Data monetization new business opportunities, IT...

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.


In this handbook:

Editor’s Letter





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.


In this handbook:

Editor’s Letter






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


In this handbook:

Editor’s Letter





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.


In this handbook:

Editor’s Letter





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


In this handbook:

Editor’s Letter





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.


In this handbook:

Editor’s Letter






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.


In this handbook:

Editor’s Letter





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.


In this handbook:

Editor’s Letter





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


In this handbook:

Editor’s Letter





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.


In this handbook:

Editor’s Letter





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.


In this handbook:

Editor’s Letter






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.


In this handbook:

Editor’s Letter





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


In this handbook:

Editor’s Letter





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


In this handbook:

Editor’s Letter





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.


In this handbook:

Editor’s Letter





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.

Data monetization new business opportunities, IT...

Documents

Transcript of Data monetization new business opportunities, IT...