Here's how you can enhance your data analysis processes with artificial intelligence.

Pour moi moi ce qui l’IA apporte de plus c’est 2 choses: - la 1ere chose c’est que l’IA traite les problèmes beaucoup plus rapidement que l’humain. Les puissances de calculs de l’IA sont beaucoup plus sophistiquées pour des sujets complexes et demandant beaucoup d’informations à prendre en compte. - la 2eme chose c’est que l’IA peut considérer plus d’infos pour répondre à une question qu’un humain ne peuvent le faire. Parexemple pour prédire un phénomène l’IA engrange des dizaines d’entrée mais l’humain ne saurait faire cela efficacement et rapidement.

One caveat of using LLMs - One needs strong prompt engineering skills to obtain a high degree of accuracy, completeness, and precision in model responses. Otherwise, you might end up doing rework which would be more time consuming that analyzing the data manually to begin with. A good mental model to determine where to use AI for market research is to recognize what the models are good at. Currently, this include structuring language, summarizing long-form content, creativity, and logic. Working backwards from this, LLMs add value in prediction, classification, extracting themes, and brainstorming ideas. As a PM, LLMs simplifies the work of generating first drafts of user discovery docs, brainstorming solutions, & categorizing pain points.

Diving into market research with AI, particularly through machine learning / Data science, promises to revolutionize market analysis and rewards strategies. AI/ML empowers systems to learn from vast datasets, uncovering intricate patterns and correlations that evade human observation. With AI-driven predictive analytics, businesses can anticipate market dynamics and tailor rewards programs to meet consumer preferences with unprecedented precision, fostering deeper engagement and loyalty. Understanding supervised and unsupervised learning is essential for crafting personalized market insights, while reinforcement learning enables continuous adaptation of reward strategies based on real-time market interactions.

Exploring AI basics, especially in machine learning and data science, offers exciting prospects for reshaping market research and strategy. By utilizing AI algorithms, businesses uncover hidden patterns in vast datasets, enhancing predictive analytics for tailored rewards programs. Understanding supervised and unsupervised learning is vital for personalized insights, while reinforcement learning allows continual refinement of strategies based on real-time feedback, fostering growth in dynamic markets.

Data mining plays a pivotal role in dissecting market trends and understanding consumer behavior. With AI-powered data mining, we can delve deep into vast datasets to unearth valuable insights and patterns that drive strategic decision-making. By leveraging AI-driven analytics, businesses can identify hidden opportunities and mitigate risks, leading to informed product development, targeted marketing campaigns, and enhanced customer engagement. It's crucial to embrace AI tools tailored for data mining to ensure efficient and precise analysis. Let's harness the power of AI to unlock actionable insights and drive business success!

AI is a powerful tool for data mining, but remember it's all about setting clear goals and choosing the right AI technique for the job. Pattern Recognition: AI algorithms can analyze vast amounts of data to identify complex patterns and relationships that might be missed by humans. This can reveal hidden trends, customer preferences, or anomalies. Automated Insights: AI can automate data cleaning and feature selection, saving you time and ensuring your models are trained on high-quality data. Predictive Power: AI lets you build models to predict future trends or customer behavior. This can inform strategic decisions and help you stay ahead of the curve.

Before using AI on a large data set: * Try on a small scale set of data to make sure that there is no obvious bias introduced by the AI or the modeling technic used. * Make sure the data is not corrupted. AI hallucinations are real and prompt injection is also a reality you must take into account. * Question the interest of "old" data for the use case you are covering. Processing vast amount of data comes at a cost and you might not need to go 6 years back to identify recent trends

While working on developing an application to detect the type of activity/workout a person is doing based on data collected by any smart wearables or phones, data mining played a crucial role in demystifying different types of analytics based on gathered data. They were not possible without AI-driven data mining techniques. Different data mining techniques such as Clustering, Classifications, Regression etc helped us find hidden analytics based on our data.

Imagine sifting through mountains of data to find a golden nugget of insight. AI automates data mining, efficiently uncovering hidden patterns and trends you might miss. For example, an AI system can analyze customer purchase history to identify buying habits and recommend targeted promotions. For example a retail chain uses AI to analyze customer purchase data. The AI discovers a correlation between purchases of diapers and beer. This unexpected insight might reveal dads stocking up on groceries after picking up diapers for their babies. The retailer can then tailor promotions to target this specific customer segment.

Predictive AI and GenAI models can only succeed when business teams are in control of them and rapid and risk free iterations are possible. These are the musts for these great next gen tools: -nocode, easy and clean use -use case and rapid ROI approach -team collaboration, even inter departmental teams -continuous self learning and explanations -business SME and decision maker in the loop -always WIP company culture

Incorporating AI into your predictive modeling can dramatically enhance your efforts. AI algorithms allow you to develop highly accurate models for forecasting consumer behavior, sales trends, or market dynamics. Utilizing AI improves the precision of your forecasts and speeds up the analysis process, enabling quicker decision-making.

AI excels at building predictive models. By analyzing historical data and identifying patterns, AI can forecast future trends with surprising accuracy. This allows businesses to make proactive decisions and optimize strategies. For example, a logistics company uses AI to predict equipment failures based on sensor data and historical maintenance records. This enables them to schedule preventive maintenance, preventing costly downtime and ensuring deliveries run smoothly.

Predictive models can be used for various business activities and on different business cases. You can use predictive models to traffic the amount of traffic that comes into your store using the footage and thus equip all the tills with enough staff to ensure high customer service and customer satisfaction

Regularly update and validate your models to maintain their accuracy over time. Data drift can significantly impact performance. Implement cross-validation techniques to check the robustness of your models.

AI shines in real-time analysis by continuously crunching data. Faster Processing: AI algorithms can analyze massive amounts of data instantly, identifying patterns and anomalies in real-time. This allows you to react quickly to situations, like sudden changes in customer behavior. Predictive Analytics: AI can use real-time data to predict future events or trends. This can help you make informed decisions on the fly, like personalizing user experiences in real-time. Automated Actions: AI can even be programmed to take automated actions based on real-time insights. This can streamline processes and improve efficiency, for example, automatically flagging suspicious activity or triggering targeted marketing campaigns.

Depending on the use case, real time analysis is a must. If you are monitoring systems, you have to have real-time analysis, especially to catch system failures or fraud or security issues. For use cases like fraud, security it is apparent that you need real time predictions. In the workflows where the data about customer or documents are updated, the predictions will need to be redone to enable the next set of actions.

Real-time is not necessarily a must have. System architecture is not defined based on some hype, but on the need of the business / application. It is critical to understand the need to define if there is such a need as real-time comes at a cost.

Leverage cloud computing resources to handle large data streams efficiently. Utilize AWS or Azure for scalable real-time data processing capabilities.

No more waiting for reports! AI can analyze data streams in real-time, providing instant insights that inform immediate action. This is crucial in fast-paced environments like stock markets or social media monitoring. For example, a financial services company uses AI to analyze real-time stock market data. The AI identifies sudden fluctuations in a specific stock and alerts traders of potential buying or selling opportunities.

Automation offers myriad benefits: increased efficiency, cost savings, improved accuracy, scalability, better decision-making, enhanced customer experience, and improved compliance and risk management. It streamlines processes, reduces errors, and allows businesses to adapt and innovate more effectively.

Artificial intelligence does not replace the researcher but can supercharge your productivity. Consider 3 automations: 1. Reduce repetitive work. Why manually enter, extract, or check (and double check) the same task over and over? Automate with AI to free you up to be humanly! 2. Detect changes & duplication + de-duplication. Why not produce timely and precise output? Come to think there was a time when we used the “lookup” function that took a long time to look up or even the occasional “eyeballing” method… 👀 3. Be aware of AI “hallucination”! It refers to when the model generates unfounded text. Try not to use AI to conduct literature review, and if you must, carefully vet the sources that they are factual and align with your input.

AI can automate repetitive data analysis tasks, freeing up human analysts for more strategic work. Imagine AI handling data cleaning, formatting, and generating basic reports, allowing you to focus on interpreting the insights and making strategic decisions. For example, a healthcare provider uses AI to automate patient data analysis. The AI sorts through medical records, flagging potential health risks and generating preliminary reports. Doctors can then prioritize cases and tailor treatment plans based on the AI's insights.

Automation, significantly enhances efficiency by streamlining various processes involved in data collection, analysis, and reporting. Through AI-driven automation, tasks that once required considerable time and effort can now be completed swiftly and accurately, freeing up resources for more strategic endeavors. Additionally, AI enables scalability, allowing market research efforts to seamlessly adapt to varying data volumes and dynamic market conditions without the need for extensive manual intervention. This scalability ensures that businesses can efficiently handle large datasets and remain agile in responding to market changes, thereby maintaining a competitive edge.

Automation used to help when you had an idea on what kind of rules to apply. Supporting automation with AI is a must, to avoid "exception"s. Especially when the business keeps changing, automation has to be updated with AI, since rules will be impossible to manage.

We found the use of explanation models, simulation scenarios and constant documentation of data and models to help a lot with understanding and avoiding biases in the models. Explanations have to be available not for technical but also business teams. Ability to give feedback or to correct automatically when something is not right is also extremely important.

AI in data analysis offers transformative potential, but its implementation must be approached ethically. Transparency is crucial; companies should clearly communicate how AI systems operate and make decisions. Data Privacy is paramount, necessitating robust protection measures and adherence to privacy regulations to secure personal information. Bias Mitigation requires using diverse datasets and implementing techniques to identify and correct biases to prevent unfair outcomes. Ethical AI practices protect consumers, enhance credibility, and build trust in market research findings.

There has to be a long and tough discussion on the ethical values of AI it’s also has to be unanimous so that it would be the preceding policy for all AI driven systems to follow through to keep the integrity and transparency of the system

Ethical considerations are paramount in the implementation of AI in various domains. Ensuring transparency, fairness, and privacy protection in data usage is essential to maintain trust with stakeholders. Additionally, measures must be taken to mitigate biases inherent in AI algorithms to prevent discrimination or exclusion. Striking a balance between data-driven insights and ethical principles is crucial for building sustainable and responsible AI systems. Handling ethical considerations in AI implementation requires: Transparency and accountability, Fairness and bias mitigation, Privacy protection, Inclusive design, Continuous monitoring and evaluation, Ethics committees and guidelines and Education and awareness.

Ethics is a key element. However, this is a new subject for private organizations. We recommend to pu in place an adequate governance, at local as well as at central levels. For the latter, it should gain from being composed of external and independent experts —among with philosophers and sociologists to broaden the view.

Another major statement one has to consider before jumping into AI: those project are all but only technical! Business Consulting is crucial to fully grasp and address the different stakes: ethical, legal & compliance, change management and adoption, financial mastering, carbon emissions and ecological issues, strategic independance or even sovereignty topics. AI projects are deeply transformative and have to be led with great responsibility and conscious.