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
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