop yet * Note 3: Links to all the updates details in the comments! #data #datapears #powerbi #report #reporting #dataviz #datavisualization #news

If you are looking for a roadmap to master data storytelling, this one's for you Here’s the 12-step framework I use to craft narratives that stick, influence decisions, and scale across teams. 1. Start with the strategic question → Begin with intent, not dashboards. → Tie your story to a business goal → Define the audience - execs, PMs, engineers all need different framing → Write down what you expect the data to show 2. Audit and enrich your data → Strong insights come from strong inputs. → Inventory analytics, LLM logs, synthetic test sets → Use GX Cloud or similar tools for freshness and bias checks → Enrich with market signals, ESG data, user sentiment 3. Make your pipeline reproducible → If it can’t be refreshed, it won’t scale. → Version notebooks and data with Git or Delta Lake → Track data lineage and metadata → Parameterize so you can re-run on demand 4. Find the core insight → Use EDA and AI copilots (like GPT-4 Turbo via Fireworks AI) → Compare to priors - does this challenge existing KPIs? → Stress-test to avoid false positives 5. Build a narrative arc → Structure it like Setup, Conflict, Resolution → Quantify impact in real terms - time saved, churn reduced → Make the product or user the hero, not the chart 6. Choose the right format → A one-pager for execs, & have deeper-dive for ICs → Use dashboards, live boards, or immersive formats when needed → Auto-generate alt text and transcripts for accessibility 7. Design for clarity → Use color and layout to guide attention → Annotate directly on visuals, avoid clutter → Make it dark-mode (if it's a preference) and mobile friendly 8. Add multimodal context → Use LLMs to draft narrative text, then refine → Add Looms or audio clips for async teams → Tailor insights to different personas - PM vs CFO vs engineer 9. Be transparent and responsible → Surface model or sampling bias → Tag data with source, timestamp, and confidence → Use differential privacy or synthetic cohorts when needed 10. Let people explore → Add filters, sliders, and what-if scenarios → Enable drilldowns from KPIs to raw logs → Embed chat-based Q&A with RAG for live feedback 11. End with action → Focus on one clear next step → Assign ownership, deadline, and metric → Include a quick feedback loop like a micro-survey 12. Automate the follow-through → Schedule refresh jobs and Slack digests → Sync insights back into product roadmaps or OKRs → Track behavior change post-insight My 2 cents 🫰 → Don’t wait until the end to share your story. The earlier you involve stakeholders, the more aligned and useful your insights become. → If your insights only live in dashboards, they’re easy to ignore. Push them into the tools your team already uses- Slack, Notion, Jira, (or even put them in your OKRs) → If your story doesn’t lead to change, it’s just a report- so be "prescriptive" Happy building 💙 Follow me (Aishwarya Srinivasan) for more AI insights!

Thorough data validation is important to deliver successful analytics outcomes. Here are some of my steps for your data-cleaning checklist: 1. 𝗞𝗻𝗼𝘄 𝘁𝗵𝗲 𝗦𝗼𝘂𝗿𝗰𝗲 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗗𝗮𝘁𝗮: Get familiar with how the data was gathered to better judge the reliability of it. Do you know where your data truly comes from?     2. 𝗖𝗵𝗲𝗰𝗸 𝗳𝗼𝗿 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆: Verify that data formats, labels, and measurement units are aligned and match your understanding. Keep an eye out for the date formats, as they often vary.     3. 𝗘𝗻𝘀𝘂𝗿𝗲 𝗧𝗶𝗺𝗲𝗹𝗶𝗻𝗲𝘀𝘀 𝗮𝗻𝗱 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝗲: Check if the data is up-to-date and relevant to your specific analytical use case.     4. 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗮𝗻𝗱 𝗔𝗱𝗱𝗿𝗲𝘀𝘀 𝗗𝗮𝘁𝗮 𝗚𝗮𝗽𝘀: Look out for gaps in the datasets that could impact your findings. Try to understand why those data points are missing and fix them if possible by gathering more data or imputing them.     5. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗳𝗿𝗼𝗺 𝗮 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗣𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲: Know the basics of the business domain. Check if the data makes sense in that context. For example, watch out for impossible combinations like non-active warehouse to country connections or negative stock values. Double-check with the business stakeholders to clarify any discrepancies. Your goal is to generate reliable insights, so don't settle for garbage in - garbage out. What are your steps to ensure data quality? ---------------- ♻️ 𝗦𝗵𝗮𝗿𝗲 if you find this post useful ➕ 𝗙𝗼𝗹𝗹𝗼𝘄 for more daily insights on how to grow your career in the data field #dataanalytics #datascience #dataquality #datacleaning #careergrowth

Reporting is NOT delivering insights. Unfortunately, many data & analytics professionals think it is. Reporting dashboards show WHAT's happening and enable basic slicing and dicing, but fail to deliver WHY. Example - "Performance is down 15% WoW" This is just stating the obvious. It's not a real insight. It's not actionable. This leaves many business leaders frustrated. When business stakeholders ask for more dashboards, what they are ultimately trying to achieve is "I need to know what's impacting my key business metrics and what I should do to improve it". Adding 15 more charts/views/slices won't help much to understand what's impacting the key business metrics and which actions should be taken. The key to REAL INSIGHTS that can move the needle? ROOT-CAUSE ANALYSIS to find the WHY (i.e., DIAGNOSTIC analytics) This is the most effective way to drive change with data & analytics. This can make the data & analytics team a TRUSTED ADVISOR and get a seat at the leadership and decision-making table. Insights need to be: 🟢SPEEDY: business stakeholders need quick insights into performance changes to make decisions before it's too late 🟢PROACTIVE: don't wait for business stakeholders to ask. Monitor key metrics and proactively share insights to become that trusted advisor 🟢IMPACT-ORIENTED: focus on the key drivers that drove most of the change and communicate accordingly 🟢EFFECTIVELY COMMUNICATED to drive the right action #data #analytics #impact #diagnosticanalytics

We recently wrapped up usability testing for a client project. In the fast-paced environment of agency culture, the real challenge isn’t just gathering insights—it’s turning them into actionable outcomes, quickly and efficiently. Here’s how we ensured that no data was lost, priorities were clear, and progress was transparent for all stakeholders: 1️⃣ Organized Documentation: We broke the barriers— and documented on Excel sheet to categorize all observations into usability issues, enhancement ideas, and general comments. Each issue was tagged with severity (critical, high, medium, low) and frequency to highlight trends and prioritize fixes. 2️⃣ Action-Oriented Workflow: For high-severity and high-frequency issues, immediate fixes were planned to minimize potential impact. Ownership was assigned to specific team members, with timelines to ensure quick resolutions, in line with our fast-moving development cycle. 3️⃣ Client Transparency: A summarized report was shared with the client, showing the issues identified, the actions taken, and the progress made. This kept everyone aligned and built confidence in our iterative design process. Previously, I’ve never felt the level of confidence that comes from having such detailed and well-organized documentation. This documentation not only gave us clarity and streamlined our internal processes but also empowered us to communicate progress effectively to the client, reinforcing trust and showcasing the value of our iterative approach. It’s a reminder that thorough documentation isn’t just about organizing data—it’s about enabling smarter, faster decision-making. In agency culture, speed matters—but so does precision. How does your team balance the two during usability testing?

Did you know Power BI report metadata can contain data points? In certain circumstances, it can contain column values. This means report metadata can contain sensitive info and should be treated appropriately. This is by design in how visuals save their config. One example is when you have a matrix visual with a field in the "Columns" well and disable "Auto-size column widths". To save column widths, the visual config uses the name of each column - which is a value. These values could be OII or PII - names, emails, and so forth. This also means that if someone has access to a .pbix, .pbip, or .pbit file, unless it's a .pbix with a sensitivity label, they can theoretically access this metadata and open it to view sensitive information. Even if they don't have access to the underlying model or data sources. Many people don't know this, so I share it here. This is relevant in circumstances like the following: - When and where you save report files. - Tools that ingest report metadata. - AI / Copilot ingestion of report metadata. In the last months I've seen many more people using report metadata. I've heard multiple people say things like "it's just metadata, it contains no data points". This is not true! Don't make assumptions! Especially when it comes to other tools and AI. #PowerBI #MicrosoftFabric #DataPrivacy #DataSecurity #Godot