How can you measure the impact of data mining project solutions?

One of your objectives should always be capable of return on investment. Many classic data mining use cases are binary classification problems: fraud or not fraud, default or not, loyal or churn, etc. You should be able to estimate the financial implications of each outcome in the confusion matrix: true positive, false positive, true negative, and false negative. If you can't articulate what you are trying to predict, why that is a valuable thing to know in advance, what actions you will (or won't) take, or how the prediction has financial implications, then you should slow down and reassess until you can. If you still can't, it's a clue that you either have poor problem definition or a project that won't achieve ROI.

Data understanding is the cornerstone of any successful data mining project. Every data point is like a puzzle piece that, when correctly assembled, conveys a narrative. We unveil hidden patterns, trends, and correlations by truly understanding our data. This empowers us to ask the right questions and confidently navigate the wealth of information in a way that ultimately transforms data into valuable knowledge.

Data mining is more than just digging into numbers; it's about understanding our business's heartbeat. For our efforts to be impactful, they should always sync with our business objectives. By applying the SMART criteria - being Specific, Measurable, Achievable, Relevant, and Time-bound - we can pinpoint the right questions to ask. This method helps us uncover the most valuable data, ensuring that our time and resources are invested wisely and drive tangible benefits for the company.

Data mining should have the focus on what the effect is on the buiness primary business results an the quality of the deliverd products. It should help in better decisions in a shorter time than humans would do.

Always down to bottom line; revenue generation, cost saving or cost avoidance. If any of these 3 bottom line are unable to be derived for any data mining project. Forget about it. Don't start.

The CRoss-Industry Standard Process for Data Mining (CRISP-DM) provides helpful advice. Two distinct types of success metrics have to be identified: business success metrics and technical metrics. Sure, the technical metrics include things like AUC, overall accuracy, etc. That's not usually what projects have a tough time with. You must also identify which business metrics will define success. They should be identified during the Business Understanding phase, and then measured against during the Evaluation phase. A common mistake is to assume that the Evaluation phase is about technical metrics. It is not. That should have already been done during the assessment task of the Modeling phase.

As a healthcare data scientist, my aim goes beyond mere accuracy. I prioritize precision, recall, and the harmonic balance between them. To achieve this, I deploy a variety of ML algorithms including Decision Trees, Random Forests, Gradient Boosting, and Neural Networks. By integrating these diverse methodologies, we ensure our models are robust, efficient, and truly resonate with real-world healthcare scenarios.

Metrics selection is pivotal. Metrics should mirror your project's goals, ensuring that progress aligns with objectives. Key considerations encompass relevance, interpretability, sensitivity, and business impact. Metrics like accuracy, precision, and recall are standard, but contextual metrics like profit and customer satisfaction can be equally vital. Align with stakeholders to ensure shared objectives, consider ethical implications, and choose metrics that shed light on both model performance and business impact. Ultimately, metrics serve as the compass, guiding data mining projects toward meaningful and actionable insights.

These measurable criteria are designed to assist data mining professionals in making informed choices regarding the appropriateness of models and algorithms for their specific problems. They also aid in refining their methods for improved outcomes. At a more advanced level, they enable the assessment of actions or patterns that may be discarded if they prove successful. Moreover, as we gain insights into the underlying reasons, we can potentially alter outcomes by modifying input variables. The selection of the appropriate metric hinges on the specific characteristics of the data mining problem and the objectives of the analysis.

Nice try, but I know what you're really aiming for, and I will not help you train your A.I. with my personal train of thought for free, LinkedIn

Defining the basis for comparison is often more difficult than one might think at first sight. Not only do the data change, but also the tasks, circumstances, and possible actions. While there is already a natural place for data drift monitoring in machine learning processes, retrospective monitoring is rarely seen when time is not considered as a parameter. Another problem is the reliability of data in a non-statistical sense. There is, for example, the well-known ImageNet benchmark dataset for multi-class classification tasks in machine vision. Because of its huge size, no one has ever tested the reliability of the data. Then it turned out that excellent results could be obtained with a smaller but correctly labelled subset.

As a Healthcare Data Scientist, I undertook a comprehensive analysis comparing traditional mammography methods with advanced machine learning algorithms. The baseline for this comparative study was established using conventional radiology and mammography data. Our goal was to ascertain the predictive accuracy and efficiency of the machine learning models relative to traditional diagnostic procedures. Through rigorous testing and validation, we aimed to enhance the quality of breast cancer detection, leveraging the computational capabilities of machine learning while benchmarking against the traditional mammography standards

Establishing a baseline is foundational. It provides a reference point against which the impact of your solution can be measured. Baselines capture the current state, making progress tangible. Whether it's sales growth or process efficiency, a baseline helps quantify improvement. It's the compass guiding data mining endeavors, enabling us to gauge the significance of change and the effectiveness of our solutions.

Yes baseline is very important. Specially in Production environment, baselines for missing interventions have to be agreed upon since ,post insights , the production would have improved due to the interventions made.

I believe this article is conflating two distinct concepts: data mining and predictive model building. Data mining is a broader process that might not even encompass such testing methods! Based on our experience exploring biomedical data, the results of data mining are predominantly verified through the clinical experience of the professional in a highly qualitative yet objective manner.

Thorough testing is pivotal. It's the litmus test for your solution's performance, generalization, and resilience. Robust testing unveils its strengths, limitations, and adaptability. Employ methodologies like cross-validation, hold-out, or A/B testing, tailored to your data's characteristics. Testing ensures your solution aligns with expectations and stands ready to tackle diverse real-world scenarios, contributing to informed decision-making and value creation.

Continuous monitoring is paramount. It's the compass guiding your solution's journey post-deployment. Monitoring ensures it delivers sustained value, maintains reliability, and adapts to evolving needs. Employ tools like dashboards, reports, and feedback systems to track performance, spot anomalies, and prompt timely updates. It's a dynamic process, vital for long-term success, driving ongoing improvements, and maximizing the relevance and impact of your data mining solution.

As a Medical Data Scientist, I employ sophisticated dashboards and regularly updated reports to ensure the optimal performance of our machine learning algorithms. Continual data refreshment and meticulous hyperparameter tuning are integral to our processes, enabling us to consistently achieve superior predictive models and outcomes.

Without clear and concise communication, the valuable insights and knowledge gained from data analysis efforts remain dormant and fail to drive informed decision-making. The primary need for communication lies in bridging the gap between data experts and decision-makers. Data analysts must translate complex data into understandable narratives, visualizations, and reports that resonate with non-technical stakeholders. This ensures that decision-makers can grasp the significance of the findings without being overwhelmed by technical jargon. It allows decision-makers to ask questions and seek clarification, promoting a collaborative approach to decision-making.

In many scenarios, the progressive improvements in performance may not be directly visible or directly attributable. Taking the baselining from the orevious step, the definition of success, the historical data of performance ( or lack of it) etc needs to be carefully included for the communication. Transparence of the recommendations, given by the models plays a significant role in the acceptance.

We can observe the success of data mining projects by measuring their solutions from a strategic perspective. The models we create at the desk can perform very well with measurements, tests, and validations. "But real success is measured in the field." The real success of projects is in the gains and returns in the field. Therefore, whether a machine learning or data mining project has succeeded is indicated by field trials, the conversion of offers made in the field into new customer acquisitions, a reduction in churn rates, or, if you are running a predictive maintenance project, the amount of cost reduction. For this reason, it is necessary to have an approximately one or two-month monitoring period and establish real validation.

Organizations are dynamic, and with growth, data volumes surge. It's crucial that data mining solutions are built with scalability in mind to handle these increasing datasets effectively. Furthermore, the dynamic nature of businesses means goals and needs shift over time. As such, data solutions should not only be robust but also flexible, ensuring they can adapt and evolve in alignment with the organization's changing landscape and objectives.

It is always important to check the validity of your findings with different sources of information. To cross validate with simple logic other source before any decisions to be made. Additionally if you drill very deep into subset of full data you might miss the bigger picture and the decision made on one product might very much affect the product group or the whole store.