Marketers often have the most rich and detailed data in the enterprise yet only a small fraction of data is used due to data silos. How can data professionals help Marketing become more data driven? During the presentation we will explore the top use cases and customer examples for AI and Analytics in Marketing. Examples include how Google Analytics data can be enriched with 1P data to improve ROI, and the use of Generative AI in marketing campaigns.
10. October
Silo in data analytics community in an organization happen when data is scattered in many different systems. This talk is about how Telenor reduces data silo with data integration solution based on metadata driven approach.
How do you define and communicate a clear AI vision that resonates with all stakeholders, securing buy-in and support across your organization? It’s about mastering the balance between moving quickly to seize short-term opportunities and planning strategically for long-term growth. While at the same time, it’s about telling your story in a compelling way that convinces people that you are committed to their success. This talk will give suggestions to get support and success from my experience with working with AI & Data strategies for 10 years.
Åpne datakilder kan gi informasjon om hvor og når etterspørselen etter ulike typer varer og tjenester er størst. Informasjon om befolkning, konkurrerende og komplementære servicetilbud, trafikk og arbeidsplasser er eksempler på data som kan fortelle mye om etterspørselen etter varer og tjenester på ulike steder.
Ved å kombinere slike data med en virksomhets egne data kan man bygge maskinlæringsmodeller og dermed bruke dataene på en smart måte. Hvilken beliggenhet vil gi størst lønnsomhet når man skal etablere et nytt utsalgssted? Vil et sted med stor etterspørsel og samtidig hard konkurranse være mer lønnsom enn et sted med mindre etterspørsel og mindre konkurranse?
Eller motsatt, hvis man skal redusere antall utsalgssteder, hvilket utsalgssted vil få minst negativ effekt av å bli fjernet?
Vi vil presentere flere kundecaser, inkludert Kople, som har et landsdekkende ladenettverk med over 7000 offentlige ladere. Kople bruker maskinlæring som beslutningsgrunnlag når de planlegger nye ladestasjoner. Vi vil forklare hvilke data som benyttes, hvordan maskinlæring brukes og hvordan beslutningsgrunnlaget ser ut i praksis.
Vi vil også vise hvordan tilsvarende fremgangsmåte kan brukes for å planlegge utsalgssteder innen andre bransjer.
Join us as we explore Skatteetatens cutting-edge data platform designed to meet diverse needs—from traditional data warehousing to operational and advanced analytics—using Azure Databricks.
We will focus on how Skatteetatens data platform team leverages infrastructure as code and Terraform to seamlessly package and integrate services. Discover how we empower analysts with self-service capabilities to order personal or team-centric analytical environments/sandboxes. We’ll also share our journey in developing a custom Terraform provider, enabling us to remove manual processes and handovers, while streamlining access control management.
In this presentation, we explore the transformative potential of Generative Adversarial Networks (GANs) and synthetic data in revolutionising risk analytics. This collaboration uses GANs to enhance personal loan model accuracy by augmenting default cases with synthetic data. We detail how these technologies boost model performance through a case study comparing outcomes with and without synthetic data, highlighting realistic improvements in risk analytics.
Discover the future of risk with generative AI.
At Entur we have been attempting to apply Data Mesh Principles for the last 2 years, starting very small, and gradually scaling.
We hope to be able to share some key learnings to address the pain points from our attempt at applying these principles, so that others may not make the same mistakes.
The main point will be that our biggest failure has been in doing too many new things, too soon.
The Challenge – Aker Solutions is a big international organization operating in several large segments within the Energy Industry. Consequently, the majority of the work performed by the employees is very domain specific. How can a centralized AI investigation team approach the challenge of exploring and developing domain-specific use cases in an efficient manner?
The Solution – By implementing an application that works both as a general RAG chat service and a testing ground for more complex domain-specific use cases, the goal of this approach is to fulfil both the general and specific needs within the organization. Some domain-specific use cases the solution is solving are 1) Feedback module for constructive and positive feedback on documents, 2) Ability to compare guidelines to documents and 3) Chatbot connection to Confluence documentation.
Technical Solution – Presentation of architecture and infrastructure. Explain the general functionality and how exploration is facilitated.
Implementation of use cases – Give an explanation of how new use cases are gathered and how these use cases are explored and possibly implemented by the investigation team.
Example use case – BidAI: Brief description of how the solution works toward bids, and why this provides value for the organization.
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