Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    image fx (67)
    Improving LinkedIn Ad Strategies with Data Analytics
    9 Min Read
    big data and remote work
    Data Helps Speech-Language Pathologists Deliver Better Results
    6 Min Read
    data driven insights
    How Data-Driven Insights Are Addressing Gaps in Patient Communication and Equity
    8 Min Read
    pexels pavel danilyuk 8112119
    Data Analytics Is Revolutionizing Medical Credentialing
    8 Min Read
    data and seo
    Maximize SEO Success with Powerful Data Analytics Insights
    8 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Are Data Scientists the Next Masters of the Universe?
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Data Management > Culture/Leadership > Are Data Scientists the Next Masters of the Universe?
CommentaryCulture/LeadershipData WarehousingExclusiveHadoopMapReducePredictive AnalyticsRisk ManagementUnstructured Data

Are Data Scientists the Next Masters of the Universe?

paulbarsch
paulbarsch
5 Min Read
SHARE

Back in the late 1970s, traders buying and selling mortgages were pushed aside for new masters of the universe—“quants” or individuals that used mathematics to slice and dice mortgages into debt tranches. And in the same way, today’s traditional Business Intelligence (BI) professionals must be looking over their collective shoulders as business and IT publications tout the emerging role of “data scientist”.

Back in the late 1970s, traders buying and selling mortgages were pushed aside for new masters of the universe—“quants” or individuals that used mathematics to slice and dice mortgages into debt tranches. And in the same way, today’s traditional Business Intelligence (BI) professionals must be looking over their collective shoulders as business and IT publications tout the emerging role of “data scientist”.

Before Lew Ranieri came on the scene, mortgages were a very staid business. Banks would loan money and keep assets on the books for up to thirty years (depending on how quickly the loan was paid back). Except for underwriting skills, there wasn’t much complexity to the mortgage business.

More Read

How to Stay Ahead of the Data Protection Curve in 2016
Updates on Dronegate
Keeping Sales & Marketing Data in Order: What startups must learn from the enterprise.
AOL Advertising on the Need for Speed in Yield Optimization
Potential Hurdles Limiting the Internet of Things

As a trader for Salomon Brothers, Lew Ranieri changed all that.  Ranieri’s insight was that mortgages could be bundled together and then sliced into different tranches of varied risk.  This slicing exercise was quite complex because of a buyer’s ability to prepay their loans early or refinance.  Michael Lewis, of Liar’s Poker fame writes; “Mortgages were acknowledged to be the most mathematically complex securities in the marketplace. The complexity arose entirely out of the option the homeowner has to prepay his loan…mortgages were about math.” 

Suddenly the very boring business of home loans became a very complex business challenge in how to slice the pie based on risk profiles and cash flows from interest and principal. Lewis writes; “Different investors place different prices on risk. Risk could be canned and sold like tomatoes.” And this mathematical complexity demanded a new skill set—quantitative analysis—to perform the necessary mathematical modeling to ensure investment banks remained profitable in this new business.

Pushed out by a new breed of mathematical whizz-kids, many former investment bankers and traders either retired or left for smaller financial firms. And the rise of the quants—or the new masters of the universe—was complete by the mid-1980s.

Is a similar shift happening in the field of Business Intelligence with the emerging “data scientist” role? The skill set of today’s data scientist is much more robust than one who solely performs BI or ETL application development.  With new sources and types of data (i.e. multi-structured), the data scientist must be able to develop new data driven products such as churn models, create recommendation algorithms, assist marketers with behavioral segmentation and targeting and more. 

But that’s not all. Fellow SmartDataCollective contributor Daniel Tunkelang says the data scientist; “Also needs to possess creativity and strong communication skills. Creativity drives the process of hypothesis generation, i.e., picking the right problems to solve that will create value for users and drive business decisions.”  Tall order to find all these skill sets in one person, much less build an internal competency center with such talent.

Perhaps for the foreseeable future, there’s room for both traditional BI professionals and the new breed of data scientists, as today both are valuable contributors in the field of analytics. However, with data growth on a fast paced exponential curve, much less the complexity and velocity of multi-structured data, it’s easy to see how the mix of skill sets to succeed in the future will tilt more in favor of the data scientist role.

The mortgage bankers never saw Lew Ranieri coming. Regarding the rise of data scientists—should traditional BI professionals be worried?

 

TAGGED:Data Scientistmichael lewis
Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

image fx (2)
Monitoring Data Without Turning into Big Brother
Big Data Exclusive
image fx (71)
The Power of AI for Personalization in Email
Artificial Intelligence Exclusive Marketing
image fx (67)
Improving LinkedIn Ad Strategies with Data Analytics
Analytics Big Data Exclusive Software
big data and remote work
Data Helps Speech-Language Pathologists Deliver Better Results
Analytics Big Data Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

Data Scientist Team: Question and Answer

11 Min Read

The Promise of Big Data: How it will Impact Roles, Company Culture and the Industry in High Performance Environments

10 Min Read

Rules Based Decision Making Shouldn’t Be Followed Religiously

4 Min Read
data science upskilling
Big Data

Upskilling for Emerging Industries Affected by Data Science

10 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

giveaway chatbots
How To Get An Award Winning Giveaway Bot
Big Data Chatbots Exclusive
AI and chatbots
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence Chatbots Exclusive

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?