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#DataTalkPredictive Data Analytics to Help Your Customers
Join our #DataTalk on Thursdays at 5 p.m. ET
This week, we tweeted with Michael Beygelman, Co-founder and
CEO of Joberate, Berry Diepeveen, Partner and Enterprise
Intelligence Leader at EY, and Chuck Robida, Chief Scientist for
Experian Decision Analytics.
Check out all tweets from this Twitter chat:
ex.pn/predictive
What is predictive analytics?
Michael Beygelman
CEO, Joberate
@beygelman @joberate ex.pn/datatalk
#DataTalk
Predictive analytics is extracting
information from data sets to determine
patterns, predict outcomes and trends.
Chuck Robida
Chief Scientist, Experian
@ExperianDA ex.pn/datatalk
#DataTalk
Predictive analytics is the ability
to use data to predict future behavior
based on past behavior.
Berry Diepeveen
Partner, EY
@Berry_Diepeveen ex.pn/datatalk
#DataTalk
I think it is as old as business.
Nobody can perfectly predict the future,
but you want to be more accurate about
what is likely to happen.
Chuck Robida
Chief Scientist, Experian
@ExperianDA ex.pn/datatalk
#DataTalk
It’s the ability to analyze data in a way
that can scale, be reproduced, and
provide unbiased results.
Michael Beygelman
CEO, Joberate
@beygelman @joberate ex.pn/datatalk
#DataTalk
Clever marketers are redefining
predictive analytics into whatever suits
them today, so we need to beware.
Chuck Robida
Chief Scientist, Experian
@ExperianDA ex.pn/datatalk
#DataTalk
Some techniques get more attention
than others like machine learning,
but all are used to solve business problems.
Berry Diepeveen
Partner, EY
@Berry_Diepeveen ex.pn/datatalk
#DataTalk
It is about being able to intervene.
What is the point of finding out we
lost a customer after he left?
We need to prevent losing one before it happens.
Michael Beygelman
CEO, Joberate
@beygelman @joberate ex.pn/datatalk
#DataTalk
I’ve always said that predictive analytics
needs to be actionable like a brake system
in a car. When you press, it does something.
Chuck Robida
Chief Scientist, Experian
@ExperianDA ex.pn/datatalk
#DataTalk
Predictive analytics isn’t a crystal ball,
but the value comes in identifying
the propensity of certain behaviors.
How trustworthy is
predictive analytics?
Berry Diepeveen
Partner, EY
@Berry_Diepeveen
#DataTalk
ex.pn/datatalk
Predictive analytics is very trustworthy,
but not when used in pure isolation.
#DataTalk
ex.pn/datatalk
Chuck Robida
Chief Scientist, Experian
@ExperianDA
Predictive analytics has a natural life
and models need to be continually
validated and aligned to changes in behavior.
Berry Diepeveen
Partner, EY
@Berry_Diepeveen
#DataTalk
ex.pn/datatalk
It’s not just about the predictive modeling,
statistics and the algorithms. It’s also about
the play, experiments, intuition and innovation.
#DataTalk
ex.pn/datatalk
Chuck Robida
Chief Scientist, Experian
@ExperianDA
Behaviors change so should your models.
Economic change-inflation, housing,
unemployment. Life change: marriage, kids, job ...
#DataTalk
ex.pn/datatalk
Michael Beygelman
CEO, Joberate
@beygelman @joberate
In terms of relevance, if associated with
some decisions of value or have meaning,
predictive analytics can be very relevant.
Berry Diepeveen
Partner, EY
@Berry_Diepeveen
#DataTalk
ex.pn/datatalk
And you must deal with in a sensible way,
especially around sensitive use cases
such as fraud detection.
#DataTalk
ex.pn/datatalk
Chuck Robida
Chief Scientist, Experian
@ExperianDA
Simply put, predictive analytics still comes
down to a cost/benefit decision.
Use it as your compass.
What type of data do companies
use for predictive analytics?
Depends on the business goal.
Generally a mix of fit-for-purpose internal
and external data types, structured
or unstructured data.
#DataTalk
ex.pn/datatalk
Chuck Robida
Chief Scientist, Experian
@ExperianDA
Sometimes companies start by using
internal data. In my world, payroll data,
promotions, performance reviews, etc.
#DataTalk
Michael Beygelman
CEO, Joberate
@beygelman @joberate ex.pn/datatalk
Depending on how much success
they have with internal data, and how quickly,
they’ll usually broaden out to third-party data.
#DataTalk
Michael Beygelman
CEO, Joberate
@beygelman @joberate ex.pn/datatalk
For lenders: asset evaluations for loans,
address change for collections -- all good data,
in compliance with regulation.
#DataTalk
ex.pn/datatalk
Chuck Robida
Chief Scientist, Experian
@ExperianDA
For marketing: social, contact history,
profile data, all good data.
#DataTalk
ex.pn/datatalk
Chuck Robida
Chief Scientist, Experian
@ExperianDA
If we go back to the fraud detection
use case; you’d have to rely on
internal, external, structured and
unstructured data.
Berry Diepeveen
Partner, EY
@Berry_Diepeveen
#DataTalk
ex.pn/datatalk
The beauty is that there is no limit about
what data sources you want to tap into.
It’s always driven by the business and use case,
not the other way around.
Berry Diepeveen
Partner, EY
@Berry_Diepeveen
#DataTalk
ex.pn/datatalk
Only limitations are legal, compliance
and your imaginations.
#DataTalk
ex.pn/datatalk
Chuck Robida
Chief Scientist, Experian
@ExperianDA
How much data preparation
needs to be done before
executing predictive analytics?
It requires a very tight collaboration between
business and data science in order
to determine the iterations.
Berry Diepeveen
Partner, EY
@Berry_Diepeveen ex.pn/datatalk
#DataTalk
Data preparation is arguably as
important as the rest of the process.
ex.pn/datatalk
#DataTalk
Michael Beygelman
CEO, Joberate
@beygelman @joberate
Garbage in, garbage out.
Data preparation is the most important step.
Incorrect or insufficient data equals
bad business decisions
ex.pn/datatalk
#DataTalk
Chuck Robida
Chief Scientist, Experian
@ExperianDA
We see three phases in any predictive analytics
program: 1) strict data management, 2) building
and applying advanced analytics models, and
3) using data visualization to bring the
insights back to the end user.
Berry Diepeveen
Partner, EY
@Berry_Diepeveen ex.pn/datatalk
#DataTalk
If the sample size is massive, it might
be more practical to sample the data;
else you can use the whole sample.
ex.pn/datatalk
#DataTalk
Michael Beygelman
CEO, Joberate
@beygelman @joberate
Without strict and rigorous data management,
you should question your investments
in data science.
Berry Diepeveen
Partner, EY
@Berry_Diepeveen ex.pn/datatalk
#DataTalk
Decide what to do with incomplete data,
discard it or take guesses at missing data points
by looking at other data in the sample.
ex.pn/datatalk
#DataTalk
Michael Beygelman
CEO, Joberate
@beygelman @joberate
Be careful before tossing any data. Bias!
ex.pn/datatalk
#DataTalk
Chuck Robida
Chief Scientist, Experian
@ExperianDA
Many activities like selecting, combining,
and aggregating data are important,
especially when defining the form for training.
ex.pn/datatalk
#DataTalk
Michael Beygelman
CEO, Joberate
@beygelman @joberate
How often should
models get updated?
It’s more of a business decision.
If your data is updated quarterly,
no point in updating a model
more often than that.
ex.pn/datatalk
Michael Beygelman
CEO, Joberate
@beygelman @joberate
#DataTalk
Frequent model evaluation or validation
is critical + results should be taken
in context of other solutions
and external factors.
ex.pn/datatalk
#DataTalk
Chuck Robida
Chief Scientist, Experian
@ExperianDA
Building good models is the science.
It involves experimentation,
sufficient quality data
and is time consuming.
Berry Diepeveen
Partner, EY
@Berry_Diepeveen
#DataTalk
ex.pn/datatalk
If data is updated daily, and you
choose to update the model quarterly,
you might have to live with some
bad assumptions.
ex.pn/datatalk
Michael Beygelman
CEO, Joberate
@beygelman @joberate
#DataTalk
Expect a model to naturally deteriorate
over time. Predictive analytics
needs to be continually validated for
fit for purpose.
ex.pn/datatalk
#DataTalk
Chuck Robida
Chief Scientist, Experian
@ExperianDA
Regardless of the use case, you need
to update models regularly and
structurally, but additional ad hoc
updates depend on use case.
Berry Diepeveen
Partner, EY
@Berry_Diepeveen
#DataTalk
ex.pn/datatalk
Models are fit-for-purpose and consider
things like economy, home values...
Tests + benchmarks exist to ensure
models are robuts.
ex.pn/datatalk
#DataTalk
Chuck Robida
Chief Scientist, Experian
@ExperianDA
What is often forgotten is that new
models have to be retrained
with the updated data sets -
and results verified.
Berry Diepeveen
Partner, EY
@Berry_Diepeveen
#DataTalk
ex.pn/datatalk
What are the best ways to
test the effectiveness of
predictive analytics?
There are many scientific ways to test,
but the real question is did the analytics
provide you with actionable insights,
at the right time.
#DataTalk
ex.pn/datatalk
Berry Diepeveen
Partner, EY
@Berry_Diepeveen
Splitting data at the outset could be
a good idea so you’re not accidentally
creating a super model that only
works on one set.
#DataTalk
ex.pn/datatalk
Michael Beygelman
CEO, Joberate
@beygelman @joberate
Deploy them in a manner where
their impact can be measured in a
controlled environment like
champion-challenger testing.
#DataTalk
ex.pn/datatalk
Chuck Robida
Chief Scientist, Experian
@ExperianDA
Use a majority of the data (say 65% or so)
for the build of the model, and
use the 35% of the data for the
test of the model.
#DataTalk
ex.pn/datatalk
Michael Beygelman
CEO, Joberate
@beygelman @joberate
There are numerous ways to test
models, and some people swear
by some approaches almost like religion.
#DataTalk
ex.pn/datatalk
Michael Beygelman
CEO, Joberate
@beygelman @joberate
Test models by using data not
used during development.
Validation won’t yield same results,
so benchmarking plays a big role.
#DataTalk
ex.pn/datatalk
Chuck Robida
Chief Scientist, Experian
@ExperianDA
One can use lift charts, decile tables,
some people like to use target shuffling.
#DataTalk
ex.pn/datatalk
Michael Beygelman
CEO, Joberate
@beygelman @joberate
What are ways companies can
use predictive analytics
in new ways?
Possibilities are endless,
but business focus is key.
ex.pn/datatalk
Berry Diepeveen
Partner, EY
@Berry_Diepeveen
#DataTalk
Predictive analytics used to scientifically
predict anything from the future state
economy + weather to spread
+ cures for disease.
#DataTalk
ex.pn/datatalk
Chuck Robida
Chief Scientist, Experian
@ExperianDA
Newest technologies allow you
to quite efficiently translate
unstructured into structured
such that it can be included in models.
ex.pn/datatalk
Berry Diepeveen
Partner, EY
@Berry_Diepeveen
#DataTalk
#DataTalk
Michael Beygelman
CEO, Joberate
@beygelman @joberate ex.pn/datatalk
I spoke to a gentleman at
Goldman Sachs. They were using
predictive analytics in the hiring process.
#DataTalk
Michael Beygelman
CEO, Joberate
@beygelman @joberate ex.pn/datatalk
Goldman Sachs used analysis of
incoming CVs to compare to
top performers and those who had
a cultural fit.
What are the challenges when
working in predictive analytics?
Michael Beygelman
CEO, Joberate
@beygelman @joberate ex.pn/datatalk
#DataTalk
Challenges? Too many :)
But making sure you have ample
relevant data is important,
and making sure you have tested models
#DataTalk
ex.pn/datatalk
Chuck Robida
Chief Scientist, Experian
@ExperianDA
Challenges working with predictive
analytics: data availability,
quality, volume, and statistically
robust sample size.
Berry Diepeveen
Partner, EY
@Berry_Diepeveen
#DataTalk
ex.pn/datatalk
We need to seriously consider
data ownership and data privacy
in every single engagement.
#DataTalk
ex.pn/datatalk
Chuck Robida
Chief Scientist, Experian
@ExperianDA
Compliance and control over
permissible purposes present
challenges, especially when
rich data can’t be used.
Berry Diepeveen
Partner, EY
@Berry_Diepeveen
#DataTalk
ex.pn/datatalk
Too many examples of not treating
data as a real asset that
ultimately belongs to the customer.
Berry Diepeveen
Partner, EY
@Berry_Diepeveen
#DataTalk
ex.pn/datatalk
Another challenge is resources
and finding professionals who have
skills around technology, techniques,
modeling and business acumen.
Michael Beygelman
CEO, Joberate
@beygelman @joberate ex.pn/datatalk
#DataTalk
Globalization of predictive analytics
is another challenge.
Michael Beygelman
CEO, Joberate
@beygelman @joberate ex.pn/datatalk
#DataTalk
In more mature markets, the uptake
is “simpler” while in other markets
less so, which creates challenges
for global organizations.
What trends are happening in
predictive analytics?
Michael Beygelman
CEO, Joberate
@beygelman @joberate ex.pn/datatalk
#DataTalk
In terms of trends, machine learning
to automate the analytics process
itself is certainly one of the
bigger trends.
ex.pn/datatalk
#DataTalk
Berry Diepeveen
Partner, EY
@Berry_Diepeveen
We are predicting the future of
predictive analytics now.
We need an algorithm and a model. :)
ex.pn/datatalk
#DataTalk
Chuck Robida
Chief Scientist, Experian
@ExperianDA
Available data + advanced statistics
+ new processing tech = businesses
+ can build more meaningful
+ relationships with consumers.
Michael Beygelman
CEO, Joberate
@beygelman @joberate ex.pn/datatalk
#DataTalk
Another trend hard to ignore is the
datafication of our lives;
basketballs to tennis rackets, and
Lumo Lift to help you stop slouching
Michael Beygelman
CEO, Joberate
@beygelman @joberate ex.pn/datatalk
#DataTalk
Along the datafication continuum,
data privacy laws are severely lagging
and will need attention.
ex.pn/datatalk
#DataTalk
Berry Diepeveen
Partner, EY
@Berry_Diepeveen
Look at fantastic innovations from
enterprise technology providers
and new ventures that revolutionize
the markets
Any final tips for companies
working in predictive analytics?
#DataTalk
ex.pn/datatalk
Michael Beygelman
CEO, Joberate
@beygelman @joberate
Become more community based
rather than managed by centralized
IT at big companies, or siloed in
some underfunded organization. :)
#DataTalk
ex.pn/datatalk
Berry Diepeveen
Partner, EY
@Berry_Diepeveen
Absolutely! The internet of things is
creating great opportunities where we
have seen completely new
business models.
#DataTalk
ex.pn/datatalk
Chuck Robida
Chief Scientist, Experian
@ExperianDA
Business new to predictive analytics
maintain robust model validation
methodology. Using a broken model
will cost you money.
#DataTalk
ex.pn/datatalk
Michael Beygelman
CEO, Joberate
@beygelman @joberate
The input of community into the
evolution of predictive analytics
can have profound open-source
like benefits.
#DataTalk
ex.pn/datatalk
Chuck Robida
Chief Scientist, Experian
@ExperianDA
Sophisticated users of predictive
analytics, remember to start with
the business problem and
work backwards.
#DataTalk
ex.pn/datatalk
Michael Beygelman
CEO, Joberate
@beygelman @joberate
My best tip for working in
predictive analytics is walk,
don’t run. Make sure you take very
deliberate first steps.
#DataTalk
ex.pn/datatalk
Chuck Robida
Chief Scientist, Experian
@ExperianDA
Predictive analytics serves to exist
for solving complex business problems.
Start with end in mind.
#DataTalk
ex.pn/datatalk
Michael Beygelman
CEO, Joberate
@beygelman @joberate
Decide that you will have a culture
of analytics and then move
into that area. Don’t “test” analytics
to see if they’re “for you.”
#DataTalk
ex.pn/datatalk
Berry Diepeveen
Partner, EY
@Berry_Diepeveen
Do not underestimate how important
the data visualization is to end
user adoption of predictive analytics.
#DataTalk
ex.pn/datatalk
Chuck Robida
Chief Scientist, Experian
@ExperianDA
The world of analytics and data
is exploding. It’s critical to prioritize
analytical opportunities to stay
ahead of competition.
#DataTalk
ex.pn/datatalk
Berry Diepeveen
Partner, EY
@Berry_Diepeveen
And do not underestimate
how important the data visualization
is to end user adoption of
predictive analytics.
#DataTalk
ex.pn/datatalk
Berry Diepeveen
Partner, EY
@Berry_Diepeveen
The business needs to work with
the insights and it is not about
developing the most accurate and
complex model.
#DataTalk
ex.pn/datatalk
Chuck Robida
Chief Scientist, Experian
@ExperianDA
Simply put, predictive analytics
still comes down to a cost/benefit
decision. Use it as your compass.
Join our #DataTalk on Twitter
on Thursdays at 5 p.m. ET.
experian.com/datatalk

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