There's No Such Thing as Too Much Data

The uses of AI compels us to decide how to store and access massive amounts of data.

Verily, a life-sciences firm owned by Google's parent company, Alphabet created 5 TB of data sequencing the human genome. More importantly, there are 10 times more microbial cells than human cells in the body, each with millions of their own genes. Researchers have concluded that it is these microbial cells that cause many human diseases, the genetic sequencing of which necessitates ever greater data storage. This calls for Big Data.

The need for capable storage and retrieval of large amounts of data is apparent when 95% of patients’ DNA sequencing revealed information useful for future drug use discoveries, according to a Children’s Hospital University of Michigan Health System study. 46% of the DNA sequencing was directly useful to the individual patient’s care team, with 25% of the information leading to actionable changes in existing treatment plans.

Data of this size and complexity needs fast memory and substantial storage. Micron Ventures plans to invest $100 M in start-ups and ventures focused on AI and applications because they believe that AI will drive the demand for memory and storage. Appropriately, 20% of the money will fund minority-backed startups led by women, minorities and other underrepresented groups.

Micron's CEO Sanjay Mehrotra says that

Over the course of the last several years, the technology has advanced such that hardware is becoming the essential disruptor. This is what we mean by intelligence accelerated: the data center, devices at the edge, and AI and algorithms creating new worlds that we never would have imagined. Data and data storage is very much at the heart of these trends.

Now that Big Data is coming into its own, memory and storage that use AI to understand and manage the deluge of data are urgently needed. These new advances in data usage are fundamentally changing the way that business and research are done.

Read more here.

Reality Changing Observations:

Q1. How can Big Data help other fields of research?

Q2. Why is it important in data analysis to have a strategy set in place to record information and analyze it?

Q3. Is Big Data still in the future or is it here and now?

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