Data Analysis Skills Training

Our TAM is 20K. So why do we have 100K accounts in Salesforce? This is a common situation in B2B. But don’t blame RevOps. The traditional data providers trained everyone to “filter and flood.” Set some filters → Query their database → Pull some lists → Flood accounts into CRM → Manual review sessions → Repeat. Account selection is the starting point for ABM. But “filter and flood” is broken. Boolean filters don’t describe nuanced ICPs. One-time data dumps don’t enable continuous iteration. And don’t get me started on the data quality issues — rigid filters force you to treat estimations like hard facts. Here's another option. 1.) Define an ICP model — Analyze nuanced patterns in your best customers. — Weight different factors in a back tested model. — Go beyond size and industry, include clues about situations, problems, and priorities. — Use Keyplay AI or build DIY. Either way a dynamic model beats rigid filters. 2.) Use gradients (instead of drawing hard lines). — Transition from filters to scoring. — Decide on thresholds based on the specific segment, play, or campaign. — Have a dynamic score so that you can continually surface opportunities. 3.) Create a continuous selection process. — Make it a program, not a one-time project. — Set a cadence to find new accounts and try new ideas. Gamify it a bit. — Work in tandem with sales territory planning cycles to keep everyone coordinated. Account selection is not the most glamorous part of building ABM. But we all know it matters. Even our most brilliant account-based engagement program is doomed if we target the wrong accounts. So it’s worthwhile to get it right. #marketing #sales #ABM #ICP

Learning the tools is the 𝘦𝘢𝘴𝘺 part. Learning them in the 𝗿𝗶𝗴𝗵𝘁 𝗼𝗿𝗱𝗲𝗿? That’s where most aspiring analysts fall off. Here’s the progression I always recommend:  1. 𝗘𝘅𝗰𝗲𝗹: Learn to clean, transform, and analyze data fast.  2. 𝗦𝗤𝗟: Learn to query data like a pro.  3. 𝗕𝗜 𝘁𝗼𝗼𝗹𝘀 (𝗧𝗮𝗯𝗹𝗲𝗮𝘂 / 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜): Learn to visualize insights clearly.  4. 𝗣𝘆𝘁𝗵𝗼𝗻: Learn to automate, model, and scale. Each step prepares you for the next one. When you master Pivot Tables in Excel... ↳ You'll better understand GROUP BY in SQL. When you visualize in Tableau... ↳ You’ll appreciate how queries power dashboards. Python is amazing. But it’s the 𝘤𝘩𝘦𝘳𝘳𝘺 𝘰𝘯 𝘵𝘰𝘱, not the base layer. Master the first three. Then go Python. If you're just starting → copy this progression. It’ll save you months of confusion. Repost to help someone learning data right now ♻️ 

4 Steps to See What Your Averages Are Hiding (A simple example using basket size) Two stores report the same average basket size. One is stable. The other is losing margin every week. The number is identical. The business outcome is not. The failure isn’t in asking, “Are averages holding up?” It’s in missing the real question: “What drives that average, and is it sustainable?” If your dashboards show only averages, you cannot detect risk. Actionable Diagnostics: 1. Inspect the distribution, not the average • Plot basket sizes. • Healthy: consistent middle. • Risk: many small baskets + a few very large ones = Mid-tier customers are disappearing. 2. Separate price from behavior • Compare full-price vs discounted baskets. • If “strength” appears only under discounting = Buying volume, not generating demand. 3. Test what “typical” actually means • Compare mean vs median. • Example: median down 10%, mean flat = Top customers are masking base decline. 4. Link patterns to decisions • Hollow middle → bundling or assortment failure • Discount dependence → margin compression risk • Outlier-driven revenue → concentration risk in customer mix Rethink “average” before approving pricing, promotions, or assortment changes. This is a basic diagnostic and one of the most common points of failure in business analytics. ------------------ - Dr. Kruti Lehenbauer of Analytics TX, LLC  #DataScience, #PostitStatistics, #Economics, #AI P.S.: Move from “average” stories to defensible analytics: Professional Certificate in Business Analytics: Data-Informed Decision Making at https://lnkd.in/gfduYgg9

One thing I've noticed when working with clients and doing discovery calls is that a lot of companies are not using customer signals to be proactive instead of reactive. Being proactive rather than reactive is the key to ensuring customer satisfaction and retention. One effective strategy to stay ahead of potential issues is by documenting and understanding "customer signals" – subtle behaviors and indicators that can serve as red flags. Recognizing these signals across the organization allows businesses to engage with customers at the right moment, preventing issues from escalating and ultimately fostering a more positive customer experience. Teams should not just try to save the account once there is a request to cancel or an escalation. You need to pay attention to the signs before you hit this point. Ensuring the entire team knows what to look for means that everyone is empowered to care and improve the customer experience. Here's a list of customer behaviors that could be potential red flags, gradually increasing as they check out or consider leaving: 🔷 Reduced Engagement: Decreased interactions with your product or service. Limited participation in surveys, webinars, or other engagement opportunities. 🔷 Decreased Usage Patterns: A decline in frequency or duration of product usage. Reduced utilization of features or services. 🔷 Unresolved Support Tickets: Multiple open support tickets that remain unresolved. Frequent escalations or dissatisfaction with support responses. 🔷 Negative Feedback or Reviews: Public expression of dissatisfaction on review platforms or social media. Consistently low scores in customer feedback surveys. 🔷 Inactive Account Behavior: Extended periods of inactivity in their account. No logins or interactions over an extended timeframe. 🔷 Communication Breakdown: Ignoring or not responding to communication attempts. Lack of response to personalized outreach or engagement efforts. 🔷 Changes in Buying Patterns: Drastic reduction in purchase frequency or order size. Shifting to lower-tier plans or downgrading services. 🔷 Exploration of Alternatives: Visiting competitor websites or exploring alternative solutions. Engaging in product comparisons and evaluations. 🔷 Billing and Payment Issues: Frequent delays or issues with payments. Unusual changes in billing patterns.

Understanding statistics is essential for effective quantitative research and data-driven decision-making. However, many professionals without formal statistical training often find statistical analyses intimidating. "Statistics for Non-Statisticians" (2nd Edition) by Birger Stjernholm Madsen is an excellent resource for anyone seeking to develop foundational statistical skills. This book provides clear explanations of critical statistical concepts, ensuring accessibility and practicality for professionals across fields such as business, economics, social sciences, and management. Key topics covered include data collection methods, descriptive statistics, hypothesis testing, analysis of variance (ANOVA), and regression analysis. The text effectively bridges theoretical understanding and real-world application through practical examples, straightforward language, and minimal mathematical complexity. Professionals looking to enhance their statistical literacy and confidently perform quantitative analysis will find this book a valuable resource. #DataAnalysis #QuantitativeResearch #Statistics #DataLiteracy Statistics 4 non-Statisticians #share

As a Data_Analyst, SQL has been important l for conducting in-depth data analysis. Here are some advanced SQL techniques that can significantly enhance your analytical capabilities: 1. Window Functions: • Advanced Analytics: Master the use of OVER() for complex analytical tasks. Window functions are essential for calculating running totals, rankings, and performing lead-lag analysis within datasets. Explore functions like ROW_NUMBER(), RANK(), DENSE_RANK(), and NTILE() to gain nuanced insights into your data. • Partitioning and Ordering: Learn how to partition your data and order within partitions to perform segmented calculations efficiently. 2. CTEs and Temporary Tables: • Simplifying Complex Queries: Common Table Expressions (CTEs) and temporary tables are invaluable for breaking down and simplifying complex queries, especially when dealing with large datasets. • Recursive CTEs: Utilize recursive CTEs for hierarchical data processing and recursive algorithms, which can be critical for tasks like organizational chart creation and graph traversal. • Performance Considerations: Understand when to use CTEs versus temporary tables for optimal performance and resource management. 3. Dynamic SQL: • Flexibility and Responsiveness: Learn to construct SQL queries dynamically to enhance the flexibility of your database interactions. Dynamic SQL allows you to create more adaptable and responsive applications by building queries based on variable inputs and user interactions. • Security Best Practices: Implement best practices for securing dynamic SQL, such as using parameterized queries to prevent SQL injection attacks. 4. Query Optimization: • Performance Tuning: Delve into advanced techniques for optimizing query performance. This includes the strategic use of indexing, query restructuring, and understanding execution plans to significantly boost efficiency. • Indexing Strategies: Explore different types of indexes (e.g., clustered, non-clustered, covering indexes) and their appropriate use cases. • Execution Plans: Gain expertise in reading and interpreting execution plans to identify bottlenecks and optimize query performance. 5. PIVOT and UNPIVOT: • Data Transformation: These operations are crucial for transforming rows into columns and vice versa, making your data more accessible and analysis-friendly. • Advanced Pivoting: Combine PIVOT and UNPIVOT with aggregate functions to summarize data dynamically. This is particularly useful for creating cross-tab reports and reshaping data for better visualization and analysis. • Complex Transformations: Implement complex data transformations using nested PIVOT/UNPIVOT operations to handle multi-dimensional data structures effectively. #Dataanayst #SQLskills

Comparing Qualitative and Quantitative Analysis Techniques Qualitative Analysis focus on exploring non-numerical data to understand themes, patterns, and narratives Content Analysis Examines the frequency of specific words, themes, or concepts Example Counting the use of "sustainability" in corporate reports Narrative Analysis Interprets the stories people share to understand their meanings Example Studying autobiographies to explore trauma coping strategies Thematic Analysis Identifies recurring themes or patterns in qualitative data Example: Analyzing opinions from focus group discussions Grounded Theory Analysis Develops theories by systematically analyzing data Example Building a theory on consumer behavior from interviews Discourse Analysis how language is used in social contexts through written or spoken communication Example Analyzing political speeches to understand power dynamics Ethnographic Analysis Observes cultural or social practices to gain insights into group dynamics Example Studying workplace interactions for team behavior insights Text Analysis Applies computational tools to analyze textual data for trends and insights Example Conducting sentiment analysis on product reviews Sentiment Analysis Classifies emotions in text using computational methods Example Gauging public opinion on a movie through tweet analysis Quantitative Analysis Rely on numerical data and statistical techniques to measure Inferential Statistics Draws conclusions or predictions from a sample to generalize for a population Example: Comparing average income between two cities using a t-test Descriptive Statistics Summarizes dataset features with measures like mean or median Example: Calculating students' average math test scores Correlation Analysis Measures the relationship between two variables Example Analyzing the link between study hours and exam scores Regression Analysis how dependent variables relate to independent variables Example Predicting house prices using size and location Factor Analysis Identifies patterns or clusters within large datasets Example Grouping survey responses into themes like loyalty and satisfaction Chi-Square Tests relationships between categorical variables. Example Assessing if gender affects product preferences Time Series Analysis Analyzes trends or patterns in time-based data. Example Forecasting monthly sales using past sales data Structural Equation Modeling (SEM) Analyzes relationships between variables using advanced multivariate techniques Example Evaluating how training impacts employee satisfaction ANOVA (Analysis of Variance) Compares group means to determine if they differ significantly Example Assessing student performance across different teaching methods Cluster Analysis Groups data points based on similarities Example Segmenting customers by purchasing behavior Survival Analysis Studies the time until a specific event occurs Example Estimating the lifespan of a machine

Here are some steps you can take to practice data analysis effectively: 1-Identify a dataset: Start by finding a dataset that interests you or is relevant to your goals. You can find datasets on platforms like Kaggle, UCI Machine Learning Repository, or government/open data portals. 2-Understand the data: Spend time exploring the dataset, understanding the variables, and getting a sense of the data structure and quality. Check for missing values, outliers, and any potential data quality issues. 3-Perform exploratory data analysis (EDA): Conduct an initial exploration of the data using techniques like descriptive statistics, data visualization, and data transformations. This will help you understand the relationships between variables and identify any patterns or insights. 4-Formulate questions: Based on your EDA, come up with specific questions you want to answer using the data. These questions will guide your subsequent data analysis. Choose appropriate analytical techniques: Depending on your questions, select the right data analysis techniques, such as regression, classification, clustering, or time series analysis. Learn about the assumptions and limitations of each technique. 5-Implement the analysis: Use programming languages like Python, R, or SQL to implement the data analysis techniques you've chosen. This will help you develop hands-on experience with the tools and libraries used in data analysis. 6-Interpret the results: Carefully interpret the output of your analysis, drawing insights and conclusions. Consider the limitations of your analysis and any potential biases or assumptions. 7-Communicate the findings: Practice presenting your data analysis results in a clear and compelling way, using visualizations, reports, or presentations. This will help you improve your communication and storytelling skills. 8-Iterate and refine: After completing an analysis, reflect on what worked well and what could be improved. Incorporate feedback and new ideas into your next data analysis project. 9-Expand your skill set: Continuously learn new data analysis techniques, tools, and best practices. Participate in online courses, workshops, or data analysis competitions to challenge yourself and gain new insights. -------------------------------------------------------------- Here are some of the best sites to practice data analysis: Kaggle: Kaggle is a popular platform for data science and machine learning competitions. 2-UCI Machine Learning Repository 3-Dataquest: Dataquest is an interactive learning platform 5-FiveThirtyEight: FiveThirtyEight is a well-known data journalism website that publishes data-driven articles and analysis. 6-Statsmodels and Scikit-learn: These Python libraries provide a wide range of tools for data analysis, machine learning, and statistical modeling. 7-Tableau Public Activate to view larger image,

Most people jump into tools - SQL, Python, dashboards - but real data mastery comes from understanding the full ecosystem: From data concepts ➝ pipelines ➝ analytics ➝ ML ➝ governance ➝ business insights. If you are serious about mastering Data Analytics, this is the roadmap you wish you had on Day 1. Here is a breakdown of what the Data Analytics Periodic Table teaches: 1. Core Concepts & Terminology (Your Foundation) Understand essentials like data analytics, BI, data science, ETL, EDA, warehousing & mining, the fundamentals every analyst must know. 2. Data Engineering & Pipelines (How Data Moves) Explore ingestion, batch & streaming pipelines, wrangling, feature engineering, and everything needed to transform raw data into usable insights. 3. Tools & Platforms (Your Daily Workspace) SQL, Python, Power BI, Tableau, Excel, BigQuery - the stack every analyst uses across analytics, visualization, and machine learning. 4. Analytics & Visualization (Turning Data → Decisions) Master segmentation, forecasting, dashboards, KPIs, optimization, and visual storytelling that drives business impact. 5. ML & Predictive Analytics (Future-Ready Skills) Regression, classification, anomaly detection, deep learning, recommendations & model operations to build AI-driven solutions. 6. Governance, Quality & Security (The Most Overlooked Skill) Data lineage, metadata, privacy, version control, quality monitoring - the backbone of reliable data systems. 7. Business Use Cases (Where Value Is Created) Marketing analytics, HR insights, sales analytics, financial analytics, and supply chain analytics - learn how data solves real problems. 8. Collaboration & Workflow (The Analyst Superpower) Communication, documentation, task management, stakeholder clarity — skills that separate good analysts from great ones. Data Analytics isn’t just SQL + dashboards. It’s an interconnected system of skills - technical, analytical, strategic, operational, and collaborative. Master the system, and you become truly industry-ready.

I’m a CFO today, but I still remember the first time someone told me: "You are not strategic enough." At the time, I was a Controller at a $100M+ company. I had direct and indirect reports, And I remember thinking, how? 🤷🏾♀️ I thought I was already doing all the right things, but I didn’t even know where to start. If that’s you right now, save this post. Being more strategic means understanding the bigger picture, (How things work beyond your role, your department, and your organization) ↪️ So you can think ahead about risks, opportunities, and trade-offs ➕ And prioritize actions that create the most value accordingly. Now, how can you do that when you feel stuck in the weeds? Here are 3 ways to get started: 1️⃣ Block 20-30 minutes on your calendar 2-3 times weekly - Use this time solely for pattern recognition and asking "why?" - Focus on one specific dataset or process each session 2️⃣ Find ways to make your existing work strategic For example: - Include a "Business Implications" section to your regular report. - Add a 5-minute "Patterns We're Seeing" discussion at the end of a team meeting 3️⃣ Make it a habit to go one layer deeper. When reviewing any financial data, Ask one question that goes beyond the surface. For example: - Surface: Did we meet budget? => Deeper: Which assumptions in our budget proved most/least accurate, and why? - Surface: What's our DSO this month? => Deeper: Which customer segments show the biggest changes in payment behavior? Start small, but start today! I wrote a full guide on this for my newsletter subscribers. You can access it here: https://lnkd.in/eh42pXMX ---------------------------- If you are new here - Welcome 👋🏽 My name is Wassia (pronounced Wa-see-Ya), and I post about what it really takes to reach and thrive at the executive level, especially in finance and accounting. If that resonates with you, click "visit my website" below my name to sign up for my newsletter. Cheers!