The presentation discusses the critical balance between performance and innovation in analytics, highlighting how organizations can foster a culture that effectively drives both through strategic mindset, skillsets, and organizational structures.
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that'll work for you yeah thank you very
much uh hey good morning everyone and um
for those of you that don't know gangas
uh they're up there on the right on the
top uh and uh gangas is actually a uh
decision intelligence organization so we
work with clients to help them gather an
gather data we work with clients to help
them do uh data sciences and analytics
on that data and then we work with
clients to help them um curate their
insights and uh help them to be more
persistent uh throughout the Enterprise
so people can access their insights and
uh we were supplier of the year for
Coca-Cola the year before last uh for
their knowledge and insights group been
around for about 20 years
uh me my name's Jason ruso uh my last
name actually if you spell it backwards
is oh sugar
so uh true story and
um and uh you know for those of you that
don't know me I actually have the best
job in the world I get to work with uh
clients and uh Executives that have very
challenging and very complex analytics
problems and I've been doing it for a
long time and uh I've had the privilege
of working with Walmart United
Healthcare sap helping sap do their
analytics uh Starbucks uh
InterContinental Hotels Nestle uh
General Motors and so over the course of
the last 10 years and helping to grow mu
Sigma and helping to uh deliver all this
uh work I thought I'd compile some
learnings and uh some of the uh what I
think are uh unintended consequences of
people going a little too fast when it
comes to analytics and where people get
where people get snagged
snagged
actually go back one second here uh so
the title of this presentation is Arch
contacting an intelligent organization
so what is intelligent or how am I
defining intelligent um you know intell
an intelligent organization to me is one
that uh balances uh performance um Eric
was talking about uh Precision uh you
know accuracy speed quickness but then
also the ability to innovate and um you
know the
I Big Data you know the word Big Data
means a lot of things to a lot of people
um for me I think it's a metaphor for
disruption I think data's always been
big uh but I think if you actually take
the two words big and data I think they
represent uh two sides of the coin you
know the the big part is um is the part
that allows CEOs to come up with new
business models Coos to come up with new
ways of uh driving driving efficiency in
their business CMOS new ways of reaching
their customers and uh you know that
part of Big Data you you know the big
part I think is is represented by Albert
Einstein's quote it's you know
imagination is more important than
knowledge you know that's where the
Innovation comes from you the data part
of it to me is uh you know how you
gather and store and access and uh
assimilate that information in a quick
and uh accurate manner I think is more
presented by you know the W her quote
you know fast is fine but accuracy is
everything uh and the reality is for
just about everybody in this room to
some degree if you're involved in
analytics in your Enterprise you're
accountable to both performance and
Innovation and so you know how does that
feel well unfortunately you know speed
doesn't equal learning and and accuracy
doesn't equal experimentation and so the
ability to deliver significant
performance a lot of times actually is
you know compromises your ability to
innovate and so you know this is uh you
know I think a picture says a thousand
and so there's performance on the left
and there's Innovation on the right and
uh and you know there and we all have
that look on our face that Homer has
right there hanging from the wrecking
ball as uh you know our um as as a new
technology comes in because what happens
happens
it can deliver both performance and
Innovation and then suddenly it's the
you know it's incredibly hyped and then
and and so what I want to really cover
today is
is I want to talk about the mindset
that's required for successful uh
analytics I want to talk about uh the
skill set or at least my perspective on
skill set I think actually par of and I
have some very similar uh opinions on
this and then and then I want to talk a
little bit about a level set um I want
to talk a little bit about what this
cultures and so if you think about
analytics uh regardless of the actual
industry you're in you know the business
that we're in as as analytics Executives
is the business of connecting data to
decisions decisions to data that is that
is our business how we do it so we will
take the outcomes or we'll take the uh
you know the exhaust the digital exhaust
from a business environment create these
data stores and then we'll do
analytics on the data and then people
will make decisions and that influences
the business environment and and this is
how it goes right and so and then to
kind of operationalize it you know in
the lower left corner you have your data
infrastructure and that's everything
from your relational databases all the
way to your Hadoop clusters and all
points in between and then you have your
analytics which is everything from your
descriptive to your interpretive to your
predictive to your prescriptive although
I sometimes debate whether prescriptive
actually is its own thing or if it's
just an attribute of descriptive
interpretive and predictive I mean all
analytics really should be prescriptive
and say so what and now what and I guess
while I'm opining I'll also say that I
think it's kind of a sad commentary that
in the times we're living in today we
call it datadriven decision-making you'd
think we just call it decision
making uh so uh you know as we evolve
though and you know analytics gets more
mature then there's the process of
insight Logistics and what I mean by
Insight Logistics uh I think someone
touched on this yesterday although
thankfully they didn't call it this is
you know getting the right information
into the right executive's hand at the
right time in the right way so they can
make the right decision and and it's the
skills required to properly deliver
analytics in my opinion are inherently
cross functional the idea that your data
uh Architects can be in a silo and you
know not really you know be in the same
Department as your your your
statisticians who aren't you know in the
same Department as your uh as your folks
that are actually uh you know working
with the business to help them uh Drive
business outcomes I think that that's
going to have to go away because uh the
the the speed at which we're
moving almost incapacitates true
specialization the way it was once upon
a time that the ability to be cross
functional the ability to work across
all of these uh different concepts and
for someone in the data environment to
still be able to connect the dots with
okay if I store it this way and I want
to access it this way what does that
mean for the end user there has to be
that level of accountability to what
they're doing but what happens got all
this great technology and I think that
uh and part of you know not a silver
bullet I could not agree more I what I
do think is important is that people
need to be able to play around with
these things and figure out the context
and how they relate to one another and
then figure out if it's meaningful but
uh you know Cass whether it's Cassandra
or whether it's uh you know spark or
what have you I think that uh people
often times are like oh gosh we got to
have this skill set inhouse before
they've even really validated whether or
not it's truly meaningful for them and truly
truly
complimentary and so let's go into
mindset for one more
second you've got this business
environment you know and you you've got
this data and you got this analytics you get
get
decisions to me this is actually a
performance mindset when I think about
big data and what big data is really all
about you know especially
combined I think that there's more to it
I I think that if you're going to drive
Innovation you have to think a little
bit differently you have to say well
wait a minute you know what decisions do
I want to be making and what analytics
do I need to be doing and what data do I
need to
have and then can I reinvent the
business environment and it's our
Charter as analytics Executives and the
ones that I work with to make sure that
they're capable of doing both for their
Enterprise so how do they do that or
what's the you know what's the what's
the way that they do it well I believe
that it's it's it's Unique to each
culture and there are um there are
different cultures there are first of
adopt well you know some will build and
some will buy and uh I'm not here to
pass judgment I think that there's uh I
think there's a case that you know both
are are good uh but they also have you
know every strength is a weakness uh so
if you build something you obviously you
know you own it and it's but it can be
slow uh but over time you can achieve
certain uh scale benefits you buy it you
can have it very quickly but sometimes
you know there's a little bit of
fast and then you have deployment and so
you know should I be centralized you
know having all these data scientists in
one room there's a lot of benefit for
that that for all the sharing or should
I be decentralized I could be closer to
my customer I could be more responsive
and you know what happens is companies
tend to wind
up falling into buckets and so
centralized companies that uh build
their own Technologies or their own
capabilities you know they have great
structure they're
slow and uh you know for folks that want
to be
Innovative I I hate to break to you but
if you're come from this quadrant you
are going to be challenged in driving
Innovation you will be out of this world
when it comes to driving performance but
you know you every strength is a
weakness right and then you have the
outsourcers so you know centralized you
know they tend to align around one or
two technology companies you know
they've got they've got IBM they got
Oracle and then you know fill in the
blank for the third one and you know
maybe uh TCS and and they wind up
Outsourcing a lot and they go very fast
in some regards but there's also
sometimes uh you know it's it's a lot of
expense of course and they're current
but um you know they can be challenging
sometimes because uh they've outsourced
a lot of capability there and so when
new technologies and New Concepts come
in they have a hard time uh really
knowing what that means they have to
just listen kind of to what their
Partners tell them and there's some risk in
in
that and then you have the Islanders and
I see this one probably the most in the
companies that I work with frankly well
probably the bottom to
but um you know the Islanders are um you
know so they're
decentralized uh and uh you know they
tend to build a lot of their own
capabilities I see this a lot in cpg
because multinational companies they
want to be close to their markets uh and
so what happens in these kind of
cultures is uh very very effective and
uh you know kind of very good at get
capturing what they need to capture
within their markets not so good at
communicating with each other and they
don't collaborate as effectively as they
could and so um you know you tend to
have have
um communication gaps and uh
opportunities lost uh in the way that
they could be sharing better and then
you have the Trailblazers you know
they're decentralized they buy things
they move very quickly and uh and so you
have um you know very fast but also
chaotic maybe even I'd call it
schizophrenic but uh they're actually
very um you know they're they're very
passionate about customer service and uh
and these are usually more uh your
startups like uh I did a lot of work
with uh Airbnb they were like this and
uh and so their challenge is how do I
how do I deliver you know um
consistently so I'm not just Reinventing
time and so the reality is as you guys
you know leave here today and you take
all this knowledge and you go back and
you lead your organizations you're going
to have to build these capabilities
inside the culture that you that you
know is going to be hosting uh these
analytics so I just wanted to give you
guys a few case examples and takeaways
from uh experience and also um uh some
potentially so how do I drive service
and and to me like the the controllers
like I said I find that they have some
Innovation challenges uh there's a book
called The innovators dilemma uh it's a
good one I think it's related to these
cultures in particular they're very good
at doing what they do and then then
that's kind of the optic through which
they look at everything um I think that
the the best advice that I could give
for companies that come from this group
is to rethink your sandboxes a lot of
times the you know they'll they'll
create a Dev environment and then uh uh
and then they'll uh you know they'll
invite some power users and uh they're
like you know okay well we can play
around check and uh you know we got some
people from the business the power users
but more often than not the power users
in these kind of cultures aren't
actually that close to the business uh
you know because they're such power
users and so I think you got to try to
invite people into the sandbox that are
also like almost naive about uh what the
technology is or what the tools are and
just hear what they're trying to
do uh a good book for uh to read is uh
where good ideas come from by Steve
Johnson uh it's a very powerful book
about how um how you know one of the
examples is you know Newton was thinking
about gravity for a hell of a long time
before the Apple hit him in the head it
was just that moment that suddenly it
crystallized and so there's a lot of
good ideas about how you can work within
your Enterprise to um to learn and uh
way the
outsourcers you know how do I instill
ownership you know
um so the you know the companies in this
group tend to throw money at problems
but they won't throw executive time and
so there's there's a lot of leadership
issues there uh as far as commitment and
so uh my advice to the the the folks in
this group is um try make sure that you
build an internal competency as well
like uh you have to having worked with
these companies I can tell you that um
there's a little bit of a a confusion
about well what am I paying you for why
do I need to have you know analytics
expertise inhouse if I'm bringing you on
and and my answer is that if you don't
have some analytics expertise and some
fluency inhouse like like solid then
your leadership will never uh really be
able to understand what we're doing for
you because you know we're delivering it
through you as my sponsor and uh if
you're not you know capable of doing a
lot of this stuff yourself or or or
being out and Walking The Halls and
socializing it the right way and people
understand what you're talking about it
won't be sticky the way you want it to
be uh a great book is a vested out
Outsourcing very good book on how to get
Partnerships and then there's the
Islanders so uh like I said lot lot I
see this a lot in multinational
companies cpgs uh how do I promote
collaboration and uh and I'm not just
talking about even just like in
marketing analytics across all the
different continents for example I even
mean like
um and uh Parts have use the word
Federated so I actually like that word
quite a bit I like to call it the Jedi
Council but the idea is how do I get
people that are responsible for
analytics period uh you whether they're
in the supply chain or whether they're
in finance or whether they're in uh you
know marketing or whether they're in
digital can I get them all together so
they can start sharing because at the
end of the day regression techniques can
be used across the Enterprise in
different capacities and so hearing the
different use cases will spur ideas it
will also facilitate a lot of sharing
and um and you can start to do some
really great stuff uh I've seen it time
and time again and the the only other
trick to this if you're going to make it
work is um you have to have somebody
that isn't the CIO although it probably
could be but uh somebody at a very
senior level that oversees this Jedi
Council so when they get together and
they knowledge share when there is a
really good idea that individual then
can go to the the sea level talk about
what we're doing and analytics starts to
become a true function uh again
borrowing from part to but I think that
that's that that's when that starts to
happen great book to read is group
Alchemy it's all about how uh you can
take folks that have separate
um agendas if you will and uh get them
working together so they're more uh
distinctively collaborating and uh
winning for the
group and then the Trailblazers uh I so
personally I probably the way I think in
my life I'm probably a little like this
uh and so you know the challenge here is
how do I ensure scalability and uh you
know how do you channel that
schizophrenia a little bit because
because I because I do think that
there's a distinct advantage to moving
fast and these people do tend to move
very fast but being an employee in this
environment can be very challenging it
can feel very schizophrenic because you
know the priorities of the day can
change you know from morning to after
lunch and uh and that could be very
challenging and um confusing frankly and
uh so a good book to read is a one
called traction
the uh the bias of the author is a
little bit more to uh eradicate
schizophrenia I don't know that you have
to totally eradicated but I do think it
needs to be channeled uh and uh because
these you know kind of take it a step
back you know these groups right here
the uh where's the pointer right there
you know this one right here real good
at uh at performance not so good at
Innovation guess what these guys you
know not not good at per performance
graded Innovation so you know it's uh
I'm not passing judgment by the way I
want to make very clear that all of
these are uh have distinctive strengths
and weaknesses so I just want to make
sure that I'm not like it doesn't appear
that I'm being dismissive of any of these
these
cultures so I'll leave you with a quote
from uh you know a data scientist far
before his time you know we cannot solve
problems by using the same kind of
thinking we used when we created them so
as you go back to your uh your day jobs
and uh you think about how I can drive
the effectiveness of my analytics
organization you know be mindful that uh
you're accountable to both the uh the
performance and The Innovation and uh
questions oh there we go so
so
situ so uh let me just restate the
question so uh you're talking about in a
very large company where you'll have a
group that behaves like controllers and
a different group that's behaving like
Islanders um so I think in both of those
cases uh a lot of times the Oru
facilitates that and uh and and what
you're trying to do is often times
especially when it comes to Big Data is
to drive more Innovation and so uh what
I found I'm working with a mortgage
company and uh what we've done is we've
actually taken and broken off some of
the high performing more fixed parts of
their business and there's a group of
data scientists that are in that group
and all they're doing is taking the same
8 to 12 models that they've built that
run their business and they're
constantly trying to drive drive
performance and accuracy on that and
then there's a different group that's uh
not not quite a statistici if you will a
little bit more uh business user
oriented and uh and they're accountable
they roll up to the same person but they
have different uh metrics by which
they're judged and by creating that
differentiation I found that um you can
kind of Tear Down the Walls a little bit
H because the the business users they're
always talking about like what's
possible and that gets people excited
about that and then uh once they've
galvanized around what they want to be
doing they can then try to
operationalize it that's that's what I
found the best the biggest challenge I
find for analytics Executives frankly is
that they don't have enough time Walking
The Halls they spend more time with
their their um you know their sleeves
rolled up hacking out SAS code or trying
to figure out why two reports aren't
tying out as opposed to like literally
talking to business users about you know
what's working and what's not working
and you know I delivered this model for
you how come you're not using it it and
well I don't really believe it okay well
the math is right well I don't trust
your assumption so you haven't really PE
that yeah well or some senior leader
um I think it so to the degree companies
have I think there's a lot of people
that kind of are filling the role of a
CDO or a CAO these days uh as opposed to
people that are actually have the title
of Chief analytics officer and then sit
at the same level as the CMO and the You
Know Chief people officer what have you
um regardless I think the the the point
that I was trying to make is that it
needs to be someone that has the ears of
all of those uh you know operational
heads you know all of those uh sea Suite
leaders so when a good idea happens it
doesn't happen in a vacuum it you know
it's able to be communicated across the
organization and socialize so people can
get more excited about the possibility
enables do that make sense
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