Decoding Machine Learning: 4 How to Create a Solution Approach

Often, clients will communicate their overarching vision and a set of requirements to us. So, our responsibility is to carefully analyze their objectives and organize it into an appropriate problem solving approach. In this article, we will delve into the steps involved in navigating this particular process.

Building on the insights from the previous articles of this series, a systematic way to arrive at a solution approach for a business problem involves these steps:

1. Defining the end goal
2. Identifying all data sources
3. Listing all the possible ideas that achieve the business objective.
4. Refining ideas.
5. Putting it all together, i.e. defining broad level tasks and their flow.

Start with the End in Mind

In the first instalment of this series, we emphasised the significance of conducting comprehensive background research into the client’s business problem. It involves clearly articulating the desired outcome or objective that the client aims to achieve. By having a well-defined end goal, all subsequent decisions and actions of the solution approach can be aligned to ensure they contribute towards its attainment.

Once the end goal is identified, it should be formulated in a way that is specific, measurable, achievable, relevant, and time-bound (SMART) using Key Performance Indicators (KPIs). This ensures that the goal is clear and can be objectively evaluated. For example, instead of stating a general objective like “increase sales,” a SMART end goal could be “increase sales revenue by 15% within the next six months.”

The defined end goal serves as a guiding principle throughout the solution approach. It helps in making informed decisions, prioritising actions, and assessing progress.

Dealing with Data

Based on the foundation that has been set so far, we turn our attention towards the data involved and gain a deeper understanding of its characteristics.

To effectively deal with data, several key considerations come into play:

1. Identifying relevant data sources: Start by identifying all potential data sources that may be valuable for solving the problem. This could include internal databases, third-party data providers, publicly available data, or even data generated through user interactions. It is essential to explore and evaluate different sources to ensure comprehensive coverage.
2. Assessing data quality and suitability: Once the data sources are identified, evaluate the quality and suitability of the data. Consider factors such as accuracy, completeness, consistency, and reliability. Data cleansing and preprocessing techniques may be necessary to address any inconsistencies or errors.
3. Understanding data limitations: Recognise any limitations or biases present in the data. Data may have inherent biases due to collection methods or sample selection. Understanding these limitations helps in interpreting results accurately and making informed decisions.
4. Data integration and aggregation: Determine how different data sources will be integrated and aggregated to create a unified dataset. This step may involve combining data from various formats or platforms and ensuring compatibility and consistency.
5. Data privacy and security: Safeguarding data privacy and security is of paramount importance. Ensure compliance with relevant regulations and implement appropriate measures to protect sensitive information throughout the data handling process.

Brainstorming Ideas

When it comes to data-related projects, brainstorming ideas is a valuable technique for finding innovative solutions. Here are some simplified steps to effectively brainstorm ideas for data science projects:

1. Conduct background research: Start by conducting preliminary research on the problem domain, available data sources, and relevant literature. This research will provide valuable insights and inspiration for generating ideas.
2. Gather a diverse team: Assemble a diverse group of individuals with different expertise and backgrounds, including domain experts and stakeholders. This diverse team brings varied perspectives and knowledge, contributing to a wider range of ideas.
3. Feature engineering: Explore the existing datasets and identify what additional features can be derived or created from the available data. This step involves extracting meaningful information and transforming it into useful features for analysis.
4. Identify techniques: Make a list of data science techniques, algorithms, and processes that can be potentially used to solve the business objective. Consider techniques such as classification, regression, clustering, or data visualisation that are relevant to the problem solving approach at hand.

By following these simplified steps, you can tap into the collective knowledge and creativity of your team to generate diverse and effective ideas for data science-related projects. Remember to create a collaborative and supportive environment that encourages participation and fosters innovative thinking throughout the brainstorming process.

Refining Ideas

Refining ideas is a crucial step in the brainstorming process for data science projects. It involves evaluating and enhancing the initial ideas generated to ensure their feasibility, effectiveness, and alignment with the project goals. Here are some steps to effectively refine ideas:

1. Alignment with project goals: Review the refined ideas and ensure they align with the project goals and objectives. Check if they contribute to solving the specific problem or addressing the desired outcome. Discard ideas that deviate from the project’s core objectives.
2. Evaluation and feasibility assessment: Consider factors such as available resources, technical complexity, data availability, and time constraints. Eliminate ideas that are not feasible or realistic given the project constraints.
3. Collaboration and feedback: Seek input and feedback from team members, domain experts, and stakeholders. Engage in discussions to refine and improve the ideas further. Incorporate diverse perspectives and valuable insights into the refinement process.
4. Iterative refinement: The refinement process is iterative. Continuously revisit and refine the selected ideas as the project progresses. New insights, data, or feedback may lead to further enhancements or modifications of the ideas.

The refinement process allows for continuous improvement and ensures that the final chosen ideas are well-suited to address the data science project’s requirements.

Putting it all Together

Once the ideas have been refined and prioritised, it is time to consolidate them into a cohesive solution. This stage involves structuring the various components and tasks identified throughout the brainstorming and refinement process. Here are the key steps for putting it all together:

1. Define the solution architecture: Outline the high-level architecture of the solution, including the key components, data flow, and interactions between different elements. This provides a visual representation of how the solution will come together.
2. Identify dependencies: Determine any dependencies or interdependencies between the different components of the solution. This helps in understanding the sequence of activities and ensures that necessary prerequisites are met before moving forward.
3. Break down tasks: Divide the solution into smaller, manageable tasks or subtasks. This enables better planning, resource allocation, and tracking of progress throughout the implementation phase.
4. Create a project plan: Develop a comprehensive project plan that incorporates all the identified tasks, timelines, milestones, resource allocations, and communication channels. The project plan serves as a roadmap for executing the solution and helps in managing the project effectively.
5. Test and iterate: As the solution implementation progresses, conduct regular testing and evaluation to identify any issues or areas for improvement. Iterate on the solution based on feedback and insights gathered during testing to ensure its effectiveness.

By putting all the elements together and following a well-defined plan, the solution approach can be implemented in a structured and organised manner. Regular monitoring and iteration during the implementation phase will ensure that the solution effectively addresses the business problem and achieves the desired outcomes.

In the final article of this series, we will delve deeper into the intricacies of creating a comprehensive project plan.

Key Takeaways

• Clearly articulate the desired outcome that the client aims to achieve, ensuring it is specific, measurable, achievable, relevant, and time-bound (SMART).
• Thoroughly analyse the characteristics of the data involved to make informed decisions.
• Conduct background research, gather a diverse team, engage in feature engineering, and identify techniques and processes that can be used to solve the business objective.
• Evaluate and enhance the initial ideas to ensure their feasibility, effectiveness, and alignment with project goals.
• Structure the solution by defining the solution architecture, identifying dependencies, breaking down tasks, and creating a comprehensive project plan.

About Us

Data Science Discovery is a step on the path of your data science journey. Please follow us on LinkedIn to stay updated.

About the writers:

• Ujjayant Sinha: Data scientist with professional experience in market research and machine learning in the pharma domain across computer vision and natural language processing.
• Ankit Gadi: Driven by a knack and passion for data science coupled with a strong foundation in Operations Research and Statistics has helped me embark on my data science journey.