What do you do if your boss's expectations are unrealistic as a data science professional?

It is common that non-data people underestimate the complexity of building new solutions. When this is the case, the best strategy is to set up a detailed plan, which encompasses all steps of the process, from accessing the data, preprocessing, training, testing, to deploying and tracking model performance. The plan should comprise not only the technical part, but also the intricacies of involving business people, and everyone who will need to collaborate to make the project a success. Spending time constructing and reflecting upon this plan will help your boss better understand the process and recalibrating her expectations.

When your boss's expectations seem unrealistic for your role as a data scientist, it's important to address the situation diplomatically and strategically. Here are some steps you can take: 🟧Understand the expectations 🟧Communicate clearly and honestly 🟧Present alternatives 🟧Demonstrate value 🟧Negotiate 🟧Document everything If, despite all efforts, your boss's expectations continue to be unrealistic and impossible to meet, it may be necessary to reconsider whether this is the right environment for you to develop your career as a data scientist. Sometimes, it's better to seek opportunities in an environment where your skills are valued and your contributions are realistically recognized.

When facing unrealistic expectations, start by evaluating the situation realistically. As a data scientist, assess the feasibility considering your available time, data, and resources. Remember, tasks like data cleaning can consume up to 80% of your project time, and insights from unstructured data are often unpredictable. It’s essential to communicate these realities clearly to manage expectations effectively.

As a data science professional facing unrealistic expectations from your boss, it’s crucial to address the situation effectively to ensure a productive and sustainable work environment. Here are steps to manage this challenge: - Communicate Clearly - Set Realistic Goals - Educate and Inform - Seek Support and Resources - Document Everything By addressing unrealistic expectations through clear communication, education, and proposing practical solutions, you can foster a more realistic and supportive working relationship with your boss.

As a data scientist, when faced with unrealistic expectations about data science from a boss, the first step would be to contextualize by searching for similar use cases and how data supported achieving the objective. Next, it s crucial to break down these expectations into stages. Within each stage. to consider : 👉 Identify the objectives of each stage that will support achieving the expectations. 👉 Identify the data-level requirements and technological architecture involved in all stages. 👉 Each stage should lead to decision-making that contributes to project advancement. Integrating these stages coherently, a comprehensive approach can be achieved that allows expectations to be met more realistically and effectively.

If a boss's expectations seem unrealistic as a data science professional, it's important to communicate this concern openly and professionally. Provide a clear explanation of the challenges and constraints, using data and facts to support your points. Suggest alternative solutions or timelines that are more realistic, while still aiming to achieve the overall objectives. It's also beneficial to educate your boss about the data science process, helping them understand the time and resources required for different tasks. Regular updates on progress and any potential issues can also help manage expectations. Remember, effective communication and negotiation are key in such situations.

Clarify Goals: Make sure you fully understand what your boss expects. Ask for detailed requirements, timelines, and the desired outcomes. Document Expectations: Write down the expectations as clearly as possible, including any specific metrics or deliverables mentioned. Evaluate Resources: Consider the resources available to you, including time, data, tools, and team members. Determine if these resources are sufficient to meet the expectations. Technical Feasibility: Assess whether the technical requirements are achievable with the current state of technology and your team's expertise. Timeline Assessment: Compare the timeline given with the typical duration of similar projects. Determine if the deadlines are reasonable.

When facing unrealistic expectations from your boss as a data science professional, it's crucial to communicate openly and transparently. Start by discussing the specific challenges and constraints you're encountering, propose feasible alternatives or adjustments, and emphasize the importance of setting realistic goals aligned with available resources and timelines. Building a collaborative dialogue can help manage expectations effectively while maintaining a focus on delivering valuable insights and results.

I think this can be resolved with simple steps: 1. Clarify expectations 2. Provide realistic estimates and offer solutions 3. Communicate challenges transparently 4. Negotiate and collaborate on achievable goals

When faced with unrealistic expectations from your boss as a data science professional, it's crucial to assess the reality of the situation. Start by clearly communicating the limitations and feasibility of the given tasks or deadlines based on available resources and data quality. Provide alternative solutions or adjustments to align expectations with practical outcomes. Additionally, prioritize tasks based on their impact and feasibility to manage workload effectively. Open and transparent communication with your boss about challenges and constraints fosters mutual understanding and helps set realistic expectations moving forward.

Educate them on what data science can and cannot do. Get support/buy-in from a senior peer if hitting a dead end. Open a clear dialogue and break problems down into skill gaps vs lack of support gaps

Gather Data: Collect evidence to support your points, including project timelines, resource availability, and examples of similar past projects. Identify Key Points: Outline the main issues you need to address, such as unrealistic deadlines, resource constraints, or technical challenges. Propose Solutions: Think of alternative approaches or compromises that could help align the expectations with reality. Schedule a Meeting: Set up a dedicated time to discuss your concerns. This ensures you have your boss’s full attention. Private and Professional Environment: Choose a setting where you can speak openly without interruptions, maintaining a professional atmosphere.

In data science, clear communication is crucial, especially when managing expectations. If faced with unrealistic goals, initiate a constructive dialogue with your boss. Begin by affirmatively acknowledging the project’s vision, then carefully explain the practical constraints and potential risks of rushed analytics, such as compromised decision quality. Emphasize how a realistic timeline can enhance accuracy and lead to more informed business decisions. This approach not only sets feasible expectations but also demonstrates your commitment to the project’s success.

Communicate your assessment to your boss clearly and professionally. Explain the challenges and the reasons why the expectations might be unrealistic. Be honest but respectful in your communication.

Assuming the boss is technical enough to have a fair perspective and estimate, it is beneficial to exchange notes. Have they considered reusing components of existing solutions? Are they expecting an MVP rather than a comprehensive solution? Does their definition of MVP differ from mine? Can they suggest a shortcut? Did they miss any essential aspects of the project? Is the ask only a feasibility study rather than a comprehensive solution requiring a few sprints to train, tune and test in the real world?

Initiate a respectful and constructive dialogue with your boss. Clearly and honestly communicate your concerns about the unrealistic expectations. Use data and evidence to support your points, such as project timelines, resource availability, and case studies or benchmarks from the industry.

Unrealistic expectations from your boss as a data scientist? Don't panic, communicate! 🗣️ Initiate an open dialogue to explain the feasibility of the task 🧠 Break down complex concepts in a way non-technical stakeholders understand 💼 Relate limitations to business outcomes 🔍 Focus on finding solutions, not just highlighting problems Never forget: 50% of your job is "managing up." Master this skill to thrive in your data science career.

Escolha um bom momento para ter uma conversar franca e aberta com seu líder, especialmente se ele for uma pessoa muito ocupada. - Deixe claro logo de início que desejar entender como é a visão dele sobre o projeto em que está trabalhando e como ele formou as expectativas de resultado. - Escute com muita atenção e faça anotações. - Tenha consigo uma lista do que considera os prós e os contras do projeto, assim como as partes que entende que não poderão ser endereçadas. - Apresente alternativas sempre.

This is a very common case in data science. It is one of the most hyped things in this decade. A lot of companies hire data scientist with unrelaistic expectation that it will solve all of their problems. but the reality is you cannot solve all the problems. sure you can build models improve efficiency, improve process but again having the right data and data quality is readily important. it s not a magic and that can fix everything. it is really important to discuss what can be and what cannot be done, and the amount of time and resources it will take to get their. setting this expectation early on will be extremely helpful in the long run

I have found that setting up regular check-ins with my boss to discuss ongoing projects can be very effective. During these meetings, I present progress updates, discuss any encountered issues, and adjust timelines if necessary. This proactive approach not only keeps everyone informed but also builds trust and transparency. I also use visual aids, like charts and graphs, to make technical concepts more accessible.

When addressing unrealistic expectations, frame your approach around data-driven decision-making. For instance, if tasked with developing a predictive model under tight deadlines, propose an initial phase of exploratory data analysis. This phase helps in identifying key variables and potential data issues, crucial for the model's accuracy. Use this scenario to illustrate the value of phased approaches—ensuring each step enhances overall project credibility and reduces long-term risks. This method underscores your strategic foresight and dedication to quality outcomes.

Prioritization should always be considered ... High-impact tasks: Identify the tasks with the greatest potential impact on business objectives and prioritize them over less important tasks to make the best use of resources. Communicate constraints: Clearly communicate resource constraints and technical limitations to your boss so they know within which boundaries expectations should fall. Stakeholder alignment: Align with key stakeholders on project priorities and goals to foster collaboration and support realistic expectations.

In data science, setting clear priorities is key when facing unrealistic expectations. Engage your boss in discussions to align on the primary business objectives. Identify the most impactful tasks and prioritize them, focusing on quick wins to demonstrate value early. This strategic approach not only manages workload but also ensures that critical analyses receive the necessary attention, fostering a productive and realistic work environment. Push back on less urgent tasks to maintain focus on priorities.

Identify Key Deliverables: List all the tasks and deliverables required for the project. Evaluate Impact: Determine the impact of each task on the overall project goals. Identify which tasks are critical and which are less essential. Estimate Time: Calculate the time required for each task based on past experience and available resources. Assess Resource Availability: Identify the resources (e.g., team members, tools, data) available for the project and any constraints. Critical Tasks: Mark tasks that are crucial for achieving the project's main objectives. Secondary Tasks: Identify tasks that are important but not critical. Tertiary Tasks: Highlight tasks that are nice-to-have but can be deferred or scaled back if necessary.

If your boss's expectations are unrealistic, prioritize by: Clarifying Objectives: Request a meeting to understand the critical goals and their impact on business outcomes. Data-Driven Evidence: Present data or case studies to illustrate the feasibility and resource requirements of the tasks. Actionable Plan: Propose a phased approach, breaking down tasks into manageable milestones with clear timelines. Resource Allocation: Highlight the need for appropriate resources, be it time, tools, or personnel, to meet the expectations realistically.

From my idealogy I am sure that Setting priorities is crucial when faced with unrealistic expectations as a data science professional. Start by identifying key objectives that align with the organization's goals and focus on delivering value in those areas. Communicate with your boss to clarify expectations and discuss the feasibility of the tasks at hand. Prioritize tasks based on their impact and feasibility, and be prepared to negotiate timelines or resources if necessary. By setting clear priorities and managing expectations, you can work more effectively towards achieving realistic goals.👌✔

Prioritization should always be considered a dynamic process. One approach I use is the MoSCoW method (Must have, Should have, Could have, Won’t have) to categorize tasks and features. This method helps in clearly communicating what is essential versus what can be delayed or omitted, ensuring that resources are allocated efficiently to achieve the most critical business outcomes first.

In discussions with your boss, it is helpful to set clear priorities. With a multitude of possible analyzes and directions on any given data set, data science can quickly become overwhelming. Work with your boss to understand what the ultimate business goals are and prioritize tasks that align with those goals. Tips: * Identification of commercial objectives. * Priority map. * Quick profits. * Impact and effort analysis. * Planning and adjustment of deadlines. * Continuous reevaluation. Taking this prioritization approach will not only help you manage work more effectively, but will also demonstrate your alignment with business objectives and your ability to add strategic value through data science.

Discuss which aspects of the project are most critical and propose focusing on these areas first. This helps in managing resources better and achieving the most important goals.

Data Science can be overwhelming, especially when starting up something. Once you address the issues and their inapplicability, try to prioritise the tasks to clarify the requirements and expectations. This helps you and your boss understand the actual expectations.

A great way to deal with a boss who has unrealistic expectations is to get yourself fired. As a data scientist you have a couple of ways to accomplish this - Log in to the employee database and drop any data corresponding to yourself - Drop every critical database instances from all production, QA, local, hyperlocal and even your boss' laptop. then update your progress on Jira. - Convert all ML workloads to python2 workloads and remove any try catch block from production code. Make sure this passes QA by pushing it on Friday night as an EBF (Emergency Bug Fix) . The best way to not have expectations is to not have a job and these steps should help you accomplish this within a reasonable timeframe.

Extend Timeline: Propose extending the project timeline to allow for thorough analysis and high-quality results. Scope Reduction: Suggest narrowing the project scope to focus on the most critical components first. Additional Resources: Recommend allocating additional resources, such as more team members, better tools, or increased budget, to meet the expectations. Phased Approach: Propose a phased approach where the project is divided into manageable phases, each with its own deliverables and deadlines. Be Clear and Concise: Present each alternative clearly, explaining how it addresses the challenges of the current plan.

When presenting problems or challenges, it’s also helpful to provide alternatives. If the original expectations are unrealistic, suggest practical alternatives that can achieve similar results but are more attainable. This could involve proposing different methodologies, extending timelines, or reallocating resources.

Offering phased approaches or smaller, incremental goals can help manage expectations while still delivering value. In one project, when a comprehensive predictive model was unrealistic within the given timeframe, I suggested starting with a basic regression analysis. This initial step provided actionable insights quickly and laid the groundwork for more complex models to be developed later.

Consider employing predictive analytics to set realistic goals. For example, use historical data to simulate project outcomes under different conditions. This could highlight the likelihood of success within the given constraints, offering a data-driven rationale for adjusting timelines or resources.

If certain expectations are really unrealistic, don't just say no; offers alternatives. As a data scientist, you have the experience to propose different approaches that may be more feasible while still providing valuable insights. Here are some strategies to address unrealistic expectations: * Initial exploratory analysis. * Phased approach. * Parallel projects. * Simulations or prototypes. * Simplified approach. Taking a proactive and collaborative approach when offering alternatives will not only help manage expectations, but will also demonstrate your ability to think strategically and find effective solutions within existing constraints.

If the original goals are unattainable, suggest alternative solutions or approaches that could meet the objectives more realistically. This shows your initiative and commitment to finding workable solutions.

Once the expectations and issues are assessed, understood and listed off. Come up with an alternative solution or plan with proof of applicability that is more realistic to the business use case. Some tips are: - Divide the timeline is a few phases, rather than going for a big one at once - Give justification for the time consumption. Explain the need for good data preparation and model evaluation. - Strategically allocate the work among teams (in case you're a lead) and do timely meetings to ensure everyone is on the same page

If certain expectations are unattainable, propose realistic alternatives. Suggest different approaches or solutions that could achieve the desired results within the existing constraints.

There is no single solution for a project. If you find the expectation is unrealistic, try to run a few experiments to prove your point. Else, provide alternative perspective solution for the same project but different goal. E.g. Issue/Alert pattern recognition is impossible, but anomaly detection might work in the same project.

Identify Knowledge Gaps: Determine what your boss may not understand about the data science process, its complexities, and realistic timelines. Understand Their Perspective: Understand their expectations and why they might have them. This will help tailor your educational efforts. Explain the Lifecycle: Educate your boss on the data science lifecycle, including data collection, cleaning, exploration, modeling, validation, and deployment. Highlight Uncertainties: Emphasize the uncertainties and iterative nature of data science projects. Relevant Analogies: Use analogies relevant to your boss’s experience to explain complex concepts.

Being a Data Scientist in an organization implies also bean a Data Evangelist and a Data Educator. Explaining the limitations of technology clearly will help your team and management to focus their energy and creativity in what can be achieved. It will give them a focus point and a frame of reference. At the same time, you should not discard any "fringe" idea out right, the challenge is to work with the team to bring that idea down to earth, step by step, to define a goal that is possible achieve to the organization in the available time and resources.

I have been using in the state of Continuous education which is essential in the field of data science to keep up with evolving technologies and methodologies. As a data science professional, you can educate yourself continuously by staying updated on the latest trends, tools, and techniques in the field. This can involve attending conferences, workshops, and webinars, as well as reading books, research papers, and online resources. By investing in your education, you can enhance your skills, stay competitive in the job market, and contribute more effectively to your organization's success.👌✔

Education is a continuous process in data science. Take every opportunity to educate your boss and colleagues about what data science can and cannot do. The goal is to foster a better understanding of the time, effort, and complexity involved in extracting meaningful information from data. General considerations: * Informal talks. * Formal presentations. * Practical workshops. * Documentation and resources. * Question and answer sessions. * Internal case studies. Implementing an ongoing educational approach will not only improve understanding and collaboration within your team, but will also help align expectations and create a more informed and efficient work environment.

I try to explain data science concepts, methodologies, It is important to never assume that the non-technical stakeholder know these processes. This ongoing education helps bridge the gap between technical capabilities and business expectations, ensuring everyone is on the same page. Additionally, I share relevant articles and case studies to illustrate real-world applications and limitations.

Consider presenting case studies from past projects to illustrate the complexity and variability inherent in data science. For instance, explain how a model that predicted customer behavior required adjustments over six months due to unforeseen market changes. Use this scenario to underscore the adaptive, often unpredictable nature of our field, reinforcing the need for flexible expectations.

Use this as an opportunity to educate your boss about the data science process, the typical challenges faced, and the time requirements for various tasks. This can help set more realistic expectations in the future.

There's a thing among employees nowadays. They commit to do work with half-knowledge. Before jumping into the solution, plan the solution. Try to research and educate yourself continuously with the industrial trends and better alternative solutions. Also, try to draw down your approach. This will help you identify the knowledge gaps.

Educate your boss about the complexities involved in your work. Regular updates about the challenges and intricacies of data science projects can help in setting more realistic expectations.

In terms of documentation, it is important to consider ... Clarity: Documenting expectations, requirements and constraints provides clarity and transparency and helps to avoid misunderstandings and miscommunication. Communication: Use documentation to support discussions with your boss and stakeholders by providing evidence of resource constraints, technical challenges and potential risks. Accountability: Documenting decisions and agreements ensures accountability and alignment between stakeholders, reducing the likelihood of unrealistic expectations and increasing project success.

Haltet alle besprochenen Punkte und getroffenen Entscheidungen schriftlich fest. Dokumentation hilft, Missverständnisse zu vermeiden und bietet eine Referenz für zukünftige Diskussionen.

Detailed documentation has been a lifesaver in many projects. I use project management tools like Notion or AzureDev-ops to keep track of tasks, progress, and changes. For each project, I maintain a comprehensive log that includes meeting notes, decisions made, rationale for those decisions, and any changes in scope or timeline. This documentation not only provides clarity but also serves as a valuable resource for future projects.

Keep records of communications and decisions made regarding project expectations and revisions. This documentation can be useful for referencing in future discussions or evaluations.

Keep detailed records of all communications, agreed-upon objectives, timelines, and any compromises or changes made. Documentation can protect both parties and serve as a reference point for future discussions.

In most case this happens if the manager climbed up the ladder without actually experiencing the real world or corporate climber or does not have hands on experience doing the exact same things during his/her early days of career. Years back read a good article by Harvard business review on this topic. Here is what I recall. It is very unlikely that anyone plan to be unrealistic. It’s much more likely that they have a rationale that they haven’t conveyed clearly, or may not even recognize themselves. Here are few pointers you can consider. - Agree in principle and later explain the real situation and details - Evaluate if things go well and keep evaluating the situation. Its going to be a iterative process

Additional factors that should also be considered ... Resources: Assess the availability of resources such as time, budget and manpower required to meet expectations. A lack of resources often lead to unrealistic requirements. Technical feasibility: Assess whether the expectations match the technical capabilities of the available tools and technologies. Unrealistic requirements may result from a lack of understanding of technical limitations. Risk assessment: Identify potential risks associated with meeting unrealistic expectations, such as compromising data quality or exceeding project deadlines. Mitigate risks through careful planning and communication with stakeholders.

Cundo comienza un nuevo proyecto de análisis o de exploración de nuevos datos, es clave ir acordando con tu líder entregas o revisiones parciales para ir viendo avances. Dado que la.mayoria de las veces los tiempos son acotados, es preferible tomar algunas aproximaciones de lógicas complejas de negocio con el fin de poder ir generando entregables útiles para la toma de decisiones. Y pautar para entregas posteriores refinamientos o trabajos más detallados.

As a data scientist the unrealistic/realistic view of the boss can be defined qualitatively through few important factors like documents, numbers, roadmap, alternatives, & priorities. Another powerful factor is through use cases sharing with the boss.

If your boss's expectations are unrealistic as a data science professional, it's crucial to have a candid conversation with them. First, clarify what they expect and explain the feasibility of those expectations based on available data, resources, and time constraints. Offer alternative solutions or compromises that align with both their goals and realistic outcomes. Emphasize the importance of setting achievable goals to ensure the success of projects and maintain a positive working relationship. Additionally, provide evidence or examples from similar projects to support your points. If necessary, involve stakeholders or other team members to provide input and perspective on the situation.

When facing unrealistic expectations as a data science professional, start by clearly communicating the constraints and complexities of your work. Educate your boss on key concepts and typical timelines. Set realistic goals by breaking projects into manageable tasks with clear milestones. Demonstrate feasibility through small-scale proofs of concept and industry benchmarks. Prioritize high-impact objectives aligned with business goals. Document all discussions and progress. Seek support from knowledgeable stakeholders and your team. Propose alternative solutions and advocate for an iterative approach to refine and improve over time.

If your boss's expectations are unrealistic, it's essential to address the issue promptly and professionally. 1. *Clarify expectations* 2. *Assess feasibility* 3. *Communicate concerns* 4. *Offer alternatives* 5. *Negotiate and collaborate* 6. *Set clear goals and objectives* 7. *Regularly review progress* 8. *Seek support if necessary* Remember to approach the conversation with your boss in a respectful and professional manner, focusing on finding solutions and achieving shared goals.

Understand the requirement, analyse self with his expectations discussed, make a model with analysed gap of information in expectations shared and make a model with few modifications with better understanding for both , and explain the difference, the variations will help to understand better.

In a globalized world that has blurred traditional boundaries, we need to reevaluate of our standards and benchmarks. I believe it is essential to adopt two distinct but complementary standards to navigate this new landscape effectively: 1) World-Class Excellence: Aspiring to world-class standards has become imperative. This standard represents the pinnacle of achievement, only limited by infrastructure, hardware and financials. 2) Best in Province/Region/Country: benchmarks that aligns with regional infrastructure, finances, and hardware capabilities while fostering a sense of pride and identity within the community/company/staff. I believe this needs to be conveyed to all businesses, employers and line-managers(bosses)