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#17 - Return of Investment for Data Quality (Nor)

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Conteúdo fornecido por Winfried Adalbert Etzel - DAMA Norway. Todo o conteúdo do podcast, incluindo episódios, gráficos e descrições de podcast, é carregado e fornecido diretamente por Winfried Adalbert Etzel - DAMA Norway ou por seu parceiro de plataforma de podcast. Se você acredita que alguém está usando seu trabalho protegido por direitos autorais sem sua permissão, siga o processo descrito aqui https://pt.player.fm/legal.

Can you put a value on quality data? Definitely! But how?
Who better to ask than Kristin Otter Rønnevig and Espen Hjelmeland? They both dedicated their master thesis to explore this topic with really interesting results. Their thesis "An Investment Perspective on Data Quality in Data Usage" asks "How can an organization optimize its investments in data quality?"
To answer the question Kristin and Espen posed three Research Questions:

  1. What are the main drivers for willingsness to pay for data quality?
  2. What part of data quality aspects should one invest in to maximize opportunities and minimize risk?
  3. How can the quality of data be improved, and what are the costs?

Here are some of their key observations, I found particularly interesting:

  • Increasing confidence in data in order to enhance company operations is one of the
    motivations for willingness to pay for data quality.
  • A greater level of knowledge in data quality gives a higher willingness to pay for data quality.
  • Demonstrating to the customer how quality improvements may enhance profit at each step
    of the value chain contributes to the willingness to pay for data quality.
  • Prioritizing improvements are based on the time and cost of the particular improvement. A way to reverse-engineer the impact of various improvements
    can help to identify how to improve the quality.
  • It is critical to invest in a professional, highly skilled team environment to succeed
    in data quality investments.
  • To cope with data quality, it is necessary to invest in security.
  • To know whether the organization is investing in data quality optimally, it will need a deep understanding of the business and experience with it.
  continue reading

57 episódios

Artwork
iconCompartilhar
 
Manage episode 324542798 series 2940030
Conteúdo fornecido por Winfried Adalbert Etzel - DAMA Norway. Todo o conteúdo do podcast, incluindo episódios, gráficos e descrições de podcast, é carregado e fornecido diretamente por Winfried Adalbert Etzel - DAMA Norway ou por seu parceiro de plataforma de podcast. Se você acredita que alguém está usando seu trabalho protegido por direitos autorais sem sua permissão, siga o processo descrito aqui https://pt.player.fm/legal.

Can you put a value on quality data? Definitely! But how?
Who better to ask than Kristin Otter Rønnevig and Espen Hjelmeland? They both dedicated their master thesis to explore this topic with really interesting results. Their thesis "An Investment Perspective on Data Quality in Data Usage" asks "How can an organization optimize its investments in data quality?"
To answer the question Kristin and Espen posed three Research Questions:

  1. What are the main drivers for willingsness to pay for data quality?
  2. What part of data quality aspects should one invest in to maximize opportunities and minimize risk?
  3. How can the quality of data be improved, and what are the costs?

Here are some of their key observations, I found particularly interesting:

  • Increasing confidence in data in order to enhance company operations is one of the
    motivations for willingness to pay for data quality.
  • A greater level of knowledge in data quality gives a higher willingness to pay for data quality.
  • Demonstrating to the customer how quality improvements may enhance profit at each step
    of the value chain contributes to the willingness to pay for data quality.
  • Prioritizing improvements are based on the time and cost of the particular improvement. A way to reverse-engineer the impact of various improvements
    can help to identify how to improve the quality.
  • It is critical to invest in a professional, highly skilled team environment to succeed
    in data quality investments.
  • To cope with data quality, it is necessary to invest in security.
  • To know whether the organization is investing in data quality optimally, it will need a deep understanding of the business and experience with it.
  continue reading

57 episódios

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