Knowledge Graphs can represent complex relationships among organizations, technologies and markets. Taking e-mobility in the public sector as an example, the session describes an ongoing project for the automated creation of a knowledge graph from market-relevant news feeds and linked open data sources. Quantitative measurements of mentions as well as changes in the knowledge graph over time are combined to offer insights into structures and developments in the e-mobility market environment.

Target Audience: Decision Makers, Business Development Specialists, Innovation Managers
Prerequisites: Basic knowledge of Data Analysis
Level: Basic

Working as a Data Scientist in a Start-Up can be challenging and exciting at the same time. The amount of available data, lack of data quality and fast-changing business needs are some of a Data-Scientist's biggest enemies. To overcome such hurdles you need to be creative to get as many insights as possible from the available data in order to establish scalable processes and take the right decisions at the right time.
How we did that? We will elaborate in this talk.

Target Audience: Data Scientists, Project Leader, Project Manager, Analysts, Data Engineers, everyone who's juggling with Data
Prerequisites: Basic knowledge
Level: Basic

Extended Abstract
As usual for a (grown up) Start-Up, there are many construction sites that need to be handled or optimized. And although Cluno is still very young, it already has a Data Team consisting of 9 employees, 3 of whom work in the Data Science team - because Cluno wants one thing above all others: to support decision making processes with data driven intelligence.
The Data-Science team is currently working on two major building blocks that are essential for Cluno: The purchase of the right vehicles in the right quantity and the matching of potential customers with the most convenient available vehicles. As for every data intensive project, using state of the art machine learning techniques is very tempting. Unfortunately, in theory this is often easier said than done. Even though we have the necessary data infrastructure and machine learning expertise, the amount of data collected over the two years of Cluno existence and its quality prevents us from leveraging the fanciest techniques. Under these conditions, the Data Science team had to get extra creative in order to distill as much intelligence as possible from the existing data to support our different departments - without losing ourselves in the depths of the most complex machine learning algorithms.
In this talk we want to give you some insights on how ideas for the optimization of existing processes were turned data-driven, what (data-) challenges we faced in a Start-Up environment and how both projects are not only leveraging simplicity and providing our business with the much needed insights, but also building the foundations of the next generation of machine learning based data products.