How do you identify a business problem that can be solved using data science and communicate the process to non-technical stakeholders?

Identifying a business problem that can be solved using data science involves a process of understanding the organization's challenges, goals, and available data, and then framing those challenges in a way that can be addressed using data-driven techniques. Communicating this process to non-technical stakeholders requires clear and straightforward language, focusing on the business value and potential impact rather than on complex technical details. Here’s how to approach both:

Identifying a Business Problem for Data Science:

1. Understand the Business Context: The starting point is to gain a comprehensive understanding of the organization's goals, challenges, and operational processes. This involves talking with different stakeholders across departments, including management, sales, operations, marketing, and customer service. Asking the right questions, and doing proper research about the business is critical. For example, for a retail company, the goal might be to increase sales, reduce costs, or improve customer satisfaction. Understanding the business environment and the key areas that are important to the stakeholders is vital.

2. Identify Pain Points and Challenges: Look for specific areas where the organization is facing challenges, inefficiencies, or missed opportunities. These could be anything from high customer churn rates, low conversion rates, excessive operational costs, supply chain bottlenecks, or ineffective marketing campaigns. For a hospital system, the challenge might be reducing patient readmission rates, or optimizing resource allocation to improve patient care and reduce costs. These challenges usually require a data-driven solution.

3. Assess Data Availability: Evaluate the types and quality of data the organization collects. Assess whether there is enough relevant data to address the business problem. This includes understanding the volume, variety, and veracity (accuracy and reliability) of the available data. For example, an e-commerce company would need to look at their transaction data, customer data, website clickstream data, and inventory data. Ensure that the data is available and that it will be enough to create a model. If the data is not available, assess whether the data can be obtained through other means, for example through manual data entry or setting up new data collection processes.

4. Frame Problems as Data Science Problems: Once the business problem is understood, the next step is to reframe the problem in a way that it can be addressed using data science. This typically involves identifying specific questions that can be answered using data analysis or predictive modeling. For instance, a problem like "high customer churn" can be reframed into a question like "can we predict which customers are likely to churn using their historical behavior?" or "what are the key factors that influence customer churn?" Similarly, "inefficient inventory management" can become "can we optimize our inventory levels using historical sales data to minimize costs?" It is important to formulate the business problem into a data science problem by clearly identifying input variables and outputs.

5. Prioritize Problems Based on Business Value and Feasibility: Not all problems are equally important or feasible to solve using data science. Prioritize potential projects based on the potential business value (revenue increase, cost reduction, efficiency improvement) and technical feasibility (availability of data, technical resources, level of complexity). If there are several business problems, choose the ones that will have the most impact, or that are the most feasible to be addressed with the available tools, resources and data.

Communicating the Data Science Process to Non-Technical Stakeholders:

1. Use Clear and Non-Technical Language: Avoid using technical jargon or complex statistical terms when explaining the data science process to non-technical stakeholders. Instead, use simple language that they can easily understand. Use analogies and real-world examples to explain the process in ways that the stakeholders can relate to. The goal is for them to understand the process, without understanding the specific technical aspects.

2. Focus on Business Value and Impact: Instead of focusing on the technical details of the algorithms or models, emphasize the business value and potential impact of the data science project. Communicate the expected benefits, such as increased revenue, cost savings, improved customer satisfaction, or reduced operational inefficiencies. For example, rather than saying “we will use a Random Forest model”, it would be more useful to explain the model will identify which customers are likely to churn, which will help the business to take measures to reduce churn.

3. Explain the Process in Simple Terms: When explaining the data science process, break it down into simple, easy-to-understand steps. Focus on the big picture rather than getting into the weeds of the process. For example, instead of going into details of data preprocessing techniques, explain that the data will be cleaned and organized in such a way that the models will work well. You can explain the steps as "First, we gather the data; then we clean and prepare the data; then we create a model to make predictions; finally, we monitor the model".

4. Use Visual Aids and Examples: Use visual aids like charts, graphs, and simple diagrams to help non-technical stakeholders understand the data and the results of the analysis. Use examples that are relevant to their understanding of the business. For example, showing a chart of a company’s customer churn rate over time might be a good way to visualize the churn problem, or showing a chart of how a model is able to predict churn, with certain important features being highlighted. This can make the explanations clearer and more engaging for people who do not have a technical background.

5. Emphasize Transparency and Collaboration: Explain how the team will be transparent about the data science process, and that there will be ongoing collaboration with the business stakeholders. Transparency helps to build trust and ensures that the business stakeholders are involved in the process, that they will receive regular updates, and that they can participate in discussions and decision-making. Explain that this will not be an abstract process but one that will involve input from the stakeholders throughout the different stages of the project.

6. Provide Real-World Examples: When explaining how data science can solve business problems, use real-world examples of how data science has benefited other companies in the same industry. This helps non-technical stakeholders understand that this approach is useful and that it can benefit their business as well. For example, show examples of how machine learning models can predict future sales, which will allow the stakeholders to see the benefits.

In summary, identifying a business problem for data science involves understanding the organization's goals, challenges, and data, and then translating those challenges into data science problems. Communicating this process to non-technical stakeholders requires clear language, emphasis on business value, and collaboration. By doing this, you can build trust and support for your data science projects.