MetaDAMA - Data Management in the Nordics

#9 - Demand Side Data Management (Eng)

October 18, 2021 Winfried Etzel VP Activities DAMA Norway Season 1 Episode 9
#9 - Demand Side Data Management (Eng)
MetaDAMA - Data Management in the Nordics
More Info
MetaDAMA - Data Management in the Nordics
#9 - Demand Side Data Management (Eng)
Oct 18, 2021 Season 1 Episode 9
Winfried Etzel VP Activities DAMA Norway

Demand Side Data Management comes from the notion to provide value to customers as our highest outcome and has been around as a concept for demand side data quality for a while. We let the consumer decide what data quality he needs to create value with the data.

  •  But can we widen that approach to a Data Management as a Service model?
  • Can we put Data Management into a chain of custody?

 Aiko Yamashita, Senior Data Scientist at the CoE at DNB, and Karl-Aksel Festø, Head of Advanced Analytics CoE at DNB, gave their input to these questions, supported by examples form their work at DNB.

 We talked about:

  • What is the difference between Data Science and Analytics?
  • What skills do we need to build data literacy?
  • How do we get from analysis to insight, and how do we get from insight to action?
  • Who is demanding Data Management?
  • How is Data Management linked to the data value chain?
  • What does it mean to manage a non-depletable asset that is a common good for many different stakeholders?
  • What is the value of good Data Management?
Show Notes

Demand Side Data Management comes from the notion to provide value to customers as our highest outcome and has been around as a concept for demand side data quality for a while. We let the consumer decide what data quality he needs to create value with the data.

  •  But can we widen that approach to a Data Management as a Service model?
  • Can we put Data Management into a chain of custody?

 Aiko Yamashita, Senior Data Scientist at the CoE at DNB, and Karl-Aksel Festø, Head of Advanced Analytics CoE at DNB, gave their input to these questions, supported by examples form their work at DNB.

 We talked about:

  • What is the difference between Data Science and Analytics?
  • What skills do we need to build data literacy?
  • How do we get from analysis to insight, and how do we get from insight to action?
  • Who is demanding Data Management?
  • How is Data Management linked to the data value chain?
  • What does it mean to manage a non-depletable asset that is a common good for many different stakeholders?
  • What is the value of good Data Management?