In this webinar fact sheet, Marie Goodell, Strategic Marketing Lead, Big Data, talks to Dr Helmut Linde, Head of Data Science Solutions at SAP, about why data science is one of the most sought-after skills in town, and how it can transform data into business value.

1. Big Data, Big Thinking:
Data Science in Action
According to Harvard Business Review, being a data scientist is the sexiest job of the 21st century.
In this webinar, Marie Goodell, Strategic Marketing Lead, Big Data, talks to Dr Helmut Linde, Head of Data
Science Solutions at SAP, about why data science is one of the most sought-after skills in town, and how it
can transform data into business value.
SAP Big Data, Big Thinking webinar series

2. “A data scientist
is somebody who
uses mathematics
and information technology to solve
business problems. ”
“Someone with
the training,
programming languages and curiosity
to build statistical
models and make discoveries in the world of
Big Data.”
SAP Big Data, Big Thinking webinar series
2

3. How does data science differ
from traditional analytics?
SAP Big Data, Big Thinking webinar series
Traditional business
intelligence is usually
about providing standard
reporting functionality
and ad hoc analysis. But
in data science, there’s a
mathematical component.
1.
Data mining involves
applying mathematical
standard algorithms to a
data set to find patterns
or create clusters.
2.
Forecasting relies on
modeling – the process
of identifying the key
drivers and then inventing
a formula that describes
the business process.
3.
Optimization is the most
mature stage, determining
the ideal choice of parameters
to achieve the best possible
outcome in a model.
4.
3

4. An approach based
on data science uses
mathematical algorithms
to identify patterns,
and then automatically
appends the statistically
probable attributes to the
appropriate records en
masse.
SAP Big Data, Big Thinking webinar series
Data science helps
you know more
Use Case: Utilities – Regulatory compliance
Utility companies have millions of asset records
(for pipes, valves, power cables, gas lines, and more)
which, for compliance purposes and reasons of
operational efficiency, they need to track across the
grid. Sometimes the information is incomplete, which
can be a challenge to maintaining the infrastructure,
so the data needs to be enriched before the analysis
can be run. One approach is to manually inspect each
asset. But that means in order to complete a record
on a power cable, the company would have to dig up
the street, examine the cable and enter the missing
information in its database, which simply isn’t practical
or cost-effective. An alternative approach is to get a
subject matter expert to define a set of business rules,
which are then coded into a program so they can be
applied to the dataset. However, this is only valid for
certain attributes so gaps still remain. However, an
approach based on data science uses mathematical
algorithms to identify patterns, and then automatically
append the statistically probable attributes to the
appropriate records en masse.
4

5. Consumers tend to buy
a new product out of
curiosity. Only once they
have tried the product do
they decide whether to
buy it again.
SAP Big Data, Big Thinking webinar series
Data science helps
you look deeper
Use case: Retail & CPG – Sales analysis
Because some new product introductions fail, a
retailer might pilot two products (A and B) on shelf for
two months to see which sells best. At the end of the
first month, Product A appears to be more successful,
so it’s likely that the category manager would keep
Product A and delist Product B. However, in reality,
consumers tend to buy a new product out of curiosity.
Only once they have tried the product do they decide
whether to buy it again.
To evaluate the long-term prospects of a product,
you need to take into account the secondary effect
of repeat purchases. That means as well as analyzing
the amount of product sold each week, knowing which
product has been purchased more than once by the
same individual (attributed, for example, to a loyalty
card number). This demands a data platform that can
perform deep analysis at speed.
5

6. Simulation of a spare
parts supply chain
allows improved service
levels while reducing
inventory holding cost.
Use Case: Transportation – Supply chain analysis
SAP Big Data, Big Thinking webinar series
Data science helps
you look ahead
A railway company needs to manage an inventory of
spare parts, from nuts and bolts to entire train engines.
It has a complex network of different stock-holding
locations and supplier relationships. It operates a
policy to trigger replenishment when stock levels drop
to a designated reorder point. But who determines
where that reorder point should be?
Rather than focus on the moment the stock level hits
the threshold, the company needs to simulate future
demand. It takes a snapshot of the supply chain based
on historical information, examines the statistical
distribution of demand, and calculates key indicators
like inventory levels and costs. Because available
stock never gets close to zero, the reorder point can
be reduced by optimizing parameters so the average
inventory is smaller.
6

7. The publisher saves
€2m per annum – just
through feeding more
accurate numbers
into its replenishment
calculations.
Use Case: Media – Sales and stock optimization
SAP Big Data, Big Thinking webinar series
Data science helps
you plan better
A newspaper publisher prints millions of papers and
delivers them daily to tens of thousands of points of
sale. The risk of failing to optimize the distribution lies
with the publisher, who supplies the papers on a sale
or return basis: over-supply and swallow the cost, or
lose the revenue through stock-outs. To mitigate, the
publisher allows a safety margin of 50% extra copies.
Two newsagents sell the same number of papers on
average, but one shop has variable demand, while the
other is more consistent. The publisher can improve
forecast quality by taking into account factors such
as calendar events which might affect footfall. The
shop with variable demand needs more safety stock
than the shop with regular business. By reducing the
safety margin, the publisher saves €2m per annum
– just through feeding more accurate numbers into
its replenishment calculations, rather than having to
change its business process.
7

8. SAP Data Scientists – who
are we and what do we do
Join us
You can watch this and previous
events on demand, or register to attend
the remainder of the Big Data, Big
Thinking webinar series to understand
how you can get started on your own
Big Data journey.
Visit bit.ly/BigDataBigThinking
We’re a team of over 60 data scientists with expertise in mathematics,
visualization and Big Data technologies. We’ve worked on over 100 customer
projects which normally follow a four-stage process:
•• Identify and evaluate use cases – we usually do this in a workshop that
involves both lines of business and IT, to identify the business potential
•• Drive proof of concept – we test our hypothesis and develop the business
case and roadmap
•• Pilot a solution – we develop core functionality to use for validation and
change management
•• Develop a productive solution – we deliver the full software with all the
features, functions and simplified user interface needed by the business.
Copyright statement – © 2014 SAP SE or an SAP affiliate company. All rights reserved. SAP Big Data, Big Thinking webinar series
8