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Data monetization strategies add new business opportunities, IT needs
FEBRUARY 2017INFORMATION MANAGEMENT
HANDBOOK
FO
TO
LIA
2 DATA MONETIZATION STRATEGIES ADD NEW BU SINESS OPP ORTUNITIES, IT NEEDS
In this handbook:
Editor’s Letter
Missions for monetizing data need lift from upfront groundwork
Building data science teams takes skills mix, business focus
Why physicists are a good fit for data science jobs
INFORMATION MANAGEMENT HANDBOOK
Efforts to monetize data should be built for the long haul
CRAIG STEDMAN
Perhaps the most obvious way to monetize data is simply to sell it to other
organizations. But that isn’t the only data monetization path, nor is it the most
likely one for companies that aren’t information services providers at heart. For
such businesses, the more common approach is embedding data along with
tools for analyzing it in the products and services they sell.
And there are real opportunities to do so, “often quite significant ones,” in most
companies, according to MIT researchers Barbara Wixom and Jeanne Ross.
In an article published by the MIT Sloan Management Review in January 2017,
the two wrote that “wrapping” products and services with data that enriches
them can help ward off commoditization and improve customer satisfaction.
Ideally, that leads to increased sales and stronger customer loyalty, even with
higher pricing on the enriched products.
3 DATA MONETIZATION STRATEGIES ADD NEW BU SINESS OPP ORTUNITIES, IT NEEDS
In this handbook:
Editor’s Letter
Missions for monetizing data need lift from upfront groundwork
Building data science teams takes skills mix, business focus
Why physicists are a good fit for data science jobs
INFORMATION MANAGEMENT HANDBOOK
In most cases, however, companies also need to upgrade their IT and analytics
capabilities to avoid possible data-related problems that could damage their
standing with customers, Wixom and Ross cautioned. New investments may
be needed in things such as data quality programs, big data platforms and data
science skills to keep efforts to monetize data on track, they said.
Gartner analyst Doug Laney similarly urged IT and business execs to think
more broadly about prospects for monetizing data in a July 2016 blog post. But
organizations should still quantify the financial impact of what he character-
ized as indirect data modernization methods. Otherwise, Laney asked, “how
can they claim they’re monetizing it?”
Positive results, Wixom and Ross wrote, “stem from a clear data monetization
strategy, combined with investment and commitment.” This handbook offers
guidance on how to build that kind of an initiative as you move to monetize data.
4 DATA MONETIZATION STRATEGIES ADD NEW BU SINESS OPP ORTUNITIES, IT NEEDS
In this handbook:
Editor’s Letter
Missions for monetizing data need lift from upfront groundwork
Building data science teams takes skills mix, business focus
Why physicists are a good fit for data science jobs
INFORMATION MANAGEMENT HANDBOOK
Missions for monetizing data need lift from upfront groundwork
CRAIG STEDMAN
So, your company is looking to monetize its data? That’s a logical plan: Data
products, analytics services and other data-centric offerings are on the
upswing, as organizations look to turn their growing stockpiles of data into
both actionable information and a revenue-generating corporate asset.
Clearly, there’s money to be made from data, both in large enterprises and
data-driven startups.
But your data isn’t going to monetize itself. Various steps need to be taken to
get data monetization strategies off the ground and push them forward. Let’s
look at some of the key to-do items that IT, data management and analytics
teams will likely have to factor into formal plans for monetizing data.
Setting up a suitable -- and scalable -- data processing architecture. Data
monetization initiatives often involve a lot of data, and that calls for some
heavy-duty processing power -- in many cases provided by Hadoop, Spark
5 DATA MONETIZATION STRATEGIES ADD NEW BU SINESS OPP ORTUNITIES, IT NEEDS
In this handbook:
Editor’s Letter
Missions for monetizing data need lift from upfront groundwork
Building data science teams takes skills mix, business focus
Why physicists are a good fit for data science jobs
INFORMATION MANAGEMENT HANDBOOK
and other big data technologies. Webtrends Inc. is a good example: It uses a
160-node Spark system to stream user activity data from websites and mobile
devices into a Hadoop cluster and then runs machine learning algorithms
against the info so corporate clients can personalize webpages and marketing
offers on the fly. “The idea is that this data moves seamlessly through our
system, and it’s happening in real time,” Webtrends CTO Peter Crossley said in
a 2016 interview. Otherwise, the data would be less useful to the Portland, Ore.,
company’s customers -- and less monetizable as a result.
Hiring data scientists with advanced analytics skills. If big data is involved,
you’re going to need some data scientists or other skilled data analysts to
monetize it effectively. They’re the ones who can build, test and run the analyti-
cal algorithms and predictive models that will produce insights as part of ana-
lytics services or go into data products sold to customers. Data engineers also
have a possible role to play in helping data scientists pull together data sets
and prepare them for analysis. But finding the right people isn’t easy: A short-
age of data scientists with the needed know-how continues to be the biggest
roadblock companies face in big data analytics efforts, according to a survey
of 370 IT and business professionals conducted in August 2016 by research
and educational services provider TDWI.
6 DATA MONETIZATION STRATEGIES ADD NEW BU SINESS OPP ORTUNITIES, IT NEEDS
In this handbook:
Editor’s Letter
Missions for monetizing data need lift from upfront groundwork
Building data science teams takes skills mix, business focus
Why physicists are a good fit for data science jobs
INFORMATION MANAGEMENT HANDBOOK
Preparing your data for monetization success. Data products have to meet the
diverse analytical needs of different customers, so a one-size-fits-all approach
to structuring the data that goes into them could dampen user satisfaction
and diminish the data’s business value. For example, James Powell, CTO at
The Nielsen Company, advised against designing data products “for the low-
est common denominator” during a panel discussion on monetizing data at the
May 2016 MIT Sloan CIO Symposium in Cambridge, Mass. Nielsen, an audi-
ence measurement and marketing research company based in New York, does
“a lot of careful modeling” of data for analytics uses, Powell said. But he added
that underlying data models need some flexibility for external users, which
could mandate a new modeling mindset in organizations.
Embedding usable and accurate analytics capabilities. Successful monetiza-
tion of data depends on it being useful to paying customers. That means pro-
viding the right data, potentially from a mix of internal and external sources.
The data also needs to be clean and consistent, same as if it was going into a
data warehouse or Hadoop system for internal use. And built-in analytics tools
must be easy to use and produce accurate results. For business intelligence
applications, embedded BI tools from various vendors are a potential option
for smoothing the path. For more advanced analytics uses, analytical models
7 DATA MONETIZATION STRATEGIES ADD NEW BU SINESS OPP ORTUNITIES, IT NEEDS
In this handbook:
Editor’s Letter
Missions for monetizing data need lift from upfront groundwork
Building data science teams takes skills mix, business focus
Why physicists are a good fit for data science jobs
INFORMATION MANAGEMENT HANDBOOK
have to be tested, or “trained,” to ensure that they deliver valid results -- then
updated on an ongoing basis to keep them current and relevant to customers.
Creating business processes to support data monetization. Efforts to mon-
etize data depend on actually selling it so it’s profitable. That could require new
pricing models and sales processes alike. “We underestimated how difficult it
would be to sell these products,” said Ivan Matviak, an executive vice president
at State Street Corp. and head of data and analytics platforms for its Global
Exchange unit. Speaking as part of the MIT panel discussion, Matviak added
that the Boston-based company had to educate its sales team to sell a data-
as-a-service platform, a risk analytics service and other new offerings to reap
the rewards of its monetization effort.
Monetizing data isn’t for everyone -- not all companies have the wherewithal
to become a data business, including data that lends itself to the concept. But
for organizations that do fit the mold, implementing a data monetization strat-
egy can almost literally turn data into business gold. Just be prepared for what
needs to be done to unlock the treasure chest.
8 DATA MONETIZATION STRATEGIES ADD NEW BU SINESS OPP ORTUNITIES, IT NEEDS
In this handbook:
Editor’s Letter
Missions for monetizing data need lift from upfront groundwork
Building data science teams takes skills mix, business focus
Why physicists are a good fit for data science jobs
INFORMATION MANAGEMENT HANDBOOK
Building data science teams takes skills mix, business focus
CRAIG STEDMAN
Hiring data scientists can be a big challenge, partly because the available
supply doesn’t meet the demand for them. But that’s only the first of the hurdles
organizations face in building data science teams with the technical skills,
business acumen and analytics bent needed to take full advantage of all the
information flooding into big data systems.
In a panel discussion yesterday at Strata + Hadoop World 2016 in San Jose,
Calif., a group of experienced data science team managers offered advice on
finding, managing and retaining skilled data scientists, both for internal analyt-
ics initiatives and efforts to build data products for marketing to external cus-
tomers. They said it starts with hiring the right types of people at the right time,
then working to ensure the assembled data scientists are both productive for
the business and satisfied by what they’re doing.
9 DATA MONETIZATION STRATEGIES ADD NEW BU SINESS OPP ORTUNITIES, IT NEEDS
In this handbook:
Editor’s Letter
Missions for monetizing data need lift from upfront groundwork
Building data science teams takes skills mix, business focus
Why physicists are a good fit for data science jobs
INFORMATION MANAGEMENT HANDBOOK
That’s easier said than done, though. Here are some of the topics that were
addressed during the session, and what the panel members had to say about
them:
Don’t hire data scientists before the analytics “lab” is ready for them. Monica
Rogati, an independent consultant who previously built and led a data sci-
ence team at San Francisco-based wearable device maker Jawbone, said
it’s a mistake to “hire data scientists thinking that they’re just going to sprinkle
learning-pixie magic dust” around an organization and start generating action-
able business insights. If the data needed to do that isn’t available for analysis,
Rogati added, the data scientists can become frustrated and restless -- “and
the company feels cheated, too, because they’re expensive and they’re doing
nothing.”
Yael Garten, director of data science at LinkedIn, agreed it isn’t a good idea to
“bring in someone whose goal in life is to implement machine learning algo-
rithms when there’s no data available to them.” She noted, though, that it can
be helpful to have someone with data science skills in-house “who can help lay
the foundations” for an analytics program, especially in the case of a startup
that’s pursuing a data monetization strategy. Otherwise, “there’s a lot of techni-
cal debt to be paid later on,” Garten said.
10 DATA MONETIZATION STRATEGIES ADD NEW BU SINESS OPP ORTUNITIES, IT NEEDS
In this handbook:
Editor’s Letter
Missions for monetizing data need lift from upfront groundwork
Building data science teams takes skills mix, business focus
Why physicists are a good fit for data science jobs
INFORMATION MANAGEMENT HANDBOOK
Expertise with algorithms isn’t all there is to being a data scientist. Rogati,
who also worked at LinkedIn as a senior data scientist in the past, said tech-
nical skills clearly are among the traits she looks for in job candidates. But
another that’s high on her hiring-priority list “is being grounded and having this
very realistic, get-things-done attitude.” Garten similarly pointed to a strong
business sense as a vital trait of effective data scientists -- an idea of “what’s
doable, what’s feasible and what’s important,” she said.
In addition, Rogati said data science teams need strong communication skills
so they can explain analytical findings to business executives in understand-
able terms. Admonitions to speak more clearly to execs “used to really make
me mad,” she said. “But if you don’t simplify it, someone else will. So, it’s in your
best interest to do it yourself.”
Data science generalists and specialists both have their place. Early in the pro-
cess of building data science teams, “when you’re going from zero to 80” on
the analytics speedometer as quickly as possible, jack-of-all-trades general-
ists who can work across various business units and departments are good
to have along for the ride, said Daniel Tunkelang, a former data science direc-
tor at LinkedIn. Later, when a team is up to speed and the new goal is making
11 DATA MONETIZATION STRATEGIES ADD NEW BU SINESS OPP ORTUNITIES, IT NEEDS
In this handbook:
Editor’s Letter
Missions for monetizing data need lift from upfront groundwork
Building data science teams takes skills mix, business focus
Why physicists are a good fit for data science jobs
INFORMATION MANAGEMENT HANDBOOK
incremental improvements, data scientists who specialize in particular func-
tional areas can be more useful, added Tunkelang, who also has worked at
Google and other companies, and is currently an independent consultant.
Commingling data scientists and data engineers can promote togetherness.
Rogati said data scientists often talk about having to “bribe” data engineers,
who help prepare data for analysis, to do what’s needed to enable analyt-
ics work to proceed. “You can skip all that by having a common team that has
the same goals and is working toward the same thing,” she added. Tunkelang
said putting data scientists and engineers together on one team can also help
“avoid having resentment created on one side or the other if they can’t do the
work they need to” because of a lack of cooperation across the aisle.
It’s good to keep data scientists happy -- but not at the expense of business
needs. While retaining the data scientists you hire clearly should be a priority,
it can’t be the only one in building data science teams. Garten said promoting
“continuous technical growth,” partly by adding new analytics tools and meth-
odologies, can help keep data scientists in the fold by enhancing their profes-
sional skills.
12 DATA MONETIZATION STRATEGIES ADD NEW BU SINESS OPP ORTUNITIES, IT NEEDS
In this handbook:
Editor’s Letter
Missions for monetizing data need lift from upfront groundwork
Building data science teams takes skills mix, business focus
Why physicists are a good fit for data science jobs
INFORMATION MANAGEMENT HANDBOOK
She also advocated allotting time for a data science team to do exploratory
analytics work that isn’t tied to specific business initiatives or parts of a data
monetization plan. “But you need to make it clear upfront that the goal is to get
things done for the company,” Garten said, advising that team managers spell
out how much time data scientists should devote to practical analytics versus
exploring data for possible insights.
In an interview at the Strata conference today, Bill Loconzolo, vice president of
data engineering and analytics at Intuit Inc., said he focuses on the business
problem-solving aspects of data science jobs when interviewing candidates
for the Mountain View, Calif., vendor of financial and accounting software. “We
talk about the impact of the work they’re going to do -- for example, how much
money they’re going to put back in the pockets of people at tax time,” Locon-
zolo said. “That’s very attractive to data scientists. They want to solve real
problems.”
Craig Stedman is executive editor of SearchBusinessAnalytics. Email him at
[email protected], and follow us on Twitter: @BizAnalyticsTT.
13 DATA MONETIZATION STRATEGIES ADD NEW BU SINESS OPP ORTUNITIES, IT NEEDS
In this handbook:
Editor’s Letter
Missions for monetizing data need lift from upfront groundwork
Building data science teams takes skills mix, business focus
Why physicists are a good fit for data science jobs
INFORMATION MANAGEMENT HANDBOOK
Why physicists are a good fit for data science jobs
ED BURNS
Kiril Tsemekhman earned a doctorate in physics before moving into the online
ad industry, eventually taking on his current role as chief data officer at Integral
Ad Science Inc. So how does a physicist go on to lead cutting-edge big data
analytics projects for a company that has built its business around its data col-
lection and analysis capabilities?
“If I reflect on it, I think certain backgrounds prepare you better for data sci-
ence,” Tsemekhman said.
Increasingly, physics is one of those backgrounds. Tsemekhman said he’s see-
ing a lot of people stepping out of academia and into data science jobs. On his
team, his background in genetic physics is complemented by researchers with
degrees in chemistry, computational neuroscience and linguistics. Other team
members also have degrees in physics.
14 DATA MONETIZATION STRATEGIES ADD NEW BU SINESS OPP ORTUNITIES, IT NEEDS
In this handbook:
Editor’s Letter
Missions for monetizing data need lift from upfront groundwork
Building data science teams takes skills mix, business focus
Why physicists are a good fit for data science jobs
INFORMATION MANAGEMENT HANDBOOK
This is perhaps a necessary evolution of the data science field. At a time when
demand for data scientists is rising, driven partly by the growing ranks of ana-
lytics services providers and other companies looking to monetize data, the
supply isn’t keeping pace. Traditional data scientists, with degrees and experi-
ence in advanced math, computer science and business disciplines, remain
scarce. So, businesses are looking elsewhere -- and researchers from the hard
sciences can be a good fit.
In many scientific fields, physics chief among them, statistical analysis of large
data sets is common. Tsemekhman said he cut his teeth modeling the interac-
tion of genes based on large sets of observed data. It should be no wonder that
one of the most well-known people in data science circles, Kirk Borne, a princi-
pal data scientist at management consulting firm Booz Allen Hamilton who has
a large social media following, got his start in astrophysics and remains active
in the field today.
NO STRANGERS TO ANALYTICS ALGORITHMS
The Large Hadron Collider, the world’s biggest particle accelerator, operated
in Switzerland by CERN, offers a good example of why physicists make good
15 DATA MONETIZATION STRATEGIES ADD NEW BU SINESS OPP ORTUNITIES, IT NEEDS
In this handbook:
Editor’s Letter
Missions for monetizing data need lift from upfront groundwork
Building data science teams takes skills mix, business focus
Why physicists are a good fit for data science jobs
INFORMATION MANAGEMENT HANDBOOK
data scientists. The particle accelerator generates data at a rate of 1 MB per
collision event, and such events happen at a rate of about 600 million per sec-
ond. It’s the mother of all big data problems. Physicists write algorithms to sift
through the data in real time to collect and save only potentially interesting
data. It’s not hard to see how the experience translates to commercial big data
projects.
In fact, investment-portfolio analytics software vendor Omega Point Research
Inc. employs several people who have experience at CERN. “High-energy
physics is a great training ground for data science,” said Omer Cedar, co-
founder and CEO of the New York company, which has built a data science
platform that combines an analytics engine, machine learning algorithms and a
set of data feeds it has assembled for customers to use.
Not only does the experience translate from one field to the other, but big
data technology is building a bridge between the research community and
enterprises.
For example, Cedar, whose company uses the Databricks distribution of
Spark, said academic researchers have been among the early adopters of the
16 DATA MONETIZATION STRATEGIES ADD NEW BU SINESS OPP ORTUNITIES, IT NEEDS
In this handbook:
Editor’s Letter
Missions for monetizing data need lift from upfront groundwork
Building data science teams takes skills mix, business focus
Why physicists are a good fit for data science jobs
INFORMATION MANAGEMENT HANDBOOK
open source data processing engine. He’s had success hiring people from aca-
demic fields after meeting them in Spark-related discussion forums.
Still, none of this means physicists or any other types of true scientists auto-
matically make good candidates for data science jobs. Working for an enter-
prise presents many challenges that are distinct from academic research,
even if the nuts and bolts of data analysis are similar.
“If you bring in a lot of people who have no clue about the business, it becomes
difficult to guide people to practical solutions,” Tsemekhman said.
DATA SCIENCE TEAM PLAYERS WANTED
When looking for new employees for Integral Ad Science, which is also based
in New York, Tsemekhman tries to assess job candidates’ personalities and
ability to function as part of a team as much as their data analysis skills. He asks
candidates to solve some kind of data science problem and then present their
findings to the rest of his team to get a sense of how the person will mesh with
others. And while some academics excel at this type of challenge, others do
not, Tsemekhman admits.
17 DATA MONETIZATION STRATEGIES ADD NEW BU SINESS OPP ORTUNITIES, IT NEEDS
In this handbook:
Editor’s Letter
Missions for monetizing data need lift from upfront groundwork
Building data science teams takes skills mix, business focus
Why physicists are a good fit for data science jobs
INFORMATION MANAGEMENT HANDBOOK
Also, the level of experience most academic researchers have may be more
than most businesses need. Speaking at the TDWI Accelerate conference in
Boston in July, James Kobielus, a big data evangelist at IBM, said many busi-
nesses can get by with building a team of business analysts, data visualization
specialists, software developers and systems architects to do the job of data
scientists. Enterprises can also send employees back to school or encourage
upskilling to fill any leftover skills gaps.
“You don’t necessarily need a Ph.D. and five to 10 years of experience to
be effective as a data scientist,” Kobielus said. “Data science is not rocket
science.”
So rather than intentionally setting out to find academic researchers to fill
data science jobs, corporate enterprises and organizations that are focused
on data monetization might do better to cast their nets broadly and be open-
minded to possible candidates with a range of experiences. “It’s more by
accident than design, but we have people from different backgrounds,”
Tsemekhman said.