ISESLab’s Post

‘Predictive’ is not the beginning or the end of your Advanced Industrial Analytics journey. It’s the middle! Manufacturers often place a disproportionate amount of importance on being able to predict asset failures and out-of-spec process parameters, so much so that they don’t consider the necessary steps before and after that. An ideal Advanced Industrial Analytics journey begins with basic exploratory descriptive and diagnostic analytics on good quality and contextualized data that provides engineers and business users answers to questions like “what happened” and “why did it happen”. On the other hand, companies that have predictive analytics in place don’t know what to do with the predictions. It is almost always upon the individual engineer, operator, or business user’s discretion to decide what to do with the predictions. Also, in many cases, these predictive insights are not embedded as part of the company's decision-making processes, and don't end up being operationalized. To maximize value out of these predictive models, companies need to have prescriptive and prognostic analytics that provides them with actionable recommendations, and likely results of those recommendations. Finally, these predictions, prescriptions, and prognosis need to be part of a closed-loop model that learns over time to improve accuracy. The root cause of these challenges lies in today's common ways of providing technology solutions to business problems. Successful and scalable analytics requires investing in not just technology and data, but also the organizational culture, an agile mindset, and willingness to redesign business processes wherever necessary. #AdvancedIndustrialAnalytics #DataAnalytics #PredictiveAnalytics #PrescriptiveAnalytics #BusinessInsights LNS Research

🚀 PostHoc Analysis in R: Unveiling Deeper Insights After Hypothesis Tests 📊 Understanding the why behind your findings is crucial. Posthoc analysis in R empowers you to delve deeper after a hypothesis test, revealing significant differences and relationships within your data. 🤔 What does it solve? Uncovers nuanced relationships: Identifies which specific groups differ significantly, beyond the initial hypothesis test's broad conclusion. Explores complex interactions: Explores how multiple factors influence your outcome variable. Avoids false positives: Correctly identifies significant differences, minimizing the risk of spurious results. 💡 Examples: Comparing sales performance across different marketing campaigns: Identify which campaigns truly drive higher sales, not just that there's a difference. Analyzing customer satisfaction scores across product lines: Pinpoint which product lines are most impactful on customer satisfaction. Evaluating treatment effectiveness in clinical trials: Determine which specific treatment groups show statistically significant improvements. 📈 Key Benefits: Enhanced understanding: Gain a more comprehensive view of your data. Improved decisionmaking: Make datadriven choices with greater confidence. Reduced risk of errors: Minimize the chance of drawing incorrect conclusions. 🛠️ Software & Tools: R: The powerful statistical computing language, with numerous packages for posthoc analysis. RStudio: A userfriendly integrated development environment (IDE) for R. 📚 Methodologies & Frameworks: Tukey's HSD: Commonly used for comparing multiple group means. Scheffé's test: A more conservative approach for multiple comparisons. Dunnett's test: Useful when comparing multiple groups to a control group. 💼 Use Cases: Market research: Analyze consumer preferences and behaviors. Clinical trials: Evaluate treatment effectiveness. Business analytics: Identify key drivers of performance. #BusinessAnalytics #DataAnalysis #RProgramming #Statistics #HypothesisTesting #PostHocAnalysis #DataScience

Is your advanced analytics program not delivering the business value you expected? You are not alone. After putting so much time and effort into data and analytics, many aren't seeing the ROI expected. Why is that? ❗ Some lack the vision for what advanced analytics can be. Don't think about predictive as the end game, but as a starting point to the vision to value pivot. (See the post below from my colleague Vivek Murugesan) ❗ We are pushing data so hard we have started to overwhelm people with TOO MUCH data. It's not just the quality of data, but the quantity of data received at one time and the method of delivery. ❗ The one size fits all approach does not work. Analytics should be personalized. Providing just what's needed, when it's needed, to the right person, at the right time, is most important. Advanced analytics is a critical aspect of digital transformation. Taking a persona-based approach can help manufacturers significantly improve the ROI. Want to learn more? Visit the LNS Research blog "The three user personas of advanced industrial analytics software" 👉🏻 https://lnkd.in/gxBXuY-3 #AdvancedAnalytics #Data #BigData #DataAnalytics #Manufacturing

Power of OR and Analytics professionals in society…

HOW CAN REGRESSION ANALYSIS REVOLUTIONIZE YOUR WORK? Have you ever wondered how companies and researchers predict future trends and make informed decisions? The secret often lies in the power of regression analysis. This versatile statistical tool helps unravel the relationships between variables, allowing for precise predictions and trend identification. Imagine being able to foresee disease progression in patients by analyzing their physiological data—this is exactly what regression analysis enables in biomedical engineering. It aids in early diagnosis and personalized treatment plans, potentially saving lives. In the financial sector, regression analysis can forecast stock prices and market trends, guiding savvy investment decisions and robust risk management. Here’s how it can revolutionize your work: Predict Disease Progression: In biomedical engineering, regression analysis can analyze patient data to foresee disease progression, enabling early diagnosis and personalized treatment plans. Optimize Treatment Plans: By understanding the relationships between various physiological factors, regression models help tailor medical treatments to individual patients, improving outcomes and efficiency. Forecast Financial Trends: In finance, regression analysis can predict stock prices and market trends by examining historical data, guiding investment strategies and risk management. Improve Risk Management: By identifying key variables that influence market behavior, regression analysis helps in developing robust risk management strategies to mitigate potential losses. Enhance Marketing Strategies: Companies can use regression analysis to understand consumer behavior, predict sales, and optimize marketing campaigns, ensuring a better return on investment. Drive Product Development: By analyzing customer feedback and market trends, regression models can inform product development, ensuring new products meet market needs and preferences. #DataSolutions #DataAnalysis #StatisticalAnalysis #BigData #DataScience #DataDriven #DataInsights #DataAnalytics #BusinessIntelligence #MachineLearning #ArtificialIntelligence #PredictiveAnalytics #DataVisualization #DataMining #DataManagement #DataEngineering #DataStrategy #DataIntegration #Analytics #DataQuality #BusinessAnalytics #DataTrends #DataConsulting #DataTransformation #DataInnovation #TechTrends #Innovation #TechSolutions #DigitalTransformation #SmartData #DataExperts

When developing optimization models, one crucial lesson stands out: a model, no matter how technically robust, can fall short if it doesn’t align with the needs of business. Here are some key reflections on why an optimization model might not meet business expectations: 1️⃣ Misaligned Objectives: The heart of any model is its objective function. If this doesn’t encapsulate the core business goals, the model’s output won’t deliver the desired impact. It’s essential to have continuous dialogue with business stakeholders to ensure alignment. 2️⃣ Overlooking Constraints: Each business operates within its unique constraints, from regulatory compliance to resource limitations. An effective model must reflect these real-world boundaries clearly and accurately. 3️⃣ Data Discrepancies: The old adage “garbage in, garbage out” holds especially true in mathematical modeling. Models built on outdated or incorrect data can lead to decisions that are out of step with current market realities. 4️⃣ Lack of Flexibility: Business environments are ever-changing. Models that lack the ability to adapt to new information or shifting priorities quickly become obsolete. 5️⃣ Insufficient Testing and Validation: Rigorous testing against historical and simulated data is crucial. Without this, models might perform well under theoretical conditions but fail in actual application. The secret? 🤫 ✅ Engage regularly with stakeholders, update data inputs, and continuously iterate on your model. ✅ Remember, a successful model is not just a technical success but a business one. Would love to hear thoughts from my network on how you keep your models relevant and aligned with business needs! #Optimization #BusinessAlignment #DataScience #OperationalExcellence #ContinuousImprovement

The analytical process: It refers to the systematic approach of gathering, analyzing, interpreting, and deriving insights from data to solve problems, make decisions, or generate knowledge. It involves several key steps including defining the problem or question, collecting and preparing data, exploring and analyzing the data, developing models or algorithms, interpreting results, and making decisions based on insights derived from the analysis. The analytical process is used across various domains such as business, science, engineering, healthcare, and more, to extract meaningful information from data and drive informed decision-making. #dataanlytics #databasemanagement #ibmdatascience #datasciencecommunity

Explore our stories and discover how Mogital Analytics can turn your data into your most powerful asset. https://lnkd.in/enmART-7 #Mogitalanalytics #projectportfolio #datascience #stories

Omniconsultancy: Pioneering the Future of Universal Advisory Integration In the vanguard of future-centric disciplines, omniconsultancy emerges as a transformative paradigm that integrates interdisciplinary expertise to address the complexities of an increasingly interconnected world. This forward-thinking field synthesizes insights from diverse domains—ranging from artificial intelligence and quantum computing to socio-economic theory and advanced neuroscience—to provide comprehensive solutions for multifaceted global challenges. Omniconsultancy redefines the role of advisory services by emphasizing the seamless integration of knowledge across traditional boundaries. It leverages sophisticated analytical tools and holistic frameworks to offer multidimensional insights that transcend conventional problem-solving approaches. By harnessing the power of real-time data analytics, predictive modeling, and advanced simulation techniques, omniconsultants can deliver anticipatory guidance that is both precise and adaptable. At its core, omniconsultancy operates on the premise that complex systems—whether they are socio-economic structures, environmental ecosystems, or technological networks—demand an integrative approach to understanding and intervention. This discipline advocates for a paradigm shift from isolated expertise to a collaborative model where insights from various fields are synthesized to address pressing issues in a cohesive manner. The future of omniconsultancy will likely see the advent of dynamic advisory platforms that utilize cutting-edge technologies such as artificial general intelligence (AGI) and quantum-enhanced analytics. These platforms will facilitate real-time integration of diverse data sources, offering unparalleled foresight and strategic recommendations. As the global landscape becomes increasingly intricate, the capacity for omniconsultants to navigate and orchestrate solutions across multiple dimensions will become indispensable. Omniconsultancy represents a revolutionary approach to advisory services, promising to redefine how we tackle the complexities of the 21st century by harmonizing diverse streams of expertise into a unified, actionable framework. #Omniconsultancy #FutureOfAdvisory #InterdisciplinaryIntegration #AIandQuantumComputing #HolisticSolutions #AdvancedAnalytics #StrategicGuidance