Data Science Summit

Making Analytics Work for Business

Businesses today are collecting vast amounts of data from operations, manufacturing, supply-chain management, customer behavior, marketing campaign performance, workflow procedures, and so on. At the same time, information from external sources such as market trends, industry news, and competitors’ movements are also widely available. The volume and variety of data have far surpassed the capacity of manual analysis. The good news is, computing power, too, have become far more advanced with network ubiquity and well-developed algorithms. The convergence of these phenomena has given rise to data science application in businesses. Data Science Summit at Geospatial World Forum 2019 will highlight best practices in extracting useful information and knowledge from data that improve business performance.

Speakers

Data Science Phenomenon: Impact to Geospatial Business4 Apr 2019 0900 - 1030 hrs
Moderator & Lead SpeakerGeorge Percivall, CTO & Chief Engineer, Open Geospatial Consortium (OGC), USA
Moderator & Lead Speaker
George Percivall
CTO & Chief Engineer
Open Geospatial Consortium (OGC)
USA
Christian Heipke, Professor, Photogrammetry and GeoInformation, Leibniz University of Hannover, Germany
Christian Heipke Professor
Photogrammetry and GeoInformation
Leibniz University of Hannover
Germany
Linda Ochwada, Geo/Data Scientist, Supper & Supper, Germany
Linda Ochwada Geo/Data Scientist
Supper & Supper
Germany
Dr. Patrick Vetter, Head of Data Science, Supper & Supper, Germany
Dr. Patrick Vetter Head of Data Science
Supper & Supper
Germany
Matthew Pennells, Digital Transformation BD Manager, Esri, UAE
Matthew Pennells Digital Transformation BD Manager
Esri
UAE
Kumar Navulur, Sr. Director, Strategic Business Development, DigitalGlobe, USA
Kumar Navulur Sr. Director, Strategic Business Development
DigitalGlobe
USA
4 Apr 2019 1130 - 1300 hrs
ModeratorAthina Trakas, Director Regional Services, Open Geospatial Consortium, Germany
Moderator
Athina Trakas
Director Regional Services
Open Geospatial Consortium
Germany
Weigang Yan, Senior Data Scientist, Cambridge City Council, United Kingdom
Weigang Yan Senior Data Scientist
Cambridge City Council
United Kingdom
Data-driven Products and Services4 Apr 2019 1400 - 1530 hrs
ModeratorRamana Mallysetty, Chief Technology Officer, Eos Digital, USA
Moderator
Ramana Mallysetty
Chief Technology Officer
Eos Digital
USA
Erwin Folmer, Product Owner Data Platform, Kadaster, The Netherlands
Erwin Folmer Product Owner Data Platform
Kadaster
The Netherlands
Adrian Badi, Senior Data Analyst, Business Intelligence, William Demant, Denmark
Adrian Badi Senior Data Analyst, Business Intelligence
William Demant
Denmark
Ipsit Dash, Senior Consultant, Government (Space), CGI Nederland, The Netherlands
Ipsit Dash Senior Consultant, Government (Space)
CGI Nederland
The Netherlands

SESSION THEMES

Building Effective Machine Learning Models for Business

Model fitting is the essence of machine learning. A properly fitted model can capture the complex relationships between known variables and the target variable, allowing it to find relevant insights or make accurate predictions. This is crucial in order to solve specific real-world business problem with a high level of accuracy. Data scientists will discuss best practices in building an accurate predictive model, from data cleaning to deployment.

Data-driven Products and Services

Developing data-driven products is certainly a very innovative way to use and monetize data, but also a complex one. Ability to handle data consistently and securely is the key for any business to succeed in this domain. Data-driven products are powered by analytics, business domain knowledge, and scalable IT architectures. It requires organizations to become truly digital – in their products as well as in their business models. Data-driven products get additional value when advanced analytics is embedded. Data scientists from major data-driven businesses will share their know-hows for successful and sustainable data-driven products and services.

Extracting Business Value from Unstructured Data

It is estimated that between 80 and 90 percent of data in an average organization is unstructured. And most of the data remains to be untapped or simply disposed. A few examples of unstructured data are word documents, images, emails, social media posts, product reviews, digital audio files, etc. The value of unstructured data comes from the patterns and the meanings that can be derived from it; this includes identifying issues, market trends, or overall customer sentiment towards a brand. Extracting intelligence in unstructured data can help businesses yield deeper insights and drive strategic business decisions. Data scientists will discuss the best solutions for the analysis of unstructured data via AI and machine learning.