Data-Driven Decision Making

𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗺𝗶𝘀𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗼𝗼𝗱 𝘁𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲. Because most people explain it from the inside out: policies, councils, standards, stewardship. But the business does not buy any of that. The business buys outcomes: → trustworthy KPIs → vendor and partner data you can actually use → faster financial close → fewer reporting escalations → smoother M&A integration → AI you can deploy without creating risk debt Most AI programs fail for boring reasons: nobody owns the data, quality is unknown, access is messy, accountability is missing. 𝗦𝗼 𝗹𝗲𝘁’𝘀 𝘀𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗶𝘁. 𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗳𝗼𝘂𝗿 𝘁𝗵𝗶𝗻𝗴𝘀: → ownership → quality → access → accountability 𝗔𝗻𝗱 𝗶𝘁 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘃𝗲𝗿𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝗶𝗻 𝟰 𝗹𝗮𝘆𝗲𝗿𝘀: 1. Data Products (what the business consumes) → a named dataset with an owner and SLA → clear definitions + metric logic → documented inputs/outputs and intended use → discoverable in a catalog → versioned so changes don’t break reporting 2. Data Management (how products stay reliable) → quality rules + monitoring (freshness, completeness, accuracy) → lineage (where it came from, where it’s used) → master/reference data alignment → metadata management (business + technical) → access controls and retention rules 3. Data Governance (who decides, who is accountable) → data ownership model (domain owners, stewards) → decision rights: who can change KPI definitions, thresholds, and sources → issue management: triage, escalation paths, resolution SLAs → policy enforcement: what’s mandatory vs optional → risk and compliance alignment (auditability, approvals) 4. Data Operating Model (how you scale across the enterprise) → domain-based setup (data mesh or not, but clear domains) → operating cadence: weekly issue review, monthly KPI governance, quarterly standards → stewardship at scale (roles, capacity, incentives) → cross-domain decision-making for shared metrics → enablement: templates, playbooks, tooling support If you want to start fast: Pick the 10 metrics that run the business. Assign an owner. Define decision rights + escalation. Then build the data products around them. ↓ 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗮𝗵𝗲𝗮𝗱 𝗮𝘀 𝗔𝗜 𝗿𝗲𝘀𝗵𝗮𝗽𝗲𝘀 𝘄𝗼𝗿𝗸 𝗮𝗻𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀, 𝘆𝗼𝘂 𝘄𝗶𝗹𝗹 𝗴𝗲𝘁 𝗮 𝗹𝗼𝘁 𝗼𝗳 𝘃𝗮𝗹𝘂𝗲 𝗳𝗿𝗼𝗺 𝗺𝘆 𝗳𝗿𝗲𝗲 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://lnkd.in/dbf74Y9E

(FMCG Blueprint) Sales forecasting in FMCG is both an art and a science. Let’s break it down using some basic matrices with a relatable example. Imagine we’re working for a brand that sells a spicy instant noodle, “HotBowl Ramen”. 1. Historical Sales Data (Your Crystal Ball) The first step is to look at past sales. For example: Month Sales (Units) January 10,000 February 11,000 March 10,500 April 12,000 Now, let’s assume you notice a 5% growth trend every month. For May, you might forecast: May Sales = April Sales * (1 + Growth Rate) = 12000 * (1 + 0.05) = 12600 Tip: This works well unless your sales suddenly nosedive because people discovered a new health fad: “No-Spice Life!” 2. Seasonality (Your FMCG Calendar) People eat more noodles in winter because “cozy food” vibes. Let’s adjust for seasonality: • Winter months: Add 10% • Summer months: Subtract 15% If your May forecast is 12,600 units but May is peak summer, adjust like this: Adjusted Sales = Base Sales * (1 - 0.15) = 12600*0.85 = 10,710 Reality Check: Your product is spicy. Some brave souls will still eat it even in May, sweating like they’re in a sauna. 3. Market Dynamics (Your Frenemy) Suppose your competitor, “MildBowl Ramen,” launches a huge promotion in May. You estimate a 10% impact on your sales. Final Sales Forecast = Adjusted Sales * (1 - 0.1) = 10710*0.9 = 9,639 4. Promotional Impact (Buy One, Cry One Free?) Now, your marketing team swoops in with a “Buy 1 Get 1 Free” promo. Promotions can boost sales by 20%, so: Promo Adjusted Sale = 9639*1.2 =11,566.8 Realistic Case Summary Step Forecasted Sales Base Sales Forecast 12,600 Seasonality Adjustment 10,710 Competitor Impact 9,639 Promo Impact 11,566 Funny Perspective Imagine your boss: • Before Forecast: “We need 15,000 units this month!” • After Your Analysis: “Hmm… okay, but let’s add another promo to reach 12,000 at least!” Your real hero? The customer who eats your spicy noodles even in May, sweating but happy. Moral: Forecasting is like cooking ramen—balance your ingredients (data) and adjust for taste (market trends)!

In last 15 years , I've seen database technologies evolve dramatically. Here's a comprehensive guide on when to use various database types: 1. Relational (MySQL, PostgreSQL):    - When: For structured data with complex queries and ACID compliance needs.    - Use case: Financial systems, ERP applications. 2. Key-Value (Redis, DynamoDB):    - When: Need ultra-fast, simple data lookups.    - Use case: Caching, session management, real-time leaderboards. 3. Document (MongoDB, CouchDB):    - When: Handling semi-structured data in JSON-like formats.    - Use case: Content management systems, catalogs, user profiles. 4. Graph (Neo4j, ArangoDB):    - When: Data has complex relationships and interconnections.    - Use case: Social networks, recommendation engines, fraud detection. 5. Wide-Column (Cassandra, HBase):    - When: Dealing with large-scale, high-write-throughput scenarios.    - Use case: IoT sensor data, time-series for large systems. 6. In-Memory (Redis, Memcached):    - When: Need microsecond response times and can trade durability for speed.    - Use case: Real-time analytics, caching layers, message queues. 7. Time-Series (InfluxDB, TimescaleDB):    - When: Handling time-stamped or sequential data efficiently.    - Use case: Monitoring systems, financial trading platforms, IoT data analysis. 8. Object-Oriented (db4o, ObjectDB):    - When: Data model closely mirrors object-oriented programming structures.    - Use case: CAD/CAM systems, scientific simulations. 9. Text-Search (Elasticsearch, Solr):    - When: Full-text search and complex text-based queries are primary needs.    - Use case: Search engines, log analysis, content discovery platforms. 10. Spatial (PostGIS, SpatiaLite):    - When: Working with geographic data and location-based services.    - Use case: GIS applications, location-based recommendation systems. 11. Blob (Amazon S3, Azure Blob Storage):    - When: Storing and managing large binary objects like media files.    - Use case: Content delivery networks, backup systems, data lakes. 12. Ledger (Hyperledger Fabric, Amazon QLDB):    - When: Immutability and audit trails are crucial.    - Use case: Financial records, supply chain tracking, digital identity systems. 13. Hierarchical (IBM IMS, Windows Registry):    - When: Data naturally fits into a tree-like structure.    - Use case: File systems, organization charts, XML databases. 14. Vector (Singlestore, Chroma):    - When: Dealing with high-dimensional vector data and similarity searches.    - Use case: Machine learning models, recommendation systems, image recognition. 15. Embedded (SQLite, Berkeley DB):    - When: Need local data storage within applications, especially mobile or IoT.    - Use case: Mobile apps, edge computing devices, local caches. Pro Tip: Modern applications often benefit from a multi-database approach. Don't hesitate to combine different types to optimize for various data patterns and access needs.

Over the past 10+ years, I’ve had the opportunity to author or contribute to over 100 #datagovernance strategies and frameworks across all kinds of industries and organizations. Every one of them had its own challenges, but I started to notice something: there’s actually a consistent way to approach #data governance that seems to work as a starting point, no matter the region or the sector. I’ve put that into a single framework I now reuse and adapt again and again. Why does it matter? Getting this framework in place early is one of the most important things you can do. It helps people understand what data governance is (and what it isn’t), sets clear expectations, and makes it way easier to drive adoption across teams. A well-structured framework provides a simple, repeatable visual that you can use over and over again to explain data governance and how you plan to implement it across the organization. You’ll find the visual attached. I broke it down into five core components: 🔹 #Strategy – This is the foundation. It defines why data governance matters in your org and what you’re trying to achieve. Without it, governance will be or become reactive and fragmented. 🔹 #Capability areas – These are the core disciplines like policies & standards, data quality, metadata, architecture, and more. They serve as the building blocks of governance, making sure that all the essential topics are covered in a clear and structured way. 🔹 #Implementation – This one is a bit unique because most high-level frameworks leave it out. It’s where things actually come to life. It’s about defining who’s doing what (roles) and where they’re doing it (domains), so governance is actually embedded in the business, not just talked about. This is where your key levers of adoption sit. 🔹 #Technology enablement – The tools and platforms that bring governance to life. From catalogs to stewardship platforms, these help you scale governance across teams, systems, and geographies. 🔹 #Governance of governance – Sounds meta, but it’s essential. This is how you make sure the rest of the framework is actually covered and tracked — with the right coordination, forums, metrics, and accountability to keep things moving and keep each other honest. In next weeks, I’ll go a bit deeper into one or two of these. For the full article ➡️ https://lnkd.in/ek5Yue_H

If your SQL tables are messy, your analytics will always lie to you. Data cleaning is not optional, it is the foundation of trustworthy insights. Here’s a simple breakdown of 13 essential SQL techniques every data engineer and analyst should know: 1. Replace NULL with a Default Value Use COALESCE to safely fill missing values during queries. 2. Delete Rows with NULL Values Remove incomplete records when they can’t be repaired. 3. Convert Text to Lowercase Standardize fields like names and emails for clean comparisons. 4. Find Duplicate Rows Identify values that appear more than once using GROUP BY. 5. Delete Duplicate Rows (Keep One) Remove duplicates while preserving a single valid entry. 6. Remove Leading & Trailing Spaces Trim whitespace so joins and comparisons don’t break. 7. Split Full Name into First & Last Extract components using SUBSTRING functions (simple cases only). 8. Standardize Date Formats Convert inconsistent date strings into a unified format. 9. Eliminate Special Characters Strip symbols while keeping alphanumeric data clean. 10. Identify Outliers Spot values outside expected upper/lower thresholds. 11. Remove Outliers Delete invalid or extreme values when necessary. 12. Fix Typo or Incorrect Values Correct inconsistent categories to avoid fragmentation. 13. Standardize Phone Number Format Keep only digits for clean, uniform phone fields. Messy data leads to messy decisions. Small SQL cleanup steps like these dramatically improve model accuracy, dashboards, and business reporting.

Hey Salespeople: Here is a collection of current use cases for AI in sales & CS: ** GenAI in Sales ** --> Draft messaging for personalized email outreach --> Generate post-call summaries with action items; draft call follow ups --> Provide real-time, in-call guidance (case studies; objection handling; technical answers; competitive response) --> Auto-populate and clean up CRM --> Generate & update competitive battlecards --> Draft RFP responses --> Draft proposals & contracts --> Accelerate legal review & red-lining (incl. risk identification) --> Research accounts --> Research market trends --> Generate engagement triggers (press releases; job postings; industry news; social listening; etc.) --> Conduct role-play --> Enable continuous, customized learning --> Generate customized sales collateral --> Conduct win-loss analysis --> Automate outbound prospecting -->Automate inbound response --> Run product demos --> Coordinate & schedule meetings --> Handle initial customer inquiries (chatbot; voice-bot / avatar) --> Generate questions for deal reviews --> Draft account plans ** Predictive AI in Sales ** --> Score leads & contacts --> Score /segment accounts (new logo) --> Automate cross-sell & upsell recommendations --> Optimize pricing & discounting --> Surface deal gaps / identify at-risk prospects --> Optimize sales engagement cadences (touch type; frequency) --> Optimize territory building (account assignment) --> Streamline forecasting (incl. opportunity probabilities; stage; close date) --> Analyze AE performance --> Optimize sales process --> Optimize resource allocation (incl. capacity planning) --> Automate lead assignment --> A/B test sales messaging --> Priortize sales activities ** GenAI in CS ** --> Analyze customer sentiment --> Provide customer support (chatbot; voice-bot / avatar; email-bot) --> Draft proactive success messaging --> Update & expand knowledge base (incl. tutorials, guides, FAQs, etc.) --> Provide multilingual support --> Analyze customer feedback to inform product development, support, and success strategies --> Summarize customer meetings; draft follow-ups --> Develop customer training content and orchestrate customized training --> Provide real-time, in-call guidance to CSMs and support agents --> Create, distribute, and analyze customer surveys --> Update CRM with customer insights --> Generate personalized onboarding --> Automate customer success touch-points --> Generate customer QBR presentations --> Summarize lengthy or complex support tickets --> Create customer success plans --> Generate interactive troubleshooting guides --> Automate renewal reminders --> Analyze and action CSAT & NPS ** Predictive AI in CS ** --> Predict churn; score customer health; detect usage anomalies, decision maker turnover, etc. --> Analyze CSM and support agent performance --> Optimize CS and support resource allocation --> Prioritize support tickets --> Automate & optimize support ticket routing --> Monitor SLA compliance

🚨 My dashboard is useless when the dataset is incorrect !!!!! I once made it to the final round of an interview for a Data Analyst role. The task? Build a dashboard in Excel or Power BI based on the company’s requirements. At that time, I was super confident in my Power BI skills. I built a beautiful dashboard with almost every feature from the meme — colorful visuals, interactive filters, drill-down magic, even a clean schema from Power Query. But… I forgot one small thing: removing duplicates. And here’s the truth: no matter how fancy your dashboard looks, stakeholders won’t care if the data feeding it is wrong. If your dataset isn’t reliable, your insights are useless. That experience taught me an important lesson: before you think about making a “wow” dashboard, make sure the dataset is correct. Here are a few expanded steps I now follow to keep my data clean: 1. Scan and understand your dataset - Start with a data audit — what kind of dataset is it? Transactional, customer, operational, or something else? - Understand the logic of rows and columns: are they events, unique IDs, or aggregated summaries? - Profile the data by running quick checks: number of rows, missing values, duplicate counts, and overall structure. - Treat duplicates carefully. Sometimes they’re errors, but sometimes they’re valid (e.g., multiple transactions from the same customer on the same day). 2. Check column types and validate formats - Classify every column: categorical (e.g., product category), numeric (e.g., sales amount), or time/date (e.g., transaction date). - Verify consistency: Categorical fields → spelling consistency (“USA” vs. “U.S.” vs. “United States”). Numeric fields → make sure they’re truly numeric and not stored as text. Dates → standardize to one format (e.g., YYYY-MM-DD) across the dataset. - Review NULL or missing values. Decide whether to impute, drop, or escalate — but never ignore them. 3. Spot anomalies and outliers - Check for extreme values that don’t make sense (e.g., negative sales, a customer age of 400). - Use descriptive statistics (mean, median, standard deviation) to highlight outliers. - Always validate with the business context before removing or adjusting. Sometimes outliers are the most important story! 4. Document every step of cleaning - Keep a “data diary” — document what transformations you applied, what errors you found, and how you handled them. - Track unresolved issues. For example: “Column X had 125 NULL values — awaiting stakeholder input.” “Customer IDs had 15 duplicates — validated as system error, removed.” - This makes your process transparent, reproducible, and easy to explain in future audits. ✅ In short: data cleaning isn’t “extra work,” it’s the foundation of reliable dashboards. A fancy front end might impress once, but clean, trustworthy data keeps stakeholders coming back. ✨ let’s connect and share ideas! #DataAnalytics #PowerBI #DataCleaning #DataStorytelling

𝙅𝙪𝙨𝙩 𝘽𝙚𝙘𝙖𝙪𝙨𝙚 𝙄𝙩’𝙨 𝘿𝙖𝙩𝙖-𝘿𝙧𝙞𝙫𝙚𝙣 𝘿𝙤𝙚𝙨𝙣’𝙩 𝙈𝙚𝙖𝙣 𝙄𝙩’𝙨 𝘽𝙧𝙖𝙞𝙣-𝘿𝙚𝙖𝙙. 𝙒𝙝𝙮 𝘾𝙧𝙚𝙖𝙩𝙞𝙫𝙞𝙩𝙮 𝙈𝙖𝙩𝙩𝙚𝙧𝙨 𝙈𝙤𝙧𝙚 𝙏𝙝𝙖𝙣 𝙀𝙫𝙚𝙧 𝙞𝙣 𝙍𝙚𝙨𝙚𝙖𝙧𝙘𝙝 Someone once told me, "You're a Research Analyst? Must be all numbers and no creativity." I smiled because that couldn’t be further from the truth. In fact, the deeper I’ve gone into data, the more I’ve realized: Creativity isn’t optional — it’s what separates insights from noise. Here’s why creativity is critical in data-driven roles (and how I use it every day): 📍𝗗𝗮𝘁𝗮 𝗱𝗼𝗲𝘀𝗻’𝘁 𝘁𝗲𝗹𝗹 𝘀𝘁𝗼𝗿𝗶𝗲𝘀, 𝗮𝗻𝗮𝗹𝘆𝘀𝘁𝘀 𝗱𝗼 Anyone can pull numbers. But turning those numbers into insights that matter to stakeholders? That requires framing, context, and narrative — aka, creative thinking. 📍𝗥𝗶𝗴𝗶𝗱 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗹𝗶𝗺𝗶𝘁 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗯𝗹𝗲𝗺-𝘀𝗼𝗹𝘃𝗶𝗻𝗴 I’ve worked on projects where the real insight came not from the expected dashboard… …but from asking a different question, slicing the data differently, or visualizing it in a new way. 📊 Creativity is what turns static dashboards into strategic tools. 📍𝗣𝗮𝘁𝘁𝗲𝗿𝗻 𝗿𝗲𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻 = 𝗰𝗿𝗲𝗮𝘁𝗶𝘃𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 Spotting anomalies. Connecting dots across markets. Forecasting trends. These aren’t robotic tasks — they’re acts of creative judgment built on logic + intuition. 📍𝗘𝘃𝗲𝗻 𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗰𝗿𝗲𝗮𝘁𝗶𝘃𝗲 You can have the smartest dataset in the room but if your visuals confuse or bore people, your insight is lost. I use creativity to design dashboards, visuals, and reports that stick. A McKinsey study found that companies that prioritize creativity outperform peers in revenue growth by up to 2x. Even in research. Especially in research. So no, creativity doesn’t belong outside of data, it belongs at the center of how we interpret, communicate, and act on it. #CreativityAtWork #DataStorytelling #ResearchMindset #InsightsThatStick #AnalystLife

Your board wants 20% growth next year. Your team hears that number and their souls leave their bodies. 20%??? After they just killed themselves to hit this year's number? Todd Caponi , during this past week's Revenue Manager Lab at Sales Assembly, broke down a formula that should hopefully result in folks who are faced with goals like this exhaling a huge sigh of relief. The Results Formula: Revenue = (Qualified Opportunities × Deal Size × Win Rate) ÷ Cycle Length. Now here's where it gets interesting. Improve each metric by just 5%: - 5% more qualified opportunities (literally one more per rep). - 5% higher deal sizes ($2K on a $40K deal). - 5% better win rate (win one more deal you'd normally lose). - 5% faster cycle time (close 3 days faster). Result: 22% revenue growth. Don't believe Todd? Run it through whatever spreadsheet you want. Change the variables. Use different baseline numbers. ALWAYS comes out to 22%. Try 10% improvements across all four? You get 46% growth. But here's a mistake many leaders make: They pick one metric and try to double it. "We need MORE PIPELINE!" So they hire more SDRs, blast more emails, book more meetings. Pipeline goes up 50%. Revenue goes up 8%. Why? Because they flooded the zone with bullshit opportunities that destroyed their win rate and extended their cycle time. The magic is in the compound effect of tiny optimizations. A 5% improvement is nothing: - One better discovery call per month. - One less discount given. - One deal closed three days faster. - One bigger upsell identified. Stack those improvements. Compound them. Watch what happens. Your team doesn't need to raise their hand another foot higher. They need to raise it one inch higher in four places. Stop asking for heroics. Start asking for tweaks. The math is undefeated.