Elder Research’s Post

Process Intelligence   In today's fast-paced business landscape, understanding and optimizing operational processes is paramount. For a business to compete in the market, we can use #process_intelligence, a field at the intersection of data analytics and business process management.   Process intelligence relies on the power of data, specifically event logs and process execution data. These logs serve as the foundation for process monitoring and control, offering insights into the intricacies of business workflows. However, extracting meaningful data for process intelligence can be a challenge, requiring precision in capturing details related to cases, tasks, and timestamps. (that's why we use #bpms)   The key points to consider regarding process intelligence are:   1. #Automatic_process_discovery stands out as a key aspect of process intelligence. Techniques like the α-algorithm create process models based on real-world log data, illustrating how processes unfold. While the α-algorithm is a valuable tool, newer process mining algorithms address its limitations, enhancing robustness in uncovering process dynamics. 2. The #performance_assessment of a process involves exploring dimensions such as time, cost, and quality. The Devil's Quadrangle framework delves into these aspects, emphasizing the importance of detailed task execution times in cost calculations. Quality is scrutinized through repetitions encountered in logs, offering a holistic view of process efficiency. 3. #Conformance_checking adds another layer to process intelligence, involving the validation of constraints. Control flow constraints, including mandatoriness, exclusiveness, and activity ordering, play a crucial role. Fitness calculations, based on replaying logs in a normative process model, aid in ensuring processes align with predefined standards.   Before delving deeper, it's essential to distinguish process intelligence from #AI_driven_processes. While process intelligence sets the stage, AI implementation takes it a step further, bringing advanced analytics and decision-making capabilities to the forefront. 📖 Resource: Fundamental of Business Process Management - Book

Data-driven doesn't mean dehumanised. A speciality of Decision Lab is Human-AI Teaming (HAT), and we're pioneering its implementation in industry. There are three practical benefits: 🔸Automation of frequent, repetitive decisions—freeing up decision makers 🔸Augmented human decision-making—fewer errors and greater impact 🔸Support for difficult decisions from forecasting and analysis Decision Lab combines market-leading optimisation, simulation, and AI technologies to produce transformative decision intelligence. Come discover more and speak with us at the Gurobi Live! The Decision Intelligence Summit in Barcelona Oct 18–19: https://lnkd.in/d5rAvUNP Decision intelligence: Elevate your performance. #decisionintelligence #optimization #datadriven #humanAIteaming

Read how the AI-PROFICIENT team shares the #prognosticmodels and #algorithms that are developed within the project. Not only do these models offer insights, but they also provide a glimpse into the effectiveness of these cutting-edge methodologies!

As we all know in today's dynamic business landscape, the mantra "customer focus" is our priority. As organizations strive to stay ahead of the curve, the power of data science and artificial intelligence (AI) and applied automations has emerged as a game-changer in understanding and serving customers better. Our unwavering focus lies in harnessing the potential of predictive and prescriptive analysis through cutting-edge data science and AI technologies to drive unparalleled value for our clients. #automation #data #analytics Data: The Golden Asset. We firmly believe that data is the new goldmine, and our approach revolves around extracting, refining, and leveraging this precious resource to its fullest potential. #customerexperience Unlike traditional methods characterized by linear progressions and incremental improvements, AI represents an exponential leap forward in technological advancement. Through years of experience and continuous refinement, we have identified key success factors that underpin advanced analytical capabilities: -Technical Expertise -Mix of Data Science and AI Tools  -High-Quality Consistent Data -Ease of Access -Clear Strategies for Effective Use of Analytics Let's delve into strategies for maximizing the benefits of this new era of technological revolution. In conclusion, the era of data-driven decision-making is upon us, we stand at the forefront of this revolution. By leveraging the transformative potential of data science and AI, we empower organizations to unlock new frontiers of customer value and propel themselves towards sustained success in an increasingly competitive landscape.

As model drift (e.g. definition of target changes, covariates shift) occurs over time, all predictive/ML models need to be updated eventually. Here are three levels of updating predictive/ML models. Model Refreshing – The model structure (e.g. GLM) remains the same. The predictors or features remain the same. We simply use new data to get new parameter estimates for the features and generate new predictions. Relatively less time and effort are required. Model Refitting – The model structure remains the same but we are open to considering new predictors or features and may drop existing features. We obtain new parameter estimates for both existing and new features, with moderate time and effort required. Model Rebuilding – When model performance deteriorates beyond acceptability, we rebuild the model from scratch. Not only may we select new variables and create new features, but also we may choose completely different types of model (e.g. from GLM to XGboost, or Random Forest). This level requires the most time and effort. Why does this matter? Why not always build new models? There are several main reasons. First, cost. Rebuilding models requires considerable investment in time and money including FTEs (e.g. data scientists, data engineers), storage, computing, quality control and so on. In the business world, the level of update has to be commensurate with the model’s current performance and potential gains to be had. Why kill a few ants with a sledgehammer? Second, regulation. In industries such as the P&C insurance, if new models affect rates, they may need to be filed with the department of insurance (DOI), which can be a lengthy and laborious process with no guarantee for approval. Third, continuity, drastically different models may lead to different segments and classes that make reporting and comparison difficult. Why not just use AI? AI may improve the efficiency of model monitoring and updating but it still needs to fulfill the same regulatory requirements. Also training AI models or developing AI applications can be costly too. The bottom line is deciding the level of model update in the business environment is not just a technical issue. We need to take into consideration cost and benefit analysis, regulation, and alternative solution comparison. #Data #Analytics #modeling #AI #machinelearning

Trying to build AI into your products but worried about potential failure cases? There's a wide gap between a cool demo and something that's production ready. You can bridge that gap with a good data flywheel and solid eval sets. LLM applications need continuous evaluation, monitoring, and improvement to maintain performance over time. Here's a streetfighting cheatsheet for LLM evaluation: 1. Define clear, task-specific success metrics by examining actual LLM outputs, not just theorizing about potential failures. 2. Implement code-based, LLM-based and human judged eval metrics. The order of value is generally human > LLM > code. This also maps to how expensive the evals are to run. 3. Use binary (True/False) metrics when possible for easier alignment and consistency. 4. Validate inputs as well as outputs. 5. Keep metric implementations aligned by regularly sampling and labeling production data. 6. Use dynamic few-shot examples in prompts, retrieved based on input similarity and weighted by recency. 7. Manually iterate on prompts or pipelines based on metric scores and patterns in low-performing instances. 8. Bootstrap improvement by fixing low-scoring outputs and using them as few-shot examples. 9. Break down "good" outputs into multiple dimensions for more accurate labeling and targeted improvements. 10. Consider uncertainty quantification for LLMs to enhance alignment and prioritize data for human review. 11. If your LLM system has many points of call, graph it out. Whiteboarding your system and the potential failure points is very helpful. 12. Log all your data. Use tools like langfuse to make sure no production interaction goes to waste.

Hello! as we stand at the helm of AI CULTURE, my fellow founders, my team and I are thrilled to witness our predictive analysis technology redefine the landscape of data driven decision making strategies. Our commitment to transforming complex data into strategic foresight is not just about predicting the future it is all about creating it. We are crafting the tomorrow success stories. Together with our brilliant team, we have harnessed the power of AI to offer deep, actionable insights that empower our clients to make proactive decisions. It is not just data analysis, it is a leap into a future where every business move is informed and intentional. Our predictive tools are more than just algorithms they are personalized, anticipated strategies that elevate business experiences to new heights. As COO, I take pride in how our innovations have become integral to our client’s success stories. With our leaders and team ideas and innovation converge to create pioneering solutions that resonate with our client’s aspirations. Our technology is a testament to what happens when ambition meets precision. From finance to healthcare, AI CULTURE’s predictive analysis is a beacon of change. We are not just part of the industry we are leading it, one prediction at a time. Join us on this venture. With AI CULTURE, the future is not just bright it will become predictive. #Leadership #Innovation #PredictiveAnalysis #AICULTURE

What new technology are you adopting to resolve complex business problems? We have solutions that can help your business. One of our AI models create dynamic mathematic models that produce iterations of outcomes based on criteria that you give us. The goal is an optimized solution that creates growth and sustainability. For more details, connect with us. https://lnkd.in/gk7iKUuk #AI #ArtificialIntelligence #BusinessSolutions #Automation #BusinessOwners #Houston #Texas

Are you curious about how time series foundation models are revolutionizing data analysis and forecasting? Discover the key insights from an illuminating article by visiting the bobweb.ai blog. This engaging piece delves into the emergence of these models and how they are shaping modern data science practices. By breaking down complex concepts into digestible information, this article offers a clear understanding of the significance of time series foundation models. One crucial takeaway highlighted in the article is how these models enhance the accuracy of forecasts by capturing underlying patterns in time-series data. By leveraging advanced algorithms and historical data, businesses can now make more informed decisions and predictions to drive success. Additionally, the article emphasizes the growing importance of data analysis in today's digital landscape and how time series foundation models play a pivotal role in unlocking valuable insights. Take your understanding of data analysis and forecasting to the next level by exploring the insightful article on time series foundation models at bobweb.ai. With a commitment to providing comprehensive insights into cutting-edge technologies, bobweb.ai empowers businesses to leverage AI automation for enhanced efficiency and decision-making. Visit the blog post here: https://lnkd.in/gEF_-RUR.

Revolutionize Your Data Management Strategy! A study by QuerySurge, involving over 200 companies, reveals a startling truth: the majority of businesses barely scratch the surface when it comes to data validation. With the majority validating less than 25% of their data, the impact on financials and reputation is too significant to ignore. QuerySurge, your smart data testing solution, is here to transform data chaos into clarity. Enhance data health, optimize critical data with AI, and see tangible ROI. https://lnkd.in/dej-egHv