Que faites-vous si vous souhaitez améliorer l’analyse des données grâce à l’intelligence artificielle ?

AI is not new and it has been around for many years. With the advancements of LLMs, more powerful computers and data storage, we are able to perform in more exciting ways. AI involves more than just LLMs. Neural networks, for example, have been used by researchers for well over 25 years, but with carefully curated, cleaner datasets. Learning the basics of AI and the history of how it evolved can be helpful to more deeply understand why there are ethical and performance risks to consider, the importance of the quality of data being consumed, the best methods for answering which questions, and in understanding both the power and limitations of certain models.

Improving data analysis through AI encompasses various strategies and methods. One crucial step is ensuring the quality of your data; Gather relevant, high-quality data, and meticulously clean and preprocess it to eliminate errors, missing entries, and discrepancies.

Learning how to use AI effectively, by incremental and progressive prompting, is one of the most valuable skills to develop and practice. Rigorous prompt engineering will help you advance your data analytics projects. Generative AI can give you detailed step-by-step for the workflow to follow for specific cases. Prompting can be applied as method of scientific inquiry to help you quickly understand and deepen your knowledge of machine learning (e.g., supervised, unsupervised, and reinforcement learning). Use AI to learn AI, because AI knows itself quite well. And finally use AI to discover and reveal the limitations of AI. Be careful what you ask for, because you will get it.

A IA é uma ferramenta poderosa, mas sua eficácia depende da perícia, conhecimento e cuidado do usuário. A crença de que a Inteligência Artificial (IA) pode, de forma autônoma, resolver problemas e analisar dados com precisão é falaciosa. O conhecimento de base é fundamental não apenas para operar ferramentas de IA, mas também para interpretar seus resultados e selecionar as informações de entrada adequadas. Usuários leigos podem cometer erros significativos na seleção dos dados e na interpretação dos resultados, comprometendo a validade e a utilidade dos modelos. A colaboração entre a capacidade analítica da IA e a expertise humana é crucial para a extração de insights valiosos e a tomada de decisões embasadas. Mas há de se tomar cuidados!

AI Basics encompass understanding the core principles of artificial intelligence (AI), which involves machines emulating human-like intelligence to perform tasks. It encompasses learning, reasoning, and self-correction. Within AI, machine learning plays a pivotal role in data analysis. It enables computers to learn from data, make predictions, and derive insights. Key concepts include supervised, unsupervised, and reinforcement learning, employing algorithms such as decision trees, neural networks, and clustering. Mastery of these fundamentals is essential for effective utilization of AI in enhancing data analysis capabilities.

Every AI project feeds itself of data... gabbage in, gabbage out. Hence,more actions should be done before any AI project to ensure data quality through out the data supply chain. Data is the most valuable asset of a business, it should be treated as a product hence its quality should always be the priority.

When "cleaning data" for use in, or for training AI for "quality" results that can be legally used in the future, consider: What source and methods were used to obtain it that could result in inaccuracy or error? Did the sources provide consent? What is the reliability of the source for accuracy? Did the data collection violate licensing, content ownership, privacy or copyright laws? Does the data contain consumer or commercial data - if so did the intellectual property or rights owner (consumer or business) overtly consent? Does your AI intended use violate EEOC or FTC deception standards? An old saying comes to mind: "garbage in, garbage out". The more thoughtful the data collection and cleaning process, the better the result.

For some companies, data completeness better indicates data quality than data accuracy. Improving data quality is much more than “clearing” out bad data; it’s a dedicated process that involves considering all aspects of your collection process. A well-maintained database can drive 66% higher conversion rates—a fact that should ensure that data quality is high on the list of every organization, no matter the size.

Understanding the importance of data preprocessing and feature engineering is essential in ensuring the quality and relevance of input data for machine learning models. Data preprocessing involves cleaning, transforming, and organizing data to remove noise and inconsistencies, while feature engineering focuses on selecting and creating relevant features that enhance the performance of the model. Additionally, it's crucial to grasp the concept of model evaluation and validation techniques to assess the effectiveness and generalization of machine learning models on unseen data. By mastering these, one can effectively leverage AI and ML techniques to enhance data analysis and drive impactful insights for various applications across industries.

AI also can streamline the data preprocessing stage by automating tasks such as data cleaning, normalization, and imputation of missing values. Using AI techniques like natural language processing (NLP) and machine learning algorithms can help standardize and clean datasets more efficiently.

select AI and ML tools and platforms that align with your project’s needs and your organization’s infrastructure. Options are many, from cloud-based platforms like AWS, Google Cloud AI, or Azure ML, to programming libraries such as TensorFlow, PyTorch, and Scikit-learn. Make your choice based on factors like model complexity, data volume, and available computational resources.

Augmenting data analysis with AI tools will accelerate data exploration and identify insights. The tools you use to infuse analytics with AI first need to handle your data sources. Whether structured, unstructured, quantitative, or narrative the tool needs to index the data so it can be used by the AI algorithm(s). Then with automated pattern finding, natural language processing, or other predictive algorithms, the best AI tools generate accurate outputs in the form of insights, recommendations, or process decisions. There are many tools in the market that integrate the automated steps run by AI. Highly configurable SAAS tools include Alteryx, Tableau, and MS PowerBI. APIs with OpenAI/Anthropic can AI-ify your data processing.

It is crucial to select the appropriate tools for conducting data analysis using artificial intelligence. This may encompass machine learning platforms, open-source libraries, and other technologies that cater to your particular analysis requirements.

Next to Tensorflow and Pytorch, Sklearn is a great tool for more "traditional" or classical model based approaches. Huggingface and Spacy are great libraries specific for NLP models.

Not only Tool Selection but properly Architecture definition. It’s essential to define an architecture that is secure and that is able to scale properly and in a clean way as your models and data flows grow up. Other way you will have to re work several times as your analysis grow.

MLOps. Training is essential not only before the AI models release but during the whole model lifecycle. Most AI models rely on the characteristics of the data they work with. Data and behaviors evolve during time so it’s essential to have a good re-training strategy for your models so they keep (or improve) their precision and accuracy.

Do a serious Exploratory Data Analysis process. Then apply Feature Engineering AFTER cleaning your data well. Feature Engineering is the cornerstone of high-quality predictive models. Feature Engineering and can often improve your results 10-fold.

Having a strategy for the development, training, evaluation and finally deployment are essential to the development successful machine learning tools. This cycle is often referred to as MLOps, and is fundamental to the operationalization of machine learning in the real world. It’s no longer enough to build and train a model with a simple split dataset, as both the data and the model performance will drift over time. Employing a MLOps strategy and toolset ensures an organization is better prepared for long term modeling success.

This involves selecting an appropriate algorithm, defining a loss function, and optimizing the model parameters. Remember to split your data into training and testing sets to validate your model.

Enhancing data analysis with artificial intelligence hinges on meticulously training your AI model. This phase allows the algorithm to learn from your dataset, uncovering trends or forecasting. Prepare representative, unbiased data to avoid skewed results. As the model trains on diverse data, it refines predictive or pattern recognition capabilities. Choosing the appropriate machine learning model is critical for effective data interpretation based on analytical goals. The iterative process requires repeated evaluation and adjustment cycles to fine-tune accuracy. Continuous training, testing, and tuning are crucial for delivering insightful, actionable analysis, elevating the sophistication and utility of your data analysis efforts.

Data models details how to take real world ideas or objects and make them useful to a computer system. Open GIS Consortium (OGC) introduced a white-paper using data models for interoperability Interoperability is achieved when software and hardware developed by various vendors communicate on multiple machines Data models have three levels of abstraction ensuring interoperability from different perspectives: conceptual, logical physical

Communicating the result in a very simple and concise way to stakeholders and non-technical people is very essential, using of visualization tool for charts to explain the results can also help to interpret it well.

Interpretability is crucial in machine learning for several reasons. It helps debug algorithms, identify and address any biases, and transfer insights into a broader knowledge base. It also enhances reliability and trust in ML systems by enabling stakeholders to understand how models make their predictions and evaluate trade-offs. Moreover, interpretability ensures compliance with industry standards, company policies, and government regulations. Finally, interpretability helps fix errors when a model predicts accurately but for the wrong reasons

This could involve understanding the feature importance, analyzing the confusion matrix, or measuring the accuracy, precision, and recall of your model. Furthermore, interpreting result is one thing, conveying and explaining those results in a formidable way to the business audience is also an important skillset to have.

Interpreting results goes beyond just scratching the surface; it's about extracting actionable insights that can propel business growth. So, you need to go in depth in your data, not only to identify trends but also to uncover hidden correlations and causations. By understanding the story behind the numbers, you can make strategic decisions that align with organizational goals and objectives. Moreover, communicate these findings effectively to stakeholders, translating complex data into digestible insights that drive action across the organization.

Incorporating ethical considerations into AI-driven data analysis is not just a checkbox; it's a fundamental responsibility. I advocate for comprehensive data governance frameworks that prioritize privacy protection, fairness in algorithmic decision-making, and transparency in data usage. By embedding ethical principles into every stage of the data lifecycle, from collection to interpretation, you can mitigate potential risks associated with AI bias or misuse. Also prioritizing ethical considerations is a strategic advantage that can build long-term resilience and credibility for your organization in an increasingly data-driven world.

In my experience, one of the most critical aspects of enhancing data analysis with AI is the iterative process of model training and evaluation. It's rare to get everything right on the first try. Therefore, being prepared to iterate on your model based on performance metrics and feedback is key. Additionally, staying updated with the latest AI research and methodologies can provide new insights and techniques to improve your models further. Engaging with the AI community through forums, conferences, and workshops is invaluable for learning and innovation.

The world that we are building around data is not a blank slate; it presents pre-existing social conditions, some of which may sometimes be unfair, unequal, and discriminatory. Always remember the presence of confirmation bias, the natural tendency of the human brain to interpret information in a way that supports our beliefs, even disregarding contradictory evidence. This requires those of us who work with data & AI to think with an open mind, always receive new data evidence, and always question our findings and potential impacts. AI resembles a mirror, reflecting our society, where the users who utilize it and those impacted by it are not necessarily the same person, and the impacts also vary.

Here are a few key areas where ethics play a significant role: 1. Privacy: Ensuring that data is collected, stored, and used in ways that respect individuals' privacy is fundamental. 2. Bias and Fairness: AI systems can inadvertently perpetuate or amplify existing biases in data. It's essential to actively work to identify and mitigate biases to ensure fair and equitable outcomes. 3. Ethical issue: Ensuring that AI is used for beneficial purposes and not for harmful applications, such as surveillance or discrimination. We can prioritize to help ensure that AI contributes positively to society and supports ethical practices in data analysis.

Data analysis for me comprises two phases. The exploratory part and in most cases, at least from my experience, should take 80% of the work if not more. This phase is where I advocate against being automated or being AI-based. This part can be semi investigated through an AI. However, any AI-generated analysis should be guided. For instance, the use of parametric and nonparametric methods can be easily misleading if the assumptions of these methods are not met. Also, these methods can lead to an easier to digest analysis. Not every data analysis requires sophisticated and fancy predictive models. Conversely, most analyses are required to be informative without the "modelling" part. The second phase, the modelling part, needs another post.

Well structured and simple steps to get to an effective AI Strategy. In my experience while all other steps are intuitive data quality is often the determinant of the success

To enhance data analysis with artificial intelligence, employ techniques like machine learning for predictive modeling, deep learning for pattern recognition in unstructured data, and natural language processing for text analysis. Utilize AI algorithms for anomaly detection, clustering, and classification to extract insights efficiently. Implementing AI-driven tools and platforms, such as TensorFlow or PyTorch, can streamline analysis pipelines. Additionally, ensure data quality, establish clear objectives, and continually refine models to improve accuracy and relevance. Collaboration between data scientists, domain experts, and AI specialists fosters innovation and maximizes the value derived from AI-driven analysis.

To enhance data analysis using artificial intelligence, I would start by identifying the specific business problem or analytical challenge. Then, I would explore AI techniques such as machine learning, natural language processing, or computer vision, depending on the nature of the data and the analysis goals. It's important to select the right AI tools and algorithms, preprocess the data effectively, and validate the results to ensure they align with the project objectives.

AI offers powerful ways to transform data analysis. Machine learning algorithms can excel at pattern recognition within huge datasets, identifying trends or anomalies humans might miss – this could streamline quality control processes or predict customer churn. Natural language processing (NLP) can analyze customer feedback or social media sentiment, providing insights that would take teams weeks to uncover manually. Even predictive modeling becomes accessible with AI tools, helping forecast sales or resource demand. The key is exploring how AI techniques align with your specific data challenges and goals