Okay, I need to create a wiki-style document about "데이터 기반 의사결정" (Data-Driven Decision Making) under the categories of Technology → Data Science → Analysis. Let me start by understanding the requirements.

First, the structure: H1 title, overview section, multiple sections with H2 and H3, and references if needed. The content needs to be accurate, objective, detailed, easy to read, and use technical terms with explanations. Also, markdown formatting with proper headings, lists, tables, code blocks where necessary. It should be at least 800 characters.

Starting with the title: "데이터 기반 의사결정" as H1. Then an overview section explaining what it is, its importance, and maybe a brief history or key concepts.

Next sections could include components of data-driven decision making, steps involved, tools and technologies, challenges, case studies, and references. Each of these can be H2 headings. Under each H2, I can have H3 for sub-sections.

For the overview, I should define what data-driven decision making is, mention its role in modern organizations, maybe touch on how it differs from traditional methods. Then in components, break down into data collection, analysis, interpretation, and implementation. Steps could be problem identification, data gathering, analysis, decision-making, evaluation. Tools might include Python, R, SQL, Tableau, etc. Challenges could involve data quality, privacy issues, skill gaps. Case studies can provide real-world examples.

I need to ensure that technical terms are explained. For example, when mentioning machine learning models, briefly explain what they are. Also, use code blocks for examples like Python code snippets using pandas or scikit-learn. Maybe include a table comparing different tools.

Check the length: 800 characters is about 150 words. The document should be longer than that, so I need to elaborate each section sufficiently. Avoid being too brief. Make sure each part has enough detail without being redundant.

Also, think about the audience. Since it's a wiki, it should be accessible but still informative for someone with some background in data science. Use clear examples and avoid overly complex jargon unless explained.

Need to verify accuracy: For instance, when discussing steps in data-driven decision making, ensure that each step is correctly described. Mentioning tools like Python, R, SQL, Tableau, Power BI, etc., are correct. Challenges like data quality, privacy, and skill gaps are valid points.

In the case studies section, maybe use a hypothetical example or reference real-world companies if possible. However, since it's a wiki, perhaps keep it general unless specific examples are known.

References: Maybe include links to authoritative sources, but since this is a text-based response, just mention them in the references section with titles and authors.

Now, structure each part step by step, ensuring markdown formatting. Use H1 for the title, H2 for main sections, H3 for subsections. Use bullet points or numbered lists where appropriate. Include code examples if possible, like a simple data analysis script.

Check that all requirements are met: structure, content quality, markdown format, length. Make sure there's no markdown in the thinking process but the final answer should have it.