Using Data To Improve Efficiency

#PeopleAnalytics: Turning #HRMetrics into #Strategic Insights In today’s data-driven organizations, HR is evolving from a support function to a strategic powerhouse. These HR Metrics are more than just numbers; they’re lenses through which we can understand workforce dynamics, organizational health, and business impact. Let’s break it down: 🔹 Absenteeism Rate: A high rate may signal burnout, disengagement, or systemic issues in workplace culture. Tracking it helps identify patterns and intervene early. 🔹 Employee Attrition & Retention: These twin metrics reveal the stability of your workforce. High attrition can be costly and disruptive, while strong retention often reflects good leadership and employee satisfaction. 🔹 Internal Promotion Rate: A key indicator of talent mobility and succession planning. Promoting from within boosts morale and reduces hiring costs. 🔹 Cost Per Hire & Time to Hire: Efficiency metrics that reflect the effectiveness of your recruitment strategy. Long hiring cycles or high costs may point to process inefficiencies or misaligned sourcing channels. 🔹 Offer Acceptance Rate: A direct measure of your employer brand and candidate experience. Low acceptance rates might mean your value proposition isn’t resonating. 🔹 Human Capital ROI: This is the ultimate business case for HR—how much return you’re getting from your investment in people. It’s a powerful metric for aligning HR with financial performance. 🔹 Employee Engagement: Often measured through surveys, this metric captures how emotionally and cognitively invested employees are in their work. High engagement is correlated with productivity, innovation, and employee retention. 💡 Why it matters: These formulas empower HR teams to move from reactive to proactive. They help diagnose problems, forecast trends, and make evidence-based decisions that drive business value. People analytics isn’t just about tracking—it’s about transforming. #PeopleAnalytics #HRStrategy #HumanCapital #WorkforceInsights #EmployeeExperience #DataDrivenHR #Leadership #FutureOfWork #LinkedInHR #HRLeadership

In today’s data-driven world, the ability to quickly understand and act on data is more critical than ever. One of the most powerful tools to achieve this is data visualization, especially when using Excel. By transforming raw data into visual representations, we can not only identify trends and patterns but also communicate insights in a more digestible format. 𝐿𝑒𝑡’𝑠 𝑑𝑖𝑣𝑒 𝑖𝑛𝑡𝑜 ℎ𝑜𝑤 𝑦𝑜𝑢 𝑐𝑎𝑛 𝑙𝑒𝑣𝑒𝑟𝑎𝑔𝑒 𝐸𝑥𝑐𝑒𝑙’𝑠 𝑓𝑒𝑎𝑡𝑢𝑟𝑒𝑠 𝑡𝑜 𝑒𝑛ℎ𝑎𝑛𝑐𝑒 𝑦𝑜𝑢𝑟 𝑑𝑎𝑡𝑎 𝑎𝑛𝑎𝑙𝑦𝑠𝑖𝑠 𝑎𝑛𝑑 𝑑𝑒𝑐𝑖𝑠𝑖𝑜𝑛-𝑚𝑎𝑘𝑖𝑛𝑔 𝑝𝑟𝑜𝑐𝑒𝑠𝑠𝑒𝑠: 📈 Charts and Graphs: Visualizing data with charts and graphs helps highlight important trends and patterns at a glance. Whether it’s a bar chart, line graph, or pie chart, these visuals are perfect for simplifying complex data and making it easier to interpret. ℹ️ Conditional Formatting: Want to quickly spot outliers or key data points? Conditional formatting is your go-to tool. By applying color scales, data bars, or icon sets, you can instantly identify critical information without having to sift through every row of data. 📊 Pivot Charts: Pivot charts allow you to create dynamic visual summaries of your data, giving you the flexibility to explore different perspectives on the fly. With the ability to adjust and manipulate the data, you can uncover insights that might have been overlooked in static tables. 🌟 Sparklines: These mini-charts inside a cell are perfect for showcasing trends within a single row of data. Use sparklines to get a snapshot of trends without taking up too much space on your sheet. 〰️ Dashboard Integration: A dashboard consolidates multiple visualizations into one interactive view, making it easier to track key metrics and make informed decisions. With Excel, you can integrate different charts and graphs into a dashboard that provides a holistic view of your data. Data visualization isn’t just about creating pretty pictures—it’s about making data more accessible, understandable, and actionable. Whether you’re tracking business performance or analyzing trends, these tools can turn raw numbers into strategic insights that drive decisions. How do you currently use data visualization to inform your decision-making process, and which Excel feature do you find most effective? Share your thoughts in the comments below! #DataVisualization #ExcelTips #ExcelDashboards #DataInsights #DataDrivenDecisionMaking

Real-time data becomes more useful when processes can act on it immediately. With intelligent automation, companies can detect issues faster, trigger alerts, and support decisions with fresher operational evidence. For operations leaders, IPA turns data analysis into faster execution: - Real-time data collection gives teams a clearer view of production, supply chains, and customer interactions. - Automated analysis helps identify anomalies before they become larger operational problems. - Alerts and predictive models support quicker responses when conditions change. - Repetitive tasks can be reduced, freeing people to focus on higher-value decisions. - Integration and security remain essential because automation depends on trusted data flows. Intelligent Process Automation creates value when data, workflows, and controls are designed to work together from the start. #ProcessAutomation #RealTimeAnalytics

If you were evaluating quality improvement projects in industries or internship projects, you would find yourself in the familiar territory on the usage of control charts. But it would be another matter when you see the usage in other business processes, where the distributions are non-normal. I was particularly alarmed by this tendency when one of my best students came up with a Control Chart on Covid 19 distribution of ailments / death, a couple of years back. The distribution of COVID-19 cases across populations and over time does not follow a simple statistical distribution such as the normal distribution, power law distribution, or any other standard distribution. Instead, it exhibits complex dynamics influenced by various factors including human behavior, government interventions, healthcare capacity, and the characteristics of the virus itself. Use of control charts does not arise. Then recently I sat on a panel reviewing MBA internships and almost one in five used Control Charts to depict business process stability whereas the underlying data and the distribution had little reason for such a denouement as either the statistic used was wrongly chosen or the period under question. The first test to be adopted for the use for Control Charts is whether the underlying data follows a normal distribution, Binomial distribution, Poisson Distribution or Power Law or the sample size is large enough that Central Limit Theorem can be applied. Control charts rely on the Central Limit Theorem that states that the distribution of sample means tend to be approximately normal as sample sizes increase. The underlying premise is that we want to look at a process that has variation due to random causes (not assignable) and special causes (assignable). Here are some ways to effectively use Control Charts in business processes: 1.     Consumption data: Here are a few scenarios where control charts can be applied to consumption data: Inventory Management, Resource Consumption, Demand Forecasting, Service Consumption 2.     Service of Defective Parts data: For variation in the frequency and variability of defective parts, two dominant distributions are: Binomial Distribution & Poisson Distribution. We should therefore use P-Charts 3.      For Non-Normal Distributions: If the data is not normally distributed and exhibits non-constant variance (heteroscedasticity), alternative control charts such as the Exponentially Weighted Moving Average (EWMA) chart or the Cumulative Sum (CUSUM) chart may be more suitable. 4.     For Power Law Distributions: Power law distributions are characterized by a heavy tail and a high frequency of low-value occurrences, which deviate significantly from the assumptions underlying traditional control chart methodologies, alternative statistical methods and visualization techniques may be more appropriate. Read my full article. #businessprocess #variation #controlchart #SPC #powerlaw

New Video: How to Apply Key Inferential Statistics Methods In the final part of my Inferential Statistics series, I break down four essential methods every analyst should master: - Chi-Squared Tests – for analyzing categorical data relationships - T-Tests – for comparing means between two groups - ANOVA – for comparing multiple groups - Tukey Tests – for post-hoc comparisons after ANOVA Whether you’re working with marketing data, research studies, or product performance metrics, these methods are foundational for uncovering meaningful insights and making data-driven decisions. What You’ll Learn: • When and how to use each test • Step-by-step demos in Excel and Google Sheets • How to turn data into actionable insights You'll find the full video here: https://bit.ly/3DQsBVe Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

If you think data visualization and statistics don’t apply to FP&A -- consider just how much valuable information is hidden away in those financial processes. For instance, understanding not only the average days payable but also the variance around those payables can shed light on potential risks or opportunities. The same approach can be applied to other metrics, such as sales forecasts or overhead expenses: analyzing forecast accuracy, identifying anomalies, or even spotting correlations between different expense lines can significantly enhance strategic decision-making. Of course, transforming raw spreadsheets and disparate systems into a structured, analysis-ready format requires effort, but it pays off once those cleansed datasets are in place. With the right data visualization and statistical techniques, these metrics become more than just numbers on a page -- they become actionable insights that drive better decisions. FP&A actually benefits substantially from this kind of analysis, and those who overlook its potential may be missing out on valuable guidance. Embracing data analytics and visualization can help surface insights that might otherwise remain buried and give organizations a more comprehensive view of their financial health and future direction.

What connects Industrial IoT, Application and Data Integration, and Process Intelligence? During my time at Software AG, my attention has shifted in line with the company's strategic priorities and the changing needs of the market. My focus on Industrial IoT, moved into Application and Data Integration, and now I specialise on Business Process Management and Process Intelligence through ARIS. While these areas may appear to address different challenges, a common thread runs through them. Take a typical production process as an example. From raw material intake to finished goods delivery, there are countless interdependencies, processes and workflows, and just as many data sources. Industrial IoT plays a key role by capturing real-time data from machines and sensors on the shop floor. This data provides visibility into equipment performance, production rates, energy usage, and more. It enables predictive maintenance, reduces downtime, and supports continuous improvement through real-time monitoring and analytics. Application and Data Integration brings together data from across the value chain, including sensor data, manufacturing execution systems, ERP platforms, quality management systems, logistics, and supply chain management. Synchronising these systems with integration creates a unified, reliable view of production operations. This cohesion is essential for automation, traceability, quality management and responsive decision-making across departments and geographies. Process Management, including modelling, and governance, risk, and controls, takes a different yet equally critical perspective. Modelling helps design optimal process flows, while governance frameworks ensure controls are in place to manage quality, risk, and enforce conformance for standardisation. Process mining uncovers bottlenecks, rework loops, and compliance deviations. It focuses on how the production process actually runs, rather than how it was designed to operate. Despite their different vantage points, each of these domains works toward the same goal: aggregating, normalising, and structuring data to transform it into information that can be easily consumed to create meaningful, actionable insights. If your organisation is capturing process-related data through isolated tools, such as diagramming or collaboration platforms, quality management systems, risk registers, or role-based work instructions, it is likely you are only seeing part of the picture. Without a unified approach to integrating and analysing this data, the deeper insights remain fragmented or out of reach. By aligning physical operations, applications & systems, and business processes, organisations can move beyond surface-level visibility to uncover the root causes of inefficiency, unlock hidden potential, and govern change with clarity and confidence. #Process #Intelligence #OperationalExcellence #QualityManagement #Risk #Compliance

Behind every great insight is a solid statistical foundation. Here are the 4 methods every data analyst must master: 𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬: Data visualization is just the tip of the iceberg. The real power comes from understanding the statistical methods that reveal relationships, patterns, and predictive insights. 𝐓𝐡𝐞𝐬𝐞 4 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐦𝐞𝐭𝐡𝐨𝐝𝐬 𝐩𝐨𝐰𝐞𝐫 𝐞𝐯𝐞𝐫𝐲 𝐝𝐚𝐭𝐚-𝐝𝐫𝐢𝐯𝐞𝐧 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧: 1. 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 → Predict outcomes and identify what drives them → "How does marketing spend impact revenue?" → Master: R² for model fit, RMSE for prediction accuracy → Pro tip: Always check residuals - they tell the real story 2. 𝐇𝐲𝐩𝐨𝐭𝐡𝐞𝐬𝐢𝐬 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 → Make confident, evidence-based decisions → "Is this A/B test result actually significant?" → Master: t-tests for comparing means, ANOVA for multiple groups → Remember: Statistical significance ≠ business significance 3. 𝐂𝐨𝐫𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 → Measure relationships between variables → "How strongly do these factors move together?" → Master: Pearson for linear, Spearman for non-linear → Warning: Correlation ≠ causation (but you knew that) 4. 𝐓𝐢𝐦𝐞 𝐒𝐞𝐫𝐢𝐞𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 → Uncover trends, cycles, and seasonality → "What will demand look like next quarter?" → Master: ARIMA for trends, Exponential Smoothing for patterns → Always: Decompose first to understand components 𝐖𝐡𝐲 𝐦𝐚𝐬𝐭𝐞𝐫 𝐭𝐡𝐞𝐬𝐞 𝐧𝐨𝐰: ↳ Every dashboard needs statistical validation ↳ Every recommendation requires evidence ↳ Every model must be interpretable ↳ Master these = become indispensable The best part? Once you think statistically, data tells stories you never noticed before. Master the stats. Master the insights. Get 150+ real data analyst interview questions with solutions from actual interviews at top companies: https://lnkd.in/dyzXwfVp ♻️ Save this for your next analysis 𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 18,000+ readers here → https://lnkd.in/dUfe4Ac6

🎙️ "AI transformation in HR can help supercharge business transformation because the business strategy starts with the people strategy." This HR playbook authored by Jacqui Canney and Brandon Roberts, respectively Chief People and AI Enablement Officer and Global Head of People Analytics, AI and Product at ServiceNow, provides a practical framework for HR leaders to navigate the AI revolution. They rightly frame that this is not just as a technological shift, but as a "human renaissance." The playbook lays out a comprehensive three-point plan for HR to lead the AI transformation. 🔎 First, Reimagine an AI-powered HR function. This includes partnering with IT, establishing an HR AI operating model, use case prioritisation, taking the lead on AI governance and identifying HR roles of the future. 🔎 Second, Enable AI in HR and across the organisation. This is about building AI literacy and governance across the company, ensuring employees have the skills and policies needed to responsibly adopt new tools. 🔎 Finally, Transform the Workforce by equipping people with the skills to partner with AI, preparing them for new roles that will emerge from this shift. The playbook also highlights practical examples of AI in action at ServiceNow, such as an AI-powered chatbot to resolve employee requests, which provides instant answers for common questions, and leveraging generative AI to summarise complex documents. These examples demonstrate how AI delivers tangible value by driving efficiency and improving the employee experience, proving that a holistic, agile approach can lead to both quick and sustainable progress. 🚀 The article is featured in the August edition of the Data Driven HR Monthly, which you can access here: https://lnkd.in/e733FXTD 🚀 #humanresources #peopleanalytics #chiefpeopleofficer #learning #hrtech #workforceplanning #employeelistening #leadership #culture

Overheard in a client meeting last week: “We have 8 regional managers sending their monthly reports in Excel. One of our guys spends 1-2 days combining them all.” Ok… You're paying a skilled analyst to: • Download eight separate spreadsheets • Copy & paste everything into a single master file • Fix formatting differences (especially dates) • Match up product codes • Hunt down discrepancies • Update pivot tables • Distribute the final report manually We sorted this with a simple SQL database setup and a simple process. 1. Excel attachments on emails are automatically saved to a folder using Power Automate 2. A simple daily process gobbles up those files and ingests them into SQL database tables 3. They now run a simple SQL query to extract the data. Takes seconds, not days. 4. Kettle on. Now their analyst actually analyses data instead of wrestling with Excel. Mad, isn't it? The power of SQL. #dataanalysis #businessintelligence #automation #sql #learnsql #corporatetraining #uniquetraining