Hier erfahren Sie, wie Sie die Entscheidungsfindung in einer Karriere im Bereich Data Science durch strategisches Denken verbessern können.

In the field of data and AI, professionals tend to skip the basics of programming, statistics, algorithms, data manipulation, and other fundamental principles. However, this foundational knowledge is essential for better understanding problems and making informed decisions.

Along with all mentioned, a good understanding of the business is also important to make decisions in data science. It plays a key role that helps in making sense from the DS outputs and validating them.

To make better decisions in Data Science with strategic thinking, one needs to also understand the business context, have a solid grasp of data fundamentals, be statistically literate, and be familiar with various modeling techniques. Critical analysis skills are essential for questioning data and model assumptions, while ethical awareness is crucial for responsible data use. Clear communication of insights to stakeholders and a commitment to continuous learning are also key components for success in the field.

To enhance decision-making in Data Science: Understand Business Goals: Align your work with company objectives and stay in touch with stakeholders to grasp their needs. Solve Problems: Define the issue clearly and test hypotheses with data. Use Advanced Tools: Apply predictive models and optimization techniques to forecast trends and suggest best actions. Promote Data Use: Help others understand data and ensure your results are trustworthy. Combine and Clean Data: Integrate data from various sources and ensure its accuracy. Communicate Clearly: Use visuals and simple language to present findings and recommendations. Review and Improve: Track outcomes, adapt based on feedback, and stay updated with trends.

Improving decision-making in a data science career through strategic thinking involves a few key steps. Start by understanding the big picture and long-term goals of your projects and organization. This helps you prioritize tasks that align with these objectives. Analyze data with a focus on actionable insights that can drive meaningful change. Regularly review your decisions and their outcomes to learn from successes and mistakes. Stay informed about industry trends to anticipate future needs and opportunities. Collaborate with colleagues to gain diverse perspectives, and always keep a flexible mindset to adapt to new challenges and information.

Effective decision-making in data science hinges on a strong foundation of inquiry. By cultivating a culture of questioning, data scientists can uncover deeper insights and drive more impactful outcomes. Precise Question Formulation: Crafting well-defined questions is essential to focus analysis and problem-solving efforts. Critical Inquiry: Challenging assumptions and exploring alternative perspectives can lead to innovative solutions. Data-Driven Exploration: Utilizing data to answer complex questions is the core of a data scientist's role. Iterative Questioning: Continuously refining questions based on emerging findings enhances the decision-making process.

I think we must always begin by identifying the problem , trying to solve and understanding who will be affected by the analysis and the long-term implications. For example, if I'm working on a project to improve customer retention, I have to ask questions like, "What factors are causing customers to leave! ?" and "How will our findings impact our retention strategy?" By focusing on these questions, we can gather the most relevant data and use the right analytical techniques, ensuring project and work is technically sound and aligned with business goals.

Asking questions in the data analytics field is the most phase of successful data driven decisions. It is the steering wheel of your ship. if you deviate in the problem-centered questiones, your voyage would reach the wrong destination where pirates awaiting to destroy your business.

Frame the problem first. Always ask yourself myriad questions. Do I understand the economics of the division, product, industry, or company I’m analyzing? Is this a data-only problem or does it need to be augmented with focus groups and surveys? Does the analysis need additional tables beyond what’s inside the organization or does the company need to buy additional datasets to paint a better picture making the analysis much more impactful? Do you need to reinvent the wheel and create a new model organically or leverage an existing one? Also, it is key to map out multiple scenarios leveraging lateral thinking such as coming up with good, better, best, or halt (worst case) scenarios with planned metrics.

In my experience, asking questions is key to making better decisions in data science. If you don’t start by asking yourself things like: What are we trying to achieve? or Why is this important? it’s easy to get lost in the details. Whenever I take the time to question what data I need or what would happen if I tried something different (This brings surprises haha), the analysis improves, and the decisions I make are more aligned with the goals. In the end, asking the right questions always makes a difference.

Absolutely! The dataset provided at the start of any business project is often raw, with several columns being irrelevant to the scope of analysis. In such cases, it's crucial to prepare strategically and ask yourself questions aligned with the project's goals and objectives. To ensure effective analysis, you can follow some basic hygiene practices: reduce bias and errors, ensure trustworthy data sources, and identify outliers. Apart from this you can also: 1. Prioritize Feature Selection: Focus on identifying and selecting the most relevant features from your dataset. 2. Data Normalization: Ensure that your data is normalized or standardized as needed.

usually not all data is equally useful, so must assess its reliability, completeness, and potential biases. For example, in analyzing customer feedback to improve a product, we shuld consider whether the feedback is from a representative sample of users or if it's skewed towards a particular group. This strategic approach ensures that we are not just looking at what the data shows but also understanding what might be missing or misrepresented, leading to more meaningful insights and better decisions. There are many different aspect and ideas, but it what I think.

Evaluating data quality and relevance is crucial in strategic decision-making. As a data scientist, you must critically assess datasets to ensure they yield meaningful insights. This involves examining: 1. Reliability: Is the data accurate and trustworthy? 2. Completeness: Is the data comprehensive and representative? 3. Bias: Are there any underlying biases or assumptions? Strategic thinking takes it a step further by considering: 1. Omissions: What is the data not telling you? 2. Obscurations: What might the data be hiding or distorting? 3. Context: How does the data fit into the broader business context?

Is the data collected will help us to make a decision or calrify a pattern towards our Questions (or goals)? If the data is not relevant we won't make progress at all. Here we should matvh the features that our data reveas with the features that has been asked before.

A lot of research recently emphasizes the importance of data quality. Some data could poisson your model and overthrow all the good conclusions. Before taking step on the modeling training, it could be beneficial to perform an EDA of the data collected. Sometimes we need data cleaning and sometimes we need to augment data. Those works are intense but very important and necessary.

we look beyond immediate results to see how the insights can benefit the organization and bussines. For example, if we discover a trend in customer behavior, we must think about how this can inform future strategies or lead to new products. By developing predictive models, we help guide decisions that align with our company's goals. This approach ensures analysis isn't just data-driven but also strategically aligned with our long-term objectives.

Data always has a story to tell. Being able to understand that story is what can be hard. When you have data analyzed, visualized, and overall combed through, it is important to think of the human side of things. Strategic thinking involves looking beyond immediate results to understand how your insights can be applied for maximum benefit. An example could involve ML & predictive models. Your analysis should serve as a motivator for actions that align with your company's goals along with human insight of said analysis to look past the bottom line for other implications.

When analyzing data, it's crucial to consider the potential impact of your findings. Reflect on how your insights will influence decision-making within your organization. Strategic thinking involves extending beyond immediate results to understand how your analysis can drive meaningful action. Consider developing predictive models that guide future strategies or identifying trends that could inspire innovative products or services. Your analysis should not only address current needs but also serve as a foundation for strategic decisions that align with your company's goals. By focusing on the broader implications, you ensure that your work contributes to long-term success and aligns with organizational objectives.

La ciencia de datos esta orientada a tener un impacto 100% en las organizaciones , entre mejor se tengan los modelos y se implementes los procesos que nos permitan generar Máquinas que aprendan y que automáticamente generen un mejor conocimiento de nuestra información y de la de nuestros clientes eso se verá impactado en beneficios para los procesos y problemas que se quieran resolver, actualmente en los proyectos en los que he estado involucrado tanto en sector privado como en púlbico la expectativa de imapcto es alta en todas las instituciones que se enfocan en implementar este tipo de soluciones. Y como he comentado en otros temas no importa el tamaño de la empresa e institución

When analyzing data, always consider the potential impact of your findings. How will they influence decision-making within your organization? Strategic thinking means looking beyond immediate results to understand how your insights can drive future strategies or spark innovation. Your analysis should be a springboard for actions that align with your company's goals, ensuring your work not only informs but also propels the organization forward.

Data science, data analysis and etc can be complex, so focus on making news insights understandable and actionable. For example, when we want to discover a pattern in customer data, we can use visualizations and simple narratives to explain it to stakeholders without a technical background. This way, everyone can grasp the significance of the data and make informed decisions. Clear communication ensures strategic thinking leads to meaningful actions within the organization.

In my experience, being able to take messy data and turn it into human centered/readable data is something that is never entirely straightforward. What is the goal, is there bias in the data, who are you presenting to? Being able to understand all of this and being able to display the data in such a way that is a readable by people with non-technical backgrounds, even someone not in a certain industry, is a great goal to have. Crystal clear communication ensures that your strategic thinking translates into informed decisions across an organization.

Effective strategic thinking relies on clear communication of findings. Data science is complex, but simplifying and presenting insights is crucial for adding value. When presenting your findings: Understand Your Audience: Tailor your message to their background and expectations. Use Simple Visualizations: Create clear visuals that tell a story and make complex data accessible. Provide Context: Use narratives to explain the significance and implications of the data. Be Clear and Precise: Ensure your insights are easily understood and actionable for informed decision-making. Clear communication transforms complex data into actionable insights, influencing decisions across the organization.

Clear communication is essential for strategic thinking in data science. While the field is complex, your ability to simplify and convey insights effectively makes your work truly valuable. Use visualizations and narratives to make your analysis accessible to non-technical stakeholders. This ensures that your strategic insights lead to informed decisions and meaningful actions across the organization.

Effective communication is a key component of decision-making in data science. Presenting your findings in a clear and concise manner ensures stakeholders understand the insights and their significance. This facilitates informed decision-making and aligns teams around common goals. Tools: Utilize data visualization tools like Tableau or Power BI to create compelling presentations. Focus on key metrics and actionable insights, and tailor your message to your audience's level of technical expertise. Real-Life Example: When presenting to executives, focus on high-level insights and strategic recommendations rather than technical details. Use visuals to highlight trends and correlations.

I think that after each project, we must take time to assess what went well and what could be improved. For example, if a predictive model that developed was accurate but slow, we will reflect on how we can optimize it for speed next time. By constantly refining these approach, we develop a deeper understanding of leveraging data science strategically, ensuring better outcomes in future projects. This ongoing process helps me grow and improve these decision-making skills.

Every time you do data analysis/science, the process will be different. The data will change, how you get the data will change, the client will change, the goal will change, and the technology behind it all will change. Learning from errors, from others, from new technologies, and reflecting on them to better your skills is vital to stay competitive in data science.

EL proceso de reflexión una vez implementada estas soluciones , en caso de haber sido exitosas el impacto será grande y la utilización muy fuerte incluso sorprendentes del poder al que se pueda llevar sin saber los límites de esto , de ahí también se tiene que reflexionar sobre el imapcto en la sociedad del futuro cercano, es algo que se tienen que reflexionar

Make your processes reproducible and historized. By automating them as much as possible, you formalise how your data is transformed, and therefore provide a unambiguous explanation of your methodology. It also fosters smoother evolutions of this methodology, by small but continuous increments. Track the impact of these changes with dedicated tools, such as MLFlow.

Strategic thinking in data science is an iterative process of learning and adaptation. Reflecting on project outcomes and refining decision-making strategies fosters continuous improvement. This ongoing refinement enhances proficiency in leveraging data strategically, driving future success and innovation in data science careers.