
English: 
Hi everybody!
In this video, we will focus on a fascinating
topic – the step-by-step process IBM’s
data science team applies when working on
a consulting project. We believe this overview
can be highly beneficial for both experienced
professionals and data science beginners.
We’ll explore a best-practice framework
applied by one of the pioneer and leading
companies in the field. This way, you’ll
get an insider’s look at how a consulting
project that involves data analysis and data
science unfolds.
In addition, we’ll examine the results achieved
in IBM’s data science consulting projects
with major clients from different industries.
Why is that important? Well, each of these
initiatives serves as an invaluable lesson
to the rest of the companies in the respective
industry. If, for example, Carrefour managed
to leverage AI to improve its supply chain
processes, the rest of the global hypermarket
chains would basically be obliged to follow,
if they want to keep up.
Alright.
Let’s get right in and outline the five

English: 
hi everybody in this video we will focus
on a fascinating topic the step by step
process IBM's data science team applies
when working on a consulting project we
believe this overview can be highly
beneficial for both experienced
professionals and data science beginners
will explore a best practice framework
applied by one of the pioneer and
leading companies in the field this way
you'll get an insider's look at how a
consulting project that involves data
analysis and data science unfolds in
addition we'll examine the results
achieved in IBM's data science
consulting projects with major clients
from different industries why is that
important well each of these initiatives
serves as an invaluable lesson to the
rest of the companies in the respective
industry if for example Carrefour
managed to leverage AI to improve its
supply chain processes the rest of the
global hypermarket chains would
basically be obliged to follow if they
want to keep up all right let's get
right in and outline the five stages of
a data science consulting project stage

English: 
stages of a data science consulting project.
Stage one - engage the firm’s CTO.
Stage two - meet with the company’s SMEs
and brainstorm.
Three – Data collection and modeling through
coding sprints;
Four - Visualization and communication of
findings;
And finally - Follow-up projects;
Each of these steps of the process is vital,
so let me elaborate a bit further by describing
them one by one in more detail.
Things start with a conversation with the
firm’s Chief Technology Officer.
He needs to be sold on the project. Hopefully,
this would result in him championing and endorsing
the initiative across the organization. Such
buy-in enables cooperation and improves the
project’s chances of success. At this stage,
the consulting team and the CTO will define
the scope of work and the ‘lowest hanging
fruits’, which will give an immediate boost
in terms of bottom-line results. What we mean
by ‘lowest hanging fruit’ is an opportunity
that the data science team knows is available
for most companies in an industry and is easiest

English: 
one engage the firm's CTO stage two meet
with the company's SMEs and brainstorm
three data collection and modelling
through coding sprints for visualization
and communication of findings and
finally follow-up projects each of these
steps of the process is vital so let me
elaborate a bit further by describing
them one-by-one in more detail things
start with a conversation with a firm's
chief technology officer he needs to be
sold on the project hopefully this would
result in him championing and endorsing
the initiative across the organization
such buy-in enables cooperation and
improves the project's chances of
success at this stage the consulting
team and the CTO will define the scope
of work and the lowest hanging fruits
which will give an immediate boost in
terms of bottom-line results what we
mean by lowest hanging fruit is an
opportunity that the data science team
knows is available for most companies in
an industry and is easiest to implement
for example they have seen in a few

English: 
to implement. For example, they have seen
on a few occasions that supermarket chains
can greatly reduce food waste if they implement
a predictive AI model able to adjust the timing
of deliveries. So, an absolute best practice
when working on consulting projects is to
address such opportunities first, because
this gives instant credibility to the project
team and wins support across the organization.
Once the project scope has been identified
with the firm’s CTO, the data science consulting
team will proceed to brainstorm on how AI
can be applied in the particular use cases
that have been pre-selected.
To envision this a bit better, the team needs
to conduct a series of interviews and meetings
with Subject Matter Experts - the people who
work in the business day in and day out and
who are able to contribute greatly in terms
of identifying actionable and meaningful solutions.
Also, in most cases, SMEs are the ones who
have a good idea of what data is available
and can be used for the purposes of the project
at hand.
The next stage consists of coding sprints.
This is the main chunk of the work, so IBM’s

English: 
occasions that supermarket chains can
greatly reduce food waste if they
implement a predictive AI model able to
adjust the timing of deliveries so an
absolute best practice when working on
consulting projects is to address such
opportunities first because
this gives instant credibility to the
project team and wins support across the
organization once the project scope has
been identified with a firm CTO the data
science consulting team will proceed to
brainstorm on how AI can be applied in
the particular use cases that have been
pre-selected to envision this a bit
better the team needs to conduct a
series of interviews and meetings with
subject matter experts the people who
work in the business day in and day out
and who are able to contribute greatly
in terms of identifying actionable and
meaningful solutions also in most cases
SMEs are the ones who have a good idea
of what data is available and can be
used for the purposes of the project at
hand the next stage consists of coding
sprints this is the main chunk of the
work so IBM's team organizes it in three

English: 
team organizes it in three parts.
One for Collecting data and feature modeling
Data collection sounds like ‘getting the
data from all places’, but it may be much
trickier. Depending on the scope of the project,
the consulting company may need to first consolidate
all data in one place, called ‘a data warehouse’.
In some cases, not enough data is being collected
and new data sources must be set up. Feature
modeling is inside this step as features may
be chosen from the available data. Sometimes,
however, very important metrics are not being
measured. The consulting firm can then suggest
starting to collect data on that, thus changing
the data collection structure of the client.
Another sprint for feature selection and running
the model for the first time
Once data has been collected and features
have been modeled, it is time for some data
science.
While features were modeled and kind of selected
during the first sprint, they were never tested
in a model. So, in the second coding sprint,
features are evaluated, transformed, or new

English: 
parts one for collecting data and
feature modeling data collection sounds
like getting the data from all places
but it may be much trickier depending on
the scope of the project the consulting
company may need to first consolidate
all data in one place called a data
warehouse in some cases not enough data
is being collected and new data sources
must be setup feature modeling is inside
this step as features may be chosen from
the available data sometimes however
very important metrics are not being
measured the consulting firm can then
suggest starting to collect data on that
thus changing the data collection
structure of the client another sprint
for feature selection and running the
model for the first time once data has
been collected and features have been
modeled it is time for some data science
while features were modeled and kind of
selected during the first sprint they
were never tested in a model so in the
second coding sprint features are
evaluated transformed or new features

English: 
are engineered this time for predictive
modeling purposes once this is done the
first models come to life showing the
potential to the stakeholders in the
client company and a third sprint to
fine-tune the model and adjust it as per
client requirements the moment a solid
model has been thought through and
executed the fine-tuning begins there
are many ways in which a model can be
improved but 1% increase in accuracy
could imply millions of dollars in
savings for the client company therefore
this step should not be overlooked
even if it sounds like the least
exciting one okay moving on to the
fourth stage data visualization data
visualization plays a critical role in
most data science projects however
please bear in mind that the specialists
who build a model are not always the
ones best equipped to work on the
visualization of its findings when
presenting in front of a non-technical
business team it is much better to show
tableau or power bi graphs rather than a
jupiter notebook and hence the data
science consulting team needs skills

English: 
features are engineered, this time for predictive
modeling purposes. Once this is done, the
first models come to life, showing the potential
to the stakeholders in the client company.
And a third sprint to fine-tune the model
and adjust it as per client requirements
The moment a solid model has been thought
through and executed, the fine-tuning begins.
There are many ways in which a model can be
improved. A 1% increase in accuracy could
imply millions of dollars in savings for the
client company. Therefore, this step should
not be overlooked even if it sounds like the
least exciting one.
Okay.
Moving on to the fourth stage -data visualization.
Data visualization plays a critical role in
most data science projects. However, please
bear in mind that the specialists who build
a model are not always the ones best equipped
to work on the visualization of its findings.
When presenting in front of a non-technical
business team it is much better to show Tableau
or Power BI graphs rather than a Jupyter notebook.
And hence, the data science consulting team
needs skills related to chart and dashboard

English: 
creation, as well as the ability to communicate
in an effective way. It is not uncommon to
have a person whose job is to solely style
such findings, giving the final touch to the
presentation.
And this is how we reach the fifth stage,
namely, Follow-up projects
As with any other type of consulting, the
secret sauce of being a successful consultant
is to be able to sell the next project. And
then to sell the next one after that. And
so on.
The premise is that if the consulted company
sees a measurable bottom-line improvement,
they will certainly want to retain the consulting
team and will be willing to purchase additional
services - from IBM in our example. This is
also why consulting firms prefer to start
with low hanging fruits – this allows them
to show they can create value very fast. And
hence they improve their chances of being
hired again.
Alright.
Now that we’ve figured out the typical cycle
of a data science consulting project, let’s
take a look at some of the successful use
cases IBM’s elite data science consulting
team helped with.

English: 
related to chart and dashboard creation
as well as the ability to communicate in
an effective way it is not uncommon to
have a person whose job is to solely
style such findings giving the final
touch to the presentation and this is
how we reach the fifth stage namely
follow-up projects as with any other
type of consulting the secret sauce of
being a successful consultant is to be
able to sell the next project and then
to sell the next one after that and so
on the premise is that if the consulted
company sees a measurable bottom-line
improvement they will certainly want to
retain the consulting team and will be
willing to purchase additional services
from IBM in our example this is also why
consulting firms prefer to start with
low-hanging fruits this allows them to
show they can create value very fast and
hence they improve their chances of
being hired again alright now that we've
figured out the typical cycle of a data
science consulting project let's take a
look at some of the successful use cases
ibm's elite data science consulting team
helped with starting with Nedbank in the

English: 
case of Nedbank a south african bank a
model predicting ATMs need for repair
was implemented and this led to
important efficiencies in terms of ATM
reliability and maintenance timeliness
in another project IBM's data science
team helped JPMorgan implement a model
which prevented the banks traders from
engaging with trades that are not
recommended by JP Morgan's powerful
predictive models Experian is one of the
leading companies in the information
business industry they analyzed credit
payments on a global scale for a number
of institutions in this case IBM's team
helped Experian leverage unstructured
data and combine it with structured data
that was traditionally used in
experienced models to build a more
comprehensive view of the businesses
Experian is higher
you analyze one can argue the data
science and AI consulting is a business
in its infancy man that appears that the
most important ingredient IBM's team has
mastered is the combination of technical
know-how in terms of data science
modeling and business understanding

English: 
Starting with… Nedbank.
In the case of Nedbank, a South African bank,
a model predicting ATMs’ need for repair
was implemented and this led to important
efficiencies in terms of ATM reliability and
maintenance timeliness.
In another project, IBM’s data science team
helped JP Morgan implement a model, which
prevented the bank’s traders from engaging
with trades that are not recommended by JP
Morgan’s powerful predictive models.
Experian is one of the leading companies in
the information business industry. They analyze
credit payments on a global scale for a number
of institutions. In this case, IBM’s team
helped Experian leverage unstructured data
and combine it with structured data (that
was traditionally used in Experian’s models)
to build a more comprehensive view of the
businesses Experian is hired to analyze.
One can argue that data science and AI consulting
is a business in its infancy. And it appears
that the most important ingredient, IBM’s
team has mastered, is the combination of technical
know-how in terms of data science modeling
and business understanding.

English: 
Truth is, a successful data science project
needs both. This is precisely why we try to
teach you how data science can be applied
in a business context in every course of the
365 Data Science program. So, if you’d like
to explore this further or enroll using a
20% discount, there’s a link in the description
you can check out.
We hope you found this video helpful. If you
enjoyed the topic, don’t forget to press
the like button and subscribe to our channel
here on YouTube. In the upcoming months, we
will prepare tons of other useful career-oriented
data science videos you don’t want to miss
on.
Thanks for watching!

English: 
truth is a successful data science
project needs both this is precisely why
we try to teach you how data science can
be applied in a business context in
every course of the 365 data science
program so if you'd like to explore this
further or enroll using a 20% discount
there's a link in the description you
can check out we hope you found this
video helpful if you enjoyed the topic
don't forget to press the like button
and subscribe to our channel here on
YouTube in the upcoming months we will
prepare tons of other useful career
oriented data science videos you don't
want to miss on thanks for watching
