Data Science & Analytics: What do Data Scientists Do in 2020 and a Pioneer Practitioner’s Portfolio of Algorithm-based Decision Support Systems for Operations Management in Several Industrial Verticals

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Data Science & Analytics: What do Data Scientists Do in 2020 and a Pioneer Practitioner’s Portfolio of Algorithm-based Decision Support Systems for Operations Management in Several Industrial Verticals

December 2, 2019 by 2012pharmaceutical

Data Science & Analytics: What do Data Scientists Do in 2020 and a Pioneer Practitioner’s Portfolio of Algorithm-based Decision Support Systems for Operations Management in Several Industrial Verticals

Curator: Aviva Lev-Ari, PhD, RN

Based on Jesse Anderson’s work on data teams Kathleen Walch in Why Data Scientists Aren’t Data Engineers makes several keen distinctions between the two skill sets.

I can attest that she is absolutely correct. See below, a Pioneer Practitioner’s Portfolio of Algorithm-based Decision Support Systems for Operations Management in Several Industrial Verticals

These key distinctions are:

Data Scientists vs Data Engineers

In the mid-2000s, we saw the emergence of the Data Scientist position. As cited in the O’Reilly article: “This increase in the demand for data scientists has been driven by the success of the major Internet companies. Google, Facebook, LinkedIn, and Amazon have all made their marks by using data creatively: not just warehousing data, but turning it into something of value.” Not surprisingly, any organization that has data of value is looking at data science and data scientists to increasingly extract more value from that information.

Originating from roots in statistical modeling and data analysis, data scientists have backgrounds in advanced math and statistics, advanced analytics, and increasingly machine learning / AI. The focus of data scientists is, unsurprisingly, data science — that is to say, how to extract useful information from a sea of data, and how to translate business and scientific informational needs into the language of information and math. Data scientists need to be masters of statistics, probability, mathematics, and algorithms that help to glean useful insights from huge piles of information. These data scientists usually have learned programming out of necessity more than anything else in order to run programs and run advanced analysis on data. As a result, the code that data scientists have usually been tasked to write, is of a minimal nature – only as necessary to accomplish a data science task (R is a common language for them to use) and work best when they are provided clean data to run advanced analytics on. A data scientist is a scientist who creates hypothesis, runs tests and analysis of the data, and then translates their results for someone else in the organization to easily view and understand.

On the other hand, data scientists can’t perform their jobs without access to large volumes of clean data. Extracting, cleaning, and moving data is not really the role of a data scientist, but rather that of a data engineer. Data Engineers have programming and technology expertise, and have previously been involved with data integration, middleware, analytics, business data portal, and extract-transform-load (ETL) operations. The data engineer’s center of gravity and skills are focused around big data and distributed systems, and experience with programming languages such as Java, Python, Scala, and scripting tools and techniques. Data engineers are challenged with the task of taking data from a wide range of systems in structured and unstructured formats, and data which is usually not “clean”, with missing fields, mismatched data types, and other data-related issues. These data engineers need to use their programming, integration, architecture, and systems skills to clean all the data and put it into a format and system that data scientists can then use to analyze, build their data models, and provide value to the organization. In this way, the role of a data engineer is an engineer who designs, builds and arranges data.

Can there be a combined Data Scientist-Engineer role?

While it might seem that the roles of a data scientist and data engineer are distinct, data scientists and data engineers share many traits and skill sets. These overlapping skills include the necessity to work with and manipulate big data sets, programming skills to apply operations to the data, data analytics skills, and general fluency with systems operations.

Rather than engineering and programming-centric tools, data scientists need data science-centric tools. Right now there’s a growing collection of these tools, often emerging from data or predictive analytics environments that suit the needs of data scientists. However, it’s possible that even more business-centric tools might be appropriate, especially as the data scientists become more embedded with the line of business. For example, decades ago if you wanted to operate on large volumes of data in a spreadsheet-like format, this involved programming, but tools like Excel introduced things like pivot tables and now business managers are able to perform all sorts of analyses. It’s only a matter of time before tools like Excel embed data science capabilities, or business-centric data mining and analysis tools into their products.

As the talent gap for data scientists continues to widen, there is no doubt that we will see new tools created out of necessity to allow non-technical (read: business) people to run, test, and analyze data. Strategic business managers will begin to learn data science, without needing or wanting programming or data integration experience. Traditional data scientists will still be needed to run very complex analysis of data. For the most part however, basic analysis will move more to the business unit due to increasingly easy-to-use tools. This means we have still yet to see which tool or technology will be the dominant one for ML and data science in the enterprise.

My SOURCES for the evolution of the field of Data Science are the following:

Jesse Anderson’s work on data teams

Learn How to Create and Manage Big Data Teams

This Free, 73 Page E-Book is the Complete Guide to Successful Big Data projects

I’m really tired of seeing Big Data projects fail. They fail for both technical and managerial reasons. They all fail for similar reasons and that’s just sad because we can fix or prevent them. Gartner’s research shows that 85% of Big Data projects don’t even make it into production.

“Only 15 percent of businesses reported deploying their big data project to production, effectively unchanged from last year (14 percent).”

October 4, 2016 Gartner Press Release

https://www.bigdatainstitute.io/data-engineering-teams-book/

December, 1, 2019, 9:48 am

Why Data Scientists Aren’t Data Engineers

Kathleen Walch

Managing Partner & Principal Analyst at AI Focused Research and Advisory firm Cognilytica

https://www.forbes.com/sites/cognitiveworld/2019/12/01/why-data-scientists-arent-data-engineers/amp/?__twitter_impression=true

Translating Between Computer Science and Statistics

Posted on December 1, 2019

Gil Press

https://whatsthebigdata.com/2019/12/01/translating-between-computer-science-and-statistics/

Jan 8, 2019, 06:18am

The AI Chronicles: Combining Statistical Analysis And Computing From Hollerith To Zuckerberg

Gil Press Contributor

Enterprise & Cloud

https://www.forbes.com/sites/gilpress/2019/01/08/the-ai-chronicles-combining-statistical-analysis-and-computing-from-hollerith-to-zuckerberg/#23cf507c73b3

Jan 2, 2015, 10:48am

A Very Short History Of The Internet And The Web

Gil Press Contributor

Enterprise & Cloud

https://www.forbes.com/sites/gilpress/2015/01/02/a-very-short-history-of-the-internet-and-the-web-2/#a45c9307a4e2

May 28, 2013, 09:09am

A Very Short History Of Data Science

Gil Press Contributor

Enterprise & Cloud

https://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science/#1e7db3e155cf

May 9, 2013, 09:45am

A Very Short History Of Big Data

Gil Press Contributor

Enterprise & Cloud

https://www.forbes.com/sites/gilpress/2013/05/09/a-very-short-history-of-big-data/#16c2043b65a1

Apr 8, 2013, 09:16am

A Very Short History of Information Technology (IT)

Gil Press Contributor

Enterprise & Cloud

https://www.forbes.com/sites/gilpress/2013/04/08/a-very-short-history-of-information-technology-it/#3f5491022440

A Pioneer Practitioner’s Portfolio of Algorithm-based Decision Support Systems for Operations Management in Several Industrial Verticals: Analytics Designer, Aviva Lev-Ari, PhD, RN

On this landscape about IT, The Internet, Analytics, Statistics, Big Data, Data Science and Artificial Intelligence, I am to tell stories on my own pioneering work in data science, Algorithm-based decision support systems design for different organizations in several sectors of the US economy:

Startups:

TimeØ Group
Concept Five Technologies, Inc.
MDSS, Inc.
LPBI Group

Top Tier Management Consulting: SRI International, Monitor Group;
OEM: Amdahl Corporation;
Top 6th System Integrator: Perot System Corporation;
FFRDC: MITRE Corporation.
Publishing industry: was Director of Research at McGraw-Hill/CTB.
Northeastern University, Researcher on Cardiovascular Pharmaco-therapy at Bouve College of Health Sciences (Independent research guided by Professor of Pharmacology)

Type of institutions:

For-Profit corporations: Amdahl Corp, PSC, McGraw-Hill
For-Profit Top Tier Consulting: Monitor Company, Now Deloitte
Not-for-Profit Top Tier Consulting: SRI International
FFRDC: MITRE
eScientific Publishing: LPBI Group: Developers of Curation methodology for e-Articles [N = 3,700], electronic Table of Contents for e-Books in Medicine [N = 16, https://lnkd.in/ekWGNqA] and e-Proceedings of Biotech Conferences [N = 70].

Autobiographical Annotations: Tribute to My Professors

Pioneering implementations of analytics to business decision making: contributions to domain knowledge conceptualization, research design, methodology development, data modeling and statistical data analysis: Aviva Lev-Ari, UCB, PhD’83; HUJI MA’76

https://pharmaceuticalintelligence.com/2018/05/28/pioneering-implementations-of-analytics-to-business-decision-making-contributions-to-domain-knowledge-conceptualization-research-design-methodology-development-data-modeling-and-statistical-data-a/

Recollections of Years at UC, Berkeley, Part 1 and Part 2

Recollections: Part 1 – My days at Berkeley, 9/1978 – 12/1983 – About my doctoral advisor, Allan Pred, other professors and other peers

https://pharmaceuticalintelligence.com/2018/03/15/recollections-my-days-at-berkeley-9-1978-12-1983-about-my-doctoral-advisor-allan-pred-other-professors-and-other-peer/

Recollections: Part 2 – “While Rolling” is preceded by “While Enrolling” Autobiographical Alumna Recollections of Berkeley – Aviva Lev-Ari, PhD’83

https://pharmaceuticalintelligence.com/2018/05/24/recollections-part-2-while-rolling-is-preceded-by-while-enrolling-autobiographical-alumna-recollections-of-berkeley-aviva-lev-ari-phd83/

Accomplishments

The Digital Age Gave Rise to New Definitions – New Benchmarks were born on the World Wide Web for the Intangible Asset of Firm’s Reputation: Pay a Premium for buying e-Reputation

For @AVIVA1950, Founder, LPBI Group @pharma_BI: Twitter Analytics [Engagement Rate, Link Clicks, Retweets, Likes, Replies] & Tweet Highlights [Tweets, Impressions, Profile Visits, Mentions, New Followers] https://analytics.twitter.com/user/AVIVA1950/tweets

Thriving at the Survival Calls during Careers in the Digital Age – An AGE like no Other, also known as, DIGITAL

Reflections on a Four-phase Career: Aviva Lev-Ari, PhD, RN, March 2018

Was prepared for publication in American Friends of the Hebrew University (AFHU), May 2018 Newsletter, Hebrew University’s HUJI Alumni Spotlight Section.

Aviva Lev-Ari’s profile was up on 5/3/2018 on AFHU website under the Alumni Spotlight at https://www.afhu.org/