Written by: Udi Dotan
Some
of the best and brightest in the Massachusetts data and technology space
gathered on the last Friday in February to discuss how the world of big data
and computers might impact knowledge workers in the 21st century keynoted by
esteemed author Tom Davenport.
of the best and brightest in the Massachusetts data and technology space
gathered on the last Friday in February to discuss how the world of big data
and computers might impact knowledge workers in the 21st century keynoted by
esteemed author Tom Davenport.
Tom
spoke on the displacement of workers over time.
In the 18th and 19th centuries, it was farm workers, in the 20th
century, it was service jobs, today, it’s knowledge workers that are being
displaced by technology. Artificial
intelligence is growing and taking over roles that humans used to do and they will
take over more work. Computers can do
many tasks faster and more efficiently than humans and computers are cheaper,
easier to manage, and don’t complain about the cost of healthcare.
spoke on the displacement of workers over time.
In the 18th and 19th centuries, it was farm workers, in the 20th
century, it was service jobs, today, it’s knowledge workers that are being
displaced by technology. Artificial
intelligence is growing and taking over roles that humans used to do and they will
take over more work. Computers can do
many tasks faster and more efficiently than humans and computers are cheaper,
easier to manage, and don’t complain about the cost of healthcare.
Should
we welcome these changes as another in a string of technological revolutions
that have enabled humans to flourish, or fear our new computer overlords? Some technology leaders such as Elon Musk and
Bill Gates have voiced concerns about these changes positing that AI is the
most dangerous development in history and should be looked upon with skepticism.
we welcome these changes as another in a string of technological revolutions
that have enabled humans to flourish, or fear our new computer overlords? Some technology leaders such as Elon Musk and
Bill Gates have voiced concerns about these changes positing that AI is the
most dangerous development in history and should be looked upon with skepticism.
Where do these fears
come from and how did we get here?
Since
the dawn of the age of the internet 20 years ago (yes, it’s been more than two
decades since you saw your first AOL CD), information has been generated and
has flowed more freely. With more data,
they has been more desire to analyze which led to enormous growth of analytics. Early analytics were descriptive, utilizing
simple graphs and charts to understand our world.
the dawn of the age of the internet 20 years ago (yes, it’s been more than two
decades since you saw your first AOL CD), information has been generated and
has flowed more freely. With more data,
they has been more desire to analyze which led to enormous growth of analytics. Early analytics were descriptive, utilizing
simple graphs and charts to understand our world.
Today,
companies are utilizing predictive and prescriptive analytics to help make
better decisions (think Amazon’s recommender engine). Going forward, more and more companies are
leveraging larger stores of data, more compute power, and sophisticated
algorithms to automate analytics. One
such example was given by Ed
Macri, Senior Vice President of Marketing and Analytics at Wayfair. They are using analytics to personalize one
million emails a day based upon their prior visits, versus a single email carbon
copied to one million people.
companies are utilizing predictive and prescriptive analytics to help make
better decisions (think Amazon’s recommender engine). Going forward, more and more companies are
leveraging larger stores of data, more compute power, and sophisticated
algorithms to automate analytics. One
such example was given by Ed
Macri, Senior Vice President of Marketing and Analytics at Wayfair. They are using analytics to personalize one
million emails a day based upon their prior visits, versus a single email carbon
copied to one million people.
Another
example given by a member of our keynote panel, Bruce Weed, Program
Director, Global Watson and Big Data Ecosystem Development at IBM, is how Watson, who through the use of its
massive library, is helping medical doctors diagnose in a much faster and
efficient manner.
example given by a member of our keynote panel, Bruce Weed, Program
Director, Global Watson and Big Data Ecosystem Development at IBM, is how Watson, who through the use of its
massive library, is helping medical doctors diagnose in a much faster and
efficient manner.
What jobs are computers
doing that have or will displace humans and how do we service our new masters?
doing that have or will displace humans and how do we service our new masters?
Of
course, no human is capable of personalizing a million emails. But these aren’t the only roles that are ripe
for computerization. According to Tom
Davenport, here are some “at risk” jobs that computers can and will
do better than humans:
course, no human is capable of personalizing a million emails. But these aren’t the only roles that are ripe
for computerization. According to Tom
Davenport, here are some “at risk” jobs that computers can and will
do better than humans:
- Lawyers:
e-discovery – combing through thousands of documents to find the nuggets
of truth for specific court cases. - Accountants:
audits, taxes – using intelligence to improve tax preparation (think
TurboTax). - Radiology: cancer
detection – using machines to read radiology reports and highlight areas
of concern. - Reporters:
automated story generation – computers can used data to generate articles
for publishing (like this one? – not yet). - Marketing: online
ad buying and personalized emails as with Wayfair - Financial Advisor:
“robo-advisors” – generating customized portfolios for clients
based on factors such as age, income, and tolerance to risk. - Teachers: online
content and automated student evaluation – companies such as Kahn Academy,
Coursera, and EdX are delivering content online. Next generation companies such as
Dreambox and Knewton are delivering adaptive learning that modifies the
material in response to student performance.
Have the machines
already won or is there a role for us yet?
already won or is there a role for us yet?
Some
companies are already leveraging intelligent machines, has this turned their
offices into a wasteland where tumbleweeds are rolling through giant data
centers? In short, no, there are still
plenty of things that computers can’t do without us. Davenport refers to this work as augmentation. Computers are good at computationally complex
and repetitive tasks, but they can’t see the bigger picture. Humans will be needed to identify the
strengths and weaknesses of the analytics systems and algorithms. Humans will be needed to determine the
business problems to solve. And humans
will build and maintain the systems to solve those problems.
companies are already leveraging intelligent machines, has this turned their
offices into a wasteland where tumbleweeds are rolling through giant data
centers? In short, no, there are still
plenty of things that computers can’t do without us. Davenport refers to this work as augmentation. Computers are good at computationally complex
and repetitive tasks, but they can’t see the bigger picture. Humans will be needed to identify the
strengths and weaknesses of the analytics systems and algorithms. Humans will be needed to determine the
business problems to solve. And humans
will build and maintain the systems to solve those problems.
As
highlighted at the conference, big data technology enables much of the
analytics innovation as companies can manage with larger and more varied data
stores. Jeff Kelly, Principal Research
Contributor at Wikibon believes that we
are moving from early stage adoption of big data implementations built around
cost savings to a second generation whereby companies with big data strategies
are now focused on revenue generation and operational efficiency. P. Gary Gregory, SVP
& GM, Database Servers and Tools at Rocket
Software illustrated that to be successful with such data initiatives, you
need to start with a business problem and build data systems to support
solutions. Those systems don’t need to
include hadoop, but the purpose of the data and the definition of the data
sources should be clear, otherwise you end up with a data landfill, not a data
lake.
highlighted at the conference, big data technology enables much of the
analytics innovation as companies can manage with larger and more varied data
stores. Jeff Kelly, Principal Research
Contributor at Wikibon believes that we
are moving from early stage adoption of big data implementations built around
cost savings to a second generation whereby companies with big data strategies
are now focused on revenue generation and operational efficiency. P. Gary Gregory, SVP
& GM, Database Servers and Tools at Rocket
Software illustrated that to be successful with such data initiatives, you
need to start with a business problem and build data systems to support
solutions. Those systems don’t need to
include hadoop, but the purpose of the data and the definition of the data
sources should be clear, otherwise you end up with a data landfill, not a data
lake.
Several
of the panelists illustrated that the analytics revolution has led to a greater
need for humans, not a lesser need. Ivan
Matviak, Executive Vice President and Head of Data and Analytics Solutions
at State
Street Global Exchange says they are hiring more people, not fewer, to help
build and maintain its advanced analytics capabilities. In particular, they are aggressively seeking
to hire the sexiest
workers, data scientists. EMC’s data
science practice spends a great deal of effort investigating and rebuilding messy
data for analytic purposes. And Wayfair is augmenting automated ad purchases with
targeted human buys of online ad space.
of the panelists illustrated that the analytics revolution has led to a greater
need for humans, not a lesser need. Ivan
Matviak, Executive Vice President and Head of Data and Analytics Solutions
at State
Street Global Exchange says they are hiring more people, not fewer, to help
build and maintain its advanced analytics capabilities. In particular, they are aggressively seeking
to hire the sexiest
workers, data scientists. EMC’s data
science practice spends a great deal of effort investigating and rebuilding messy
data for analytic purposes. And Wayfair is augmenting automated ad purchases with
targeted human buys of online ad space.
Iran Hutchinson,
Product Manager & Big Data Software/Systems Architect at InterSystems led a lively panel
discussion illuminating success stories at companies leveraging big data and human
augmentation to gain remarkable insights.
Joe Dery,
a Senior Data Scientist at EMC relayed how EMC
increased revenues by mining internal contract data to optimize contract renewals. The key to optimization was not in the volume
or veracity of the data (although there certainly were large volumes of data),
but rather in clarifying data definitions and educating the sales team. According to Joe, the model generation was the
simplest part of the two year project.
Product Manager & Big Data Software/Systems Architect at InterSystems led a lively panel
discussion illuminating success stories at companies leveraging big data and human
augmentation to gain remarkable insights.
Joe Dery,
a Senior Data Scientist at EMC relayed how EMC
increased revenues by mining internal contract data to optimize contract renewals. The key to optimization was not in the volume
or veracity of the data (although there certainly were large volumes of data),
but rather in clarifying data definitions and educating the sales team. According to Joe, the model generation was the
simplest part of the two year project.
Gary Sloper, VP of Sales
Engineering and Operations at CenturyLink
uses big data to proactively monitor network activity and utilize machine
learning algorithms that can detect anomalies.
Such techniques can be employed to prevent hacks such as Sony and Anthem
have recently experienced.
Engineering and Operations at CenturyLink
uses big data to proactively monitor network activity and utilize machine
learning algorithms that can detect anomalies.
Such techniques can be employed to prevent hacks such as Sony and Anthem
have recently experienced.
At
Care.com, Co-founder and CTO, Dave Krupinski and his team has focused
analytic attention on optimizing the match rate between jobs posted and
caregivers seeking jobs. This has given
them guidance on the optimal flow of applications into a job posting, the
optimal number of applications per job, and the key terms that are more likely
to get a caregiver hired. The insights have led to an increase in match rate from 70% to over 80% with more opportunity to improve in the pipeline.
Care.com, Co-founder and CTO, Dave Krupinski and his team has focused
analytic attention on optimizing the match rate between jobs posted and
caregivers seeking jobs. This has given
them guidance on the optimal flow of applications into a job posting, the
optimal number of applications per job, and the key terms that are more likely
to get a caregiver hired. The insights have led to an increase in match rate from 70% to over 80% with more opportunity to improve in the pipeline.
Summary
As
the volume, velocity, variety, and veracity of big data grows and the analytics
become more complex and the opportunity for a cooperative relationship between
machines and humans will continue to grow and we will continue to find ways to employ
technology to advance society.
the volume, velocity, variety, and veracity of big data grows and the analytics
become more complex and the opportunity for a cooperative relationship between
machines and humans will continue to grow and we will continue to find ways to employ
technology to advance society.
