A 12-Step Plan for Governance of Customer Data​

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In my last Ecosystm Insight, I spoke about the 5 strategies that leading CX leaders follow to stay ahead of the curve. Data is at the core of these CX strategies. But a customer data breach can have an enormous financial and reputational impact on a brand. ​

Here are 12 essential steps to effective governance that will help you unlock the power of customer data. 

  1. Understand data protection​ laws and regulations 
  2. Create a data governance framework
  3. ​Establish data privacy and security policies
  4. Implement data​ minimisation
  5. Ensure data accuracy
  6. Obtain explicit consent
  7. Mask, anonymise and pseudonymise data
  8. Implement strong access controls
  9. Train employees
  10. Conduct risk assessments and audits
  11. Develop a data breach ​response plan
  12. Monitor and ​review

Read on to find out more.

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The Experience Economy
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5 Insights to Help Organisations Build Scalable AI – An ASEAN View

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Data & AI initiatives are firmly at the core of any organisation’s tech-led transformation efforts. Businesses today realise the value of real-time data insights to deliver the agility that is required to succeed in today’s competitive, and often volatile, market.

But organisations continue to struggle with their data & AI initiatives for a variety of reasons. Organisations in ASEAN report some common challenges in implementing successful data & AI initiatives.

Here are 5 insights to build scalable AI.

  1. Data Access a Key Stumbling Block. Many organisations find that they no longer need to rely on centralised data repositories.
  2. Organisations Need Data Creativity. A true data-first organisation derives value from their data & AI investments across the entire organisation, cross-leveraging data.
  3. Governance Not Built into Organisational Psyche. A data-first organisation needs all employees to have a data-driven mindset. This can only be driven by clear guidelines that are laid out early on and adhered to by data generators, managers, and consumers.
  4. Lack of End-to-End Data Lifecycle Management. It is critical to have observability, intelligence, and automation built into the entire data lifecycle.
  5. Democratisation of Data & AI Should Be the Goal. The true value of data & AI solutions will be fully realised when the people who benefit from the solutions are the ones managing the solutions and running the queries that will help them deliver better value to the business.

Read below to find out more.

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Artificial Intelligence Insights
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