Big Data, Big Thinking: Data Science in Action

Big Data, Big Thinking: Data Science in ActionAccording 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

is somebody whouses mathematicsand information technology

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the training,programming languagesand curiosityto build statisticalmodels and make discoveriesin the world of

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Big Data.”SAP Big Data, Big Thinking webinar series

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How does data science differ from traditional analytics?

Traditional business intelligence is usually about providing standard reporting functionality and ad hoc analysis. But in data science, there’s a mathematical component.

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Data mining involves applying mathematical standard algorithms to a data set to find patterns or create clusters.

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Forecasting relies on modeling – the process of identifying the key drivers and then inventing a formula that describes the business process.

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Optimization is the most mature stage, determining the ideal choice of parameters to achieve the best possible outcome in a model.

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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.


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.

Use Case: Utilities – Regulatory compliance

Data science helps you know more

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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.


Data science helps you look deeper

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.

Use case: Retail & CPG – Sales analysis

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Simulation of a spare parts supply chain allows improved service levels while reducing inventory holding cost.


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.

Use Case: Transportation – Supply chain analysis

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The publisher saves €2m per annum – just through feeding more accurate numbers into its replenishment calculations.


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.

Use Case: Media – Sales and stock optimization

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Join usYou 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

SAP Data Scientists – who are we and what do we doWe’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.

SAP Big Data, Big Thinking webinar seriesCopyright statement – © 2014 SAP SE or an SAP affiliate company. All rights reserved.

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Big Data, Big Thinking: Data Science in Action

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