3#7 - Heidi Dahl - Transforming Business with a Strategic Approach to Data (Nor)

MetaDAMA - Data Management in the Nordics

«Sentralt I dette med å skape verdi er tverrfaglighet og involvere hele bedriften, ikke bare et lite Data Science miljø.» / «Central to creating value is multidisciplinarity and involving the entire company, not just a small Data Science environment.»
Prepare for a journey into the landscape of data strategy with seasoned Data Scientist, Heidi Dahl from Posten Bring, one of the largest logistics organizations in Norway. She is not just engaged in strategic discussions about data, AI and ML, but also a passionate advocate for Women in Data Science, took the initiative to create a chapter of WiDS in Oslo, and co-founded Tekna Big Data.

In our chat to understand the dynamics of data science and IT, we talk about their balance between research and practical development. Heidi articulates the urgency for a dedicated data science environment, exploring the hurdles that organizations often confront in its creation.
We cross into the world of logistics, shedding light on the potential power of data science to revolutionize this industry. We uncover how strategic use of data can streamline processes and boost efficiency. Finally, we underscore the importance of nurturing an environment conducive for data professionals to hone their skills and highlight the role of a data catalog in democratizing data accessibility.
Here are my key takeaways:
Digital Transformation of Posten Bring

• An organization that is 376 year old and has been innovative throughout all of those years.
• The Data Science department was stated in 2020 under Digital Innovation, now a part of Digital technology and security.
• The innovative potential is found through use-case based work closely integrated with the business domains.
• There are several algorithms that made their way into production, and that is a goal to measure against.
• The Data Science teams consist of cross-functional skillsets, bringing together Data Science, Developers, Data Engineering and Business users.
• The exploratory phase is vital, but has to have a deadline.
• IT driven development projects do not always match with the needs of Data Scientists.
• Data and IT need to work together, but for exploratory work, Data Science should be able to set ut needed infrastructure.
• On cloud infrastructure it can be vise to think multi-cloud to ensure availability of a specter of relevant services.
• Posten/Bring is looking to build a digital twin for their biggest package terminal for better insight, control and distribution of packages.

Strategic use of data

• How can we use data to make better decisions, be more effective and smarter?
• The 4 core elements of the Data Strategy:

1. Establish distributed ownership of data and data products
2. Increase the amount of self-service.
3. Build competency tailored to your user groups needs.
4. Strive towards the goal of great services and products based on data for your users and customers.

• Role based self-service capabilities .
• A data catalog is discussed, to gain a better understanding of the data available, security, but also context of origin and data lineage.
• A data catalog needs to be able to serve different user needs.

Competency

• There are three perspectives:

1. How to recruit new and needed competency?
2. How to train and share competency internally?
3. How to retain competency?

• Data Engineer is a newer and more specialist role, that is hard to find on the market.
• You need to give your data professionals the possibility to do purposeful work, bring into production and connect to value creation.
• The entire organization should be aware of how to use data to make work more efficient and smart - think data literacy

Kapitler

1. Intro and who is Heidi Dahl? (00:00:00)

2. Data Science, Data Management and Data Engineering (00:04:38)

3. IT, Data Collaboration in Data Science (00:11:39)

4. Data Science Infrastructure and Strategic Data (00:17:42)

5. Data Catalog and Competence Development (00:24:41)

6. Exploring Data Science and Logistics Optimization (00:29:16)