Transcript of a discussion on how the role of the data scientist in the enterprise is expanding in both importance and influence that warrants a new level of business analysis professional certification.

1. Page 1 of 11
How the Data Science Profession
is Growing in Value and Impact
Across the Business World
Transcript of a discussion on how the role of the data scientist in the enterprise is expanding in
both importance and influence that warrants a new level of business analysis professional
certification.
Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: The Open
Group.
Dana Gardner: Hi, this is Dana Gardner, Principal Analyst at Interarbor Solutions, and
you’re listening to BriefingsDirect. Our next business trends panel discussion explores
how the role of the data scientist in the enterprise is expanding in both importance and
influence.
Data scientists are now among the most highly sought-after professionals, and they are
being called on to work more closely than ever with enterprise strategists to predict
emerging trends, optimize outcomes, and create entirely new kinds of business value.
To learn more about modern data scientists, how they
operate, and why a new level of business analysis
professional certification has been created by The Open
Group, we are now joined by our panel, Martin Fleming,
Vice President, Chief Analytics Officer, and Chief
Economist at IBM. Welcome, Martin.
Martin Fleming: Thanks, Dana.
Gardner: We are also joined by Maureen Norton, IBM
Global Data Scientist Professional Lead, Distinguished
Market Intelligence Professional, and author of Analytics
Across the Enterprise. Welcome, Maureen.
Maureen Norton: Thank you very much, pleasure to be here.
Gardner: And we’re also here with George Stark, Distinguished Engineer for IT
Operations Analytics at IBM. Welcome, George.
George Stark: Thank you, Dana.
Gardner: We are now characterizing the data scientist as a profession. Why have we
elevated the role to this level, Martin?
Fleming

2. Page 2 of 11
Data scientists add value
Fleming: The benefits we have from the technology that’s now available allow us to
bring together the more traditional skills in the space of mathematics and statistics with
computer science and data engineering. The technology wasn't as useful just 18 months
ago. It’s all about the very rapid pace of change in technology.
Gardner: Data scientists used to be behind-the-scenes people; sneakers, beards, white
lab coats, if you will. What's changed to now make them more prominent?
Norton: Today’s data scientists are consulting with the
major leaders in each corporation and enterprise. They
are consultants to them. So they are not in the back
room, mulling around in the data anymore. They're taking
the insights they're able to glean and support with facts
and using them to provide recommendations and to
provide insights into the business.
Gardner: Most companies now recognize that being
data-driven is an imperative. They can’t succeed in
today's world without being data-driven. But many have a
hard time getting there. It's easier said than done. How
can the data scientist as a professional close that gap?
Stark: The biggest drawback in
integration of data sources is having
disparate data systems. The financial
system is always separate from the
operational system, which is separate
from the human resources (HR)
system. And you need to combine
those and make sure they're all in the
same units, in the same timeframe, and all combined in a way that can answer two
questions. You have to answer, “So what?” And you have to answer, “What if?” And
that’s really the challenge of data science.
Gardner: An awful lot still has to go on behind the scenes before you get to the point
where the “a-ha” moments and the strategic inputs take place.
Martin, how will the nature of work change now that the data scientist as a profession is
arriving – and probably just at the right time?
Fleming: The insights that data scientists provide allow organizations to understand
where the opportunities are to improve productivity, of how they can help to make
workers more effective, productive, and to create more value. This enhances the role of
the individual employees. And it’s that value creation, the integration of the data that
Norton
You have to answer, “So what?”
And you have to answer, “What if?”
And that’s really the challenge of
data science.

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George talked about, and the use of analytic tools that's driving fundamental changes
across many organizations.
Captain of the data team
Gardner: Is there any standardization as to how the data scientist is being organized
within companies? Do they typically report to a certain C-suite executive or another?
Has that settled out yet? Or are we still in a period of churn as to where the data
scientist, as a professional, fits in?
Norton: We're still seeing a fair amount of churn. Different organizing approaches have
been tried. For example, the centralized center of excellence that supports other
business units across a company has a lot of believers and followers.
The economies of scale in that approach help. It’s difficult to find one person with all of
the skills you might need. I’m describing the role of consultant to the presidents of
companies. Sometimes you can’t find all of that in one individual -- but you can build
teams that have complimentary skills. We like to say that data science is a team sport.
Gardner: George, are we focusing the new data scientist
certification on the group or the individual? Have we
progressed from the individual to the group yet?
Stark: I don’t believe we are there yet. We’re still
certifying at the individual level. But as Maureen said,
and as Martin alluded to, the group approach has a large
effect on how you get certified and what kinds of
solutions you come up with.
Gardner: Does the certification lead to defining the
managerial side of this group, with the data scientist
certified in organizing in a methodological, proven way
that group or office?
Fleming: The certification we are
announcing focuses not only on the
technical skills of a data scientist, but also
on project management and project
leadership. So as data scientists progress
through their careers, the more senior folks
are certainly in a position to take on
significant leadership and management roles.
And we are seeing over time, as George referenced, a structure beginning to appear.
First in the technology industry, and over time, we’ll see it in other industries. But the
Stark
The certification focuses not
only on the technical skills of a
data scientist, but also on
project management and
project leadership.

4. Page 4 of 11
technology firms whose names we are all familiar with are the ones who have really
taken the lead in putting the structure together.
Gardner: How has the “day in the life” of the typical data scientist changed in the last 10
years?
Learn How to Become
Certified as a
Data Scientist
Stark: It’s scary to say, but I have been a data scientist for 30 years. I began writing my
own Fortran 77 code to integrate datasets to do eigenvalues and eigenvectors and build
models that would discriminate among key objects and allow us to predict what
something was.
The difference today is that I can do that in an afternoon. We have the tools, datasets,
and all the capabilities with visualization tools, SPSS, IBM Watson, and Tableau. Things
that used to take me months now take a day and a half. It’s incredible, the change.
Gardner: Do you as a modern data scientist find yourself interpreting what the data
science can do for the business people? Or are you interpreting what the business
people need, and bringing that back to the data scientists? Or perhaps both?
Collaboration is key
Stark: It’s absolutely both. I was recently with a client, and we told them, “Here are
some things we can do today.” And they said, “Well, what I really need is something that
does this.” And I said, “Oh, well, we can do that. Here’s how we would do it.” And we
showed them the roadmap. So it’s both. I will take that information back to my team and
say, “Hey, we now need to build this.”
Gardner: Is there still a language, culture, or organizational divide? It seems to me that
you’re talking apples and oranges when it comes to business requirements and what the
data and technology can produce. How can we create a Rosetta Stone effect here?
Norton: In the certification, we are
focused on supporting that data scientists
have to understand the business
problems. Everything begins from that.
Knowing how to ask the right questions, to scope the problem, and be able to then
translate [is essential]. You have to look at the available data and infer some, to come up
with insights and a solution. It's increasingly important that you begin with the problem.
You don't begin with your solution and say, “I have this many things I can work with.” It's
Data scientists have to understand
the business problems. Everything
begins from that.

5. Page 5 of 11
more like, “How we are going to solve this and draw on the innovation and creativity of
the team?”
Gardner: I have been around long enough to remember when the notion of a chief
information officer (CIO) was new and fresh. There are some similarities to what I
remember from those conversations in what I’m hearing now. Should we think about the
data scientist as a “chief” something, at the same level as a chief technology officer
(CTO) or a CIO?
Chief Data Officer defined
Fleming: There are certainly a number of organizations that have roles such as mine,
where we've combined economics and analytics. Amazon has done it on a larger scale,
given the nature of their business, with supply chains, pricing, and recommendation
engines. But other firms in the technology industry have as well.
We have found that there are still three separate needs, if you will. There is an
infrastructure need that CIO teams are focused on. There are significant data
governance and management needs that typically chief data officers (CDOs) are
focused on. And there are substantial analytics capabilities that typically chief analytics
officers (CAOs) are focused on.
It's certainly possible in many organizations to combine those roles. But in an
organization the size of IBM, and other large entities, it's very difficult because of the
complexity and requirements across those three different functional areas to have that
all embodied in a single individual.
Gardner: In that spectrum you just laid out – analytics, data, and systems -- where does
The Open Group process for a certified data scientist fit in?
Fleming: It's really on the analytics side.
A lot of what CDOs do is data
engineering, creating data platforms. At
IBM, we use the term Watson Data
Platform because it's built on a certain
technology that's in the public cloud. But
that's an entirely separate challenge from being able to create the analytics tools and
deliver the business insights and business value that Maureen and George referred to.
Gardner: I should think this is also going to be of pertinent interest to government
agencies, to nonprofits, to quasi-public-private organizations, alliances, and so forth.
Given that this has societal-level impacts, what should we think about in improving the
data scientists’ career path? Do we have the means of delivering the individuals needed
from our current educational tracks? How do education and certification relate to each
other?
A lot of what CDOs do is data
engineering, creating data platforms.

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Academic avenues to certification
Fleming: A number of universities have over the past three or four years launched
programs for a master’s degree in data science. We are now seeing the first graduates
of those programs, and we are recruiting and hiring.
I think this will be the first year that we bring in folks who have completed a master’s in
data science program. As we all know, universities change very slowly. It's the early
days, but demand will continue to grow. We have barely scratched the surface in terms
of the kinds of positions and roles across different industries.
That growth in demand will cause many university programs to grow and expand to feed
that career track. It takes 15 years to create a profession, so we are in the early days of
this.
Norton: With the new certification, we are doing outreach to universities because
several of them have master’s in data analytics programs. They do significant capstone-
type projects, with real clients and real data, to solve real problems.
We want to provide a path for them into certification so that students can earn, for
example, their first project profile, or experience profile, while they are still in school.
Gardner: George, on the organic side -- inside of companies where people find a variety
of tracks to data scientist -- where do the prospects come from? How does organic
development of a data scientist professional happen inside of companies?
Learn How to Become
Certified as a
Data Scientist
Stark: At IBM, in our group, Global Services, in particular, we've developed a training
program with a set of badges. They get rewarded for achievement in various levels of
education. But you still need to have projects you've done with the techniques you've
learned through education to get to certification.
Having education is not enough. You have to apply it to get certified.
Gardner: This is a great career path, and there is tremendous demand in the market. It
also strikes me as a very fulfilling and rewarding career path. What sorts of impacts can
these individuals have?
Fleming: Businesses have traditionally been managed through a profit-and-loss
statement, an income statement, for the most part. There are, of course, other data
sources -- but they’re largely independent of each other. These include sales opportunity

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information in a CRM system, supply chain information in ERP systems, and financial
information portrayed in an income statement. These get the most rigorous attention,
shall we say.
We're now in a position to create much
richer views of the activity businesses
are engaged in. We can integrate
across more datasets now, including
human resources data. In addition, the
nature of machine learning (ML) and
artificial intelligence (AI) are predictive.
We are in a position to be able to not
only bring the data together, we can
provide a richer view of what's transpiring at any point in time, and also generate a better
view of where businesses are moving to.
It may be about defining a sought-after destination, or there may be a need to close
gaps. But understanding where the business is headed in the next 3, 6, 9, and 12
months is a significant value-creation opportunity.
Gardner: Are we then thinking about a data scientist as someone who can help define
what the new, best business initiatives should be? Rather than finding those through
intuition, or gut instinct, or the highest paid person's opinion, can we use the systems to
tell us where our next product should come from?
Pioneers of insight
Norton: That's certainly the direction we are headed. We will have systems that
augment that kind of decision-making. I view data scientists as pioneers. They're able to
go into big data, dark data, and a lot of different places and push the boundaries to come
out with insights that can inform in ways that were not possible before.
It’s a very rewarding career path because there is so much value and promise that a
data scientist can bring. They will solve problems that hadn't been addressed before.
It's a very exciting career path. We’re excited to be launching the certification program to
help data scientists gain a clear path and to make sure they can demonstrate the right
skills.
Gardner: George, is this one of the better ways to change the world in the next 30
years?
Stark: I think so. If we can get more people to do data science and understand its value,
I'd be really happy. It's been fun for 30 years for me. I have had a great time.
We are in a position to be able to
not only bring the data together, we
can provide a richer view of what’s
transpiring at any point in time, and
also generate a better view of
where businesses are moving to.

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Gardner: What comes next on the technology side that will empower the date scientists
of tomorrow? We hear about things like quantum computing, distributed ledger, and
other new capabilities on the horizon?
Future forecast: clouds
Fleming: In the immediate future, new
benefits are largely coming because we
have both public cloud and private cloud in
a hybrid structure, which brings the data,
compute, and the APIs together in one
place. And that allows for the kind of tools
and capabilities that necessary to
significantly improve the performance and
productivity of organizations.
Blockchain is making enormous progress and very quickly. It's essentially a data
management and storage improvement, but then that opens up the opportunity for
further ML and AI applications to be built on top of it. That’s moving very quickly.
Quantum computing is further down the road. But it will change the nature of computing.
It's going to take some time to get there but it nonetheless is very important and is part of
that what we are looking at over the horizon.
Gardner: Maureen, what do you see on the technology side as most interesting in terms
of where things could lead to the next few years for data science?
Learn How to Become
Certified as a
Data Scientist
Norton: The continued evolution of AI is pushing boundaries. One of the really
interesting areas is the emphasis on transparency and ethics, to make sure that the
systems are not introducing or perpetuating a bias. There is some really exciting work
going on in that area that will be fun to watch going forward.
Gardner: The data scientist needs to consider not just what can be done, but what
should be done. Is that governance angle brought into the certification process now, or
something that it will come later?
Stark: It's brought into the certification now when we ask about how were things
validated and how did the modules get implemented in the environment? That’s one of
the things that data scientists need to answer as part of the certification. We also believe
that in the future we are going to need some sort of code of ethics, some sort of methods
Benefits are largely coming
because we have both public
cloud and private cloud in a
hybrid structure, which brings the
data, compute, and the APIs
together in one place.

9. Page 9 of 11
for bias-detection and analysis, the measurement of those things that don't exist today
and that will have to.
Gardner: Do you have any examples of data scientists doing work that's new, novel,
and exciting?
Rock star potential
Fleming: We have a team led by a very intelligent and aggressive young woman who
has put together a significant product recommendation tool for IBM. Folks familiar with
IBM know it has a large number of products and offerings. In any given client situation
the seller wants to be able to recommend to the client the offering that's most useful to
the client’s situation.
And our recommendation engines can now make those recommendations to the sellers.
It really hasn't existed in the past and is now creating enormous value -- not only for the
clients but for IBM as well.
Gardner: Maureen any examples jump to mind that illustrate the potential for the data
scientist?
Norton: We wrote a book, Analytics Across the Enterprise, to explain examples across
nine different business units. There have been some great examples in terms of finance,
sales, marketing, and supply chain.
Gardner: Any use-case scenario come to mind where the certification may have been
useful?
Norton: Certification would have been useful to an individual in the past because it
helps map out how to become the best practitioner you can be. We have three different
levels of certification going up to the thought leader. It's designed to help that
professional grow within it.
Stark: A young man who works for me in
Brazil built a model for one of our
manufacturing clients that identifies
problematic infrastructure components
and recommends actions to take on those
components. And when the client
implemented the model, they saw a 60
percent reduction in certain incidents and
a 40,000-hour-a-month increase in availability for their supply chain. And we didn't have
a certification for him then -- but we will have now.
Gardner: So really big improvement. It shows that being a data scientist means you're
impactful and it puts you in the limelight.
When the client implemented the
model, they saw a 60 percent
reduction in certain incidents and a
40,000-hour-a-month increase in
availability for their supply chain.

10. Page 10 of 11
Stark: And it was pretty spectacular because the CIO for that company stood up in front
of his whole company -- and in front of a group of analysts -- and called him out as the
data scientist that solved this problem for their company. So, yeah, he was a rock star
for a couple days.
Gardner: For those folks who might be more intrigued with a career path toward
certification as a data scientist, where might they go for more information? What are the
next steps when it comes to the process through The Open Group, with IBM, and the
industry at large?
Where to begin
Norton: The Open Group officially launched this
in January, so anyone can go to The Open Group
website and check under certifications. They will be
able to read the information about how to apply.
Some companies are accredited, and others can
get accredited for running a version of the
certification themselves.
IBM recently went through the certification process. We have built an internal process
that matches with The Open Group. People can apply either directly to The Open Group
or, if they happen to be within IBM or one of the other companies who will certify, they
can apply that way and get the equivalent of it being from The Open Group.
Gardner: I’m afraid we’ll have to leave it there. You have been listening to a sponsored
BriefingsDirect discussion on how the role of the data scientist in the enterprise is
expanding both in importance and influence. And we learned how data scientists --
especially those with this new certification -- are being called on to work more closely
than ever with enterprise strategies to predict emerging trends, optimize outcomes, and
create entirely new kinds of business value.
So please join me in thanking our guests, Martin Fleming, Vice President, Chief
Analytics Officer, and Chief Economist at IBM. Thank you, sir.
Fleming: My pleasure, Dana.
Gardner: We also have been here with Maureen Norton, IBM Global Data Scientist
Profession Lead, Distinguished Market Intelligence Professional, and author of Analytics
Across the Enterprise. Thank you so much, Maureen.
Norton: Thank you, Dana. It’s been a lot of fun.
Anyone can go to The
Open Group website and
check under certifications.

11. Page 11 of 11
Gardner: And lastly, we have been here with George Stark, Distinguished Engineer for
IT Operations Analytics at IBM. Thank you, sir.
Stark: Thank you, Dana.
Gardner: And a big thank you as well to our audience for joining this BriefingsDirect
modern digital business innovation discussion. I’m Dana Gardner, Principal Analyst at
Interarbor Solutions, your host throughout the series of BriefingsDirect discussions
sponsored by The Open Group.
Thanks again for listening. Please pass this on to your IT community, and do come back
next time.
Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: The Open
Group.
Transcript of a discussion on how the role of the data scientist in the enterprise is expanding in
both importance and influence that warrants a new level of business analysis professional
certification. Copyright Interarbor Solutions, LLC, 2005-2019. All rights reserved.
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