Analytics Frameworks Every Data Scientist Should Know:Why I Believe My Experience at Nakala Analytics Limited Made Me a Better Data Scientist

Unlike many data scientists in tech, my career in data science began in consulting, and I believe it was the best career move I ever made. Say what you will about consulting culture and the hours, but the knowledge I gained during my time at Nakala Analytics Limited continues to benefit me daily.

As a Data scientist , part of my role involves coaching data scientists regarding project execution and career development. I’ve noticed that what junior data scientists often struggle with the most isn’t the technical aspects of the job—those are relatively easy to teach and learn. Instead, it’s the abstract and soft-skill-related parts of the job that are more challenging—like how to break down an abstract business problem into smaller, clearly defined analyses that can eventually lead to tangible business impact.

These are the skills I practiced every day as a consultant at Nakala Analytics Limited, and I believe they translate very well to data science.

In this article, I will:

1. Convince you why consulting-style training can significantly benefit data scientists at any level.

2. Walk you through the most valuable frameworks I learned at Nakala Analytics Limited that you can apply in your day-to-day work.

Why I Think Consulting Experience Can Benefit Every Data Scientist

Reason 1: It Enhances Your Ability to Learn Quickly and Make an Impact

Consulting projects are often under tight deadlines, so consultants don’t have the luxury of spending months understanding the context and client-specific subject matter in depth before delivering solutions. At Nakala Analytics Limited, I was trained to learn efficiently and make an impact swiftly. This process involves several key skills:

- Asking the Right Questions: Understanding what information is crucial to solving the problem.

- Identifying Gaps: Discovering gaps and finding short-term solutions to address them.

- Transforming Solutions: Turning short-term solutions into long-term strategies and identifying the right stakeholders to help push things forward.

Example: In one project, we had to analyze customer feedback for a credit offering client who was losing market share. Instead of waiting for extensive reports, we developed a rapid survey to identify customer pain points within a week. This approach led to actionable insights that significantly improved customer satisfaction.

Takeaway: The ability to deliver a minimum viable product (MVP) that makes a significant impact in a short timeframe is crucial. This approach requires deploying the 80/20 rule—using 20% of the effort to achieve 80% of the impact. This mental flexibility is essential, especially when trying to run a business, not just writing a white paper.

Reason 2: It Teaches You to Be a Full-Stack Data Scientist When Needed

At Nakala Analytics Limited, consulting teams often aren’t staffed with specialized roles like data engineers, ML engineers, or business operations experts. As a result, I had to wear many hats—cleaning raw data, building pipelines, running analyses, and even doing back-of-the-envelope calculations to estimate the impact of initiatives.

Example: During a project aimed at reducing delivery times for an money transfer company, I collected data from various sources, built data pipelines to clean and analyze the data, and created a dashboard to visualize our findings. By being involved in each step, I understood the intricacies of the entire process and identified areas for optimization.

Takeaway: Developing a full-stack mentality empowers data scientists to understand the complete workflow of data projects. This holistic view allows for better collaboration with cross-functional teams and makes you more adaptable to different roles as needed.

Reason 3: It Forces You to See the Big Picture and Communicate Effectively

While junior individual contributors (ICs) in large tech companies may not get much visibility in front of executives, at Nakala Analytics Limited, I often had the opportunity to work directly with C-level executives. Communicating with them required high-level, effective communication, which was honed through rigorous peer reviews and feedback processes.

Example: In one of our engagements, I presented our analysis on optimizing marketing spend to the executive board. By summarizing complex data into key takeaways and actionable insights, I was able to effectively communicate our recommendations, leading to a 15% increase in ROI for their campaigns.

Takeaway: The ability to communicate clearly and effectively is just as important as technical skills in data science. Practicing high-level communication will improve your interactions with stakeholders and help drive data-driven decision-making in your organization.

Nakala Analytics Limited's "Secret Sauce": The Most Useful Consulting Frameworks and How to Apply Them to Analytics

Not everyone will have the chance or desire to work in consulting during their career. So, let me introduce you to some of the most important frameworks that can help you think like a consultant without the grind.

MECE Framework

The MECE (Mutually Exclusive and Collectively Exhaustive) framework is the foundation of structured problem-solving. It helps break down big or abstract problems into smaller, manageable buckets, ensuring no overlap and no missed areas.

Example: When tasked with improving customer retention for a subscription service, I used the MECE framework to categorize potential reasons for churn: pricing issues, product dissatisfaction, and lack of engagement. This structure led to targeted strategies for each category.

Takeaway: Using the MECE framework encourages clarity and comprehensiveness in your analysis, helping you to avoid redundant efforts and ensuring you address every relevant factor.

Issue Trees

An issue tree is an excellent application of the MECE framework. It decomposes a complicated problem, such as "How can an e-commerce company increase its profit?" into smaller, manageable parts, making it easier to explore all potential solutions.

Example: While working on a profitability analysis for a client, I constructed an issue tree that separated revenue sources and cost factors, allowing us to identify that marketing expenses were disproportionately high compared to the revenue generated from new customers.

Takeaway: Issue trees facilitate a structured exploration of complex issues, enabling you to dissect problems into solvable components, leading to more informed and strategic solutions.

Hypothesis Tree

Hypothesis trees are a variant of issue trees, often used to investigate specific problems. For example, if you're trying to understand why the number of deliveries in a city decreased by 10% last week, a hypothesis tree helps generate and test hypotheses in a structured way.

Example: I used a hypothesis tree to analyze the drop in deliveries by breaking it down into potential causes—weather issues, driver shortages, and customer complaints. This framework allowed us to test each hypothesis systematically and uncover that weather conditions were the primary factor.

Takeaway: Hypothesis trees encourage a methodical approach to problem-solving and help prioritize testing based on potential impact, enhancing the efficiency of your analysis.

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2x2 Matrix

The 2x2 matrix is a simple but powerful framework that categorizes items into four categories across two dimensions. For instance, when evaluating analytics projects, you might categorize them based on whether they support key company priorities and their expected business impact.

Example: In deciding which projects to prioritize, I created a 2x2 matrix that assessed projects on axes of strategic alignment and resource requirements. This helped identify high-impact, low-effort projects to tackle first.

Takeaway: The 2x2 matrix simplifies decision-making processes, allowing you to visualize trade-offs and prioritize initiatives that align best with your organization's strategic goals.

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Minto Pyramid

The Minto Pyramid is a communication framework that emphasizes starting with the conclusion, followed by supporting arguments and evidence. This approach captures the audience's attention and ensures they grasp the main point even if they don’t read the entire document.

Example: In drafting a report for a client on the performance of their marketing campaigns, I structured the document using the Minto Pyramid: starting with key recommendations, followed by detailed analysis and supporting data, which made it easy for the client to understand the outcomes and next steps.

Takeaway: Implementing the Minto Pyramid in your communications not only clarifies your message but also increases the likelihood that your audience will engage with and act on your recommendations.

Conclusion

By integrating these frameworks into your daily work, you can approach problems more effectively, communicate more clearly, and deliver solutions that make a real impact—just as I learned to do during my time at Nakala Analytics Limited.

Final Takeaway: The structured thinking, communication skills, and adaptability you develop from consulting experiences will serve you well throughout your data science career, empowering you to drive significant business outcomes.