MetaDAMA - Data Management in the Nordics
This is DAMA Norway's podcast to create an arena for sharing experiences within Data Management, showcase competence and level of knowledge in this field in the Nordics, get in touch with professionals, spread the word about Data Management and not least promote the profession Data Management.
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Dette er DAMA Norge sin podcast for å skape en arena for deling av erfaringer med Data Management, vise frem kompetanse og kunnskapsnivå innen fagfeltet i Norden, komme i kontakt med fagpersoner, spre ordet om Data Management og ikke minst fremme profesjonen Data Management.
MetaDAMA - Data Management in the Nordics
4#3 - Pedram Birounvand - A Paradigm Shift in Data through AI (Eng)
«The notion of having clean data models will be less and less important going forward.»
Unlock the secrets of the evolving data landscape with our special guest, Pedram Birounvand, a veteran in data who has worked with notable companies like Spotify and in private equity. Pedram is CEO and Founder at UnionAll.
Together, we dissect the impact of AI and GenAI on data structuring, governance, and architecture, shedding light on the importance of foundational data skills amidst these advancements.
Peek into the future of data management as we explore Large Language Models (LLMs), vector databases, and the revolutionary RAG architecture that is set to redefine how we interact with data. Pedram shares his vision for high-quality data management and the evolving role of data modeling in an AI-driven world. We also discuss the importance of consolidating company knowledge and integrating internal data with third-party datasets to foster growth and innovation, ultimately bringing data to life in unprecedented ways.
Here are my key takeaways:
- Always when a new technology arrives, you need to adopt and figure out how to apply the new technology - often by using the new tools for the wrong problem.
- There is substantial investment in AI, yet the use cases for applying AI are still not clear enough in many companies.
- There is a gap I how we understand problems between technical and business people. Part of this problem is how we present and visualizer the problem.
- You need to create space for innovation - if your team is bugged down with operational tasks, you are canibalizing on innovative potential.
- Incubators in organizations are valuable, if you can keep them close to the problem to solve without limiting their freedom to explore.
- The goal of incubators is not to live forever, but top become ingrained in the business.
- CEOs need a combination of internal and external council.
- Find someone in the operational setting to take ownership from the start.
- The more data you have to handle the better and clear should your Data Governance strategy be.
- Small companies have it easier to set clear standards for data handling, due to direct communication.
- You want to make sure that you solve one problem really well, before moving on.
- Before intending to change, find out what the culture and the string incentives in your organization are.
LLMs as the solution for Data Management?
- ChatGP already today very good at classifying information.
- It can create required documentation automatically, by feeding the right parameters.
- It can supersede key value search in finding information.
- This can help to scale Data Governance and Data Management work.
- Data Management will become more automated, but also much more important going forward.
- RAG architecture - first build up your own knowledge database, with the help of vectorizing the data into a Vector-database.
- The results from querying this database are used by the LLM for interpretation.
- Find a way to consolidate all your input information into a single pipeline to build your knowledge database.
- Building strong controls on naming conventions will be less important going forward.
- Vectorized semantic search will be much faster.
- Entity matching will become very important.
- Fact tables and dimensional tables become less important.
Data to value
- Be able to benchmark your internal performance to the market
- undertand trends and how they affect you.
- How to use and aggregate third party data is even harder than internal data.
- You need to find ways to combine internal and third party data to get better insights.
