1. Framing and problem definition
Welcome back! I hope you enjoyed the last lesson on decision making.
2. The first step - framing the problem
In the realm of decision science, the journey from raw data to actionable insights begins with a crucial first step: framing the problem. Effectively defining the problem sets the stage for the entire decision science process, guiding data collection, analysis, and decision-making.
Imagine building a house without a blueprint. The result would likely be chaotic and structurally unsound.
Similarly, embarking on a data science project without a well-defined problem leads to wasted resources, misdirected analyses, and, ultimately, solutions that fail to address real business needs.
Problem framing ensures that your data science project is focused, with clearly defined objectives that prevent scope creep, and help keep the project on track relevant to the business context, addressing the actual problem, and actionable.
There are a number of different problem framing techniques.
3. Techniques
The 5 Whys is a technique that involves repeatedly asking "why" to drill down to the root cause of a problem. For example, if a company is experiencing declining sales, asking "why" several times might reveal underlying issues like poor customer service or ineffective marketing campaigns.
Fishbone or “Cause and Effect” Diagrams: This visualization tool helps identify potential causes contributing to a problem by categorizing them into different branches such as process, people, and technology.
SMART Goals: Framing the problem in terms of Specific, Measurable, Achievable, Relevant, and Time-bound goals provides clarity and direction. Goals not fitting into this SMART framework will usually be less useful to business problems.
When defining the SMART goals, it is important to ensure that the target audience or subject of the analysis is clearly defined. For instance, if the problem is customer churn, the target might be "customers who haven't made a purchase in the last six months."
The SMART goals also need to be very clear about defining Success. It is crucial to define clear and measurable criteria for determining whether the project has achieved its objectives. This might involve setting target values for KPIs or defining specific qualitative outcomes.
4. Context is crucial
Understanding the context in which a decision will be made is crucial. This involves identifying the business objective as well as analyzing the constraints.
In identifying the business objective, focus on identifying the specific goal the business wants to achieve. Is it to increase revenue, reduce costs, improve customer satisfaction, faster customer service, or some other goal?
In analyzing constraints, we assess the limitations that might affect the solution, such as budget, time, human resources, IT capacity, ethical considerations, or other restrictions.
This context involves understanding who the stakeholders are. These are the individuals or groups who are impacted by the problem or who have a vested interest in the solution such as decision-makers, data owners and end users.
5. Let's practice!
Problem framing is the foundation of any successful data science project. Mastering the concepts and techniques outlined in this article ensures that your analyses are focused, relevant, and actionable, ultimately leading to data-driven solutions that deliver real business value. Approach every project with clarity and purpose, and trust that a well-framed problem will pave the way for impactful and meaningful results. Let's try some exercises. You've got this!