Becoming an intelligent organization is achievable for any company by leveraging data analytics to learn, adapt, and drive action, ultimately creating a competitive advantage.
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good morning everybody and welcome to
the MSS business transformation
Institute's webinar entitled becoming an
intelligent organization is easier than
you think I'm Laurie Elliman and today
I'm here with our presenter MSS data
scientist dr. Luo hao
dr. how is a data scientist with
comprehensive experience in data
analytics and complex data visualization
she's conducted data-driven analysis to
find and develop solutions for clients
in both the federal and public sectors
and dr. hal earned both her PhD and MS
degrees in transportation engineering
from the University of California at
Berkeley before I hand it over to dr.
Hal a reminder that this webinar is
being recorded and will be available on
the MSS BTI website to stream and
download welcome dr. Hal the floor is yours
yours
Thank You Laurie and having hi everyone
thanks for joining the webinar I'm very
excited to be here to share some of my
insights on this really heated topic
data and analytics and becoming an
intelligent organization so here's the
little agenda for today first we're
going to just talk about what is
analytics what is an intelligent
organization and what is really the
relationship between these two and then
we're going to do a Mythbusters session
where we're going to identify some
common pitfalls on your journey to
becoming an intelligent organization
what's the myth versus with the reality
and lastly we're going to talk about why
does it matter to me and so some of the
benefits of analytics and intelligence
and we're going to conclude with a case
study so let's jump right into what is
analytics by definition analytics is a
discipline that applies logic and
mathematics to data to provide insight
so in the word analytics it's not about
storing or collecting of the data that's
more of a data engineer job but it is
really about using the data using it to
get inside to derive action items and
therefore to get business values out of
your data and that all that kind of work
is more in the realm of the role of a
data scientist
so that's analytics what about the
intelligent no organization wouldn't
what it means to become an intelligent
organization so this check work quote I
think explains it perfectly so being
intelligent as an organization it's
really it's really about the
organization's ability to learn and
translate that learning into action and
do it rapidly and that is ultimately
becoming the organization's competitive
advantage at its core putting this
definition in the modern business
context I think it overlays a lot with
analytics so putting these two concepts
together being intelligence really means
learning from your data making sense of
your data and then apply all that
learning into actions and then into
values so in this way you become
intelligent and you stay competitive to
see and one other thing I want to point
out here is seeing the big names like a
TV or other big names like Google's and
Facebook might make you think that
analytics and being intelligent
organization is all just a game for the
industry giants or the tag savvy big
names but that's not really the case
it's not just for a game for the big
firms really there's a solution for
everyone for
every company of every size there's a
solution to become intelligent and be
better at what the company is doing the
summarize putting analytics and the
becoming intelligent together in the
word intelligent organization has the
ability to constantly learn from the raw
data it has and the generating value out
of it constantly at its core the
critical engine keeping this the whole
process going the whole data to value
process going is the analytics engine so
what is happening really in this black
box here let's take a closer look um so
first let's revisit the definition of
analytics there are three key components
in that in this definition and they
together comprise analytics and that is
using you have to use your data and that
means you identify or acquire or combine
or manage in some way the necessary data
you need and then it's about using that
data to provide insight where you
discover hidden patterns and derive
business insights from the data and
finally all this is for the purpose of
making better decisions or taking
specific action and a front from this
from doing this with analytics we arrive
at your value so in the word endless
rows of raw data here won't help with
your business much but you have to
derive smart insights and it turned them
so why now data and analytics have been
around for a very long time
but why all the heated discussions now
that is because never before in the
history really have we had so much data
and so much processing power to analyze
the data so on the data
on the data side we're just having more
and more data as well as new sources of
data 90%
90% of all the data in the world has
been generated over the last two years
and on the other hand not only we have
the data but we have in the increasing
processing power to handle the data and
really to analyze that data at the most
granular level and that means two things
first the potential business benefits
when these two combined what they can
generate is tremendous so using data and
analytics really can bring you value and
the second is really it is crucial for
you as an organization to become
intelligent about your data because with
everyone doing it you really have to
root this insight driven or analytics
driven strategy within your organization
it's almost like a must-have for your
organization in order to stay
competitive or even relevant in the near
future so with that said how do we
become an intelligent organization it is
actually an integrated journey it's not
just about the hiring of your data
scientist and by some expensive
analytics tools it is a very integrated
journey that includes a lot of moving
parts within your organization that
includes the strategy you're deploying
and some assessment of where you are at
the current journey what kind of talent
you need what structure of the analytics
team or the governance of your analytics
and also the approach your how you're
going to use data how you're going to
use analytics and all these together
destination was becoming truly and
intelligent organization and of course
this journey is not a journey without
pitfalls and road bumps so what are some
of the pitfalls you can run into along
the journey or so we'll explore a few
common ones in our next section so talk
about some of the popular myths that's
out there and explain why it is a myth
and what is the reality the first one is
a really common one that we hear a lot
people say oh my organization is already
using analytics you know we summarized
our data in the reports or dashboards
and we're getting some insights on our
past performance so we have analytics
that is not the case so the rest the
reality is analytics it's not just about
looking back at the past it's about
making you more confident about the
future helping you better prepare for
the future by increasing your options in
the present so like when you are
something like when you are driving
using analytics it's not just looking
into the rearview mirror where you look
back at what happened behind you but
it's more about looking at a windshield
view where you look into the future
gather information about what's coming
up on the road and be well-prepared for
it so this graph explains a lot for
explains a lot the strategic is called
strategic intelligence and it's all
about timing so say if we have an event
occurring here and this triangle here is
the range of options that we have
so before the event occurs if we have
some information and we use that
information through some analysis and
make a decision of what we're going to
do the range of options for us to take
action our response range is much wider
and on the contrary if if we don't get
any information until after the event
happens a range of options really begins
to narrow very quickly and imagine
you're driving again
so like this event maybe a car is
aggressively changing Lane
ahead of you and if you look if you look
into your windshield field and you're
sort of sort of see that see some signs
of that happening like it's speeding up
or if blinkers on so that's the data you
have and you make some judgments using
your analytics with within your brain
and you think all this car is going to
change Lane so you make till then you'll
have more options to deal with it you
can either slow down or speed up it's
not your personality and you can also
change the other lengths to avoid this
aggressive driver then if you don't have
that intelligence if you only get to
respond after you see the car is
changing Lane that makes you range of
options much narrower and you have to
react with much less confidence so
you're just in the firefighting mode
dealing with the event that's already
happened so all this information that
you gather ahead of time and you analyze
to help you help prepare you to take
actions these are the value added
through data and through the predictive analytic
analytic
that we performed on that data so that's
our first myth and next myth is for our
analytics effort I need to start with
data infrastructure another very common
is and we hear we actually hear a lot of
similar comments like we all we need to
build our data base first or a better
timing for using analytics is until
after we have we build our big data
warehouse project or something like we
need to collect all the data we have and
then put them through analytics to see
what kind of results just bubble up and
along with those comments we are we are
hearing real frustrations out there as
well we have in many senior leaders
complain that they have been spend
spending powers of money on their data
infrastructure projects and find
themselves sitting on more and more data
really seeing no value out of that data
or not knowing how to derive any value
out of there
so the question is really why are we
stuck in forever data collection and
manipulation and not seeing any value so
this is a process in a lot of people's
head about how you go from data to
insights and as it might seem like a
smooth progression it's really not it's
we're seeing is more like a bubble neck
situation here with a lot of people in
the data collection and the storing
process where only a few can pass the
rule to arrive at inside and those
getting insights from the data is what
we call a truly intelligent organization
and from the insights they get values
fairly quickly
so so what is the secret for these few
intelligent organizations making that
leap from data from all kinds of efforts
around data to using the data to
generate the inside it is actually a
twist on how to start your analytics
project in the world you shouldn't you
should have data at your data should be
at your service within your control to
serve your purpose but really not
letting data to drive the entire process
from the start
so here the myth is busted and the
reality is we think every analytics
project should really start with asking
the right business questions remember
earlier we talked about how you get you
get from data to insight to value and
well to start to start this process you
need a direction for what kind of data
that you need to be analyzed rather than
just starting with all the available
data you have that's more like boiling
the whole ocean so really there's the
step zero before all these and that is
to start with a question to identify a
key business question to be answered
with the data a question that's tightly
tied to your business drivers or your
largest business pain point and this is
what I call the question to value
approach starting with a question that
sets you on the right track all the way
to value and all the way to becoming an
intelligent organization and here's our
third news which is analytics comes down
to having the technology again a very
common one it is common to think and
Linux is just about having the right
technology and the right technical
talent and when you have those you're
ready you the insights and the business
values will just come out naturally that
is really not the case well the reality
is beside technology your business
competency and the culture all these
three are necessary factors for your
analytics maturity and for a successful
analytics project so to be to get ready
for analytics to generate a value to you
you need all three of these and we
talked a lot about technology so that
your data infrastructure data quality
and your technical talent business
competency is really about having the
right leadership having the right
business decision making process to
implement analytics it's about asking
the right business question like we
talked about and also interpreting the
analytical results accurately under to
put them into your business context and
finally the culture culture is something
often involves motivating the using of
the analytic results or fostering the
willingness to implement the action
items and it is very critical because
values are not seen until
recommendations are implemented into
your organization's day-to-day
operations and that's often by employees
way beyond your analytical talents
so to summarize here when you ask a
question am I ready or it's my
organization ready for analytics think
beyond the technology you think all the
three components technology business and
the culture and always ensure a balance
the growth among the three
and that brings us to our final news
analytics is a set it and forget it this
is also very common many people
understand analytics as some operational
routine that once it's set up you can
just leave it running data will keep
flowing through the system and keep
generating values for you well that's
not true the reality is your analytics
plan should always be revisited and
adjusted to match the changes many
changes I have and they're both internal
and external changes let's first talk
about internal changes as we talked
about analytics this meant to help with
your organization's decision making
process at a very high level so as it
grows its capability changes and also as
your organization grows your needs and
the priorities for analytics will also
change the old analysis you are doing
the questions it's answering the
approaches using they may not be useful
or even relevant anymore as your
organization grows so internally
analytics should always be adjusted to
match the overall strategy development
plan of your organization and that's
internal and the looking externally
there are also all kinds of changes
happening and they are happening rapidly
first let's talk about technology and
the analytics select technology
landscape is just ever-changing and the
weave in a lot of market disruptions and
there are disruptions coming up soon so
during the past ten years we've already
shifted from a really like a coding base
to platforms where you do a lot of
coding to analyze
data to a visual basic platforms that's
where the modern business intelligence
comes in and making it a more business
user-friendly and in the next two to
five years
there are many predictions going on
about this key trend augmented analytics
where is the power of artificial
intelligence and the machine learning
will be leverage that into advanced
analytics and making it a much much more
powerful and more usable for business
users so ten years ago we struggle to
find any machine learning and they both
business analytics applications and in
ten years we'll probably struggle to
find any that don't do that so this is
just one example of a technology trend
and there are many others on the way as
well so these should all be taken into
consideration when you are monitoring
and adjusting your analytics strategy
just to make sure your organization is
keeping up with all the technology
trends the the other external factor
that's ever-changing is technical
technical talent so a few years ago the
robot data scientists didn't even really
exist and even today the definition and
the responsibilities of a data scientist
it's rapidly evolving and leading to new
possibilities for analytics role within
your organization so that's definitely
something to always keep an eye out one
teacher and we observe in the talent in
the talent is that the boundary between
technical and the business talent is
getting blurred so it's important to
find your your own mix of the right
balance of technical and business talent
so we have
analytics skill and a bottom where your
typical data scientist resides and we
have these business skills up here and
they are these two skills are reading
really getting merged so as the business
analyst within your organization doing
basic analysis for you is that
incorporated with some analytics do we
have citizens data scientists where you
do analytics for your day-to-day job on
a daily basis and even though there
aren't that many today
gartner predicts that in less than five
years the number of citizen
data centers will grow five times faster
than those highly skilled data scientist
roles and on the other side as a
business leader
you also need some analytic skills to
understand to understand data and
understand the inside and then that is a
role called analytics translator where
you really have those business leader
skills but some knowledge of analytics
as well so it's almost non-existent
today less in less than 10 years
McKinsey is predicting that the demand
for analytics translators in the US
alone may reach two to four millions so
these are really key signs to look out
for and when we are talking about
technical talent and definitely always
adjust your analytics plan to make sure
that they match all these changes okay
so we've covered what it is and busted
some myths and now is the our final
section we talked about why doesn't
matter to me so is there an analytics
really working and why should I care
okay so let's talk about it is there a
value really from analytics well the
short answer is
according to Harvard Business Review
companies are seeing five to six percent
consistently higher productivity rate
and profitability when they are
also we have all these numbers reported
as the gain from analytics from
different aspects so we are seeing
increase in productivity a higher
customer retention also are just overall
a higher profit margin per customer
besides all that advanced analytics
users are also just gaining more
insights from their customers
that's one big that's one big use case
there because customers per day today
are just becoming more and more
demanding and expecting personalized
services and using analytics do make a
difference there in telling what the
customer really needs and what they what
they always respond on the 2/3 and X
marketing efforts it's not just facing
the customer but also analytics can help
on the operational side of the business
where it is able to identify
inefficiencies in the organization and
provide data driven solutions so in a
word insights from advanced analytics
are really disrupting everything from
your customers to your internal
operations to just everything and as we
talked before it's not just a game for
industry giants or unicorns
is for everyone and according to our
Gartner survey on the MSE the mid-sized
enterprises more than 65% of the
companies interviewed reported they get
better business outcomes by increasing
their analytics and bi adoption and like
as we talked about in fact there is an
analytic solution for everyone you're
not competing with those big names or
some mums just very techie companies
you're not competing with them on the
same playground it is really all about
finding a solution that best suits your
own business model and answer your own
business question to improve upon
yourself so if it has to be a
competition it's a competition with
yourself you're evolving into a more
intelligent more analytics driven
version of yourself and you're not
so having understood that looking within
the organization where does analytics
have an impact oh well just everywhere
and it impacts everything having a great
analytics approach really penetrates
into all the key aspects of an
organization and will own it on its own
keep finding new ways to help these are
all the aspects that it might it is able
to provide solutions we've covered
customers which is definitely a big part
but it is easily transferred into
helping with your marketing effort and
also helping with your strategy and
vision and the plans and the expansion
plan and looking inside and it also
helped with increasing operational efficiency
efficiency
helping helping your performance and
also even employee engagement or HR
effort so really it impacts everything
what about industries what kind of
industry our are being impacted well
again the answer is almost every
industry on the face of ours and again
whether it's like a has a heavy customer
facing nature like a retail hospitality
or if you have a heavy operational side
of the business like you have an
infrastructure or
supply chain to manage data and the
analytics is in each and every one of
so having understand all all that
benefits finally we are seeing a case
study where the benefits are becoming
real through this one we'll see how data
and analytics are leveraged to help this
company to answer a specific question
and the help the res values from the
data for for the company so our company
is an auto aftermarket company where its
business model is it has a very large
marketing spend across their digital
channels so they do a lot of marketing
online but none of the purchases are
happening online all their revenue has
an in-store so they are see they're
seeing this enormous gap between their
online advertising and offline purchase
so they feel their online advertising
efforts are have little directions or
guidance as to what what are effective
and or not whether or not so the
question they have really is what kind
of digital touch points have
like what kind of marketing what kind of
online activities are likely to lead to
which offline purchases and by how much
by understanding this they are better
equipped to develop their online
marketing and campaign your effort so
with that question in mind that the data
they need is really to merge more
transaction data with online digital
data by some by certain way of merging
the data and through doing a analysis on
the data in this case a purchase intent
analysis specifically they quickly get
to some insights and distance outcomes
so what they find a few actions to pay
attention to include when a customer
sign up for an email newsletter or they
are applying for new financing or credit
cards that's a highly predictive of a
big service or big purchase happening in
the near future so they develop the
strategy to target these customers more
approached them more to have them come
to their business and by doing that by
utilizing that insights from data and
taking actions they saw a 67 percent
lift in their overall customer lifetime
value and they did it fairly quickly so
this is a real case of how our business
is able to start with a question using a
pulling the right data applying
analytics to the right insight and
so as we approach as we get into the end
they are probably getting excited all
about analytics and just having this
question I am ready and how do I get started
started
well our our recommendations
always start think big and start small
so thinking big really means that you
always think of the overarching
analytics strategy the approach you're
taking the business process and always
balancing technology and culture and
making them align to your overall
strategy so that's all thinking big if
you want to start start small
remember that analytics is not about the
entire transformation of your data in
structure but it's about understanding
and using the data to create value and
often to start in a quick and dirty way
so I really start with a question
start with a question and a pool of
small data set do some quick analysis to
see results quickly and approve the
value and from there your analytics is
seeded and then will keep growing and
most importantly don't wait to start
small and start today so that's pretty
much our webinar today to summarize
quickly today we talked about first what
it means to become intelligence it's
about using data to get insights and to
taking better actions and we cleared
some news along the way so we know that
it's really about the future and always
start with a business question and
analytics it's not just about technology
and you need to look out for changes and
make adjustments and the family the
benefits we we saw a lot of real values
realized across different industries and
for companies all sizes and our parking
words is really to start to start small
and start today so that concludes our
webinar today are very interested to
learn more we have
created resources on our ms MSS PGI
website there are a good collection of
articles about analytics and many other
topics that you could feel free to
explore and we are also hosting this
workshop in October where where we all
just come in for a much deeper
discussion around how to become
intelligent organization and I'm very
excited about the workshop it will be
very interactive hands-on and just a lot
of fun so check it out on our website
and stay tuned with that thank you thank
you for your time and I'm happy to take
any questions and if you have more
questions or need more information
here's our contact information thank you
thank you very much
that was excellent great information and
I'm still reeling from the statistic
that you gave us that 90% of the
available data was has been generated in
the last two years that is an amazing
idea to deal with
that's huge while you were presenting we
did have a couple of questions that
popped up on the screen that I was
monitoring one of them is you talked
about how it's crucial to start with a
question but how do you come up with the
right question to ask well that's that's
a great question
and it is actually takes a bit more
effort probably than you think it's a -
mediator flow essentially a collaborate
process to come up with a read question
to ask and that's requiring both your
analytics input from both your analytics
team and your business team so your
business team should be driving the
process with their understanding of
their of the business model they'll be
able to identify the opportunity
for further improvement or identify some
of the pressing issues that needs to be
solved soon within the organization so
from there and they might compile a list
of questions to get to ask and then
that's when your analytics team comes in
and they're they're here to provide
insights on whether the question is
suitable for analytics whether it's a
good candidate to be modeled some
questions are some questions simply
aren't suitable for mathematical
modeling so they will just make the
decision and pick the right one so with
this collaboration will be able to reach
your question that's critical to the
business and it's also a good candidate
for analytics and another thing is [Music]
[Music]
really when you're doing this and keep
asking good questions
soon you will reach a point where you
have a long list of questions waiting to
be answered at this point it will be a
good exercise to prioritize these
questions and really manage your supply
and demand for analytics how to
prioritize these largely depends on your
business judgment but to start you
should trade off factors like the value
of the question the feasibility and the
resources required to answer the
question great it's great information
sounds like it's a combination or a
partnership between the business side of
the house and the data science exam
title your house one more thing that we
had pop up can you talk more about why
culture makes a big difference in
analytics yes definitely [Music]
too many people culture is something
that you think oh I'll get to that after
the real work is done but culture is
some is the real thing and it should be
taken care of throughout the development
of your analytical effort or really any
effort you have that is because the key
component for an initiative to succeed
is communication and taking actions and
those more often involve people beyond
much beyond your technical team who does
not necessarily lis speak the technical
language so fostering the culture really
means having the right way to
communicate to them and build their
confidence build their willingness to
act upon the inside great I think we
have time for one more question what are
best practices for data collection
that's a great question that's a great
question um I think it's really the
first again on your we talked about how
different companies of different sizes
might take the journey differently so
how you collect data is definitely
different you have different the
resources to leverage and to start to
start I would say always look within
internally first looking at what kind of
data we have available what kind of
format are they in and think about how
to if we have the data but it's in the
messy form how do we transform that data
into useful formats and we after looking
internally looking at what data you
already have then you start thinking Oh
what kind of data that we might need
that we're not collecting and that's
when you start looking for collecting
new data from either within your up
or even look for external there are all
kinds of businesses out there that self
data as a product and that's also
something to explore externally as well
great well thank you very much thank you
for your insights thank you for sharing
your your deep knowledge and expertise
of data analytics we appreciate them uh
sure and before we sign off I just want
to remind everybody to please visit MSS
BTI dot-com where you can get more
information and register for dr. house
workshop on october 18th and 19th and
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