MetaDAMA - Data Management in the Nordics

«How do you develop good procedures around testing or how do you drive experimentation in a product or business setting?»

Carl Johan Rising works as Director of Data at Too Good To Go, a marketplace that enables food businesses to sell their surplus food instead of throwing it in the bin.

We talked about how to form a product team, and how to rethink the role of Data Scientists in your team, shape it in an embedded team, close to domains and with expertise and customer focus. We talked about skills, product focus, business partners and much more.

«(A career in) data gives you a bit of everything.»

• Data is a nice intersection between aspects of business, academia, physiological problems, and technical challenges.
• Business - especially understanding and decision making 
• Academia - working with hypothesis

Data at Too Good To Go

• If you look into how you want to use data to really drive decisions, it becomes more of a change management challenge, and not just a technical challenge
• «Start with a proper infrastructure foundation - a good clean data model»
• «Foundation building is invisible, and doesn’t by itself bring business value»
• The business sees the data team as one unite, without distinction between different capabilities in the team - Therefore the expectations are different
• «Make it very explicit what people can do and what their capacity is.» - gain understanding business

Product Analyst:

• «And soon as you have any emphasis on product, its development, its iterations, then it makes sense to have Product Analysts.»
• Too Good To Go works with multiple Product teams, each with their own problem spec
• in a Product team - Product Manager, Designer, Engineering Lead, Engineers, Machine Learning Engineers, and Product Analysts embedded in the team
• Product Analysts in each team to drive good identification of problem spaces and to enable the teams to do rapid experimentation
• The role will ask the "how do we drive good?" and set an experimentation agenda - driven by the Product Analyst
• Emphasis is on statistical knowledge and technical skills.
• There are two main stakeholders for Product Analyst -> Engineering Lead and Product Manager
• Focus on gathering the best resources to tackle a problem
• What skills and experiences do you need in a PA role?
  • statistical knowledge
  • programming
  • understanding of tech. aspects
  • ability to explain results

«I think the role of Data Scientist can mean a lot of different things.»

• Be a bit more explicit about what the work is and what it entails
• Minimize the possible confusion between expectations on Data Scientists In a company

Data Analytics Business Partner

• An embedded role that is part of the business with co-ownership of the outcomes
• Through this partnership it is much easier to gather context if you work with the domain

«We (DAMA) have a role to play, (…) develop the Data Management profession ultimately for the benefit of the society.»

It is good to be back with Season 3 of MetaDAMA, and as always, we start with a DAMA-focused episode.

Nino Letteriello is one of Europes most influential data leaders, president of DAMA Italy and Coordinator for the DAMA EMEA region (Europe, Middle East, Africa). Nino started his carrier in project management, educated in civil engineering, and got involved in Data Management around 2017. Since 2019 he is the regional coordinator for DAMA EMEA.

Here are my key takeaways:

What is so special about DAMA EMEA?

• Lots of passion and commitment by volunteers
• Giving back to society to proff how the society becomes more data literate
• «Whilst we are a geographical region, I see very very different scenarios, very different levels of maturity.»

• Middle East - Saudi Arabia:
  • Government drove a framework, build on DMBoK for public administration 
  • This is cascading down from public agencies to the big corporations working in Saudi Arabia, and subsequently to SME
• African Countries show a scattered approach to Data Management
  • Great appetite for knowledge in data
  • There is an enormous sense of «missing out»
• Mediterranean Countries and Central Europe
  • Initiatives of «data alphabetization» or data literacy at an early stage
  • Teaching data management at an earlier age, eg. Program in Italy to teach DM in middel school and high school (DataHigh)
• Nordics
  • Different level of maturity.
  • Staring early with digital competency development
  • Inspirational is the nordics view on data for social good and ethical handling of data

Data Literacy and awareness

• Nino was involved in a WEF (World Economic Forum) study on how SMEs (Small, medium sized enterprises) are leveraging the power of data
• Collected information form over 200 Small and medium sized enterprises
• «Interesting how many companies still consider data an IT thing, a subset of IT.»
• There is still an over reliance on IT, not seing data as a business problem
• Immature on Data Governance and formalization of roles
• Awareness is not necessary followed by clear action
• SMEs face the same issues as big corporations, but without the means to handle these issues accordingly
• SMEs have the possibility to me very agile in facing these issues
• Still a lot of «reinventing the wheel» - we should use DMBok and other existing frameworks actively as a basis to work from

Is DAMA still relevant?

•  Importance of DAMA and DM is still large, also and especially in times of AI
• «Garbage in - garbage out» is still as valid as every
• New methods, new techniques, how languages, everything is dependent on the quality of the data you put in
• Everything starts with awareness
• The real differentiator is that data is a business asset, not an IT asset

DAMA EMEA Conference

• Organized for the third year in row, first time both digital and physical in Bologna November 29th - December 2nd 2023.
• Conference provides clear, filtered, categorized, relevant information
• Possibilities to share ideas and network
• Closed session for all board members in EMEA region to discuss a declaration of intents for DAMA EMEA
• Nino likes the idea to do Data Management Maturity Assessments across countries tom compare DM maturity
• There is also a possible intent to work closer with European Commission
• Giving DAMA EMEA a vice towards legislation makers

Get more information and sign up here for the conference: https://data-emea.org

"It's about taking a step back to ask yourself: Should we even have a data-driven system for this?" («Det handler om å ta et steg tilbake for å spørre seg: Skal vi i det hele tatt ha et data-drevent system på dette her?»)

We finish season 2 with a though-provoking episode, to maybe start som debate about data-driven public administration.

Lisa Reutter is PostDoc at the University of Copenhagen connected to a project called: «Datafied Living». We talk about the importance of Social Science in Data, and how data is intertwined with our lives. Lisa is researching in the field of «Critical data and algorithm studies», at the interplay between tech, data and society.

Here are my key takeaways:

Data in Public Administration

• For a modern state to function properly and to ensure citizen rights, services, security, etc is provided the state needs data.
• Data Management by the state for its citizens is not a new concept but has a long historical foundation.
• During the last years we use more, different and new data in administrative processes, and enhance technological development and a tool box to derive value from data
• Public administration has had a monopoly over management and ownership for citizen data. But this has been challenged by private companies.
• Data-driven systems in public sector are not there for profit, but to create value for society. Therefor they need to be build on and with the purpose to enhance our democratic values.

Registers

• Norway and other Scandinavian countries have established national registers to manage and administrate society.
• There is a reason why registers are not unified in Norway, and this is to ensure a balance of powers
• The opposite example, of what can happen if a state collects information on its citizens without boundaries, is to be found in the GDR (Eastern Germany)
• If all data of all aspects of your life are collected one place, it is really easy to misuse this data
• Through data a state could see, predict, and control the behaviors of citizens.

The public debate about data-driven

• Discussions can and should be about what data are we collecting, where do we store data, what are we using data for, who could and should have access to that data, etc.
• Even with public debate about data use in public administration, limits and boundaries can never be defined clearly. Also because this is individual and relative to context.
• Datafication is a political act. The citizens need to be involved in the process of technological advancement and intelligent use of data.
• The debate around «data-driven public administration» in Norway, has not included the public actively.

Customer-centric vs. data-as-an-asset vs. democratizing data

• Is there a rhetorical ambiguity between being customer-centric and data-as-an-asset?
• Data democratization demands that citizens have to use their time, resources and energy to ensure that public administration is working correctly.
• Is making data available leading to commercial parties capitalizing on that data and building solutions, rather than creating transparency for citizens?
• The right education and skills are important, but it needs to be available and attainable for all parts of society.
• Data Literacy is an own subject that is in dispute about what it should contain.

We need to understand, that this has implications on how we...

1. Trust in the state

2. Trade - what do I give my data for? What do I get in return?

3. Build in accepted ways

4.  Weight opportunities against risk

5. Ensure that the responsibility for understanding does not lie with the citizen alone

6. Gain knowledge, and how everyone can get it

7. Should invite for debate

«How can we consolidate data and describe it in a standardized way?»

Scientific Data management has some unique challenges, but also provides multiple learnings for other sectors. We focused on Data Storage and Operations as a knowledge area in DMBok. A topic that is often viewed as basic, often not in focus, but is a fundamental part of data operations.

I talked to Nicolai Jørgensen at NMBU - Norwegian University of Life Sciences. Nicolai has a really diverse background. His journey in data started in 1983! In his free time, Nicolai spends time with photography and AI for text to image generation

Here are my key take aways:

Scientific Data Management

• To describe data in a unified way, we need standards, like Dublin Core or Darwin Core for scientific data.
• Data is an embedded part of Science and Research - you can’t have those without data.
• You need to make sure you collect the right data, the right amount of data, valid data, +++
• You need to optimize your amount of time, energy and expenses when collecting and validating data.
• You need to standardize the way you collect data, to ensure that it can be verified.
• There needs to be an audit trail (lineage) between the data you have collected and the result presented in a publication.
• Data needs to be freely available for research and testing hypothesis.
• Data needs to be findable, accessible and interoperable, but a also reusable.
• ML algorithms can help extract and find changes to scientific data, that is internationally available.
• Describing data is key to tap into knowledge - for that you need metadata.
• In times of AI and ML, Metadata is still the key to uncover data.
• The development of AI models is a race - maybe we need to pause and get a better picture of cause and effect, and most of all risk.

Standardizing Infrastructure

• How can were standardize on the infrastructure for research projects
  • Minimize or get rid of volatile data storage and infrastructure
  • Standardize data storage solutions
  • Secure what needs to be secured
  • Splitt out sensitive or classified data and store separate (eg. Personal data)
  • Train your end users and educate data stewards
• Have good guidelines for researchers on how to store, use and manipulate data.
• There is a direct correlation between disc-space use and sustainability.
• Storage is cheap, is a correct saying, if you look at its in isolation - but in the bigger picture the cost is just moved.
• Just adding more storage doesn’t solve your problems, it might just yet increase them.

Long-term Preservation & Integrity

• To preserve data for long-term you need to
   
  • Encapsulate data at a certain level
  • Standardize the way you describe the data
  • Upload data package to a common governed platform
  • Enclose if there is a government body that can take responsibility to preserve your data for the time necessary
  • Ensure that metadata is machine-readable
  • Formats like XML provide the possibility to read the data by both machines and humans 
• Research integrity: conducting research in a way which allows others to have trust and confidence in the methods used and the findings in that result.
• Ensure lineage and audit trails for your scientific data.
• Fake data, data fabrication, are serious issues in research - the understanding and methods for keeping data integrity at the highest possible level is not getting easier, but increasingly important.
• Changes to data (change logs, change data capture, etc) can be studied as well; you can build models to build scenarios around data changes.
• You can fetch data from other sources to enrich the quality of your data.

«Sometimes, you look at the problem and think it’s valuable, you spend a lot of time on it, and then you find out that this is really a NO!»

Ida Haugland and I talked about the importance of Data in Shipping, about a concrete examples for success through digitalization and mainly about the focus on Product Management.

Ida works as Principle Product Manager at Omny. At the time of recording Ida was part of Klaveness Digital and is presenting from her perspective from her work there. She started here career with data, while working for Customer Satisfaction for Facebook in 2010.

Here are my key takeaways:

Shipping & Logistics

• We are talking about really complex lines, that need to be managed. Data is the basis for everything.
• In Shipping and Logistics it is vital to «Put together a comprehensive plan to see cause and effect.»
• Shipping and Logistics is on a way to detailed insight in to their supply chain, as much as logistics for finished good.
• « A Lot of principles and thinking behind other products can be applied to shipping.»

Carbon Emissions and the role of Data

• Carbon Emissions can be minimized based on data driven insights.
• Concrete actions based on data:
  • Slow down - timing shipping and arrival at port with possibility to embark and birth the shipload.
  • Use bigger vessels - more cargo, less voyages.
  • Use the full tonnage of the vessel - fill it up to capacity.
  • Reduce ballast distance - find vessels close to you, to reduce empty voyages.

Digital Product Management

• There is a lot of hype around product management in data and digital.
• Product thinking means turning your attention to the user/consumer of the product and their needs.
• You need to collect data, ensure quality, display its in a way that makes sense, and display it in the right context - that means products.
• There has been a development in product management to become more data informed and customer focused.
• There are still misinterpretations between product thinking and project thinking.
• Projects have a set scope and an end-date. Projects are defined by their constrains, on scope, time, or quality.
• Product development does not have nan end-date and cannot be done in isolation.
• In Product management you have constrains on features, not on the entire product.
• In Product management you can deliver a lot of value, but you don’t always know when.
• You can apply Project Management methods in product management.
• When you deliver a product you need to have your consumer, customer and end-user needs in focus throughout the entire development of that product.
• Separate your own opinions and preference out of the product during development.
• Don’t just ask what products people want, but rather elicit requirements through a controlled process, to find out what they need.
• A product manager needs to facilitate and get customer, designer, software engineer, etc. on the same page.

Skills for a Digital Product Manager

• User empathy
• Storytelling
• Elicit needs
• Data Analysis
• Cross-departmental communication

And the Number One rule as a Digital Product Manager: «Don’t mistake your own opinion for the right opinion.»

«When technology evolves really fast, also the skills you need to hire for evolve really fast.»

Within a rapidly changing environment, fueled by technology and great ideas, it can be hard to define a stable career path. So I brought in an expert on developing companies and building legacies. Pedram Birounvand has a background in quantum physics, data engineering, experience from Spotify and moved into private Equity 6 years ago. Now Pedram started a new chapter in his career as the CEO of a startup working with Data Monetization.

Here are my key takeaways:

Data Skills for the Future

• As a leader in the data domain, you need to be a storyteller, to tell the story about the necessity of data, like quality or governance.
• The Job titles for Data Scientist, Data Engineer, etc stayed consistent, but what we expect from someone with that job title changed greatly through the last years
• Hard skills in data are not as important as they where for every company
• Make sure you know, what you are optimizing for in your career
  • Are you optimizing for flexibility, self-employed consultant is best
  • Are you optimizing for building a legacy, be an entrepreneur
  • Are you optimizing for leading people and see people grow, become a line manager
• Dont become a manger if your passion is within engineering. You will need to optimize your time for coaching people, not working on problem solving as an engineer.
• «The technology of applying AI and ML becomes more and more simple and becomes more and more commoditized.»
• Dont hire Data Scientist to build models that you can buy out of the box.
• Don’t hire Data Scientist if you need Data Analysts. They work entirely different and the work is not comparable.
• If you hire a Data Scientist before having good Data Engineers, then the Data Scientist cannot create any value
• «In order to be successful as an engineer, you need to have a really transformative mindset.»
• You need to enjoy the learning process, if not focus on something else in the IT-domain
• Adopt an agile mindset. Agile fundamentals are key to todays work life.
• «You need to embrace to be able to incubate things.» Build incubator squads as soon as a good idea pops up.

Recruitment

• In a small company you need to be much more flexible and broader in the way you tackle problems, than in a larger company where you can be more specialized
• As a hiring manager, don’t lean too much on the titles, but make sure you understand what you need in your company. This is key to writing a good add and attracting the right talent
• In a job advertisement be specific: What does it mean to be a Data Engineer in the context of your business?
• What is important for you as a company today, based on the trends coming?
• Building code has become so much simpler. Do you still need developers that need to know all the details about a certain language?
• Maybe a person that can be close to the business, and not so deep in programming can add more value?
• «You have to know what it is you are optimizing for.» If you have an extremely complicated technology stack you need deep knowledge, if not don’t hire it.
• In a recruitment process, focus on soft skills of rapid learners that can adjust to new situations and having an interest in understand your business use cases.
• «My interview secret: Share a whiteboard session with me»
  • Try to figure out how self-sufficient a candidate can be in understanding how the business works and where to get relevant data
  • Test how candidates react in uncomfortable situations, with customers that are not always happy about results and solutions.
  • Look for candidates that show resilience in new and uncomfortable situations
• Career models should be technology agnostic

«Infants with guns!»

Are we mature enough to track, collect and handle data responsibly, according to ethical standards? I talked with Director of Data Innovation at IIH Nordic Steen Rasmussen about the Business impact of Data Ethics.

Here are my key take aways:

• If were track, collect, and keep all data for any random opportunistic purpose, we put your companies at risk.
• This includes a «commercial curse» of budget-heavy tracking and budget-light management and business value creation through data.
• ROT, Norwegian for clutter, is an acronym for Redundant, Obsolete, Trivial - the dat that clutters your way to find valuable data.
• Collection and tracking of data is still too much dependent on people: If there is a change in personal, you get situations where new people «clutter the clutter»

Marketing & Sales

• For many companies it was Marketing & Sales that drove the data-driven agenda.
• The big value of Marketing & Sales is to add the market-dimension to the data.
• You can actually relate your product to the market, ship to where the market is.
• Analyzing market data is «putting a fixed entity on a moving target». The market changes to rapidly to provide good analysis.
• The more you push behavioral forecasting into the future, the bigger your uncertainty.

Business value & Ethics

• Corporate irresponsibility is an issue.
• Sometimes we get involved in a projects for the projects sake.
• Data projects have for a long time been theoretical, so the impact was not visible.
• Chat-GPT is a black box. Should we really give it more firepower, if we don’t know how it works?
• The market determines that there is a business value in being first.
• The speed of innovation doesn’t give time for reactive regulatory bodies to regulate efficiently.
• Companies need data ethical guidelines to say, how they will, shall and can use data.
• Who should define data ethical guidelines in a company? It is still done on a user-level, whilst senior management is looking at market situations and weighting them against ethical guidelines.
• We need regulatory and top-level guidelines that cannot be bent according to market situations.
• «Ideally, but highly unlikely we need a global set of data ethical guidelines.»
• The more trustworthy you are as a company, the more relevant data is shared with you.
• With that trust and data, you can understand the market better than companies that are not trustworthy and basically flying blind.
• Personal Data Literacy is important, and we need basic digital skills in our society.
• There is also a lack of understanding, when it comes to measures set in place for peoples benefit, eg. Cookie-banners.
• We are still lacking good privacy approved alternatives to the tools we are using on an everyday basis.
• Everyone has to follow ethical guidelines. We cannot have a DarkOps department in our company.
• Data Ethics guidelines should be something everyone can refer to.
• Ask yourself: What is the minimum of data we require to collect? Anything else should become an ethical question.

Data Protection Laws:

• There is a difference between the interpretation of regulations in the EU.
• Nordic countries interpret law relatively, is this just, fair, reasonable?
• Southern European countries use a more napoleonic or dogmatic approach, where «the law is the law, and the law must be obeyed.»
• Both Chat-GPT and Google Analytics have been handled differently by data protection authorities.
• Data Protection Authorities generalize to much, and don’t look at differences in technology.
• Is a strict, generalized interpretation creating panic, for users of eg. Google Analytics?

«There is a fundamental conflict between the essence of fashion and Machine Learning.» 

Fashion is always about change, innovation and identity. Whilst ML is good at making predictions based on historical patterns on those things, not change. How do those go together?

I had a fantastic chat with Celine Xu, who is Head Data Scientist at H&M Group, with a mixed background from Applied Mathematics and Business (MBA).

Here are my key takeaways:

Three things Data can achieve:

1. Automation of systems, where we have a clear understanding ion the process.
2. Sourcing, filtering, ranking of data input for decision making.
3. Ad-hoc analysis of all kinds of AB testing or correlation relationship.

Focus for ML in Fashion

• «ML can be embedded across the supply chain from product design to customer.»
• H&M focuses ML on two areas:

1. Future proof for customer experience: search experience, personalization, fashion inspiration, etc.
2. Demand-driven supply chain: reduce cost and be more sustainable through demand forecasting, cruise control, logistic optimization

ML in Product Design (some examples)

• 3D modeling can make the customer more comfortable in a visual way, in choosing the right size.
• Digitalizing fashion sampling is an interesting use case.
• Early detection of format, color, etc. that can generate trends.
• Optimizing logistics and storage through demand forecasting.
• Shop assistance for employees, with sales data, inventory and warehouse information.
• Language to image: use of stable diffusion for design patterns, on the material level.
• H&M is experimenting with trending words on TikTok or Instagram and transferring them to production design.
• Focus is not in understanding a sentence or the sentiment behind it, but instead use hashtag or product description to find out, what are the trends and link those back to a certain garment feature.
• Purchasing records (when and where did you buy what) and web viewing records (devices uses, pages that lead you to your purchase, etc.) are vital pieces to collect for behavioral analysis.

The Challenges of ML on behavioral and cultural data

• ML models are often evaluated on ML metrics, like accuracy, recency, etc. but not really business metrics, like revenue, profitability, etc.
• You fashion taste is influenced by your music taste, your interior design, and so much more.
• GDPR or webside viewing regulation are limiting the data source availability.
• Even harder to get the data accurate on a granular level, eg. Do you buy for yourself or doing you purchased a gift?
• With a lot of different dimensions in the data, you need to balance the accuracy with the cost.
• ML is always learning from something that already happened. If a new situation accrues, there is a lack of information and data.
• The complexity of behavioral data increases bias.
• You could artificially enrich your data set, eg. by drawing conclusions from certain data like income, area of residence, etc.
• Emotional and temperament data (what value do you hold to your fashion) can be important data to provide for your model, but also as an indication how you would react to recommendations.
• A small decision in your model or source data can lead to a butterfly effect.
• In fashion the definition of style is vague. But when you want to quantify certain feature in ML.
• Fashion trends last around 3 months on SOME, but production time is around 6 months. So how can you react in time?
• Images of material can be deceiving. Camera angle, light etc can influence how the color is represented.
• Presentation of textile is hard, because only a limited number of different textiles can be identified by image only.

«MLOps is a set of practices that bring people, process and platform together into a stream-aligned process to manage End-2-End Machine Learning lifecycles.»

MLOps is taken about a lot, so I asked an expert what we are actually talking about. Xiaopeng Li is AI business lead at Microsoft for the Western European market, located in Oslo. Xiaopeng is a passionate influencer in the field of Data and AI/ML, who was nominated as AI influencer of the Year at last years DAIR-awards in Stockholm.

Here are my key takeaways:

Patterns in AI adoption

• AI adoption projects are quite diverse, but with some patterns that are visible across. Here are use-cases that a lot of industries are working with:
  • Business Process Automation as an AI use case 
  • Adopting AI to process documents automatically and extract key-values
  • Natural Language understanding and processing, but also Natural Language generation
  • Chat GPT
  • Knowledge Mining
  • Unstructured data analysis

«Nordic countries are at the forefront when it comes to adopting AI and ML»

• Some of the most advanced search capabilities used in Microsoft are developed in Norway
• Nordic countries are typically quite tach-savvy
• Nordic countries have very good infrastructure

What is MLOps?

• MLOps is about agility, productivity, consistency and quality
• It is about creating scalability for your Data Science work
• MLOPs is a vage concept and you can probably find a variety of different definitions. Is MLOps at the intersection between DevOps, ML and Software Engineering?
• Scale ML development and deployment with constancy, with quality, with speed

The three elements that are most important are people, process and platform

• People:
  • 5 particularly important roles: Stakeholder, Cloud Infrastructure Architect, Data Engineer, Data Scientist, Machine Learning Engineer
  • There are many different roles involved in MLOps, from cleaning data to testing a model an implementing it. These roles need to be orchestrated
  • Domain experts and stakeholders play a critical role in defining the challenge in the first place. They can formulate what to achieve and what is good enough
  • Change Management is important, especially if your ML implementation triggers behavioral change
• Platform:
  • You are in need of a secure, scalable infrastructure to would your models on
  • Mature organizations who do ML at scale, have most an integrated architecture for Data Management, Analytics and Machine Learning
• Process:
  • Data collection,. Data processing and data management are processes you need to focus on in MLOps
  • You need a process and the right competencies to gather use-cases in the first place
  • Build a backlog of initiatives and then go through prioritization based on eg. Data availability, feasibility of solution given current etch-landscape, value for business, cost, time to marked,..

Path to MLOps

• Always start with assessing your current landscape and maturity
• Start by assessing your platform capabilities
• Ensure you have the right competencies and people
• If you want to operationalize MLOps, don’t look at it as a technological problem, but something that includes the entire organization
• Key is to bring key stakeholders as early as possible into the discussion

Oslo AI:
https://www.linkedin.com/company/oslo-ai/

https://www.meetup.com/oslo-ai/

Link to MS learning:

MLOps Maturity Model

«The real value in changing the status quo and getting more women to the forefront, is by having women share about the important work that they do, and not just talk about their gender as a topic.»

This episode was recorded right before Christmas, when I had the pleasure to chat with Alexandra Gunderson and Sheri Shamlou.

Alexandra inspired by a Women in Data dinner in New York, took it upon her to find likeminded people in Norway. That is how she came across Women in Data Science, the conference that was brought to Norway by Heidi Dahl in 2017. First meetup as a Community was June 2018, and this years WiDS event «Crossing the AI Chasm» is coming to Oslo (and digitally) on May 24th, 2023.

Here are my key takeaways:

Women in Data Science (WiDS)

• «Creating a meeting place, a place for people to connect and get inspired»
• Creating a platform and stage for outstanding women.
• Here are some of the events WiDS organizes:
  • «Champagne Coding» - hands on event
  • «Data after Dark» - after work event: 1-2 quick high level presentations
  • «Data for Good» - get together and solve difficult challenges for greater causes
• An important mission is to increase the number of role models in the community to look top to.
• The goal is to provide arenas to learn together, son it is as important to share stories about failure and collaborate around the learnings from those.
• WiDS is looking for sponsors, and one benefit can be, that trough events real-life uses cases can be solved.
• The focus for 2023 is «scalability» how to get unstuck from ML and AI pilots and bring your work to production?

The Quest for Diversity

• Diversity is a complex topic with several perspectives: gender, nationality, background, knowledge, expertise, and experience.
• Why is diversity important?

1. Leads to more innovative and effective solutions
2. Leads to more fair and just outcomes

• The starting point when working with diversity on a daily basis is awareness.

Diversity in the workplace

• Diversity doesn’t magically happen. You have to work for it.
• Awareness is a first step, but you also need to collaborate in broader groups.
• The value is gained when you are able to include everyone in your events and talks.
• For people to work together against biases of any kind, you need an inclusive culture from the beginning.
• Be open in your communication and foster a culture of collaboration.
• The «3rd shift» is an important requisite for women to be able to spend the same amount of time and intellectual capacity at work.
• The work for an inclusive work environment is never over. We have to continuously work on its and talk about it.

Diversity in recruitment

• You have to actively seek out to hire people with different backgrounds.
• In recruitment, be aware of how you write a job announcement.

1. Use gender neutral language (avoid stuff like «Data Science Ninja» or «Data Rock Star»).
2. There are online applications to check if your language is gender neutral and with suggestions for replacements of bias´ words.
3. You need to highlight more possibilities with a job, like growth and learning opportunities.
4. Minimize the list of requirements in a job-posting.
5. Be aware of you own biases and work with diverse teams also in recruiting.
6. When screening CVs, be aware that different people write in different styles.
7. In an interview process, eg. Women don’t like to do coding tests, with someone watching them.

Get involved: LinkedIn group, Meetup or https://www.widsoslo.com/.