How do you collect data to develop evidence-based solutions?

In today's world of Big Data, one may get to boil the ocean during data collection if the objective is not clear. The objective of data collection dovetails into the methodology. If the objective is extremely strategic and has potential financial implications, the data collected needs to 1. come from verified sources 2. be latest 3. have credibility in terms of methods/ assumptions used 4. ideally get triangulated with other means and ways to drive confidence If we are collecting data for high-level decision making, a back of the envelope assumption based extrapolation may serve us well.

The traditional model of defining scope and objectives first will remain relevant in certain contexts, there's a growing case for a more adaptive, data-first approach in today's dynamic and data-rich environment. That being said, there is still merit in having a general idea or hypothesis about what one hopes to achieve. This ensures that the data collection process has some direction and doesn't devolve into a massive, unfocused undertaking. It's about striking the right balance.

Gathering data to create evidence-based solutions involves a structured approach. The steps are: 1. Define the issue. 2. Review existing literature. 3. Determine data sources. 4. Design data collection tools. 5. Collect data accurately and ethically. 6. Organize and store data systematically. 7. Analyze data using appropriate methods. 8. Interpret findings. 9. Propose evidence-based solutions. 10. Communicate results clearly. 11. Consider peer review. 12. Implement and evaluate solutions. 13. Iterate as needed for improvement.

With the advent of large-scale data analysis capabilities and sophisticated AI models like Hugging Face Transformers, Llama2, and Claude style chat are all trainable for Large Language Models; the paradigm is shifting. The ability to quickly process and derive meaningful insights from massive datasets allows for a more informed and adaptive planning process, ensuring that potential opportunities aren't prematurely ruled out.

Traditionally, setting clear scope and objectives has anchored the direction of projects. Without this clarity, data collection risked being vast and aimless, causing 'analysis paralysis' due to an overload of information without a clear action path. With today's AI-enhanced consulting approaches, massive data can be swiftly processed, yielding actionable insights. This becomes especially vital when: The domain is unfamiliar or not well-defined. Early data insights can better steer the project than initial assumptions. Data is ever-changing and immediate. In fast-paced sectors like social media or stock markets, waiting for a fixed scope can mean missing out. The focus is on innovative exploration, not just solving a preset issue.

AI is revolutionizing data analysis and interpretation. It offers accelerated insights by identifying patterns, correlations, and anomalies rapidly. Deep learning allows AI to uncover intricate data relationships, revealing insights often missed by traditional methods. By tying data findings directly to project goals, AI ensures insights are both statistically significant and goal-aligned. Crucially, AI can highlight biases, ensuring objective analysis. Additionally, it simplifies data understanding through dynamic visualizations. While AI's role is transformative, human oversight remains essential. Together, AI and human expertise can make data analysis a strategic powerhouse.

"A man walking down a dark street finds another man searching for his lost wallet under a streetlight. When asked why he's looking there, the man responds, 'I can see better here.' Much like this scenario, gathering data for evidence-based solutions requires searching where the light of facts and evidence shines, not just where it's convenient. Don't let your solutions be lost in the darkness of assumptions."