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Digitale Services

How Data Science makes it even smarter

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Data Science isn’t something new. But with digitalization, IoT appliances like Thermomix ® and the huge amount of data being required, the field has gained new dimension and relevance. I can experience this change at Vorwerk Digital, where we are continuously working on making our appliances and digital services even smarter. What my role as a Data Scientist looks like, which challenges we are faced with and how to solve them – here are some Insights and three practical tips to really understand data.

Big Data, Smart Data, Data Mining, Data Engineering, Predictive Analytics – when it comes to data, the list of buzzwords can be long and confusing. On the other hand, there is no doubt that “data” is more than a buzzword but holds a true value. Though unlocking this value requires quite some effort. In the first place, the scary darkness of the “data jungle” that may have grown over years needs to be illuminated and structured. The task is to convert tons of abstract data into concrete information. This is where data science comes in. And against the cliché there are no “Brainiacs” working in the dark depths of a corporate lab brooding over heaps of statistics to make processes more efficient. Data Science is much more than that. It has to deliver very concrete output in terms of insights and “data products” that interdisciplinary teams can pick up and build upon.

How Data Science can help in product design

When it comes to Data Science in the context of the Thermomix®, we not only gain insights for marketing purposes, but also draw conclusions to directly improve the appliance and make it smarter. We e.g. may look at interaction patterns while cooking. Think about the preparation of onions: in our data we can see, that customers frequently use a specific sequence of functions, like, first cutting and then frying of onions. So, in the future we may consider combining certain sequences of interactions into single activities. This saves some clicks and smoothens the experience. This is just a simple example of how Data Science can help in improving a physical product.

Before a problem can be assessed by the means of Data Science, of course, some conditions must be met. Obviously a profound understanding the data is key: “What data sources are involved?” – “What quality does the data have?” – “How can we provide a wider picture by using data from other sources?” are just some questions to be answered. At this point I would like to share three learnings with you that have helped me in my own work as Data Scientist.

My personal tips for extracting value form data

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1

Everything starts with a question …

Data science is all about asking the right questions. As one person alone can never know all the relevant questions out there. A Data Scientist must strive to find questioners representing various perspectives on a problem. This can be product management, engineering or the direct sales representatives. In my colleague Maria Coronado, for example, I’ve found a competent questioner in the field of marketing & customer research.

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Strive for flexibility and agility

If you want to be innovative and driven by data, you need an environment where you can try new things and develop prototypes quickly. Because, usually, the complex problems in the digital world demand an iterative approach. I have the privilege of working in a start up-like environment at Vorwerk Digital that fosters a flexible and agile working mode. This helps to deliver results faster and readjust the approach along the way to also incorporate new insights and be close to the demand.

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Efficient work requires the right tools

There should be as few technical barriers as possible, so you have to find tools to help you cross the boundaries between different data sources and make all the relevant data readily available. At Vorwerk we have established a big data technology stack based on MapR for processing and preparing huge amounts of data. From there the results are made available as data products to other services, e.g. to provide recommendations to the customer. To facilitate generation of insights we rely on QlikSense as a “showcase” to display valuable information in comprehensive dashboards.

My most important personal learning is: As physical products and digital, data-fueled services increasingly merge into a single experience, a seamless collaboration between Digital and Product Management is absolute key to provide best value to customers. This also comes with an increasing demand for interdisciplinary thinking.