How IBM Does Data Science Consulting

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 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 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 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 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 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 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 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

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