Data Analysis Skills Training

Real-time data analytics is transforming businesses across industries. From predicting equipment failures in manufacturing to detecting fraud in financial transactions, the ability to analyze data as it's generated is opening new frontiers of efficiency and innovation. But how exactly does a real-time analytics system work? Let's break down a typical architecture: 1. Data Sources: Everything starts with data. This could be from sensors, user interactions on websites, financial transactions, or any other real-time source. 2. Streaming: As data flows in, it's immediately captured by streaming platforms like Apache Kafka or Amazon Kinesis. Think of these as high-speed conveyor belts for data. 3. Processing: The streaming data is then analyzed on-the-fly by real-time processing engines such as Apache Flink or Spark Streaming. These can detect patterns, anomalies, or trigger alerts within milliseconds. 4. Storage: While some data is processed immediately, it's also stored for later analysis. Data lakes (like Hadoop) store raw data, while data warehouses (like Snowflake) store processed, queryable data. 5. Analytics & ML: Here's where the magic happens. Advanced analytics tools and machine learning models extract insights and make predictions based on both real-time and historical data. 6. Visualization: Finally, the insights are presented in real-time dashboards (using tools like Grafana or Tableau), allowing decision-makers to see what's happening right now. This architecture balances real-time processing capabilities with batch processing functionalities, enabling both immediate operational intelligence and strategic analytical insights. The design accommodates scalability, fault-tolerance, and low-latency processing - crucial factors in today's data-intensive environments. I'm interested in hearing about your experiences with similar architectures. What challenges have you encountered in implementing real-time analytics at scale?

The unprecedented proliferation of data stands as a testament to human ingenuity and technological advancement. Every digital interaction, every transaction, and every online footprint contributes to this ever-growing ocean of data. The value embedded within this data is immense, capable of transforming industries, optimizing operations, and unlocking new avenues for growth. However, the true potential of data lies not just in its accumulation but in our ability to convert it into meaningful information and, subsequently, actionable insights. The challenge, therefore, is not in collecting more data but in understanding and interacting with it effectively. For companies looking to harness this potential, the key lies in asking the right questions. Here are three pieces of advice to guide your journey in leveraging data effectively: 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝟏: 𝐄𝐬𝐭𝐚𝐛𝐥𝐢𝐬𝐡 𝐆𝐨𝐚𝐥-𝐎𝐫𝐢𝐞𝐧𝐭𝐞𝐝 𝐐𝐮𝐞𝐫𝐢𝐞𝐬 • Tactic 1: Define specific, measurable objectives for each data analysis project. For instance, rather than a broad goal like "increase sales," aim for "identify factors that can increase sales in the 18-25 age group by 10% in the next quarter." • Tactic 2: Regularly review and adjust these objectives based on changing business needs and market trends to ensure your data queries remain relevant and targeted. 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝟐: 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞 𝐂𝐫𝐨𝐬𝐬-𝐃𝐞𝐩𝐚𝐫𝐭𝐦𝐞𝐧𝐭𝐚𝐥 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 • Tactic 1: Conduct regular interdepartmental meetings where different teams can present their data findings and insights. This practice encourages a holistic view of data and generates multifaceted questions. • Tactic 2: Implement a shared analytics platform where data from various departments can be accessed and analyzed collectively, facilitating a more comprehensive understanding of the business. 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝟑: 𝐀𝐩𝐩𝐥𝐲 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 • Tactic 1: Utilize machine learning models to analyze current and historical data to predict future trends and behaviors. For example, use customer purchase history to forecast future buying patterns. • Tactic 2: Regularly update and refine your predictive models with new data, and use these models to generate specific, forward-looking questions that can guide business strategy. By adopting these strategies and tactics, companies can move beyond the surface level of data interpretation and dive into deeper, more meaningful analytics. It's about transforming data from a static resource into a dynamic tool for future growth and innovation. ******************************************** • Follow #JeffWinterInsights to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

Data Quality isn't boring, its the backbone to data outcomes! Let's dive into some real-world examples that highlight why these six dimensions of data quality are crucial in our day-to-day work. 1. Accuracy:  I once worked on a retail system where a misplaced minus sign in the ETL process led to inventory levels being subtracted instead of added. The result? A dashboard showing negative inventory, causing chaos in the supply chain and a very confused warehouse team. This small error highlighted how critical accuracy is in data processing. 2. Consistency: In a multi-cloud environment, we had customer data stored in AWS and GCP. The AWS system used 'customer_id' while GCP used 'cust_id'. This inconsistency led to mismatched records and duplicate customer entries. Standardizing field names across platforms saved us countless hours of data reconciliation and improved our data integrity significantly. 3. Completeness: At a financial services company, we were building a credit risk assessment model. We noticed the model was unexpectedly approving high-risk applicants. Upon investigation, we found that many customer profiles had incomplete income data exposing the company to significant financial losses. 4. Timeliness: Consider a real-time fraud detection system for a large bank. Every transaction is analyzed for potential fraud within milliseconds. One day, we noticed a spike in fraudulent transactions slipping through our defenses. We discovered that our real-time data stream was experiencing intermittent delays of up to 2 minutes. By the time some transactions were analyzed, the fraudsters had already moved on to their next target. 5. Uniqueness: A healthcare system I worked on had duplicate patient records due to slight variations in name spelling or date format. This not only wasted storage but, more critically, could have led to dangerous situations like conflicting medical histories. Ensuring data uniqueness was not just about efficiency; it was a matter of patient safety. 6. Validity: In a financial reporting system, we once had a rogue data entry that put a company's revenue in billions instead of millions. The invalid data passed through several layers before causing a major scare in the quarterly report. Implementing strict data validation rules at ingestion saved us from potential regulatory issues. Remember, as data engineers, we're not just moving data from A to B. We're the guardians of data integrity. So next time someone calls data quality boring, remind them: without it, we'd be building castles on quicksand. It's not just about clean data; it's about trust, efficiency, and ultimately, the success of every data-driven decision our organizations make. It's the invisible force keeping our data-driven world from descending into chaos, as well depicted by Dylan Anderson #data #engineering #dataquality #datastrategy

Many teams overlook critical data issues and, in turn, waste precious time tweaking hyper-parameters and adjusting model architectures that don't address the root cause. Hidden problems within datasets are often the silent saboteurs, undermining model performance. To counter these inefficiencies, a systematic data-centric approach is needed. By systematically identifying quality issues, you can shift from guessing what's wrong with your data to taking informed, strategic actions. Creating a continuous feedback loop between your dataset and your model performance allows you to spend more time analyzing your data. This proactive approach helps detect and correct problems before they escalate into significant model failures. Here's a comprehensive four-step data quality feedback loop that you can adopt: Step One: Understand Your Model's Struggles Start by identifying where your model encounters challenges. Focus on hard samples in your dataset that consistently lead to errors. Step Two: Interpret Evaluation Results Analyze your evaluation results to discover patterns in errors and weaknesses in model performance. This step is vital for understanding where model improvement is most needed. Step Three: Identify Data Quality Issues Examine your data closely for quality issues such as labeling errors, class imbalances, and other biases influencing model performance. Step Four: Enhance Your Dataset Based on the insights gained from your exploration, begin cleaning, correcting, and enhancing your dataset. This improvement process is crucial for refining your model's accuracy and reliability. Further Learning: Dive Deeper into Data-Centric AI For those eager to delve deeper into this systematic approach, my Coursera course offers an opportunity to get hands-on with data-centric visual AI. You can audit the course for free and learn my process for building and curating better datasets. There's a link in the comments below—check it out and start transforming your data evaluation and improvement processes today. By adopting these steps and focusing on data quality, you can unlock your models' full potential and ensure they perform at their best. Remember, your model's power rests not just in its architecture but also in the quality of the data it learns from. #data #deeplearning #computervision #artificialintelligence

What can you do with Python in Excel for FP&A and #finance? I have received this question many times since the launch! Python in Excel can be a game changer for FP&A and finance professionals. If you learn how and for what to use it. You can now do: ✅Cohort Analysis with Heatmaps ✅Time Series Forecasting Using ARIMA ✅Outliers Identification ✅Statistical Advanced Outliers Identification ✅Headcount Analysis ✅Monte Carlo Simulations I created this cheat sheet to help you. But if you want to learn how to leverage AI and Python for Finance, Nicolas Boucher and I have a course coming up: https://lnkd.in/e4FugWeY Comment "Python in Excel is here" and I can send you the Excel file with all the code in the examples! And a bit more detail on the examples: 1) Cohort Analysis with Heatmaps Easily track and visualize customer retention or employee performance trends over time with beautiful, interactive heatmaps. 2) Time Series Forecasting Using ARIMA Predict future financial outcomes like revenue or expenses using advanced ARIMA models that can capture patterns in historical data. 3) Outliers Identification Quickly spot unusual data points (e.g., abnormally high expenses or revenues) with scatter plots and advanced visuals. 4) Statistical Advanced Outliers Identification Go deeper with statistical methods to identify outliers based on standard deviation or interquartile range, providing a robust analysis of deviations from the norm. 5) Headcount Analysis Analyze workforce trends across departments or time periods using visually engaging box plots and scatter diagrams, highlighting fluctuations and unusual spikes. 6) Monte Carlo Simulations Simulate thousands of financial scenarios to model risk and uncertainty, providing a data-driven approach to decision-making and forecasting.

Too often, data strategy is discussed as a technical plan or a tool selection process. But when I look at its true essence, I see something more foundational: the ability of an organization to think clearly about its information before acting on it. From identifying relevant data to ensuring it is stored safely, shared wisely, and integrated properly, each step reflects a mental model that must be coherent and aligned with the organization’s goals. A data strategy is not simply a sequence of actions, but a discipline of thought that links technology, policy, and purpose. Governance, in this context, is not the final checkbox. It is the structural thinking that gives meaning and continuity to all previous efforts. Without it, decisions risk becoming fragmented, and data loses its potential to support sustainable value. In the end, building a data strategy means building a way of thinking that treats information not only as a resource but as a responsibility. #DataStrategy #InformationGovernance #DataDriven #EnterpriseData

Crafting a Data and Analytics Strategy That Really Resonates For many organizations, articulating the tangible value of a data strategy can be a significant challenge. It's common to default to a technology-centric approach, leading to skepticism about solving a "problem" with a "hammer". 🔵 Strategy First, Technology Second Gaining buy-in for your data and analytics vision before diving into the technical details of the operating model. This prevents stakeholders from questioning the need for proposed technology solutions. Communication is key, and it must be segmented based on your audience – whether you're educating or informing (sideways; business partners), persuading (upwards; sponsors), or instructing (downwards; D&A teams). Each approach demands different content, length, and emphasis in your presentations. 🔵 Concise, Outcome-Led Vision Your vision statement should be remarkably concise, ideally 20-40 words, deliverable as an "elevator pitch". It should clearly state how your data and analytics team contributes to the top three organizational goals, identifies the specific stakeholders you aim to help, and outlines three mechanisms for delivering value. This also includes explicitly stating what you won't focus on, ensuring clarity and preventing dilution of effort. 🔵 Align with Business Transformations and Culture To ensure relevance, your strategy must connect with ongoing major business transformations within the organization. Furthermore, addressing cultural barriers to data-driven decision-making is paramount. I suggest framing the culture as "outcome-led" / "value-driven" and "decision-centric" rather than merely "data-driven". 🔵 Broaden The Appeal and Resonate, Wider Incorporate contemporary drivers and trends (e.g. how DA& teams are responding to Generative and Agentic AI), categorizing them as technology, internal, or market/societal factors, to demonstrate your strategy's forward-looking nature. 🔵 Defining Value and Measurable Impact Prioritize your primary stakeholders (ideally three), and for each, define the top three goals your team will help them achieve. For each goal, identify three measurable metrics, creating a "metrics tree" that clearly tracks your contribution to their success. Gartner defines three core value propositions for data and analytics: 1️⃣ Utility: Providing enterprise reporting as a service for common questions. Central team, allocated budget, data warehouse, etc. 2️⃣ Enabler: Facilitating business outcomes through self-service analytics, coaching, and projects based on business cases. 3️⃣ Innovation: Driving new initiatives like AI for decision making and prescriptive analytics. Each value prop requires a different delivery model, from service desks for utility to portfolio management for innovation, and these should be aligned. Collaborating with leaders like CIO, CISO, CAIO is also crucial for innovation efforts. Develop a D&A strategy that demonstrates tangible business value.

Most Excel users stop at formulas and PivotTables. But that’s just the surface. Would you like to stand out from the crowd? You need to start thinking like an analyst. Here are 4 data analysis techniques that will take your Excel skills to the next level. Just to be clear, PivotTables are great for summarizing data. But they're limited in helping you analyze it. Here's why. Data tables, including PivotTables, are good at two things: Looking up exact values. Comparing exact values. Quite frankly, this is more reporting than analysis. 1) Visual Analysis > Data Tables Tables summarize. Charts reveal. Visuals like: Histograms (for distributions) Scatter plots (for relationships) Line charts (for trends) ...make patterns jump out. Good luck seeing these patterns in a monster PivotTable. Instead, PivotTables feed your charts. 2) RFM Analysis: This is a simple but powerful analysis technique to evaluate customers: (R)ecency: How recently they purchased. (F)requency: How often they purchase. (M)onetary: How much they spend. RFM analysis is super simple to implement in Excel. **AND** It's not just for customers. At its core, RFM analysis is about analyzing data based on behaviors. You can define the analysis however you would like. Take healthcare as an example. Analyzing patients: (A)ge (B)lood pressure (W)eight (E)xercise minutes per week The possibilities are endless! 3) Cluster Analysis Sometimes, patterns aren’t apparent until you group the data. Two examples: Segment users by behavior Classify patients by characteristics Start with a scatter plot of two columns. Look for any clusters. Then, figure out what defines each cluster. Better yet... Use Python in Excel for cluster analysis. Python in Excel is included in Microsoft 365 subscriptions. It's your gateway to battle-tested analytics like k-means clustering. This will allow you to scale to using many columns to find hidden patterns. It's the future of Excel. 4) Logistic Regression This one’s for when you want to predict something like yes/no, true/false, approve/deny, etc. It helps answer questions like: Approve this application? Will the customer churn? Is this claim fraudulent? You can implement logistic regression using Solver. Better yet... Use Python in Excel. People have implemented logistic regression using Solver for years. But here's the problem. It's error-prone and doesn't scale. Python in Excel eliminates these problems and gives you way more insights. It's the future of Excel.

Advanced Analysis with Python in Copilot: How to work with time series data https://lnkd.in/gaXFYYd9 Time series analysis enables analysts to identify patterns, trends, and cyclic fluctuations over time. These insights are crucial for accurate forecasting, strategic planning, and informed decision-making. Despite its importance, working with dates and times in a sophisticated manner is often limited by Excel, which has become the butt of many internet jokes due to its handling of dates and times. Integrating Python within Excel has significantly enhanced its capabilities. The popular Pandas package, widely used in this environment, is even named in part after “Panel Data,” a type of time series data. To make this process even more user-friendly, we can now leverage Copilot’s Advanced Analysis features—a generative AI-assisted tool—to run various time series analyses on our data with ease. In this post, we'll explore how to derive time series insights in Excel with the help of Python using a well-known sales dataset, doing everything from basic data resampling and visualization to creating forecasts and predictive models. You can follow along with the free exercise files on my website. Integrating Python into Excel opens up a wide range of possibilities for time series analysis, far beyond what Excel can traditionally handle on its own. With tools like Copilot, Excel users can now easily perform complex tasks such as resampling data, checking for stationarity, and building advanced forecasting models like ARIMA. What questions do you have about working with time series data in Python within Excel or using Copilot’s Advanced Analysis features? Let me know below.