Home > Digitalizacija > Elements of data culture in companies by Alejandro Diaz, Kayvaun Rohweshankish and Tamin Saleh in McKinsley Quaterly, 3Q 2018

Elements of data culture in companies by Alejandro Diaz, Kayvaun Rohweshankish and Tamin Saleh in McKinsley Quaterly, 3Q 2018

  1. Data culture is decision culture.
    1. The fundamental objective is collecting, analysing and developing data to make better decision.
    2. Identify business problem. What is and how you can solve it. Solving business problems must be a part of your data strategy.
  2. Data culture need to be c-suite imperatives and the boards.
  3. The democratization of data
    1. C-suite should consider a hybrid organizational model in which agile teams combine talented professionals from both business side and the analytics side.
    2. Top down approach doesn’t work.
    3. Data, applied to business problems, creates innovation. With that you get excitement.
  4. Data culture and risk
    1. The effective data culture put risk at their core.
    2. Handling data usage in ethnical way, following regulatory request is a must.
    3. Algorithm bias must be observed and supervised.
  5. Culture catalysts
    1. Company requires people who can bridge both worlds – data science and on-the-ground operations.
    2. Someone’s got to lead a charge.
  6. Sharing data beyond company wall? Not so fast
  7. Marrying talent and culture
    1. Injecting new people and transforming existing ones.
    2. Three areas of skills:
      1. Business
        1. Business leader – lead analytics transformation across organization
        2. Delivery manager – deliver data and analytics-driven insight and interface with end user
      2. Technology
        1. Data engineers – collect, structure and analyse data
  • Analytics
    1. Workflow integrators -build interactive decision-support tools and implement solutions
    2. Visualization analysts – visualize data and build reports and dashboards
  1. Business and technical
    1. Data architects – ensure quality and consistency of present and future data flows
  2. Business and analytics
    1. Analytics translators – ensure analytics solve critical business problems
  3. Technology and analytics
    1. Data scientists – develop statistical models and algorithms
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