Description of the talk/panel discussion:
Title: Bootstrapping Data Science in an Emerging Industry: finding use cases thatbuilt trust and open doors
Abstract of the talk proposal or round table proposal:
Today, the logistics and transportation industry is an late-adopting however
rapidly evolving sector in terms of data usage, digitization, and machine learning.
Working in an emerging industry presents unique challenges when developing MLproducts. A nascent industry is often coupled with a market hesitant to consume
ML products, as well as organizations that are also learning how to adopt andbuild products high-value products that users will be willing to adopt. While there
is a high level of immaturity, there is also high potential for growth and innovation.
Our challenge is how to achieve this potential with limited resources and capacity.
The transportation and logistics industry at the early stages of adopting bestpractices when it comes to digitization and automation. The result of this has been
observed in brittleness of supply chains highlighted by various logistics crisis overthe past several years from the COVID-19 pandemic, sea port congestion delays in
the US, the Ever Given Suez Canal blockage, to the Russian-Ukraine conflict.
Transportation service providers are also faced with pressure from new ESG
initiatives requiring shippers to optimize routing, load management, and improve
resource use efficiency.
Early digital adoption and skepticism to new technology is also reflected in
products and software that support this industry, and likewise the organizations
that produce these products. With lack of digital adoption or interest, the market isalso slow in maturing to support the resources needed to bring the industry into
the digital now. While there is an understanding for potential connecting the dots
can remain to be a struggle for data teams and businesses.
To overcome these challenges we needed to find the sweet spot between a C-levelmessage and a bottom-up approach to finding and developing use cases. Bytargeting use cases with broad applications, that utilized internal data advantages,
known technology, and would be quick to test, to be a winning strategy. The result
was creating a ML based data product that will create both revenue and further
opportunities to develop our data group and future products.
Potential discussion points:

-Mitigating risks by choosing projects broad applications, and high feasibility
that can deliver value
Creating data opportunities in an emerging business sectors
Creating larger opportunities from smaller initatives to build a long-term
data product strategy
Working with analyst/data scientist to create these opportunities via POCs
and validation with key stakeholders

Relevance of this talk/panel discussion to the workshop:
Working with limited resources and in a business sector with emerging needs andslow adoption, creates a unique challenge and opportunity for data productdevelopment. These combined set of challenges forced us to think carefully about
how, when and what project to target to create value, with trying to keep any eye
on gaining adoption and buy-in that goes beyond the typical marketing orleadership level discussions on the value of data. Working with a lean and agile
group we were able to move from a strategy of bottom-up POC development todeveloping a data product that can be delivered as a standalone product or beintegrated into the company’s product suite. The course of action that was taken
can be applied to any business that is either in an emerging sector, or a company
that is starting to adopt ML practices into their software development lifecycles.
Info about the presenter:-A short bio of the main presenters (~100 words)
o Italo Sayan – a 2019 Rochester Institute of Technology alum, excels incrafting data-driven solutions for global supply chains, including
Walmart and Glovo. At Alpega Group, he is designing pivotal projects
to reduce logistical inefficiencies significantly. His tenure at Glovo
resulted in innovative product recommendation systems impacting
over 300,000 users. Italo integrates machine learning to transform
complex data into actionable business insights
o Gardiner von Trapp - completed his PhD at New York University and aFellowship at Insight Data Science NYC before moving to Vienna,Austria. He has since worked as a data scientist in large enterprisesoftware companies, and has filed several patents. In 2022, Gardinertransitioned to a Product Manager role, overseeing data science andanalytics product development working in the transportation andlogistics sector.

-A brief company or project portrait (~60 words)
Alpega Group, named a challenger in the 2024 Gartner Magic Quadrant™, is aleading provider of transport management software for both spot and contract
freight. We specialize in supporting shippers and carriers to manage their shortand long-term load requirements, while fostering strong partnerships between
companies in the logistics sector. Our mission is to empower our customers
through efficient digitalization to meet today's logistics challenges, driving smarter
logistics for a greener tomorrow.