Understand the problem you are trying to solve or the question you are trying to answer. This involves gathering relevant information from your stakeholders, customers, and users, such as their pain points and expectations. Conduct some exploratory data analysis to get a sense of the data availability, quality, and potential insights. You can formulate a clear and concise problem statement that summarizes the main goal of your project by first understanding the problem.
Most data science projects fail because of a lack of deep understanding of the issue. Many data scientists jump into building models (usually because it's the exciting part) before having clarified the needs of the users and the Product Team, ending up developing a solution for a different problem. This leads to the model being "put on a shelf" which in turn loses the trust of non-technical stakeholders.
Mastering Objective Definition in Data Science Projects Defining clear objectives is crucial for the success of any data science project. Here's a structured approach to get it right: 1. Understand the Problem: Gather information from stakeholders to comprehend the issues and expectations. 2. Data Exploration: Conduct exploratory data analysis to assess data availability and quality. 3. Formulate the Problem Statement: Summarize the main goal clearly and concisely. By following these steps, you can ensure that your data science project is well-defined and poised for success. I hope this helps boost engagement on your LinkedIn post
Business Understanding or understanding the problem is the first step toward success in your Data Science Project. When the problem is broken down into manageable chunks, the data scientists can ask specific questions to understand the problem. Sometimes I have seen magic moments saying, 'we don't need Data Science!'. The outcome from the step is functional and non-functional requirements for the project. View on key stakeholders in the project and targeted consumers of the model. Data sources (external and internal) scope and SME to support the data exploration.
Gathering data on various point of views are the most challenging dynamics of a ML project. Its all similar like any other traditional project management. Few changes in the data may give different perspectives. People’s expectations towards data science is often seasonal.
Next, define the deliverables, which are the outputs or outcomes that your project will produce or achieve. These can include data products, such as dashboards, reports, models, or applications, as well as data insights, such as findings, recommendations, or solutions. Specify the format, content, and quality of your deliverables, as well as the criteria and metrics for evaluating their success and impact. This step helps you communicate what your project will deliver and how it will be measured.
Well-defined project deliverables maintain focus and alignment with desired outcomes. By establishing clear goals, stakeholders and team members share a common understanding of what needs to be achieved, fostering seamless collaboration. These defined deliverables also serve as benchmarks for evaluating project progress and optimizing resource allocation. Ultimately, they lay the groundwork for a successful data science endeavor that delivers maximum value.
Shared understanding about what will be produced or achieved at the end of Data Science with all the stakeholders is essential. Information gathered during the business understanding/problem definition stage plays a key role here. The deliverables will vary based on the objectives. Some projects may be a Proof of Concept (PoC), some will be Minimum Viable Product (MVP), and integrates fourth. For each scenario, the deliverables may include documentation, code artifacts, model, pipelines for deployment, and the next viable steps to take the project further. These deliverables should align with the larger vision the team is working towards.
Plan the resources, which are the inputs or requirements that your project will need or consume. These can include data sources, such as databases, APIs, or files, as well as data tools, such as software, libraries, or platforms. Estimate the time, budget, and human resources that your project will require, as well as the risks and dependencies that may affect your project. Planning resources will help you estimate the feasibility, cost, and duration of your project.
Proper resource planning is often overlooked in data science initiatives. This happens often with data sources--data samples are typically easy to get and explore, but if you will need more data for your project, be sure to identify any necessary teams/individuals and involve them in the process to confirm you can get more data. This is especially important if you plan to work with a third party--check with data teams to identify any data sharing policies. It's tempting to quickly assign a data science project to a data scientist, but they might not be the only person involved in getting the data. Plan accordingly and bring in the right people to the conversation early to avoid delays and complications.
This is where product owners, engineering leaders, architects, data offices, and project managers must collaborate. One of the key reasons behind every failed Data Science project is overlooked resource requirements. We can broadly classify the resource requirements into the following buckets. 1) Human Resources (Data Scientists, Ml Engineer, Scrum Master ...) 2) Data (Internal and External) 3) Infrastructure (Definitely not the associate's Laptop/Desktop) 4) Software 5) Audit (If regulatory requirements applicable) 6) Deployment Infrastructure (Assume unknowns and known knowns) The functional and non-functional requirements gathered during the business understanding support this step. Effective planning provides clarity to leadership.
The process of breaking down your project into manageable and measurable tasks and milestones is known as the scope of work. You should use a data science lifecycle framework, such as CRISP-DM or OSEMN, to guide you through the main stages and activities of your project. Steps include data collection, data cleaning, data analysis, data modeling, data visualization, and data deployment. Use a project management tool, such as Trello or Asana, to organize and track your tasks and progress. By scoping the work, you can define the scope and objectives of your project in more detail and clarity.
Validate the scope, which is the process of verifying and confirming that your project scope and objectives are realistic, achievable, and aligned with your stakeholders, customers, and users. Use methods, such as interviews, surveys, feedback sessions, or prototypes, to collect and analyze feedback from your stakeholders, customers, and users, as well as from your peers and experts. You should also use agile methods, such as sprints, iterations, or MVPs, to test and refine your deliverables and insights. Validating the scope, helps ensure that your project scope and objectives are relevant, valuable, and desirable.
Scope validation, document, and stakeholder agreement are desirable irrespective of project type. Considering the risks and avoiding surprises in the project plan (or scope creep), validation done early stages of the project is the right step.
The final step in defining the scope and objectives of a data science project is to review and adjust, which is the process of monitoring and controlling your project scope and objectives throughout the project lifecycle. You should use regular meetings, reports, or dashboards to communicate and update your project status, achievements, and challenges with your stakeholders, customers, and users, as well as with your team and managers. Use change management methods, such as change requests, impact analysis, or scope creep prevention, to handle any changes or issues that may arise in your project scope and objectives. By reviewing and adjusting, you can ensure that your project scope and objectives are consistent, accurate, and flexible.
This point, in particular, can be applied to any stream of industry and not just data science. - Communication and Reporting: Regular communication with stakeholders, users, and team members ensure that everyone involved is informed and aligned. - Scope and Objectives Consistency: Regular reviews ensure that the project scope and objectives remain consistent, accurate, and aligned with the vision. - Flexibility and Adaptability: It enables the project team to respond to new information, challenges, or opportunities and make necessary modifications. - Continuous Improvement: Lessons learned from previous stages or projects can be incorporated into the ongoing review process, enabling continuous improvement in future data science initiatives.