A data scientist needs to analyze a company's chemical businesses and is using the master database of the conglomerate company. Nothing in the data differentiates the data observations for the different businesses. Which of the following is the most efficient way to identify the chemical businesses' observations?

The most efficient approach is to leverage domain knowledge from the business team. Since the master database lacks explicit identifiers for different business units, consulting with subject matter experts (the business team) is a critical first step. They can provide the necessary context, such as which sites, plants, or cost centers are associated with chemical operations. This allows the data scientist to create a targeted query or filter to ingest only the relevant data, saving significant time, computational resources, and storage. This practice aligns with the initial "Business Understanding" phase of standard data analysis methodologies.

A. Ingesting all data for exploratory analysis is highly inefficient. It requires processing a potentially massive, irrelevant dataset just to identify a subset, which may not even be possible without business context.
B. Performing analysis on the entire dataset is a brute-force method that is computationally expensive and time-consuming. The goal is to scope the data before analysis for efficiency.
D. This approach is based on an unsubstantiated assumption that the largest hard drive contains the relevant data. It is methodologically unsound and likely to produce incomplete or incorrect results.

1. Chapman

P.

Clinton

J.

Kerber

R.

Khabaza

T.

Reinartz

T.

Shearer

C.

& Wirth

R. (2000). CRISP-DM 1.0: Step-by-step data mining guide. The CRISP-DM Consortium. Section 2.1

"Business Understanding Phase

" emphasizes that the initial phase focuses on understanding the project objectives and requirements from a business perspective

which includes identifying key people and resources. This directly supports consulting the business team to define the data scope.

2. Provost

F.

& Fawcett

T. (2013). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. O'Reilly Media. Chapter 2

"Business Problems and Data Science Solutions

" discusses the importance of decomposing a business problem into data analysis tasks. This process requires deep business understanding

often gained by collaborating with business stakeholders

to correctly frame the problem and identify relevant data.

3. University of California

Berkeley. (2021). Data 8: Foundations of Data Science

Course Notes. Chapter 1

"Introduction

" highlights the importance of context in data analysis. The notes emphasize that data are not just numbers but relate to real-world individuals and conditions

and understanding this context is essential for meaningful analysis. Consulting the business team provides this critical context. (Available through UC Berkeley's Data 8 course materials).