To mark being named one of the world’s most innovative data and analytics leaders, Lufthansa Industry Solutions VP AI and Data Analytics Dr Susan Wegner talks about her new role, the benefits of data sharing and the future of data in the EU
What were your greatest professional achievements of 2019?
My role in Deutsche Telekom was really a headquarters role. The intention was that we would implement a use case in one country or business unit and then transfer it to another country or business unit to potentially achieve reusability of the use case.
That has two impacts. One impact is, it’s much cheaper if you just transfer use cases from one country to another. The other is, for all cases in a country, if you have a common data model, you can use that as the basis for the use case, enabling quicker and therefore cheaper implementations.
Overall, with a common data model, you have the scalability in the country and, additionally, the ability to transfer it with very low costs to another country.
What will your priorities be over the next 12 months in your new role at Lufthansa Industry Solutions?
Well, I’m just getting used to everything! However, the general topic is to generate business impact out of data with AI.
What I really like is that my new team has a lot of experience in enterprise-ready use cases with all kinds of AI technologies, including deep learning. Typical use cases involve customer experience, the ‘360 customer view’ or predictive maintenance.
It’s not about MVP [minimum viable product] and starting to put a little bit of statistical things into operation. No, the team already has lots of experience bringing AI use cases into operation and supporting the operation of these. We will definitely go further on with this.
The other thing I’d like to mention is that the team has started to set-up an ‘AI factory’ within one of the units. The business departments identify high-value use cases and add them to the AI factory’s pipeline. After a quick proof-of-concept (1-3 weeks), a detailed planning of an implementation project to deliver an enterprise-ready solution follows. There is a central budget for proof-of-concepts. But the cost of implementing a project has to be covered by the relevant business department.
The major effect is that all business departments are enabled and motivated to initiate relevant projects. The result is a quicker implementation of new use cases, concentration on the most relevant use cases, and reusability of components or even complete use cases.
Another thing that’s a lesson learned from a lot of discussions with others is that you always need a diverse team and you always need people from different areas. Best practice is to generate a team with persons who have the domain know-how, operational experts, data scientists and the data engineers all together.
What are the challenges you anticipate encountering as you embark on this new role with the Lufthansa Industry Solutions?
Larger companies in particular have different units that are largely separated and silo-oriented. This is fine under business purposes. But if you want to share data to generate insight out of data or even share solutions, it is a barrier.
In order to overcome this barrier, central guidance and governance and a team driving that and supporting the business units is essential.
You’re also a member of the European Commission’s Business-to-Government Data Sharing Group. What can you tell us about your involvement with this group?
In the group there are lots of experts from different countries and different areas. We have people from statistical institutes. We have someone from a hospital. We have people from telecoms. We have people from other companies, and we have professors. So, it’s a very diverse group of people that consequently leads to very interesting discussions.
My opinion is that we should enable overall data sharing, because data is the basis for all we are doing within artificial intelligence and data analytics. Therefore, it’s really essential.
I tend to say we should encourage and motivate people and companies. However, we should not put a high regulation burden on companies, because, especially for smaller companies, they are not able to handle that. And the clear target is to enable more AI applications in Europe.
We all need to be aware that we can enable a lot of things with data sharing – especially when it comes to healthcare, the environment, forecasting catastrophes, etc.
How do you think Europe’s data and analytics space will evolve over the coming 12 months?
My hope is that we really go into quick implementation within the European Union – that we really start delivering use cases.
This morning I read an article in a German market magazine putting together, some of the research insights from the last week and one was in molecular biology. That could have such an impact for society and the environment that I really hope that we quickly put all the things we already have from the technical side into new use cases.