What do you do if you need to make data-driven decisions as an AI professional?

“If you can’t use your data, you can’t use AI”... this statement by Julie Sweet, CEO da Accenture at the end of 2023 is self explanatory about the crirical role of having, first, reliable data, and, second, understanding their rationale

Incorporate predictive analytics techniques to forecast future trends, outcomes, and behaviors based on historical data patterns. By applying statistical models, machine learning algorithms, and predictive modeling tools, you can anticipate potential scenarios and mitigate risks proactively.

When making data-driven decisions in AI, start by gathering all the data you can. This means looking at numbers, trends, and patterns related to your problem.

Consider the broader context and constraints that may impact the decision-making process. Take into account factors such as business objectives, regulatory requirements, ethical considerations, and resource constraints when making data-driven decisions.

🔍 Comprender los datos antes de tomar decisiones 🔍 Antes de sumergirte en la toma de decisiones, asegúrate de tener una comprensión sólida de tus datos. Examina la calidad, relevancia e integridad de la información. Limpia y preprocesa los datos para evitar resultados sesgados. Considera la fuente de tus datos y los posibles sesgos inherentes. Recuerda, la base de cualquier decisión basada en datos radica en la integridad de los mismos. Sin precisión, tus decisiones pueden llevarte por mal camino. 🚀

Next, clean your data. Sometimes data can be messy or confusing. Cleaning it up means making sure everything is accurate and organized, so your decisions are based on good information. Then, use tools and techniques to analyze your data. This could mean using software to look for specific trends or asking an expert for their analysis. It's like using a magnifying glass to look closer at your clues.

Clearly define the objectives of your analysis. What specific questions or problems are you trying to address? What outcomes are you hoping to achieve? Setting clear objectives will help guide your analysis and ensure that you focus on relevant data and insights.

Define goals for decision making. One of the most common mistakes is setting too many goals. Set at max 3 goals. Define quantifiable metrics to measure the success/ failure of the goals. These metrics will help you decide on data requirements because not all data is required for everything. Ex- My goal is to increase the automation from my AI assistant in writing emails. Quantifiable metrics would be : 1. No of emails composed by AI and sent as it is/Total number of email sent- full automation 2. No of emails written by AI and edited before sent/Total number of email written by AI- Partial automation 3. % of edits in AI generated email- effectiveness of email

When objectives are well-defined, AI professionals can align their data collection and analysis efforts to directly support those objectives. This ensures that the data gathered is relevant and meaningful for informing decision-making processes. Additionally, clear objectives enable AI professionals to prioritize tasks and allocate resources effectively, maximizing efficiency and productivity.

Integrate ethical considerations into every stage of the AI development lifecycle, from data collection to model deployment. Proactively identify and address potential biases, privacy concerns, and unintended consequences of AI systems.

Rather than viewing analysis as a one-time task, treat it as an ongoing process of refinement. You can uncover deeper insights, refine your understanding of complex phenomena, and make more informed decisions.

Use appropriate data analysis techniques to identify trends and patterns in your data. This may involve statistical analysis, data mining, machine learning, or other methods. Look for correlations, anomalies, and other insights that can help inform your decision-making process.

Finally, don't forget to test your decisions. Once you've made a choice based on your data, see if it works out as expected. It's like checking to see if you've solved the mystery correctly. If not, you can always try again.

Analyzing trends allows AI professionals to identify patterns and relationships within the data that may not be immediately apparent. This deeper understanding enables them to uncover hidden insights and correlations that can inform decision-making processes. By identifying recurring patterns or shifts in trends over time, they can make proactive decisions to capitalize on emerging opportunities or mitigate potential risks, identify areas for improvement, and adapt their strategies accordingly

Analyze the data to identify trends, patterns, and correlations that can provide insights into potential drug candidates. This could involve using statistical analysis, machine learning algorithms, or other data mining techniques. Analyzing gene expression data from cancer patients can help identify genes associated with drug response, leading to the development of personalized treatments.

If you're using machine learning models or other predictive models, evaluate their performance using appropriate metrics. This may include metrics such as accuracy, precision, recall, and F1-score. Evaluate the models on a separate test dataset to ensure their generalizability.

Model evaluation → data-driven decisions in AI → Go beyond accuracy - consider precision, recall, F1-score ↳ These metrics reveal trade-offs & guide model fine-tuning → Cross-validation gauges performance on unseen data ↳ Ensures model generalisability & robustness → Evaluate models on a separate test dataset ↳ Avoids overfitting & confirms real-world applicability → Metrics are like a model's report card ↳ They highlight strengths & areas for improvement → Regularly assess model performance ↳ Ensures continued effectiveness & identifies drift

Evaluating models allows AI professionals to assess their predictive accuracy and generalization capabilities. By comparing model predictions against ground truth or known outcomes and specific performance metrics, they can determine how well the model captures the underlying patterns and relationships within the data. This helps ensure that the model can make reliable predictions on new, unseen data, thereby enhancing its utility in real-world applications

• Develop and evaluate predictive models to assess the effectiveness of potential drug candidates. • This involves training machine learning models on labelled data and evaluating their performance using metrics such as accuracy, precision, and recall. • Researchers can use machine learning models to predict the binding affinity of a compound to a target protein, helping in the selection of promising drug candidates for further testing. • Conduct experiments and tests to validate the predictions made by the models. • This could involve in vitro assays, in vivo studies, or clinical trials to assess the safety and efficacy of potential drug candidates.

Test and retest the data models on large datasets to evaluate the outcomes. This ensures the models are trained correctly for the performance and reliability. Understand the patterns between the datasets and fine tune the model before running it again for precision.

Your model might have good accuracy metrics, but at the end of the day you need to be able to measure how well it helps the businesses. If you are set up to run an A/B test there is no better way to measure actual business value. Maybe your recommender system has a great recall@K, but does it actually drive conversions?

The model implemented should meet the business objectives hence it is important to test the models on large real time data sets before implementing. Each iteration of testing on a different data set would help understand the predictability of the model and its application of to the problem we are trying to solve.

Implementing tests is critical for ensuring the reliability, robustness, and validity of data-driven decisions in AI. By systematically validating data, models, and analytical processes, AI professionals can mitigate risks, enhance the quality of decision-making outcomes, and foster trust and confidence in AI technologies across various domains and applications

Ethical considerations play a vital role in guiding decision-making processes and mitigating potential harms associated with the development and deployment of AI systems. By considering this context, industry can strive to develop AI systems that are fair, transparent, and accountable, thereby promoting equity and inclusivity in decision-making outcomes. This helps ensure that AI technologies prioritize the well-being and rights of individuals and communities. Additionally, it facilitates compliance with legal and regulatory requirements governing the use of data and AI technologies.

Throughout this whole process, I always try to take a step back and consider the broader ethical implications of how this technology could be applied in the real world. What are the potential negative consequences, even if unintended? Proactively addressing issues around fairness, accountability and transparency needs to be central rather than an afterthought. At the end of the day, working as an AI professional requires blending quantitative and qualitative methods - leveraging data while maintaining a strong ethical mindset and business acumen. It's a delicate balancing act, but getting it right can lead to incredibly impactful data-driven decision making.

"Most decisions should probably be made with somewhere around 70% of the information you wish you had. If you wait for 90%, in most cases, you're probably being too slow. Plus, either way, you need to be good at quickly recognizing and correcting bad decisions." - Jeff Bezos To paraphrase Andy Jassy, speed matters disproportionately at every stage of a business, regardless of the size of the firm. Speed is not preordained. Speed is a choice. It's very easy with AI models to overthink and over analyze. Analysis paralysis is equally if not more deadly than moving too quickly. This is why it's critical to have an early sense on how you're going to measure model/solution performance and spend time thinking about your test and learn plan

• Other factors to consider include regulatory requirements, market demand, and resource constraints. • Drug discovery timelines are often influenced by regulatory approval processes • Market demand may vary based on factors such as disease prevalence and competition from existing treatments • Resource constraints such as budget and personnel availability can impact decision-making processes and project timelines • Therefore, it's important to consider these factors when making data-driven decisions in drug discovery.

Ensuring interpretability and explainability in AI models and decision-making processes fosters transparency and understanding among stakeholders, enabling them to comprehend how decisions are made. Scalability and sustainability considerations are crucial for long-term success, encompassing the assessment of scalability, environmental sustainability, and ethical responsibility of data-driven solutions. For last, embracing continuous learning and improvement through ongoing professional development and staying updated on technological advancements and ethical considerations enhances the skills and expertise to remain at the forefront of innovation in the field

I highly recommend recognizing that pre-sales activities offer rich qualitative data on customer needs, market trends, and competitive dynamics. This information is invaluable for making informed decisions that are reactive and predictive of future market movements. I like to use a 4-step framework: 1. Assess current data strategies and the pre-sales processes to identify gaps and opportunities. 2. Align these insights with broader business objectives, ensuring decisions are geared towards strategic goals. 3. Act by integrating AI and analytics into pre-sales, transforming raw data into actionable intelligence. 4. Analyze outcomes using key performance metrics to measure the impact of these decisions and iterate where necessary.

Since it's decision making, it's important to know the domain. Therefore, domain experts should be included at every stage and in decision-making as well. They provide crucial context and understanding that AI alone cannot, ensuring that decisions are not just data-driven but also practically applicable and nuanced by industry-specific knowledge.