Q.7 Moving into a world of big data will require us to change our thinking about the merits of
exactitude. To apply the conventional mindset of measurement to the digital, connected world of
the twenty-first century is to miss a crucial point. As mentioned earlier, the obsession with
exactness is an artefact of the information-deprived analog era. When data was sparse, every data
point was critical, and thus great care was taken to avoid letting any point bias the analysis.
From “BIG DATA” Viktor Mayer-Schonberger and Kenneth Cukier
The main point of the paragraph is:
(A) The twenty-first century is a digital world
(B) Big data is obsessed with exactness
(C) Exactitude is not critical in dealing with big data
(D) Sparse data leads to a bias in the analysis

Here’s a complete, SEO-optimized article based on your query. I’ve crafted it for readers in biotechnology, data science, and bioengineering—fields where big data transforms microbial genomics, enzyme kinetics modeling, and fermentation analytics. The key phrase “exactitude in big data” is woven naturally for search visibility, with a concise title, meta description, and slug.

In the era of big data, fields like biotechnology and microbiology are drowning in datasets from genomic sequencing, microbial growth kinetics, and biochemical assays. Yet, as Viktor Mayer-Schonberger and Kenneth Cukier argue in their book Big Data, clinging to the “obsession with exactness” from the analog past hinders progress. This article breaks down a pivotal paragraph, identifies its main point via multiple-choice analysis, and explains why exactitude in big data takes a backseat to volume and velocity.

The Key Paragraph from Big Data

“Moving into a world of big data will require us to change our thinking about the merits of exactitude. To apply the conventional mindset of measurement to the digital, connected world of the twenty-first century is to miss a crucial point. As mentioned earlier, the obsession with exactness is an artefact of the information-deprived analog era. When data was sparse, every data point was critical, and thus great care was taken to avoid letting any point bias the analysis.”

This excerpt challenges traditional stats-heavy approaches in bioengineering, where sparse lab data once demanded pixel-perfect precision. Today, abundant data from tools like next-gen sequencing flips the script.

Correct Answer: (C) Exactitude is not critical in dealing with big data

The main point urges a mindset shift: exactitude in big data is outdated. In the “information-rich” digital world, data abundance trumps perfection. Sparse analog-era data made every point sacred to dodge bias, but big data’s scale—think petabytes from cell culture experiments—allows correlations to emerge despite noise. Option (C) captures this core thesis directly.

Detailed Explanation of All Options

Let’s dissect each choice, drawing parallels to real-world biotech applications like enzyme kinetics modeling or microbial fermentation data.

• (A) The twenty-first century is a digital world
  This is a supporting detail, not the main point. The paragraph mentions the “digital, connected world” as context for why old measurement habits fail, but it doesn’t stop there. In genomics, yes, we’re digital—but the focus is rethinking exactitude, not just stating the obvious.

• (B) Big data is obsessed with exactness
  This reverses the argument entirely. The authors call exactness an “obsession” of the past, an “artefact” unfit for big data. In fact, big data embraces “good enough” precision, as seen in machine learning models for protein folding predictions (e.g., AlphaFold), where volume beats exact lab measurements.

• (C) Exactitude is not critical in dealing with big data ✅
  Spot on. The paragraph demands “chang[ing] our thinking about the merits of exactitude,” contrasting sparse-data caution with big data’s tolerance for imperfection. For bioengineers, this means analyzing noisy fermentation kinetics datasets without fretting over every outlier.

• (D) Sparse data leads to a bias in the analysis
  This is a historical observation (“when data was sparse… great care was taken to avoid… bias”), not the main idea. It explains why exactness ruled before, but the thrust is big data’s liberation from that constraint. In molecular biology, sparse qPCR data did risk bias, but today’s big data pipelines (e.g., RNA-seq) handle it via statistical power.

Implications for Biotechnology and Beyond

For professionals modeling microbial growth or genetic engineering, ditching exactitude in big data unlocks insights. Tools like Python’s scikit-learn or R for bioprocess simulations thrive on volume over veracity. Mayer-Schonberger and Cukier predict this shift will revolutionize fields from drug discovery to personalized medicine.