Jorge Mauricio

How data people at different levels communicate: Junior Data Person: - Built 30 dashboards in one year, driving operational transparency. - Wrote 500 lines of SQL code for data cleaning, improving data accuracy by 15%. - Shared reports with 10 departments, impacting 100+ stakeholders. - Automated 4 data sources, saving 10+ hours of manual work weekly. Medior Data Person: - Developed integrated, user-centric dashboards, boosting decision-making efficiency by 20%. - Collaborated with key stakeholders to build cross-functional reports for better alignment. - Mentored 2 interns and 1 junior engineer, enhancing team capabilities. - Automated workflows, saving 30+ hours of manual reporting weekly. Senior Data Person: - Designed a scalable data architecture, reducing query execution time by 40%. - Mentored 3 medior and junior team members, improving team productivity by 25%. - Presented actionable insights to C-suite, directly contributing to a 15% increase in revenue. - Led end-to-end data strategy for a major project, integrating 10+ complex data sources seamlessly. Manager (Data Leader): - Defined and implemented the data strategy for the organisation, aligning it with business goals. - Spearheaded the transition to a modern data stack, reducing operational costs by 30%. - Fostered a data-driven culture, increasing data utilisation across teams by 50%. - Managed a diverse team of analysts and engineers, delivering projects 20% ahead of deadlines. #data #analytics #dataengineering #ai #ML

290

10 comentarios

Your data analysts aren't just "report people." They're strategic partners being underutilized. Want to get the most value from them? 1. Include them in planning meetings BEFORE decisions are made, not after 2. Share the full context behind data requests, not just the request itself 3. Ask them "what should we be measuring?" instead of just "can you measure this?" 4. Give them time to explore the data before demanding conclusions 5. Listen when they push back on a metric or approach Often, the difference between a team that uses data effectively and one that doesn't isn't the quality of their analysts. It's how they treat their analysts.

337

31 comentarios

Looking to take your data visualization to the next level? The ggdist package extends ggplot2 with specialized tools for visualizing distributions, uncertainties, and comparing data groups. It’s a powerful add-on to your ggplot2 toolkit, especially useful for communicating complex insights with ease. Key benefits of using ggdist: ✔️ Enhanced Distribution Visualizations: Create rich visuals like eye plots, slab intervals, and gradient intervals, making it easier to interpret complex data distributions. ✔️ Detailed Uncertainty Displays: Add flexible uncertainty intervals to your charts, helping to convey the full range of your data without clutter. ✔️ Improved Comparison Options: Effectively compare groups or distributions in a visual style that remains clear and interpretable. ✔️ Seamless Integration with ggplot2: Works smoothly with ggplot2 syntax, so you can incorporate it into your existing workflow without extra setup. With ggdist, you can go beyond the basics of ggplot2 to build plots that convey more information without losing clarity. This package offers specific solutions for data sets with high levels of uncertainty or multiple comparison groups, ideal for fields like statistics, finance, or any data-driven research. The visualization shown here is an example from the ggdist package website, highlighting its unique capabilities: https://lnkd.in/eWfqDqBP If you want to explore this and many other related topics, check out my online course, Data Visualization in R Using ggplot2 & Friends. Learn more: https://lnkd.in/ehRrzdJ7 #datasciencecourse #ggplot2 #dataviz #package #datastructure #analytics #statisticsclass #rstudio #tidyverse #datavisualization

59

Sometimes, as a Product Manager, I hate to wait for data reports and just make a call. Without data, however, it’s a pure guess. What to do? Here’s the brutal truth: Most teams either go full enterprise-mode with data (weeks of BI backlog, dashboards overloaded with vanity metrics) or skip it altogether and rely on gut feeling. Both are bad choices. The real question isn’t whether you should have proper data analytics for your product. It’s how to do it smart, fast, and within your constraints, while still making data-informed decisions that move the needle. This is where the sponsor of this post, Fiverr, can make a real difference. How Fiverr helps you bring analytics into your process, even when you're tight on time or budget? 𝗔𝗰𝗰𝗲𝘀𝘀 𝘁𝗼 𝗱𝗮𝘁𝗮 𝘁𝗮𝗹𝗲𝗻𝘁 Choose from analysts, data scientists, BI specialists, and SQL experts who can jump into your project with speed and clarity. 𝗙𝗮𝘀𝘁 𝘁𝘂𝗿𝗻𝗮𝗿𝗼𝘂𝗻𝗱 Get clean dashboards, reports, or experiments analyzed within days. It’s perfect for sprint deadlines or urgent product questions. 𝗣𝗿𝗼𝗼𝗳 𝗼𝗳 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 Browse portfolios, read reviews, and see examples of real analytics work before you commit. You’re not guessing; you’re choosing based on evidence. 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝗲 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗱𝗼𝗺𝗮𝗶𝗻 Work with data experts who already understand SaaS metrics, e-commerce funnels, mobile engagement, or marketplace liquidity. 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 Need a single query? A dashboard refresh? A full product analytics setup? Fiverr lets you scale data effort up or down as needed. 𝗔𝗳𝗳𝗼𝗿𝗱𝗮𝗯𝗹𝗲 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 You don’t need a full-time data scientist on payroll to make informed decisions. Get agency-level analytics at a fraction of the cost. 𝗧𝗶𝗽𝘀 𝗳𝗼𝗿 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗼𝘂𝘁 𝗼𝗳 𝗙𝗶𝘃𝗲𝗿𝗿 𝗱𝗮𝘁𝗮 𝘄𝗼𝗿𝗸: • Look for analysts with domain-specific experience (e.g., churn modeling in subscription products) • Test collaboration with a small request before handing over larger projects • Write a clear brief: what’s the question, what’s the dataset, what decision depends on it • Use Fiverr’s tools to iterate quickly and refine your insights Skipping analytics doesn’t make your team faster. It just pushes problems downstream: to strategy, growth, and your bottom line. You may get away with it for minor tweaks, but quality decisions will always require real data. Of course, you may decide to wait for your internal analytics team or use a self-service tool. However, when you work on a side project, a new MVP, or have an impossible deadline, Fiverr’s data experts will be your best bet! Have you seen a project suffer due to missing or poor analytics? Drop your story in the comments! #productmanagement  #data  #ad P.S. Thinking of giving Fiverr a go? Visit www.fiverr.com and use code BART10 for 10% off your first order.

95

18 comentarios

𝐃𝐚𝐭𝐚 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 + 𝐒𝐐𝐋 = 𝐓𝐫𝐮𝐬𝐭𝐞𝐝 𝐃𝐚𝐭𝐚 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬⁣ ⁣ ⁣ Modern organizations run on data. But without governance and security, even the most powerful data warehouses can become unreliable, non-compliant, or vulnerable.⁣ ⁣ ⁣ That’s why I created this comprehensive guide:⁣ “𝐃𝐚𝐭𝐚 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞𝐬 𝐰𝐢𝐭𝐡 𝐒𝐐𝐋 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧”⁣ ⁣ ⁣ - Understand key pillars of data governance — from metadata management to master data⁣ - Implement robust SQL-based data quality checks⁣ - Learn how to apply RBAC, RLS, column-level security & encryption in SQL⁣ - Explore real-world use cases from banking, healthcare, and e-commerce⁣ - Discover best practices to ensure security, compliance, and data trustworthiness⁣ ⁣ ⁣ Whether you’re building a BI solution, managing sensitive data, or preparing for compliance audits — this resource gives you the technical insights you need to govern and secure your data with confidence.⁣ ⁣ ⁣ Dive into the full guide and take control of your SQL data warehouse.⁣ ⁣ ⁣ #DataGovernance #DataSecurity #SQLforData #DataWarehouse #DataQuality #MetadataManagement #RBAC #RLS #DataCompliance #SQLServer #BI #DataManagement #SQLSecurity #DataProtection #AuditLogging #SQLQuery #EnterpriseData #SQLIntegration #InformationSecurity #DataPrivacy #ETL #DataOps #CloudDataWarehouse #DataEngineering #BusinessIntelligence #AnalyticsEngineering

110

48 comentarios

At work, the product team had launched new features. We as BI analytics had to closely monitor numbers, adoption and metrics. Three weeks into it, we noticed mixed adoption and occasional dips in engagement. Our dashboard would tell us the numbers, but not why. So this was not enough. 𝐓𝐡𝐞 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞: Well, the UAT (user acceptance testing) hadn’t caught many of the real-world problems customers would face, even if we are launching it to 10% of 400 million user base we have. I needed a way to get closer to the ground truth, get something good to take this forward, I wanted to above and beyond for these new launches. 𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐈 𝐭𝐡𝐨𝐮𝐠𝐡𝐭: Instead of waiting for structured surveys, or ratings, I pulled a mix of sample Play Store reviews and what people complaint in the tickets they created. This was a huge pile of unstructed data, no joke. And you cant really look at them directly and get something. I set up a framework to: -Tag complaints by product/feature. -Spot recurring keywords (e.g., “crash,” “payment stuck,” “too many steps”). (used few python libraries I knew) -Filter specifically for new product launches where early feedback mattered most. Surprisingly, some reviews were not just complaints but customer-suggested improvements. For example, one review about the wallet feature exposed an onboarding glitch, other had trouble creating tickets(api failures and since this was small, nobody really spotted) while another pointed to confusing eligibility on how to register for Rupay-CC that matched a drop we were seeing in funnel conversion. Now, let's talk about the solution I worked around: - I compiled these insights into a tracker that tied review themes back to dips in numbers. Shared with product teams, it gave them a direct line from customer voice → product fixes. This was not done at once, took some time, mails, and the product loved these solutions and how I stepped out to help. 𝐓𝐡𝐢𝐬 𝐰𝐚𝐬 𝐭𝐡𝐞 𝐨𝐮𝐭𝐜𝐨𝐦𝐞: This surfaced over 10+ actionable product issues in a short span. Not all were taken up but it gathered a lot of attention, and fixing them not only stabilized metrics but also created goodwill. Because customers obviously feel heard when the issues they flagged got resolved/lead to innovation in the product. We're talking about an app that had 400 million + user base. 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠𝐬 𝐟𝐨𝐫 𝐚𝐥𝐥 𝐝𝐚𝐭𝐚 𝐚𝐬𝐩𝐢𝐫𝐚𝐧𝐭𝐬 & 𝐩𝐞𝐨𝐩𝐥𝐞 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐢𝐧𝐠:- As a business analyst (or later as a senior data professional), your role can go far beyond just analyzing and creating dashboards. Look at how proactively I stepped in, saw a problem, created an untraditional approach (hunted for unstructured data), created a method to systematize it, and linked it to business metrics and voila, happy customers! *plays Wake me up by The Weeknd* in bg

131

7 comentarios

One of the simplest Tableau API wins? Automating extract refreshes. You don’t need to open Desktop or schedule everything manually. With a few lines of Python + tableauserverclient you can: ➜ Trigger refreshes programmatically ➜ Chain them after ETL jobs ➜ Add retry logic when failures happen Here’s the pattern: import tableauserverclient as TSC tableau_auth = TSC.PersonalAccessTokenAuth("TOKEN_NAME", "TOKEN_VALUE", "SITENAME") server = TSC.Server("[HTTPS]://SERVER_URL", use_server_version=True) server.auth.sign_in(tableau_auth) # get the data source item to update datasource = server.datasources.get_by_id("1a2a3b4b-5c6c-7d8d-9e0e-1f2f3a4a5b6b") # call the refresh method with the data source item refreshed_datasource = server.datasources.refresh(datasource) print("Refresh triggered successfully.") It’s a small script, but it frees you from babysitting extract schedules. And once you get comfortable with this, you can scale into full automation workflows. #Tableau #TableauOps #Automation #APIs #DataOps

81

15 comentarios