Data Scientists for Big Data

Note: This blog is based on Thomas H. Davenport's book, Big Data: Dispelling the Myths, Uncovering the Opportunities, published in 2014 by the Harvard Business Review Press; Harvard Business School Publishing Corporation; Boston, MA. The InfoGrow Corporation offers marketing and sales solutions involving data. We are pleased to share author Davenport's ideas about how big data and data scientists can help companies use big data to do what they already are doing with data and analytics (usually making decisions),  only cheaper, faster, or better.

 

As technology advances marketing techniques, new job functions arise. For example, InfoGrow's "Marketing Needs a Chief Marketing Technologist" blog (April 22, 2014) noted, "With increased complexities and the need for fast updates and upgrades to meet specific challenges and opportunities, marketers are finding that they are better off having their own Chief Marketing Technologist."

 

That is from a technology aspect for general data. However, today's "big data" creates another function for some companies: the Data Scientist.

 

"Big data" is so "big" that it: (1) requires more than one server, (2) is too unstructured (such as text, images, and video) for a row-and-column database, and (3) is too continuously flowing for a static data warehouse. Of these characteristics, the greatest involves the lack of big data's structure.

 

Big data is not just for big companies. Mid-size and smaller companies who are growing should be aware of big data to know what to expect from it and be prepared to manage it. Therefore, for those who will someday have big data, here is a "heads up."

 

 

Big Data's Most Important Part: The Human Element – the Data Scientist

The most important part of big data, surprisingly, is not technology, nor the data itself. It is the human element: a "Data Scientist," the person who produces the applications and models from data. Big data also involves managers and those who make decisions from data.

 

The "Data Scientist" function is not necessarily a new job function brought about from big data. Rather, today's Data Scientists are in demand from increasing needs for an existing, but perhaps, obscure, company job function.

 

Key Traits of a Data Scientist

 

A Data Scientist is a:

• hacker (ability to code and understands big data technology)

• scientist (evidence-based decision making, improvisation, action oriented)

• trusted advisor (strong communication and relationship skills, knows decision process)

• quantitative analyst (statistical analysis, visual analytics, machine learning, analysis of

unstructured data, such as text, video, or images)

• business expert (know how a business works and makes money, good sense of where to

apply analytics and big data)

 

Big Data
Data Scientist can interpret
unstructured data and help businesses get ahead of the curve.

 

 

What Does a Data Scientist Do?

The "scientist" attribute of a Data Scientist does not necessarily mean that these individuals have to be practicing scientist. They should, however, have the ability to: (1) construct experiments, (2) design experimental apparatus, and (3) gather, analyze, and describe what the data mean.

 

A Data Scientist is a quantitative analyst: one who knows his or her way around a variety of mathematical and statistical techniques, and who can explain them easily to non-technical people and decision makers, often through visual analytics.

 

A Data Scientist knows "machine learning" which involves automated testing and fitting of various models to a data set in order to find the best fit. But it really is only semi-automated because the data scientist usually has to tell a machine learning program where to start and what varieties of data transformations to explore.

 

Lastly, a Data Scientist understands how his company creates successful products and services and how key problems can best be solved through big data and analytics.

 

Contrary to the high-certitude approaches taken with small data, the volume and velocity of big data often require a less certain approach with continuous, more indicative analysis and decision making. This happens because by the time a company has achieved a high level of certitude of insights and implications from data, much more new data becomes available.

 

In short, a Data Scientist extracts data from obscure locations, writes programs to turn unstructured data into structured data, analyzes the data, interprets the results, and advises executives on what to do with the results – quickly.

 

 

ROI from Big Data

The bottom line with big data is to understand how it can bring new value to one's company, lower costs, increase the speed of processing data, develop new products or services, and allow new data or models for better decision making. Companies who master big data have a more complete picture of their customers and operations by combining unstructured and structured data.

 

For early adopters of big data, the two most important benefits are: (1) enabling new business capabilities for the first time, and (2) doing what they already are doing with data and analytics (usually making decisions), only cheaper, faster, or better.

 

 

To learn more about how data can help you grow your business, please contact me at 1-800-897-9807, x224.

InfoGrow Corporation, Riverfront Square, 2140 Front Street, Cuyahoga Falls, OH 44221

InfoGrow President: Robert Sullivan, 800-897-9807, x224

www.infogrowcorp.com

 

Re: Thomas H. Davenport; Big Data at Work: Dispelling the Myths, Uncovering the Opportunities; Harvard Business Review Press; Harvard Business School Publishing Corporation; Boston, MA; 2014.

by InfoGrow

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